Summary
Objectives: In a literature-based meta-analysis for time-to-event data, the hazard ratio in each
trial is often estimated from the summary statistics described in the article. Several
methods have been proposed: the direct method (Peto method); the indirect method using
a p-value by the log-rank test and the number of total events; and the survival curve
method using the Kaplan-Meier estimate. However, there has been no published report
on a detailed investigation of these methods. We evaluated the performance of these
methods by simulation.
Methods: In a set of simulation experiments, performance of five methods was evaluated by
the bias of estimated log hazard ratio and coverage probability of the confidence
interval. The methods evaluated were: 1) Cox regression analysis, 2) direct method,
3) indirect method, 4) survival curve method, and 5) modified survival curve method
with an alternative weighting scheme.
Results: The direct method was confirmed to have a high degree of accuracy. Although the indirect
method was also highly accurate, it tended to underestimate effect size when there
was a strong effect. The survival curve method tended to underestimate effect size
when event numbers were small and effect size was large. The modified survival curve
method could alleviate this tendency toward underestimation of effect size found with
the original survival curve method.
Conclusions: When the Kaplan-Meier curve is used to estimate hazard ratios in trials with small
sample size in the literature-based meta-analysis, we should check critically whether
those trials’ hazard ratios and overall hazard ratio are underestimated or not.
Keywords
Hazard ratio - meta-analysis - survival analysis - time-to-event
