key: cord-0253051-qe1twg10
authors: Witteman, H. O.; Ndjaboue, R.; Vaisson, G.; Chipenda Dansokho, S.; Arnold, R. M.; Bridges, J. F. P.; Comeau, S.; Fagerlin, A.; Gavaruzzi, T.; Marcoux, M.; Pieterse, A. H.; Pignone, M.; Provencher, T.; Racine, C.; Regier, D. A.; Rochefort-Brihay, C.; Thokala, P.; White, D. B.; Weernink, M. G. M.; Wills, C. E.; Jansen, J.
title: Clarifying Values: An Updated and Expanded Systematic Review and Meta-Analysis
date: 2021-01-25
journal: nan
DOI: 10.1101/2021.01.21.21250270
sha: 770dcb9d93a275826ebec9e53a0842f3a5478980
doc_id: 253051
cord_uid: qe1twg10

Background: Patient decision aids should help people make evidence-informed decisions aligned with their values. There is limited guidance about how to achieve such alignment. Purpose: To describe the range of values clarification methods available to patient decision aid developers, synthesize evidence regarding their relative merits, and foster collection of evidence by offering researchers a proposed set of outcomes to report when evaluating the effects of values clarification methods. Data Sources: MEDLINE, EMBASE, PubMed, Web of Science, the Cochrane Library, CINAHL Study Selection: We included articles that described randomized trials of one or more explicit values clarification methods. From 30,648 records screened, we identified 33 articles describing trials of 43 values clarification methods. Data Extraction: Two independent reviewers extracted details about each values clarification method and its evaluation. Data Synthesis: Compared to control conditions or to implicit values clarification methods, explicit values clarification methods decreased the frequency of values-disgruent choices (risk difference -0.04 95% CI [-0.06 to -0.02], p<.001) and decisional regret (standardized mean difference -0.20 95% CI [-0.29 to -0.11], p<0.001). Multicriteria decision analysis led to more values-congruent decisions than other values clarification methods (Chi-squared(2)=9.25, p=.01). There were no differences between different values clarification methods regarding decisional conflict (Chi-squared(2)=6.08, p=.05). Limitations: Some meta-analyses had high heterogeneity. We grouped values clarification methods into broad categories. Conclusions: Current evidence suggests patient decision aids should include an explicit values clarification method. Developers may wish to specifically consider multicriteria decision analysis. Future evaluations of values clarification methods should report their effects on decisional conflict, decisions made, values congruence, and decisional regret.

Shared decision making aims to foster health-related decisions that are both informed by the best available evidence and aligned with what matters to the person or people affected by the decision. [1] [2] [3] [4] Individual values are a critical ingredient in high quality individual health decision making. [5] [6] [7] What is important to one person might be different from what is important to others, and determining what is important to oneself can be difficult even if one has the appropriate information and evidence at hand. Therefore, patient decision aids should both present evidence appropriately and also support the process of clarifying and expressing patients' (and, when appropriate, other relevant stakeholders') values, with the goal of supporting alignment between values and decisions. Within patient decision aids, such support is offered by explicit values clarification methods.

Explicit values clarification methods require users to interact with something such as a worksheet or an interactive website to clarify what matters to them relevant to a health decision. Such methods have been shown to encourage desirable outcomes such as better alignment with patients' values [8, 9] and reduced decisional regret, the latter particularly among people with lower health literacy. [10] However, explicit values clarification methods are extremely diverse, [11] , and there has been little guidance regarding their comparative effects on users' decision-making processes or outcomes [12] , making it difficult for patient decision aid developers to know which explicit method to use. Patient decision aid developers might look towards the preference elicitation literature for guidance, but the guidance available [13] is often tailored towards aggregate level decision making, such as regulatory decisions [14] or health technology assessment [15] , not for supporting individual-level decision making.

This updated review sought to build upon previous versions of the International Patient Decision Aids Standards' Chapter on Values Clarification [16, 17] as well as previous evidence syntheses that have established the advantages of explicit values clarification methods over implicit methods or no values clarification. [8, 9] We sought to advance the science and practice of values clarification methods in three ways. First, we aimed to offer clear definitions and an annotated summary of existing approaches that have been or could be used as values clarification methods. Second, we aimed to synthesize evidence of different techniques' effects on health decision outcomes. Third, we aimed to foster future evidence by offering researchers a proposed set of outcomes to consider when evaluating the effects of values clarification methods.

Part of the challenge in studying or using values clarification methods is that definitions vary and terms like 'values' are used imprecisely in the patient decision support literature. [18, 19] Another challenge is that there is substantial overlap between values clarification methods used in patient decision support and preference elicitation methods used in health economics. To bring clarity to this imprecision and overlap, we adopt working definitions in Table 1 for use in this paper.

Values An umbrella term referring to what matters to an individual relevant to a health decision. Values may be directly relevant to decisions (e.g., "beliefs, feelings, or perceptions regarding attributes of a treatment option") or indirectly relevant (e.g., goals; worldviews; family, religious, or cultural values). [20] Values may be represented qualitatively or, in some cases, quantitatively. This definition is deliberately broad.

Values clarification "The process of sorting out what matters to an individual relevant to a given health decision." [11] This definition emphasizes that what matters to an individual may be broader than attribute-specific values. What matters may also include preferences, concerns, and issues to do with the context of a person's life within which they would need to implement a decision.

Values clarification methods "Strategies that are intended to help patients evaluate the desirability of options or attributes of options within a specific decision context, in order to identify which option [they] prefer." [17] Implicit values clarification methods

Strategies for facilitating values clarification that do not require people to interact with anything or anyone; e.g., describing "options in enough detail that clients can imagine what it is like to experience the physical, emotional, and social effects," [9] or simply encouraging people to think about what matters to them.

Strategies for facilitating values clarification that require people to interact with something or someone; e.g., filling out a worksheet, using an interactive website, having a semi-structured conversation with another person with the explicit purpose of clarifying values, or engaging in another structured exercise.

The extent to which a decision option or health state is desirable or acceptable, either in the abstract or in comparison to other options or health states. Preferences may be represented qualitatively or, more commonly, quantitatively. [21] Preference elicitation methods

Processes by which preferences are drawn out. [11] Preference elicitation methods may vary according to the theory informing them. They are highly related to values clarification methods. Although older terms "revealed" and "stated" preference elicitation methods are no longer recommended, readers who encounter these terms in previous preference elicitation literature should note that these may overlap with implicit and explicit values clarification methods, respectively.

