Using GitHub for collaborative analysis

Session

Inverse treatment-propensity weights are a standard method for adjusting for predictors of exposure to a treatment. As a treatment-propensity score is a balancing score, it makes sense to do balance checks on the corresponding treatment-propensity weights. It is also a good idea to do variance-inflation checks, to estimate how much the propensity weights might inflate the variance of an estimated treatment effect, in the pessimistic scenario in which the weights are not really necessary. In Stata, the SSC package somersd can be used for balance checks, and the SSC package haif can be used for variance-inflation checks. It is argued that balance and variance-inflation checks are also necessary in the case of completeness-propensity weights, which are intended to remove inbalance in predictors of completeness between the subsample with complete data and the full sample of subjects with complete or incomplete data. However, the usage ofsomersd, scsomersd, and haif must be modified, because we are removing imbalance between the complete sample and the full sample, instead of between the treated subsample and the untreated subsample. An example will be presented, from a clinical trial in which the author was involved, and in which nearly a quarter of randomized subjects had no final outcome data. A post-hoc sensitivity analysis is presented, using inverse completeness-propensity weights.