One-stage parametric meta-analysis of time-to-event outcomes.
Methodology for the meta-analysis of individual patient data with survival end-points is proposed. Motivated by questions about the reliance on hazard ratios as summary measures of treatment effects, a parametric approach is considered and percentile ratios are introduced as an alternative to hazard ratios. The generalized log-gamma model, which includes many common time-to-event distributions as special cases, is discussed in detail. Likelihood inference for percentile ratios is outlined. The proposed methodology is used for a meta-analysis of glioma data that was one of the studies which motivated this work. A simulation study exploring the validity of the proposed methodology is available electronically.
Department of Mathematics, University of Athens, Greece. fsiannis@math.uoa.gr
F
F
Siannis
J K
JK
Barrett
V T
VT
Farewell
J F
JF
Tierney
D000465:Algorithms; D003198:Computer Simulation; D005910:Glioma / Q000188:drug therapy / Q000401:mortality / Q000628:therapy; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis; D016008:Statistical Distributions; D015996:Survival Rate; D016896:Treatment Outcome