Quantifying the totality of treatment effect with multiple event-time observations in the presence of a terminal event from a comparative clinical study.
Pubmed ID: 30047148
Pubmed Central ID: PMC7021204
Journal: Statistics in medicine
Publication Date: 11/10/2018
Affiliation: Harvard University, Cambridge, Massachusetts.
MeSH Terms: Humans, Longitudinal Studies, Survival Analysis, Randomized Controlled Trials as Topic, Data Interpretation, Statistical, Proportional Hazards Models, Treatment Outcome, Models, Statistical, Time Factors, Area Under Curve
Grants: R01 HL089778
Authors: Tian L, Wei LJ, Solomon SD, Claggett B, Fu H
Cite As: Claggett B, Tian L, Fu H, Solomon SD, Wei LJ. Quantifying the totality of treatment effect with multiple event-time observations in the presence of a terminal event from a comparative clinical study. Stat Med 2018 Nov 10;37(25):3589-3598. Epub 2018 Jul 25.
To evaluate the totality of one treatment's benefit/risk profile relative to an alternative treatment via a longitudinal comparative clinical study, the timing and occurrence of multiple clinical events are typically collected during the patient's follow-up. These multiple observations reflect the patient's disease progression/burden over time. The standard practice is to create a composite endpoint from the multiple outcomes, the timing of the occurrence of the first clinical event, to evaluate the treatment via the standard survival analysis techniques. By ignoring all events after the composite outcome, this type of assessment may not be ideal. Various parametric or semiparametric procedures have been extensively discussed in the literature for the purposes of analyzing multiple event-time data. Many existing methods were developed based on extensive model assumptions. When the model assumptions are not plausible, the resulting inferences for the treatment effect may be misleading. In this article, we propose a simple, nonparametric inference procedure to quantify the treatment effect, which has an intuitive clinically meaningful interpretation. We use the data from a cardiovascular clinical trial for heart failure to illustrate the procedure. A simulation study is also conducted to evaluate the performance of the new proposal.