Exploring the relationship between surrogates and clinical outcomes: analysis of individual patient data vs. meta-regression on group-level summary statistics.
There has been an increasing interest in exploring the relationship between a surrogate and a clinical outcome. Two different statistical approaches have been taken by researchers to quantify the treatment effect on the clinical outcome explained by the surrogate endpoint: 1) analysis based on individual patient data (IPD), and 2) meta-regression based on summary statistics from published literature. An analysis based on IPD models the associations between the surrogate and clinical outcome for patients directly and is able to adjust for patient-level covariates. A meta-regression models the trial-level associations using group-level summary statistics and trial-level covariates. The results from these two approaches can be quite disparate and researchers may reach different conclusions on scientific questions that they wish to answer. We demonstrate that the typical summary statistics, such as group means and event counts, do not provide a set of sufficient statistics for estimating the underlying relationship between the surrogate and clinical outcome for patients. Consequently, the associations derived from meta-regression do not necessarily reflect the causal relationship for patients and should be interpreted with caution. A meta-analysis of antiresorptive agents for osteoporosis serves to illustrate the magnitude of differences between the two approaches.
Li
Zhengqing
Z
Department of Biometrics and Statistical Sciences, The Procter & Gamble Company, Mason, Ohio 45040, USA. li.z@pg.com
Meredith
Michael P
MP
D006801:Humans; D016015:Logistic Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D016896:Treatment Outcome