Biostatistics Seminar Series: Mélanie Prague, PhD

Abstract:  Whilst the standard parallel two-arm cluster randomized trials (CRTs) has been used for decades, there has been a range of innovative developments and alternatives proposed in the 12 years since the publication of the Murray et al. review. To cover this ground, Turner, Prague, Murray et al. wrote a series of two review papers on design and analysis of CRTs in 2017. I will present the conclusion of this review. I will particularly focus on marginal models and discuss their properties compared to conditional models.  After briefly discussing alternative semi-parametric approaches including quadratic inference functions (QIF) and targeted maximum likelihood (tMLE). I will focus on recently developed semi-parametric methodology based on generalized estimating equation (GEE) that simultaneously accounts for both baseline covariate imbalance and missing outcome data in CRTs (Prague et al. 2016, Biometrics). This approach has been developed in the causal inference framework using augmented and inverse probability weighted methods. It is doubly robust and easily implementable using a published R package called CRTgeeDR.