As noted above, we continue to use the term values clarification even though this is sometimes misinterpreted as implying a narrow definition of values. Changing terms makes it difficult for people who are new to a field to connect the dots across decades of previous research. It is clear that previous research in values clarification addressed issues that were broader than valuation of treatment-specific attributes. [16] In this update, we therefore move forward with the older terms, now with more clarity about what they mean in our presentation of the evidence.

Our interdisciplinary team determined that the theoretical rationale for values clarification required only a small edit, shown in square brackets, to reflect the focus on explicit methods. Like Fagerlin and colleagues, we assert the theoretical rationale for explicit values clarification methods as being that they, "should aim to [explicitly] facilitate at least one or more of the following six decision-making processes: 1) Identifying options, which can include either the narrowing down of options, or the generation of options that were not offered at the outset, 2) Identifying attributes of the situation and/or the options which ultimately affect the patient's preference in a specific decision context, 3) Reasoning about options or attributes of options, 4) Integrating attributes of options using either compensatory or both compensatory and non-compensatory decision rules, 5) Making holistic comparisons, and 6) Helping decision makers retrieve relevant values from long-term memory."

[17] Pieterse and colleagues provided theory-based recommendations on processes that values clarification methods could aim to facilitate. [22] Although reasoning is one of the potential processes supported by values clarification, neither the definition nor the theoretical rationale of values clarification methods requires that people who are being supported in making a personal health decision must rationally deliberate about each option, nor that the goal must always be a fully rational choice. In some decision-making situations, rational deliberation and rational choice may be desired, while in others, they may not. [23, 24] Explicit Values Clarification Methods Table 2 organizes strategies that can be used as explicit values clarification methods in patient decision aids, building upon previously-developed lists of types of values clarification methods [7, 11] and reviews of preference elicitation methods. [25, 26] Methods range from highly structured strategies that can also be used for preference elicitation in the context of health policy decision making to substantially less structured strategies. While not every use of a given method will be exactly the same, we deemed them functionally similar in terms of how they might be used and what the user experience might be in a patient decision aid. Patient decision aids may use multiple strategies. For example, a user may be asked to use a rating scale or visual analog scale whose values are then used in a decision analytic model. Table 2 .

Adaptive Conjoint Analysis (example [27] )

The user rates a series of sets of attributes and their levels, where choices presented are tailored to earlier answers.

Allocation of Points (example [28] ) The user has a "budget" to "spend" on decision attributes, according to their importance.

Analytical Hierarchy Process (example [29] )

The user is asked to compare sets of options relative to predefined decision criteria.

Best-Worst Scaling (example [30] ) The user is asked to indicate the best and the worst in sets of options with different attributes and levels.

Decision Analysis or Multicriteria Decision Analysis (umbrella term*) (resource [31, 32] )

The user is asked to directly indicate the extent to which a decision attribute or outcome matters to them or how good or bad they deem it to be. These values are then used in a model that calculates alignment between what matters to the user and the available decision options.

Discrete Choice Experiments (example [33] )

The user is asked to make a series of choices between two (or more) alternatives, where each alternative is characterized by attributes and their associated levels.

Open Discussion (example [34] ) The user makes a list and/or discusses what matters to them in an unstructured or semi-structured discussion.

Pros and Cons (resource [35] ) The user lists advantages (pros) and disadvantages (cons) of options and/or indicates the relevance ('this matters to me') or importance (e.g., on a Likert scale) of each advantage or disadvantage Ranking (example [36] ) The user is asked to place attributes in order of importance, relative to each other.

Rating Scales (example [37] ) The user indicates the importance of an attribute on a visual analog scale (e.g., paper-based visual analog scale, online slider) or Likert scale approximating a visual analog scale. If the rating is then used to calculate and show which option fits best, the method is classified as (multicriteria) decision analysis.

Social Matching (example [38] ) The user "observes different characters' decisions and/or decision-making processes and identifies 1 or more characters" with whom they identify. [11] Standard Gamble (example [39] ) The user indicates their willingness to accept a certain risk of death in order to avoid a particular health state by choosing between the certainty of living in that health state for the remainder of their life versus a gamble between two possible outcomes: life in a state of optimal health, with probability p, or immediate death, with probability (1−p).

Time Tradeoff (example [39] ) The user indicates how many years in their current health state they would be willing to 'trade off', in order to regain full health.

*Multicriteria decision analysis or decision analysis is an umbrella term. It encompasses some of the other, more specific categories (e.g., discrete choice experiments, best-worst scaling.) When applicable, we use the more specific, narrower categories. Otherwise, we use the umbrella term "multicriteria decision analysis," or, for brevity in figures, "decision analysis." Additionally, although within multicriteria decision analysis, the user may be asked to rate attributes on rating scales, what distinguishes multicriteria decision analysis from other methods such as rating scales is that the model calculates how well or poorly the options align with what matters to a user.

Our overall methods were guided by the Cochrane Handbook. We report according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [40] guidelines.

We included published reports of comparative evaluations of explicit values clarification methods, whether they were called 'values clarification methods' in the publications or not. This meant that we included trials of preference elicitation methods that had been trialed as values clarification methods; for example, multi-criteria decision analysis or discrete choice experiments. We included evaluations using comparative methods; i.e., randomized controlled trials or randomized experiments of one or more values clarification methods. The comparisons could be one or more values clarification methods compared to a control method, or compared to each other. Because we sought to understand the effects of values clarification methods, we excluded evaluations using descriptive study designs (e.g., acceptability and feasibility study, development study), observational study designs (e.g., reporting outcomes before and after use of a values clarification method), and reports of values clarification methods that did not evaluate the method independently of the patient decision aid in which it was used. Randomized experiments comparing one or more values clarification methods had to use distinctly different methods, meaning that more than the content or presentation of information in the values clarification method varied.

We did not apply language restrictions. We applied date restrictions to the portion of the review for which we had already conducted a systematic review (i.e., evaluations of values clarification methods that used the term 'values clarification'. [12, 17] Specifically, for this subgroup, we added articles indexed or published starting in 2014 to the existing set of articles indexed or published prior to 2014 that we had already identified using the same search strategy. We applied no date restrictions to the new, expanded portion of the review (i.e., evaluations of values clarification methods that did not use the term 'values clarification').

