Using individual participant data (IPD) has many advantages over using aggregate data (AD) inclinical meta-analysis. However, access to the IPD is often limited, yet the aggregate data is...Show moreUsing individual participant data (IPD) has many advantages over using aggregate data (AD) inclinical meta-analysis. However, access to the IPD is often limited, yet the aggregate data is availablefrom most clinical trials. Papadimitropoulou’s et al. [4] propose a method for studies with continuousoutcomes at baseline and follow-up measurement to generate pseudo-IPD from the aggregate data,which can be analyzed as IPD, using analysis of covariance (ANCOVA) models and linear mixed mod-els. The pseudo-IPD is generated based on the mean, standard deviation at baseline and follow-up, andthe correlation between baseline and follow-up, which are sufficient statistics of the linear mixed model.This thesis exemplified the pseudo-IPD models, standard meta-analysis models, and a Trowman meta-regression model on Obstructive Sleep Apnea Data with 2 treatment groups. We further exploredthe performance of the models under different conditions by a simulation study. The estimates of theTrowman meta-regression suffered from significant variance, and the standard AD models providedbias estimation when baseline imbalance exists. The ANCOVA models for pseudo-IPD and AD offeredmore accurate and stable results. The pseudo-IPD ANCOVA model is the most preferred since it canaccount for baseline difference and interaction between treatment and baseline, and different residualstructures can be used.Show less
