Individual patient-level and study-level meta-analysis for investigating modifiers of treatment effect

In meta-analyses of clinical trials, clinicians are often interested in examining subset effects. Meta-regression of aggregated data is a usual approach for relating sources of variation in treatment effects to specific study characteristics. However, it is known that study-level analyses can lead to biased assessments and have some limitations in explaining the heterogeneity. An individual patient data (IPD) meta-analysis offers several advantages for this purpose.

We compared some regression analyses of IPD with meta-regression analyses of the summarized data using a real-world example in order to investigate whether a binary patient characteristic is related to treatment effect. We used data from 10 randomized trials for non-small-cell lung cancer (n = 1355).

For treatment x stage interaction in IPD regression analysis, none of the tests of interactions was statistically significant. The meta-regression analysis gave a greater P-value than the IPD analysis. When excluding two studies, which had only stage I patients, the interaction was also not statistically significant in IPD analysis. On the other hand, the result of meta-regression analysis, though also showing no significant relationship, revealed a clear reversal in the direction of effect.

We suggest that the results of meta-regression analyses would not be as robust as those of regression analyses using IPD in examining potential modifiers of treatment effects. To investigate whether patient characteristics are related to treatment effects, we suggest that interaction tests and sensitivity analyses using IPD should be employed whenever possible.