We performed a systematic literature search in MEDLINE, EMBASE, Web of Science, the Cochrane Library, and CINAHL.

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We developed a draft search strategy in collaboration with an information specialist (FB, see Acknowledgments). Search strategies for each database are shown in Online Appendix 1. We reviewed search strategies with all authors to ensure they were inclusive of relevant preference elicitation methods that might be used for values clarification. We conducted hand searches by reviewing articles that cited the previous version of these standards (values clarification chapter) or a previous systematic review of values clarification methods.

We managed data with Covidence (covidence.org, Melbourne, Australia), reviewing data records at regular team meetings.

Study records: Selection process Two independent reviewers (SC, MM, TP, CR, CRB) screened titles and abstracts to assess potential relevance, with a third reviewer adjudicating discrepancies and discussions of questions and points of disagreement at regular team meetings. Two independent reviewers then reviewed the full text of all articles deemed potentially relevant based on their title and abstract. Discrepancies in inclusion and exclusion at full text were adjudicated through team discussions at regular meetings until we reached consensus.

Study records: Data collection process Two independent, trained research team members (SC, MM, TP, CR, CRB) extracted data from each article using a standardized and pilot-tested data extraction form based on a previous form [12] and adapted to this review. We resolved lack of agreement through discussion until consensus was reached. We contacted authors to collect any needed data that they did not report or were unable to report in their publication.

Regarding study participants, we recorded the sample size for control and intervention groups along with basic inclusion and exclusion criteria and whether or not they were making the actual decision or if the study was hypothetical. We defined a hypothetical scenario as one in which people are asked (explicitly or implicitly) to imagine that they are in a certain situation or facing a certain decision. We defined a real scenario as one in which people are facing a decision (e.g., because they have received a diagnosis) or are members of a population likely to face the decision in the near term (e.g., parents of children eligible to receive vaccines within the coming months.)

Regarding interventions, we recorded the type of explicit values clarification method as listed in Table 2 . We also recorded specific characteristics of each values clarification method, namely, whether it explicitly requires the user to engage with tradeoffs (i.e., considering which potential harms are acceptable in exchange for their associated potential benefits), whether it explicitly shows the user the correspondence between their options and what they value, and which, if any, theoretical or conceptual framework underpins it. Where relevant, we recorded whether a variable was collected via self-report, meaning whether responses were completed by participants themselves, or by independent researchers based on direct observation, including coded qualitative data.

For comparators (controls) we recorded whether the comparator was no values clarification method or an implicit method, and treated both as equivalent controls. The Cochrane review of patient decision aids . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint specifies that all patient decision aids must contain implicit values clarification methods at minimum [9] and it is accordingly rare to have patient decision aids that do not present potential benefits and harms of options in organized ways. In other words, in the context of patient decision aids, there is no meaningful distinction between implicit methods and no values clarification. The different terminology is simply a function of how authors choose to name their control. We also recorded studies that compared different types of explicit values clarification methods to each other.

Whenever such data were available, we extracted data regarding values congruence as our primary outcome, as well as secondary outcomes: decision readiness (worry, decision uncertainty, decision-making preparation, knowledge); decisional conflict; decision made; post-decision and post implementation health and well-being (decisional regret, longer-term health outcomes). Following data extraction by pairs of trained reviewers (SC, MM, TP, CR, CRB), three authors (HOW, SCD, JJ) mapped all outcomes into broad outcome groups: worry (including perceived risk), decision uncertainty (not including decisional conflict), decisional conflict (decisional conflict scale or any subscales), decision-making preparation (including self-efficacy for decision-making), beliefs (including beliefs about the condition or underlying decision structure), knowledge, values (including reported utilities), shared decision making, effects on communication (including quality, length, or existence of communication), satisfaction with care, preferences, decision (choice made and implemented) or decisional intent (choice intended, or made and not yet implemented), values congruence, informed decision making, post-decision feelings (including satisfaction, regret), post-decision health, and user assessment of the intervention (including acceptability, satisfaction, perceived balance.) We conducted meta-analyses on primary outcome values congruence and secondary outcome decisional conflict, as these outcomes had sufficient studies to do so.

Independent, trained research team members assessed risk of bias for each study using methods as defined in the Cochrane Handbook, section 8.5. [41] We conducted quantitative data syntheses with and without studies identified as being at high risk of bias to determine the sensitivity of overall findings to these studies.

We synthesized frequency-based results (e.g., how many values clarification methods reflect a given design) descriptively. To synthesize effects on outcomes, we pooled all experiments that evaluated a values clarification method against no values clarification method or an implicit method. For multi-armed studies in which the comparison of a decision aid with and without a values clarification method included an arm that was not relevant to our comparison of interest (for example, an information booklet serving as a control condition in an evaluation of the decision aid) we ignored the third arm. For multi-armed studies containing two or more different values clarification methods and one arm of implicit values clarification or control, we considered each comparison of a values clarification method against implicit values clarification, meaning that each of the multiarmed studies included in this review contributed multiple comparisons to the pooled set.

To meta-analyze results for values congruence, we pooled results using risk differences and applying a random effects model. We extracted dichotomous data indicating the frequency (i.e., number of events and sample size) of values discongruent decisions. To meta-analyze results for decisional conflict, we pooled results using standardized mean differences applying a random effects model. We extracted data on total scores on the Decisional Conflict Scale. We explored and reported consistency using Higgins I^2 [42] . We used the Cochrane Risk of Bias tool to assess study bias along 7 domains as well as to assess an overall risk . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint of bias. Where data permitted, we conducted subgroup meta-analyses of different types of explicit values clarification methods and of explicit values clarification methods that do and do not contain specific design features already identified in previous work [11] , namely, whether the method explicitly requires the user to engage with tradeoffs, whether it explicitly provides the user with the implications of what they value, and which, if any, theoretical or conceptual framework underpins it. We used p=0.05 as a threshold for statistical significance and conducted analyses in RevMan, version 5.4.

