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When performing a meta-analysis, interest often centres on finding explanations for heterogeneity in the data, rather than on producing a single summary estimate. Such exploratory analyses are frequently undertaken with published, study-level data, using techniques of meta-analytic regression. Our goal was to explore a real-world example for which both published, group-level and individual patient-level data were available, and to compare the substantive conclusions reached by both methods. We studied the benefits of anti-lymphocyte antibody induction therapy among renal transplant patients in five randomized trials, focusing on whether there are subgroups of patients in whom therapy might prove particularly beneficial. Allograft failure within 5 years was the endpoint studied. We used a variety of analytic approaches to the group-level data, including weighted least-squares regression (N=5 studies), logistic regression (N=628, the total number of subjects), and a hierarchical Bayesian approach. We fit logistic regression models to the patient-level data. In the patient-level analysis, we found that treatment was significantly more effective among patients with elevated (20 per cent or more) panel reactive antibodies (PRA) than among patients without elevated PRA. These patients comprise a small (about 15 per cent of patients) subgroup of patients that benefited from therapy. The group-level analyses failed to detect this interaction. We recommend using individual patient data, when feasible, to study patient characteristics, in order to avoid the potential for ecological bias introduced by group-level analyses.
Berlin et al. (Tue,) studied this question.