Causal effect estimation strategies in a longitudinal study with complex time-varying confounders: A tutorial

The Dutch Sciatica Trial represents a longitudinal study with complex time-varying confounders as patients with poorer health conditions (e.g. more severe pain) are more likely to opt for surgery, which, in turn, may affect future outcomes (pain severity). A straightforward classical as-treated comparison at the end point would lead to biased estimation of the surgery effect. We present several strategies of causal treatment effect estimation that might be applicable for analyzing such data. These include an inverse probability of treatment weighted regression analysis, a marginal weighted analysis, an unweighted regression analysis, and several propensity score-based approaches. In addition, we demonstrate how to evaluate these approaches in a thorough simulation study where we generate various realistic complex confounding patterns akin to the sciatica study.