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The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials with Discrete-Time Survival Endpoints.

Methodology 2017;13:41-60. [doi: 10.1027/1614-2241/a000121]

Shahab Jolani, PhD (Maastricht University), Maryam Safarkhani, PhD (Utrecht University, The Netherlands).

In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. This paper considers different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (CTN-0029), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome.

In conclusion, complete case analysis is wasteful and drops the power level to a large degree, resulting in an undetectable treatment effect. Also, it can introduce bias in the estimate of the treatment effects when the missingness mechanism depends on the outcome variable. This method is therefore invalid and the authors do not recommend it. Instead, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data. (Article (Peer-Reviewed), PDF, English, 2017)

Keywords: Attention Deficit Hyperactivity Disorder (ADHD) | CTN platform/ancillary study | Missing data | Statistical analysis | Statistical models | Methodology (journal)

Document No: 1258.

Submitted by the CTN Dissemination Librarians (3/7/2017).

Jolani, Shahab mail
Safarkhani, Maryam
NIDA-CTN-0029 www

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Supported by a grant from the National Institute on Drug Abuse to the University of Washington Alcohol and Drug Abuse Institute.
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