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

Shahab Jolani*, Maryam Safarkhani

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Web of Science)

Abstract

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. We consider 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 (ADHD), 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, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

Original languageEnglish
Pages (from-to)41-60
Number of pages20
JournalMethodology-European Journal of Research Methods for the Behavioral and Social Sciences
Volume13
Issue number2
DOIs
Publication statusPublished - Apr 2017

Keywords

  • discrete-time survival model
  • missing covariate
  • multiple imputation
  • power analysis
  • randomized trial
  • FULLY CONDITIONAL SPECIFICATION
  • MULTIPLE IMPUTATION
  • INDICATOR METHOD
  • VALUES
  • ADJUSTMENT
  • REGRESSION
  • OUTCOMES
  • MODEL

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