Out of 30,648 records screened at the title and abstract stage and 279 screened at the full text stage, we identified 33 articles that met our inclusion criteria describing trials of 43 values clarification methods. Twentyfour of the articles were new articles identified in this update of IPDAS. We excluded 2 of the articles previously included in the IPDAS Values Clarification Chapter because they did not meet our revised inclusion criteria requiring randomized controlled trials and instead reported, for example, pre-post study designs. The PRISMA diagram of included articles is shown in Figure 1 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

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The decision context varied across studies. Out of the 43 included trials, 25 (58%) addressed treatment decisions, 9 (21%) screening decisions, 4 (9%) prevention, 3 (7%) genetic testing, and 2 (5%) diagnostic testing. Thirteen of the 43 trials (30%) centered around a yes/no decision to take an option or not, 18 (42%) a choice between two or more options and 12 (28%) both a yes/no and a choice between two or more options. Most decisions (22/43, 51%) were real decisions, meaning that the person was making this decision in their actual life. The rest were hypothetical (18/43, 42%) or it was not entirely clear whether the decision was real or hypothetical (3/43, 3%). The most commonly-reported outcomes were decisional conflict and/or its subscales (29/43, 67%), decision and/or decisional intentions (22/43, 51%), knowledge (13/43, 30%) , and values congruence (12/43, 28%).

As shown in the overview of included studies in Table 3 , there was substantial diversity in the types of values clarification methods used. Decision analysis or multicriteria decision analysis was the most commonly trialed method. Full study details are available in Online Appendix 2. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

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The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint partner was pregnant or planning to become pregnant in the next 2 years variants in one's child

Peinado and others 2020 [10] n=1000 people aged 18-44 who were pregnant or whose partner was pregnant or planning to become pregnant in the next 2 years Whether or not to enroll their newborn child in a medical research study that would involve screening for genetic conditions Values clarification method decreased decisional regret and increased clarity of personal values but had no effect on overall decisional conflict nor on intent to have one's child tested.

Witteman and others 2020 [48] n=772 adults asked to imagine they had been diagnosed with colon cancer Choice between two hypothetical surgeries for colon cancer Values clarification method (strategy 4b in paper) reduced decisional conflict but did not change values congruence. Values clarification method had no effect. Decisional conflict, perceived values congruence, and self-efficacy for health behaviours improved with and without . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. *n is given for the study as a whole. See supplementary appendix for further details about each study. **Decision analysis or multicriteria decision analysis is an umbrella term. It encompasses some of the other, more specific categories (e.g., discrete choice experiments, best-worst scaling.) Throughout the paper, when applicable, we use the more specific, narrower categories. Otherwise, we use the umbrella term "multicriteria decision analysis," or, for brevity in figures, "decision analysis." ***Garvelink and colleagues 2014 and Witteman and colleagues 2020 each reported multiple experiments testing values clarification methods that did not differ in type nor in outcomes. Pooled results are therefore presented here.

Overall study quality was acceptable, with the majority of studies at low risk of bias on most elements. Eight studies were deemed to be at high risk of bias on one element; the majority in Blinding of Participants and Personnel (Performance Bias). Eighteen additional studies were deemed unclear on this element. Blinding of Outcome Assessment (Detection Bias) was the next most common source of potential bias, with 1 study at high risk of bias and 20 more unclear. Full details of risk of bias assessments are available in Online Appendix 3.

As shown in Figure . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

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As shown in Figure 3a , explicit values clarification methods decrease decisional conflict. For the 14/43 (33%) trials for which we had complete data, the pooled standardized mean difference for decisional conflict was -0.20 95% CI [-0.29 to -0.11], p<0.001. The I 2 of 67% represents moderate to high statistical heterogeneity. Figure 3b shows there was no significant subgroup difference by type of values clarification method (Chisquared(2)=6.08, p=.05). We found no significant subgroup differences by trade-offs, implementation, theory, implication, nor risk of bias (see Online Appendix 3). 

The five studies that compared values clarification methods to each other reported findings that align with the findings of our meta-analyses. Methods that provided users with explicit feedback regarding how the decision options align with their stated values led to somewhat better outcomes, including greater values congruence.

[48] When asked to compare methods to each other, study participants also preferred a values clarification method that explicitly showed them how the decision options align with their stated values.

[72] Different values clarification methods yielded different patterns of attribute importance. [59-61] Brief summaries of each study are available in Online Appendix 3.

Overall, our systematic review and meta-analyses confirm that explicit values clarification methods improve decision outcomes, notably by increasing values congruence and decreasing decisional conflict. Patient decision aids should include an explicit values clarification method.

While the best explicit values clarification method may depend on context-for example, urgent versus routine care or the extent to which a decision has a clear set of decision attributes-our analyses suggest that patient decision aid developers may wish to consider methods that draw on multicriteria decision analysis. The apparent advantages of such methods shown in our analyses may reflect similarities between the process and the outcome. In other words, increased values congruence yielded by decision analytic methods may be a function of the ways in which such methods transparently show people how their options align with their stated values. We additionally caution that when these methods use pre-specified attributes, there might not be the . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint flexibility for users to add new attributes, highlighting the importance of research to inform attribute selection. We acknowledge that some researchers have argued that health professionals having an unhurried, highquality conversation with patients may be a preferred approach for at least some patients, especially when decision attributes are many and varied. However, in this systematic review, trials of Open Discussion values clarification methods did not demonstrate strong results, suggesting that such an ideal may be difficult to achieve.

To advance further knowledge on the merits and pitfalls of different values clarification methods, we recommend that authors of future trials of values clarification methods report four outcomes: decisional conflict, decision or decision intention, values congruence, and decisional regret. When possible, authors should make use of validated scales that have good psychometric properties and are commonly reported, as this facilitates evidence synthesis.

Decisional conflict should be assessed before people make the decision, using a version of the Decisional regret should be assessed with a version of the Decisional Regret Scale [78, 79] after people make the decision, ideally with a sufficiently long delay that longer-term effects can be captured. An included study in this review showed that a values clarification method reduced decisional regret, but only after a year had passed following implementation of the decision. [54] For all four measures, authors should clearly report sample mean and sample standard deviation for continuous measures, numbers in each category for categorical measures, and sample size per study arm in all cases. Finally, we recommend that patient decision aid developers explain the rationale for their choice of values clarification method.

