key: cord-0814584-3n060djb
authors: Lo, Ernest; Lasnier, Benoit
title: Active smoking and severity of coronavirus disease 2019 (COVID-19): the use of significance testing leads to an erroneous conclusion
date: 2020-05-08
journal: Eur J Intern Med
DOI: 10.1016/j.ejim.2020.05.003
sha: e91d9f2682bf7f83c2a237f7e659ab56f342d4b4
doc_id: 814584
cord_uid: 3n060djb

nan

Institut While the presentation of confidence intervals is commendable, the comparison of a confidence interval limit with a null value to determine whether or not an effect exists is essentially a surrogate for a null hypothesis test with level α=5% [4] . This practice has been strongly criticized and described as a "misuse" or "debasing" of the confidence interval [5] .

The estimation approach has been proposed as a more correct and clinically informative statistical approach for interpreting the results of medical studies [6, 7] . In contrast to NHST, this approach correctly recognizes that the entire confidence interval represents a range of plausible values for the OR [6, 7] . Thus while the point estimate of 1.69 of the Lippi et al. study represents the expected or mean value, the OR has a probability distribution that extends to either side such that values from 0.41 to 6.92 are also quite probable [7] . In fact the bulk of the confidence interval extends well beyond the null value of 1 (Figure 1 ). Mathematical integration of the OR probability distribution (assuming a Normal distribution for the log odds ratio) indicates a 77% probability that the estimated OR exceeds 1, while the probability of it exceeding hypothetical clinical thresholds of 1. The inappropriateness of using null hypothesis significance testing to conclude an absence of effect has been well documented in the clinical literature [8, 9] . The Lippi et al. study is analogous to an underpowered clinical trial where elevated variability, as reflected in the wide confidence intervals, precludes the ruling out of a non-null or clinically important association. A more appropriate conclusion for this study is that a clinically important association is uncertain due to the lack of sample size, but that the effect of smoking on COVID-19 severity remains highly possible; further and larger studies should be Amidst the pressing need to conduct and disseminate new research on COVID-19, there remains more than ever the need for researchers and practitioners of evidence based medicine to apply sound methods of statistical inference in the interpretation of their results. Null hypothesis significance testing, without consideration of the range of plausible effect sizes, is known to produce conclusions that are non-reproducible and represent over-simplistic dichotomies that can impede scientific progress [2, 3] .

The estimation approach with the proper use of confidence intervals is greatly preferred, and in this case, indicates there is a substantial possibility of an association between smoking and COVID-19 severity.

Active smoking is not associated with severity of coronavirus disease 2019 (COVID-19)

Moving to a World Beyond " p < 0.05

The ASA's Statement on p -Values: Context, Process, and Purpose

Clinical epidemiology: the essentials

Disengaging from statistical significance

Confidence intervals rather than P values: estimation rather than hypothesis testing

The new statistics: why and how

Absence of evidence is not evidence of absence

The Importance of Beta, the Type II Error and Sample Size in the Design and Interpretation of the Randomized Control Trial

Smoking is Associated with COVID-19 Progression: A Meta-Analysis. Public and Global Health

Conflict of Interest Statement Re: Active smoking and severity of coronavirus disease (COVID-19): the use of significance testing leads to an erroneous conclusion The authors Ernest Lo and

The authors would like to thank Denis Hamel and Shu Qin Wei for their helpful comments on a previous version of this manuscript.