key: cord-0977569-35m4h9q8
authors: Proschan, Michael A.
title: Discussion on “Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment for binary, ordinal, and time‐to‐event outcomes”
date: 2021-06-09
journal: Biometrics
DOI: 10.1111/biom.13493
sha: 380f8f0b1460fc862208252da1adc831fbbd16ba
doc_id: 977569
cord_uid: 35m4h9q8

Benkeser et al. present a very informative paper evaluating the efficiency gains of covariate adjustment in settings with binary, ordinal, and time‐to‐event outcomes. The adjustment method focuses on estimating the marginal treatment effect averaged over the covariate distribution in both arms combined. The authors show that covariate adjustment can achieve power gains that could find answers more quickly. The suggested approach is an important weapon in the armamentarium against epidemics like COVID‐19. I recommend evaluating the procedure against more traditional approaches for conditional analyses (e.g., logistic regression) and against blinded methods of building prediction models followed by randomization‐based inference.

for their excellent and timely work to improve the statistical analysis of COVID-19 trials with binary, ordinal, and time-to-event outcomes by incorporating covariate information. The methods they describe are under-utilized and not well appreciated by the clinical trial community. Picking appropriate endpoints and analysis methods can be very challenging in the context of a pandemic . On the one hand, we want simple, robust methods that will provide unequivocal evidence about a treatment. On the other hand, we want an answer as quickly as possible. The latter consideration argues for taking full advantage of covariate information to reduce sample size.

The marginal approach to covariate adjustment evaluated in Benkeser et al. is different from the conditional approach often used in clinical trials. For example, suppose that is binary. The traditional covariate adjustment method uses logistic regression to model the conditional probability ( = 1 | , ), and then makes inferences Published 2021. This article is a U.S. Government work and is in the public domain in the USA. about the treatment effect using the coefficient for the arm variable . Correct conclusions are contingent on correctness of this conditional model. Logistic regression is only the first step for Benkeser et al. They use it to estimate ( = 1 | = 0, ) and ( = 1 | = 1, ). They compute arm-specific marginal event probabilities ( = 1 | = ) by averaging ( = 1 | = , ) over the covariate distribution of all patients (combined across arms). The treatment effect estimate is some function of these arm-specific marginal probabilities (e.g., the risk difference, relative risk, or odds ratio). Irrespective of the correctness of the logistic regression model, the marginal estimator provides a valid measure of treatment effect.

Benkeser et al. adopt a similar marginal approach for ordinal and time-to-event outcomes. A model is used to compute the conditional cumulative distribution function (CDF) given covariates, and arm-specific marginal CDFs are estimated by averaging over the covariate distribution combined across arms. The treatment effect estimate is some function of the marginal CDFs. For ordinal (resp., time-to-event) outcomes, the treatment effect estimate is the difference in means, Mann-Whitney estimand, or log odds ratio (resp., the difference in restricted mean survival time or survival probability at a specific time, or relative risk at a specific time).

An important aspect of the proposed approach is that the initial conditional model allows separate coefficient values in the two arms. A similar idea has been used with missing data; imputation models may use separate coefficients in the different arms to increase the ability to predict the value of the missing outcome. Clinical trialists view this aspect with skepticism because of the perception that the type I error rate might be inflated. Nonetheless, asymptotic arguments and simulation results of Benkeser et al. show that tests and confidence intervals for this marginal estimator have correct error rates and achieve efficiency gains relative to unadjusted estimates of treatment effect. Using separate coefficients in the two arms means there will be a nonzero treatment by covariate interaction estimate. In most randomized trials, dramatic between-arm differences in true slopes are unlikely, begging the question of how much would be lost in assuming equal slopes. The traditional view is that a model with interaction makes interpretation difficult. The marginal approach posits that the average effect over the observed distribution of covariate values remains a meaningful summary. Still, the marginal approach that uses different slopes may complicate the combining of treatment effects across trials or even across time in the same trial. For example, the authors discuss group-sequential monitoring. The distribution of covariate values might change over the course of the trial. Earlier patients might be sicker or less sick than later patients. The estimand from the marginal approach may not be estimating the same thing over time. Of course, the treatment effect could change over time in a clinical trial regardless of the analysis method, but it seems worse when the sole reason for a changing treatment effect is that the distribution of baseline covariates is changing. With the conditional approach with no interactions, the treatment effect (e.g., adjusted odds ratio) is assumed to be the same for different covariate values. If true, this assumption facilitates synthesis of results over time, across subgroups, or across trials.

A different approach proposed by Gail et al. (1988) for testing can also be used to construct confidence intervals, at least in the binary outcome case. Using all patients from both arms and without knowledge of treatment assignments, build a regression model using any techniqueexamination of diagnostic plots, stepwise regression, and so on. Let̂be the predicted value of using the final regression model, and let = −̂be the th residual. Compute the observed treatment effect aŝ=̄−̄, the difference in average residuals between treatment and F I G U R E 1 An extreme example under which blinded model selection can go wrong. The correct model (parallel dashed lines) shows a positive slope between and and a large difference in intercepts in the two arms, but blinded model selection using all data results in a negative slope between and control arms. Compute a p-value using a randomization test. For binary endpoints, one can view the estimand as the covariate-adjusted number of events saved per person treated. A confidence interval can also be computed by inverting the randomization test using the potential outcomes approach as described by Wang and Rosenberger (2020) . An important advantage is that randomizationbased methods can be used even with covariate-adaptive randomization (see Simon and Simon, 2011) . In contrast, Benkeser et al. require randomization to be independent of covariate values (although they later point out that it can be modified to accommodate stratified randomization).

The blinded alternative method described above can fail spectacularly in very unrealistic settings. For example, consider the analysis of covariance setting of a continuous endpoint and a single, continuous covariate in addition to the treatment indicator . Figure 1 depicts a scenario in which the values are dramatically different in the treatment and control arms and there is a huge treatment effect (difference in intercepts between the two dashed lines). The usual unblinded analysis estimates the common slope of the relationship between and as +1.02, but the blinded analysis yields an estimated slope of −1.24 (solid line). The resulting treatment effect estimate is greatly underestimated. Examples like this are very unlikely because they require a large difference between values in the two arms and a huge treatment effect. The problem can be avoided altogether using an automated, unblinded selection of model followed by a randomization test on the entire process. Specifically, one could follow these steps:

(1) re-randomize with the same method that was used in the actual trial, (2) apply the automated model selection procedure that includes covariates and the treatment variable, (3) compute the covariate-adjusted treatment effect estimate, (4) repeat steps 1-3 thousands of times, and (5) see where the observed treatment effect estimate from the original randomization lies with respect to this randomization distribution. Confidence intervals can also be computed using the method described by Wang and Rosenberger (2020) .

It would be interesting to see if the above randomizationbased methods that account for covariates perform similarly to the methods studied by Benkeser et al. Also of interest is how marginal approaches compare with conditional methods such as logistic regression and proportional hazards regression in terms of efficiency.

Benkeser et al. should be congratulated for showing that robust and under-utilized methods improve efficiency and reduce sample sizes, relative to unadjusted methods, for trials of infectious diseases such as COVID-19. One small correction is that the primary outcome of Beigel et al. (2020) was time to recovery, not time to death. O R C I D Michael A. Proschan https://orcid.org/0000-0002-9161-3739

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How to cite this article: Proschan MA. Discussion on "Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment for binary, ordinal, and time-to-event outcomes