Our study has three main limitations. First, the included data were of moderate quality. Although this review includes many robust trials, the included studies often measured different outcomes or the same outcomes in different ways, there were missing data in some studies, some studies had high risk of bias (often because it was not possible to prevent study participants from ascertaining the study arm to which they were assigned), and some of our meta-analyses had high heterogeneity. Together, these issues suggest a degree of caution in our conclusions. Second, we did not distinguish between subtypes of values clarification methods. For example, different adaptive conjoint analysis exercises may be very different from each other, as might open discussions, or many other values clarification methods we grouped together, particularly those we grouped under the broad umbrella term of multicriteria decision analysis. Indeed, the values clarification methods used and trialed may simply reflect authors' interests and expertise. Given the breadth of methods available, further comparative effectiveness research is needed to conclusively determine the superiority of any given method. Third and finally, our primary findings were heavily influenced by studies conducted with relatively homogenous populations making hypothetical decisions. Although our sensitivity analyses suggested no differences . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint between studies in real and hypothetical contexts, we nonetheless believe further study is needed in more diverse populations making real decisions before drawing firmer conclusions.

Our study also has three main strengths. First, we catalog definitions and resources regarding values clarification methods, as well as recommended outcomes to report in studies. In doing so, we hope to offer more clarity and structure to a literature that can be confusing to navigate, particularly for those who are newer to developing patient decision aids. Second, we begin to answer a core question that commonly arises when developing a patient decision aid: when including a values clarification method, which type of method should one use? Third and finally, we used rigorous methods and an expansive, systematic search. By conducting a systematic review, we reduced our likelihood of missing relevant studies. By including meta-analyses, we offer stronger findings and recommendations than would be possible without pooling data across multiple studies.

In conclusion, patient decision aids should include an explicit values clarification method. Patient decision aid developers may wish to consider the potential advantages of multicriteria decision analysis. Future research should further investigate which methods lead to the best outcomes across or within particular decisions, populations, and settings. Authors of randomized controlled trials in this area should report decisional conflict, decision made, values congruence, and decisional regret.

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Full data are available in the online appendices.

None.

This study was funded by the Canadian Institutes of Health Research (CIHR) FDN-148426 (PI Witteman). HOW receives salary support from Tier 2 Canada Research Chair in Human-Centred Digital Health and received salary support during this study from a Fonds de Recherche du Québec-Santé (FRQS) Research Scholar Junior 2 Career Award. The CIHR, Canada Research Chairs program, and FRQS had no role in determining the study design, the plans for data collection or analysis, the decision to publish, nor the preparation of this manuscript.

All authors contributed to the design of the study. HOW, RN, GV, SCD, SC, MM, TP, CR, CRB, JJ contributed to data collection. HOW, SCD, and JJ conducted data analysis and interpretation. HOW drafted the first version of the article with early revision by RN, GV, SCD, SC, MM, TP, CR, CRB, JJ and multiple subsequent revisions by all authors. All authors critically revised the article and approved the final version for submission for publication. HOW had full access to all the data in the study and had final responsibility for the decision to submit for publication. Authors BA, JFPB, SC, AF, TG, MM, AP, MP, TP, CR, DR, CRB, PT, MW, DBW, CEW contributed approximately equally and are listed alphabetically by last name.

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The copyright holder for this preprint this version posted January 25, 2021. [79] Decision regret scale -evaluation measures -patient decision aids -Ottawa hospital research institute, https://decisionaid.ohri.ca/eval_regret.html (accessed 20 January 2021).

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The copyright holder for this preprint this version posted January 25, 2021. #70 NOT (animals/ NOT humans/) #1 ((value* OR "patient preference" OR "treatment preferences") NEAR/5 (clarif* OR elicit*)):ti,ab,kw #2 'decision support system'/de OR 'patient decision making'/de #3 MCDA:ti,ab,kw #4

"analytical hierarchy process":ti,ab,kw #5

"best-worst scaling":ti,ab,kw #6

((conjoint OR decision) NEAR/3 analysis):ti,ab,kw #7

"data envelopment analysis":ti,ab,kw #8

"Decision conferencing":ti,ab,kw #9

"Decision models":ti,ab,kw #10

"direct rating":ti,ab,kw #11

"points allocation":ti,ab,kw #12

"discrete choice experiment":ti,ab,kw #13

("dominance-based" NEAR/3 approach*):ti,ab,kw #14

"EVIDEM framework":ti,ab,kw #15

"geometrical analysis for interactive aid OR GAIA":ti,ab,kw #16

MACBETH:ti,ab,kw #17

("Measuring Attractiveness" NEAR/4 "Categorical Based Evaluation TecHnique"):ti,ab,kw #18

"Multi-Attribute Global Inference of Quality":ti,ab,kw #19

(("Multiple attribute" OR "multiple criteria" OR multiattribute) NEAR/2 (utility OR theory OR analysis)):ti,ab,kw #20

( 

((value* OR "patient preference*" OR "treatment preference*") NEAR/5 (clarif* OR elicit*)):ti,ab #2

("Decision Making":kw AND "Social Values":kw) #3

MCDA:ti,ab #4

"analytical hierarchy process":ti,ab #5

best-worst scaling:ti,ab #6

((conjoint OR decision) NEAR/3 analysis):ti,ab #7

"Decision conferencing":ti,ab #8

"Decision models":ti,ab #9

"direct rating":ti,ab #10

"points allocation":ti,ab #11

"discrete choice experiment":ti,ab #12

("dominance-based" NEAR/3 approach*):ti,ab #13

"Elimination and Choice Expressing Reality":ti,ab #14

"EVIDEM framework":ti,ab #15

"geometrical analysis for interactive aid" OR GAIA:ti,ab #16

MACBETH:ti,ab #17

("Measuring Attractiveness" NEAR/4 "Categorical Based Evaluation TecHnique"):ti,ab #18

"Multi-Attribute Global Inference of Quality":ti,ab #19

"multiattribute objective function specification":ti,ab #20

(("Multiple attribute" OR "multiple criteria" OR multiattribute) NEAR/2 (utility OR theory OR analysis)):ti,ab #21

(MAUT OR MAVT OR MCUA OR MCA):ti,ab #22

("Novel approach to imprecise assessment and decision environments" OR NAIADE):ti,ab #23

