A method to visualize a complete sensitivity analysis for loss to follow-up in clinical trials

Enrique F. Schisterman, Elizabeth A. DeVilbiss, Neil J. Perkins

Research output: Contribution to journalArticlepeer-review

Abstract

Loss to follow-up occurs in randomized controlled trials. Missing data methods, including multiple imputation (MI), can be used but often rely upon untestable assumptions. Sensitivity analysis can quantify violations of these assumptions. Since an adequate sensitivity analysis requires evaluation of multiple scenarios, presenting this information in an easily interpretable manner is challenging. We propose to graphically represent a thorough sensitivity analysis displaying all possible outcomes for loss to follow-up in randomized controlled trial data relating a completely observed binary exposure to a binary outcome. We describe plausible results under different missingness mechanisms using data from the EAGeR Trial (n = 1228) on low-dose aspirin versus placebo on pregnancy and live birth, in which 140 participants had early withdrawal. For the effect of aspirin on live birth, sensitivity analysis risk ratios (RR) for all potential outcome scenarios ranged from 0.88 to 1.34, applicable to any possible missingness mechanism. MI produced RR = 1.10; 95% confidence interval: (0.98, 1.22). RRs from individual imputations ranged from 1.04 to 1.16, the range of results that could have been observed if data were missing at random. Under this mechanism, the conclusions about the efficacy of low-dose aspirin could have been sensitive to the missing outcome data. Rather than limiting sensitivity analysis for loss to follow-up to a few scenarios that can be presented tabularly, results of a complete sensitivity analysis can be presented in a single plot, which should be implemented in all studies with missing outcome data to convey certainty or uncertainty, confidence or caution.

Original languageEnglish (US)
Article number100586
JournalContemporary Clinical Trials Communications
Volume19
DOIs
StatePublished - Sep 2020
Externally publishedYes

Keywords

  • Binary
  • Graph
  • Missing data
  • Multiple imputation
  • Plot
  • Selection bias

ASJC Scopus subject areas

  • Pharmacology

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