On clinical trial fragility due to patients lost to follow up
Abstract
Background Clinical trials routinely have patients lost to follow up. We propose a methodology to understand their possible effect on the results of statistical tests by altering the concept of the fragility index to treat the outcomes of observed patients as fixed but incorporate the potential outcomes of patients lost to follow up as random and subject to modification. Methods We reanalyse the statistical results of three clinical trials on coronary artery bypass grafting (CABG) to study the possible effect of patients lost to follow up on the treatment effect statistical significance. To do so, we introduce the LTFU-aware fragility indices as a measure of the robustness of a clinical trial’s statistical results with respect to patients lost to follow up. Results The analyses illustrate that clinical trials can either be completely robust to the outcomes of patients lost to follow up, extremely sensitive to the outcomes of patients lost to follow up, or in an intermediate state. When a clinical trial is in an intermediate state, the LTFU-aware fragility indices provide an interpretable measure to quantify the degree of fragility or robustness. Conclusions The LTFU-aware fragility indices allow researchers to rigorously explore the outcomes of patients who are lost to follow up, when their data is the appropriate kind. The LTFU-aware fragility indices are sensitivity measures in a way that the original fragility index is not.
Citation
Baer , B R , Fremes , S E , Gaudino , M , Charlson , M & Wells , M T 2021 , ' On clinical trial fragility due to patients lost to follow up ' , BMC Medical Research Methodology , vol. 21 , no. 254 , 254 . https://doi.org/10.1186/s12874-021-01446-z
Publication
BMC Medical Research Methodology
Status
Peer reviewed
ISSN
1471-2288Type
Journal article
Description
Wells ′ research was partially supported by NIH grant U19 AI111143, PCORI IHS-2017C3-8923, and Cornell’s Center for the Social Sciences project on Algorithms, Big Data, and Inequality.Collections
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