ORESTE:ti,ab #24

"Pairwise comparisons":ti,ab #25

PAPRIKA:ti,ab #26

"Pairwise RanKings":ti,ab #27 PROMETHEE:ti,ab #28

"Preference Ranking Organization Method for Enrichment of Evaluations":ti,ab #29

"QUALItative FLEXible":ti,ab #30

"Simple Multi Attribute Rating Technique":ti,ab #31

SMART:ti,ab #32

"Standard gamble":ti,ab #33

"Swing weighting":ti,ab

TOPSIS:ti,ab #35

"Technique for Order Preference by Similarity to the Ideal Solution":ti,ab #36

("Time tradeoff" OR "time tradeoff"):ti,ab #37

"Value function methods":ti,ab #38

"Valutazione delle Tecnologie Sanitarie":ti,ab #39

VDA:ti,ab #40

VTS:ti,ab #41

"verbal decision analysis":ti,ab #42

"visual analog scale":ti,ab #43

"willingness-to-pay":ti,ab #44

scoring method*:ti,ab OR "weighting method*":ti,ab #45 REGIME:ti,ab #46

(scal* NEAR/2 (methods OR Natural OR Constructed OR Objective)):ti,ab

TS=(((value$ OR "patient preference$" OR "treatment preference$") NEAR/5 (clarif* OR elicit*))) #2

TS=((conjoint OR decision) NEAR/3 analysis) #3

TS=(MCDA) #4

TS=("analytical hierarchy process") #5

TS=("best-worst scaling") #6

TS=("data envelopment analysis") #7

TS=("Decision conferencing") #8

TS=("Decision models") #9

TS=("direct rating") #10

TS=("points allocation") #11

TS=("discrete choice experiment") #12

TS=("dominance-based" NEAR/3 approach*) #13

TS=("Elimination and Choice Expressing Reality") #14

TS=("EVIDEM framework") #15

TS=("geometrical analysis for interactive aid" OR GAIA) #16

TS=(MACBETH) #17

TS= ("Measuring Attractiveness" NEAR/4 "Categorical Based Evaluation TecHnique") #18

TS=("Multi-Attribute Global Inference of Quality") #19

TS=(("Multiple attribute" OR "multiple criteria" OR multiattribute) NEAR/2 (utility OR theory OR analysis)) #20

TS=(MAUT OR MAVT OR MCUA OR MCA) #21

TS=("Novel approach to imprecise assessment and decision environments" OR NAIADE) #22

TS=("Technique for Order Preference by Similarity to the Ideal Solution") #34 TS=("time tradeoff" OR "time tradeoff") #35

TS=("Value function methods") #36

TS=("Valutazione delle Tecnologie Sanitarie") #37

TS=("verbal decision analysis") #40

TS=("visual analog scale") #41

TS=("willingness-to-pay") #42

TS= "clinical trial" #47 randomization #48

"crossover procedure" #49 placebo #50

"prospective study" #51

(randomi$ed NEAR/1 "controlled trial") #52 rct #53

(allocat* NEAR/2 random*) #54

((single OR double OR treble OR triple) NEAR/1 blind*)) #55

#46 OR #47 OR #48 OR #49 OR #50 OR #51 OR #52 OR #53 #54 #56

(#1 OR #45) AND #55

TI ((value# OR "patient preference#" OR "treatment preference#") N5 ((clarif* OR elicit*)) #2

AB ((value# OR "patient preference#" OR "treatment preference#") N5 ((clarif* OR elicit*)) #3

MH "Values Clarification" #4

MM "Decision Support Techniques" #5 TI ((conjoint OR decision) N3 analysis) OR AB ((conjoint OR decision) N3 analysis) #6

TI MCDA OR AB MCDA #7

TI "analytical hierarchy process" OR AB "analytical hierarchy process" #8

TI "best-worst scaling" OR AB "best-worst scaling" #9

TI "data envelopment analysis" OR AB "data envelopment analysis" #10

TI "Decision conferencing" OR AB "Decision conferencing" #11

TI "Decision models" OR AB "Decision models" #12

TI "direct rating" OR AB "direct rating" #13

TI "points allocation" OR AB "points allocation" #14

TI "discrete choice experiment" OR AB "discrete choice experiment" #15 TI ("dominance-based" N3 approach*) OR AB ("dominance-based" N3 approach*) #16

TI "Elimination and Choice Expressing Reality" OR AB "Elimination and Choice Expressing Reality" #17

TI "EVIDEM framework" OR AB "EVIDEM framework" #18

TI "geometrical analysis for interactive aid" OR GAIA OR AB "geometrical analysis for interactive aid" OR GAIA #19

TI MACBETH OR AB MACBETH #20 TI ("Measuring Attractiveness" N4 "Categorical Based Evaluation TecHnique") OR AB ("Measuring Attractiveness" N4 "Categorical Based Evaluation TecHniq #21 TI (("Multiple attribute" OR "multiple criteria" OR multiattribute) N2 (utility OR theory OR analysis)) OR AB (("Multiple attribute" OR "multiple criteria" OR m #22

TI (MAUT OR MAVT OR MCUA OR MCA) OR AB (MAUT OR MAVT OR MCUA OR MCA) #23

TI ORESTE OR AB ORESTE #24

TI "Pairwise comparisons" OR AB "Pairwise comparisons" #25

TI PAPRIKA OR AB PAPRIKA #26

TI "Pairwise RanKings" OR AB "Pairwise RanKings" #27

TI SMART OR AB SMART #28

TI "Standard gamble" OR AB "Standard gamble" #29

TI "Swing weighting" OR AB "Swing weighting" #30

TI TOPSIS OR AB TOPSIS #31

TI "Technique for Order Preference by Similarity to the Ideal Solution" OR AB "Technique for Order Preference by Similarity to the Ideal Solution" #32 TI ("Time tradeoff" OR "time tradeoff") OR AB ("Time tradeoff" OR "time tradeoff") #33

TI VDA OR AB VDA

TI "visual analog scale" OR AB "visual analog scale" #36

TI "willingness-to-pay" OR AB "willingness-to-pay" #37

TI Scoring OR "weighting method#" OR AB Scoring OR "weighting method#" #38

TI REGIME OR AB REGIME #39

TI (scal* N2 (methods OR Natural OR Constructed OR Objective)) OR AB (scal* N2 (methods OR Natural OR Constructed OR Objective)) #40

(S5 OR S6 OR S7 OR S8 OR S9 OR S10 OR S11 OR S12 OR S13 OR S14 OR S15 OR S16 OR S17 OR S18 OR S19 OR S20 OR S21 OR S22 OR S23 OR S24 OR S25 OR #41

TX allocat* random* #42

MH "Quantitative Studies" #43

MH "Placebos" #44

TX placebo* #45

TX random* allocat* #46

MH "Random Assignment" #47

TX randomi* control* trial* #48

TX ((singl* n1 blind*) OR (singl* n1 mask*)) #49

TX ((doubl* n1 blind*) OR (doubl* n1 mask*)) #50

TX ((tripl* n1 blind*) OR (tripl* n1 mask*)) #51

TX ((trebl* n1 blind*) or (trebl* n1 mask*)) #52

TX (clinic* n1 trial*) #53

PT "Clinical trial" #54

MH "Clinical Trials+" #55 #52 OR #65 #56

(#1 OR #2 OR #3 OR #4 OR #40) AND #55 After information section about adjuvant chemotherapy and trial (i.e. after they had read the detailed information but before they were asked for their final decision).

