Accounting for delayed entry into observational studies and clinical trials: length-biased sampling and restricted mean survival time

Mei-Ling Ting Lee (), John Lawrence, Yiming Chen and G. A. Whitmore
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Mei-Ling Ting Lee: University of Maryland
John Lawrence: U.S. Food and Drug Administration
Yiming Chen: University of Maryland
G. A. Whitmore: McGill University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 4, No 6, 637-658

Abstract: Abstract Individuals in many observational studies and clinical trials for chronic diseases are enrolled well after onset or diagnosis of their disease. Times to events of interest after enrollment are therefore residual or left-truncated event times. Individuals entering the studies have disease that has advanced to varying extents. Moreover, enrollment usually entails probability sampling of the study population. Finally, event times over a short to moderate time horizon are often of interest in these investigations, rather than more speculative and remote happenings that lie beyond the study period. This research report looks at the issue of delayed entry into these kinds of studies and trials. Time to event for an individual is modelled as a first hitting time of an event threshold by a latent disease process, which is taken to be a Wiener process. It is emphasized that recruitment into these studies often involves length-biased sampling. The requisite mathematics for this kind of sampling and delayed entry are presented, including explicit formulas needed for estimation and inference. Restricted mean survival time (RMST) is taken as the clinically relevant outcome measure. Exact parametric formulas for this measure are derived and presented. The results are extended to settings that involve study covariates using threshold regression methods. Methods adapted for clinical trials are presented. An extensive case illustration for a clinical trial setting is then presented to demonstrate the methods, the interpretation of results, and the harvesting of useful insights. The closing discussion covers a number of important issues and concepts.

Keywords: Disease progression; First hitting time; Health state; Stochastic process; Threshold regression for survival models; Wiener process (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10985-022-09562-8

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