We introduce an inverse probability weighted approach to rebalance covariates across treatment groups while mitigating the influence of differential misclassification bias.
Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context
Through simulations, we show that, in the presence of weight model misspecification in a marginal structural Cox model context, with a rich and diverse set of candidate algorithms, 'Super Learner' can generally offer a better alternative to the commonly used logistic regression or statistical learning approaches.
Effect estimation in point-exposure studies with binary outcomes and high-dimensional covariate data – a comparison of targeted maximum likelihood estimation and inverse probability of treatment weighting
Using plasmode and Monte-Carlo simulation studies, we evaluates the performance of TMLE compared to that of IPW estimators based on a point-exposure cohort study of the marginal causal effect of post-myocardial infarction statin use on the 1-year risk of all-cause mortality from the Clinical Practice Research Datalink. Near violations of the positivity assumption are investigated in a high-dimensional covariate setting for both methods.
We illustrate the practical implementation of a newly proposed double robust estimator “Targeted Maximum Likelihood Estimation” (TMLE) and demonstrated its application in common pharmacoepidemiological settings. A real-word example using the clinical practice research datalink is used with a high-dimensional covariate space.
In order to examine the claim that the high-dimensional propensity score algorithm can adjust for unmeasured confounding, we hide information from the algorithm to examine its ability to compensate by selecting proxies of what was hidden. Performance of the algorithm within the hidden information context is then compared to its performance within the full information context.
The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study
We describe how three different propensity score methods, propensity score matching, stratification on the propensity score and inverse probability of treatment weighting using the propensity score, can be used to estimate absolute measures of treatment effect on survival outcomes.