The values clarification tasks involved a paper-based summary of the benefits and risks information for both the options situated on either side of a weigh-scale. Women performed either an implicit or an explicit task. They then were led to a screen where they ranked each of the 7 other attributes on a scale from 1-10 ("not nearly as important" to "just as important") compared to the first chosen attribute. These rankings were used by the software to generate an initial estimate of each subject's values. Next, participants were presented with 2 treatment choices (described to be "exactly the same except for the differences [provided]") and asked to select their preference (weighted scale ranging from "strongly prefer left" to "strongly prefer right"). Each treatment option provided was described with the same 2 attributes at 2 different levels. After an information section on living with osteoarthritis.

Computer-based Math based model Adaptive Conjoint Analysis Adaptive Conjoint Analysis Participants first ranked different routes of administration, they then rated different alternatives, finally they rated a series of paired comparisons before being presented with a scale comparing the relative importance of their choices.

In the first series of questions, respondents were asked to rank different routes of administration. In the second, participants rated the importance of the difference between the best and worst alternative for each treatment characteristic.

In the third series of questions respondents were asked to rate a series of paired-comparisons. Participants were subsequently given a handout illustrating the relative influence of each characteristic on their treatment preferences and a scale showing the relative ranking of the options. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted January 25, 2021. Interactive web-based tool that synthesizes key findings from published network metaanalyses on the benefits and tolerability and harms of the five most commonly used cholesterol-lowering treatments, statins. The tool allows users to specify the importance of each benefit and harm outcome according to their own preferences, and this information is combined with information on the relative effects of the statins obtained from the network meta-analyses. The ranking is therefore based on two components: 1) user preferences for different outcomes and 2) performance of individual statins on these different outcomes. While the former is directly entered by the user by moving the cursor from ''Not important'' to ''Very important,'' the latter is obtained from published network meta-analyses. Network meta-analyses forming the basis of the tool adopted a Bayesian 1 Cancer -breast Behavioral models of decision-making After information education on patterns of inheritance, benefits, limitations and risks of testing, and limitations of options.

Verbal (non-directive counseling using a semistructured protocol)

List of concerns (semistructured interview)

List of concerns Discuss only After the education section, participants receiving nondirective counseling where five sets of issues were discussed using a semistructured protocol.

The following specific issues were addressed using a semistructured protocol: (a) experience with cancer in the family, including psychosocial impact; ( Before possible information (participants randomized to receive video and leaflet after decision analysis or not)

Computer-based Math based model Decision Analysis Decision analysis Patients' utilities were assessed using a computerised selfcompleted interview with minimal input from the researcher. Individual absolute cardiovascular risk was calculated and combined with utilities using decision analysis software. The purpose of the decision tree and the individual nature of its components were explained to participants. At the end of the intervention, participants were given a printed sheet detailing their cardiovascular risk factors and summarising the decision analysis; that is, whether the 'optimal' decision determined by maximised expected utility would be to accept or decline pharmacological treatment.

Decision analysis is a technique to aid decision making when uncertainty exists over the balance between benefits and risks of treatment.The technique employed here was a simple decision tree, constructed to include likely outcomes of treatment options. These outcome health states were rated by patients to give utility values, represented on a quantitative scale between 0 and 1. The decision tree for hypertension used in the present study, sources of probability data, and descriptions of the standard gamble method of assessing patient utilities have been reported previously. Patients' utilities were assessed using a computerised self-completed interview with minimal input from the researcher. Individual absolute cardiovascular risk was calculated and combined with utilities using decision analysis software. The purpose of the decision tree Women were given information about the outcomes associated with each form of delivery. Then women were required to consider the value they attached to possible outcomes by rating each on a visual analogue scale from 0 to 100.

Women were given information about the outcomes associated with planned vaginal delivery, elective caesarean section, and emergency caesarean section. This comprised descriptions, but not explicit probabilities, of outcomes for both mother and baby. Secondly, women were required to consider the value they attached to possible outcomes by rating each on a visual analogue scale from 0 to 100. Though these ratings are not strictly equivalent to utility values, we considered this to be a pragmatic method of assessment and the information produced to be sufficient for this study. We combined the values with the probabilities of each outcome in a decision tree to produce a recommended "preferred option" based on maximised expected utility. Women received a computer printout of the outcome of the decision analysis and were encouraged to discuss . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted January 25, 2021. Randomized into one of two groups: 1) the control arm which received education via a printed educational brochure; 2) the intervention arm which received the educational brochure followed by preference assessment using a software program that used discrete-choice experiments to measure preferences for key outcomes.

Preference assessment prior to decision making reduced decisional conflict in men above and beyond improvements seen with high-quality patient education alone. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted January 25, 2021. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint

Additional Meta-analytic Results Figure S1 . Values Congruence by Tradeoffs 1 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint Figure S2 . Values Congruence by Implementation/Presentation of Results 2 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint Figure S3 . Values Congruence by Stated Use of a Theory/Framework 3 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint Figure S4 . Values Congruence by Real/Hypothetical Decision 4 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint Figure S5 . Decisional Conflict by Tradeoffs 5 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint Figure S6 . Decisional Conflict by Implications/Presentation of Results 6 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint Figure S7 . Decisional Conflict by Stated Use of a Theory/Framework 7 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint Figure S8 . Decisional Conflict by Real/Hypothetical 8 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint Figure S9 . Decisional Conflict by Risk of Bias 9 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint Head-to-Head Evaluations of Values Clarification Methods Feldman-Stewart et al. (2006) found no difference across all three groups (information only; values clarification method without a summary bar, i.e. rating scales; values clarification with a summary bar, i.e. multicriteria decision analysis) in terms of the attributes participants identified as important to their decisions nor in how difficult it was to make the decision. When trial participants were unblinded at the end of the study and shown all three options, all of them ranked the bars with the summary option (multicriteria decision analysis) as the most helpful.

