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Missing Data Handling Methods in Medical Device Clinical Trials

2009; Taylor & Francis; Volume: 19; Issue: 6 Linguagem: Inglês

10.1080/10543400903243009

1520-5711

Xu Yan, Shiowjen Lee, Ning Li,

Advanced Causal Inference Techniques

One of the major problems in the analysis of clinical trials is missing data caused by patients dropping out before study completion. The issue of missing data can result in biased treatment comparisons and can impact the interpretation of study results. Since the missing data mechanism is unknown and unverifiable in most situations, regulatory agencies often request various sensitivity analyses for handling missing data to evaluate the robustness of study results. This article discusses methods used to handle missing data in medical device clinical trials, focusing on tipping-point analysis as a general approach for the assessment of missing data impact. Tipping points are outcomes that result in a change of study conclusion. Such outcomes can be conveyed to clinical reviewers to determine if they are implausibly unfavorable. The analysis aids clinical reviewers in making judgment regarding treatment effect in the study. Three examples with a reasonably representative range of missing data rate are included to illustrate the methods referred.