A sequential Cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV Cohort Study

When estimating the effect of treatment on HIV using data from observational studies, standard methods may produce biased estimates due to the presence of time dependent confounders Such confounding can be present when a covariate, affected by past exposure, is both a predictor of the future exposure and the outcome One example is the CD4 cell count, being a marker for disease progression for HIV patients, but also a marker for treatment initiation and influenced by treatment Fitting a marginal structural model (MSM) using inverse probability weights is one way to give appropriate adjustment for this type of confounding In this paper we study a simple and intuitive approach to estimate similar treatment effects, using observational data to mimic several randomized controlled trials Each 'trial' is constructed based on individuals starting treatment in a certain time Interval An overall effect estimate for all such trials is found using composite likelihood inference The method offers an alternative to the use of inverse probability of treatment weights, which is unstable in certain situations The estimated parameter is not identical to the one of an MSM, it is conditioned on covariate values at the start of each mimicked trial This allows the study of questions that are not that easily addressed fitting an MSM The analysis can be performed as a stratified weighted Cox analysis on the joint data set of all the constructed trials, where each trial is one stratum The model is applied to data from the Swiss HIV cohort study Copyright (C) 2010 John Wiley & Sons, Ltd