The use of one-stage meta-analytic method based on individual participant data for binary adverse events under the rule of three: a simulation study.

ABSTRACT:

Objective

In evidence synthesis practice, dealing with binary rare adverse events (AEs) is a challenging problem. The pooled estimates for rare AEs through traditional inverse variance (IV), Mantel-Haenszel (MH), and Yusuf-Peto (Peto) methods are suboptimal, as the biases tend to be large. We proposed the "one-stage" approach based on multilevel variance component logistic regression (MVCL) to handle this problem.

Methods

We used simulations to generate trials of individual participant data (IPD) with a series of predefined parameters. We compared the performance of the MVCL "one-stage" approach and the five classical methods (fixed/random effect IV, fixed/random effect MH, and Peto) for rare binary AEs under different scenarios, which included different sample size setting

SUBMITTER: Cheng LL 

PROVIDER: S-EPMC6347966 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature