Nonparametric Tests of Treatment Effect for a Recurrent Event Process That Terminates

• Activity Number: 386
• Type: Topic Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Nabihah Tayob*+ and Susan Murray
• Companies: MD Anderson Cancer Center and University of Michigan
• Keywords: Recurrent events ; Correlated events ; Nonparametric tests ; Censored survival data ; Restricted mean survival

Abstract

Recurrent and terminal events are common outcomes for studying treatment effects in clinical studies. Existing approaches follow either a time-to-first event analysis approach or a recurrent event modeling approach. Recurrent event analyses are often restricted by independence assumptions on gap-times between events. Although time-to-first event analyses are not subject to this restriction, they discard information that occurs beyond the initial event and are much less powerful for detecting treatment differences. We develop two new approaches for determining treatment effects, motivated by less restrictive assumptions of time-to-first event analyses, that combine information from multiple follow-up intervals. The first testing procedure pools (correlated) short term $\tau$-restricted outcomes from pre-specified intervals starting at times $t_k$, k=1,..., b, and compares estimated $\tau$-restricted mean survival across treatment groups from this combined dataset. The second procedure calculates conditional $\tau$-restricted means from those at risk at times $t_k$, k=1,..., b and compares the area under a function of these by treatment.