Pignone et al. (2012) found that a discrete choice experiment produced somewhat different patterns of attribute importance compared to ranking and rating. Agreement between the most important attribute derived from the values clarification method and the most important attribute as reported by participants in the questionnaire was slightly higher in the ranking and rating arm than the discrete choice experiment arm. The authors found no difference between study arms in terms of values clarity, intent to be screened and unlabelled screening test preference.

Pignone et al. (2013) found that different values clarification methods produced differences in attribute importance and screening test preference. Participants who received the rating and ranking test were more likely to report the chance of dying from prostate cancer as the most important attribute compared to the balance sheet and discrete choice experiment groups. Those who received the balance sheet were more likely to prefer the unlabelled PSA-like test option compared to the two other groups. Participants who received the discrete choice experiment were somewhat less likely to select reduction of mortality as the most important attribute, and were least likely to select the PSA-like option on the unlabelled preference question. There was no difference across groups in intent to be screened (labelled PSA test option) nor on values clarity.

Brenner et al. (2014) found that different values clarification methods produced different results in terms of individuals' most important screening test attributes. Specifically, respondents who received the rating and ranking exercise, compared to a discrete choice experiment or a balance sheet (i.e., implicit values clarification method), were the most likely to choose risk reduction as the most important attribute. They found no differences in terms of test preferences, values clarity, nor intention to be screened. Witteman et al. (2020) found that overall, methods using mathematical models (e.g., decision analysis, allocation of points) were more promising than other methods (e.g., pros and cons, rating scales) for encouraging values-congruent decisions. All methods encouraged lower decisional conflict when this was assessed.

. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted January 25, 2021. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

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The copyright holder for this preprint this version posted January 25, 2021. ; https://doi.org/10.1101/2021.01.21.21250270 doi: medRxiv preprint

A measure of informed choice

Patients' health-care decision making: a framework for descriptive and experimental investigations

Shared decision making: examining key elements and barriers to adoption into routine clinical practice

The connection between evidence-based medicine and shared decision making

Communicating benefits and risks of screening for prostate, colon, and breast cancer

Stop the silent misdiagnosis: patients' preferences matter

Decision support for patients: values clarification and preference elicitation

Choosing treatment and screening options congruent with values: Do decision aids help? Sub-analysis of a systematic review

Decision aids for people facing health treatment or screening decisions. Epub ahead of print

Values clarification and parental decision making about newborn genomic sequencing. Health Psychol. Epub ahead of print

Design Features of Explicit Values Clarification Methods: A Systematic Review

Effects of Design Features of Explicit Values Clarification Methods: A Systematic Review

Conjoint Analysis Applications in Health-a Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force

A Framework for Incorporating Patient Preferences Regarding Benefits and Risks into Regulatory Assessment of Medical Technologies

Identifying the Need for Good Practices in Health Technology Assessment: Summary of the ISPOR HTA Council Working Group Report on Good Practices in HTA

Section D: Clarifying and Expressing Values

The vexing problem of defining the meaning, role and measurement of values in treatment decision-making

Understanding What Is Most Important to Individuals with Multiple Chronic Conditions: A Qualitative Study of Patients' Perspectives

What Matters to Patients and Families: A Content and Process Framework for Clarifying Preferences, Concerns, and Values

Humanitarian Device Exemption Applications, and De Novo Requests, and Inclusion in Decision Summaries and Device Labeling Guidance for Industry, Food and Drug Administration Staff, and Other Stakeholders. Food and Drug Administration

Theory-informed design of values clarification methods: a cognitive psychological perspective on patient health-related decision making

Combining deliberation and intuition in patient decision support

Value Awareness: A New Goal for End-of-life Decision Making

Ordinal preference elicitation methods in health economics and health services research: using discrete choice experiments and ranking methods

A systematic review of stated preference studies reporting public preferences for healthcare priority setting

Adaptive Conjoint Analysis as individual preference assessment tool: feasibility through the internet and reliability of preferences

Development and preliminary user testing of the DCIDA (dynamic computer interactive decision application) for 'nudging'patients towards high quality decisions

Patients' preferences and priorities regarding colorectal cancer screening

Quantifying preferences for asthma control in parents and adolescents using best-worst scaling

Multiple Criteria Decision Analysis for Health Care Decision Making--An Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force

Multi-Criteria Decision Analysis to Support Healthcare Decisions

Using a Discrete-Choice Experiment in a Decision Aid to Nudge Patients Towards Value-Concordant Treatment Choices in Rheumatoid Arthritis: A Proof-of-Concept Study

Pharmacists and patients sharing decisions about medicines: Development and feasibility of a conversation guide

Ottawa Personal Decision Guides -Patient Decision Aids -Ottawa Hospital Research Institute

Preferences for outcomes of treatment for rectal cancer: patient and clinician utilities and their application in an interactive computer-based decision aid

Patient or physician preferences for decision analysis: the prenatal genetic testing decision

Utilizing computerized entertainment education in the development of decision aids for lower literate and naive computer users

Utility assessment in cancer patients: adjustment of time tradeoff scores for the utility of life years and comparison with standard gamble scores

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

Cochrane handbook for systematic reviews for interventions

Measuring inconsistency in meta-analyses

Impact of educational and patient decision aids on decisional conflict associated with total knee arthroplasty

Improving informed decision-making for patients with knee pain

Preference elicitation tool for abnormal uterine bleeding treatment: a randomized controlled trial

Efficacy of a Preference-Based Decision Tool on Treatment Decisions for a First-Time Anterior Shoulder Dislocation: A Randomized Controlled Trial of At-Risk Patients

Treatment preference and patient centered prostate cancer care: Design and rationale

RESULTS Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage

Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations

Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12)

For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot

Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency

Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15)

The authors gratefully acknowledge Frédéric Bergeron, MLIS, for assistance with search strategy and Caroline Beaudoin for assistance in resolving article counts. We thank all authors of the original articles who generously gave their time to provide missing data when we were unable to extract the data needed from their papers.

na Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.

Search Appendix 1Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

Risk of bias in individual studies 12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

Summary measures 13 State the principal summary measures (e.g., risk ratio, difference in means).

Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I 2 ) for each meta-analysis.

Section/topic # Checklist item Reported on page # Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.

Appendix 3Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16] ).14-17

Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).

Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research.