Abstract Number:
3300
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Nicholas Hartman (1)
Institutions:
(1) Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A.
First Author:
Presenting Author:
Abstract Text:
Period-prevalent cohorts are often used for their cost-saving potential in observational survival analyses. Under this design, prevalent patients allow for evaluations of long-term survival outcomes without the need for long follow-up windows, whereas incident patients allow for evaluations of short-term survival outcomes without the issue of left-truncation. In practice, most period-prevalent survival analyses are designed to achieve an overall target sample size, and there is no rigorous methodology available to quantify how the relative frequencies of prevalent and incident patients impact the efficiency of the estimation and inference. In response to this gap, we propose a new method to identify the optimal mix of prevalent and incident patients that maximizes precision over the estimated survival curve, subject to a flexible weighting scheme. In addition, we derive an analytic formula for the optimal incident patient proportion that maximizes the power of the weighted log-rank test and Cox model. Simulations confirm the validity of the optimization criteria and show that substantial efficiency gains are achieved by recruiting the optimal mix of prevalent and incident patients.
Keywords:
Cox Proportional Hazards Model|Epidemiology|Kaplan-Meier Estimator|Left Truncation|Study Design|
Sponsors:
Lifetime Data Science Section
Tracks:
Miscellaneous
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