Abstract
For certain medical research questions, randomization is unethical or infeasible so causal effects have to be estimated from observational data. If confounding and exposure status are time-dependent, this requires sophisticated methodology such as target trial emulation involving longitudinal matching methods. Here we present an example from nephrology aiming to quantify the survival benefit of first kidney transplantation compared to remaining on dialysis and never receiving an organ, across ages and across times since waitlisting.
We analyzed data from the Austrian Dialysis and Transplant Registry comprising patients on dialysis and waitlisted for a kidney transplant with repeated updates on patient characteristics and waitlisting status. As often with registry data, a tricky task was data management, i.e. dealing with inconsistencies and incompleteness. Data availabilities also had to be taken into consideration when deciding on the most relevant causal effect that could be identified and estimated. We adapted the approaches of Gran et al. (2010) and Schaubel et al. (2006) by constructing a series of auxiliary trials, where each trial was initiated at the time of a transplantation (relative to time of first/second waitlisting). Transplanted patients contributed to the treatment group while patients with current active waitlisting status were classified to the control group. Controls were artificially censored if they were transplanted at a later time and their transplantation then initiated a further trial of the series. We applied pooled logistic regression adjusted for time-varying patient characteristics to estimate inverse probability of treatment weights (IPTWs) to achieve exchangeability and trial specific Cox proportional hazards models to compute yearly updated IPCWs to account for non-adherence to the assigned treatment. The marginal effect and the effect conditional on age and duration of waitlisting expressed on different scales (hazard ratios, survival probabilities and restricted mean survival times) were obtained from Cox models weighted by the product of IPTWs and IPCWs fitted to the stacked data set of all trials. A bootstrap approach was used to obtain confidence intervals. We found that kidney transplantation prolongs the survival time of persons with end-stage renal disease across all candidate ages and times spent on waiting list. Here we want to present some decisions we made throughout the analysis and discuss some possible alternatives.