Dealing with noncompliance and missing outcomes in a randomized trial using Bayesian technology: Prevention of perinatal sepsis clinical trial, Soweto, South Africa

The success of interventions designed to address important issues in social and medical science is best addressed by randomized experiments. With human beings there are often complications, however, such as noncompliance and missing data. Such complications are often addressed by statistically invalid methods of analysis, in particular, intention-to-treat and per-protocol analyses. Here we address these two complications using a statistically valid approach based on principal stratification with a fully Bayesian analysis. This analysis is applied to a randomized trial of a potentially important intervention designed to reduce the transmission of bacterial colonization between mothers and their infants through vaginal delivery in South Africa: the Prevention of Perinatal Sepsis (PoPs).