Applied Statistics Commons^™

Targeted Maximum Likelihood Estimation Of Natural Direct Effects, Wenjing Zheng, Mark Van Der Laan

Wenjing Zheng

In many causal inference problems, one is interested in the direct causal effect of an exposure on an outcome of interest that is not mediated by certain intermediate variables. Robins and Greenland (1992) and Pearl (2001) formalized the definition of two types of direct effects (natural and controlled) under the counterfactual framework. The efficient scores (under a nonparametric model) for the various natural effect parameters and their general robustness conditions, as well as an estimating equation based estimator using the efficient score, are provided in Tchetgen Tchetgen and Shpitser (2011b). In this article, we apply the targeted maximum likelihood framework …

Comparing The Cohort Design And The Nested Case-Control Design In The Presence Of Both Time-Invariant And Time-Dependent Treatment And Competing Risks: Bias And Precision, Peter C. Austin

Peter Austin

Purpose: Observational studies using electronic administrative health care databases are often used to estimate the effects of treatments and exposures. Traditionally, a cohort design has been used to estimate these effects, but increasingly studies are using a nested case-control (NCC) design. The relative statistical efficiency of these two designs has not been examined in detail.

Methods: We used Monte Carlo simulations to compare these two designs in terms of the bias and precision of effect estimates. We examined three different settings: (A): treatment occurred at baseline and there was a single outcome of interest; (B): treatment was time-varying and there …

Using Ensemble-Based Methods For Directly Estimating Causal Effects: An Investigation Of Tree-Based G-Computation, Peter C. Austin

Peter Austin

Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to the production of high-quality evidence is the ability to reduce or minimize the confounding that frequently occurs in observational studies. When using the potential outcome framework to define causal treatment effects, one requires the potential outcome under each possible treatment. However, only the outcome under the actual treatment received is observed, whereas the potential outcomes under the other treatments are considered missing data. Some authors have proposed that parametric regression models be used to estimate potential outcomes. In this study, we examined the use of …

Generating Survival Times To Simulate Cox Proportional Hazards Models With Time-Varying Covariates., Peter C. Austin

Peter Austin

Simulations and Monte Carlo methods serve an important role in modern statistical research. They allow for an examination of the performance of statistical procedures in settings in which analytic and mathematical derivations may not be feasible. A key element in any statistical simulation is the existence of an appropriate data-generating process: one must be able to simulate data from a specified statistical model. We describe data-generating processes for the Cox proportional hazards model with time-varying covariates when event times follow an exponential, Weibull, or Gompertz distribution. We consider three types of time-varying covariates: first, a dichotomous time-varying covariate that can …