Search

Summary: To unbiasedly estimate a causal effect on an outcome unconfoundedness is often assumed. If there is sufficient knowledge on the underlying causal structure then existing confounder selection criteria can be used to select subsets of the observed pretreatment covariates, X, sufficient for unconfoundedness, if such subsets exist. Here, estimation of these target subsets is considered when the underlying causal structure is unknown. The proposed method is to model the causal structure by a probabilistic graphical model, for example, a Markov or Bayesian network, estimate this graph from observed data and select the target subsets given the estimated graph. The approach is evaluated by simulation both in a high‐dimensional setting where unconfoundedness holds given X and in a setting where unconfoundedness only holds given subsets of X. Several common target subsets are investigated and the selected subsets are compared with respect to accuracy in estimating the average causal effect. The proposed method is implemented with existing software that can easily handle high‐dimensional data, in terms of large samples and large number of covariates. The results from the simulation study show that, if unconfoundedness holds given X, this approach is very successful in selecting the target subsets, outperforming alternative approaches based on random forests and LASSO, and that the subset estimating the target subset containing all causes of outcome yields smallest MSE in the average causal effect estimation. [ABSTRACT FROM AUTHOR]

Pharmacokinetics is the study of the time course of a drug and its metabolites following its introduction into the body. Population pharmacokinetic studies are becoming increasingly important as an aid to drug development. The data from such studies typically consist of dose histories, drug concentrations with associated sampling times, and often covariate measurements such as the age and weight of each subject. These studies aim to provide an understanding of the pharmacokinetics of the drug in question and so lead to an informed choice of dosage regimen. Such an understanding includes determining those covariates that are important predictors of fundamental pharmacokinetic parameters, such as clearance, defined as the volume of plasma cleared of drug in a unit of time. Determining those subpopulations (e.g., the elderly) with altered kinetics has implications for the choice of an appropriate dosage regimens, because predictive concentration profiles arising from a particular regimen in different populations may be very different. In this article a general Bayesian hierarchical model is described. Pharmacokinetic models relating concentration to time are generally nonlinear, and the data are often sparse and/or noisy. The number of individuals on whom data have been collected is often large, and so the dimensionality of the parameter space is large. Consequently, estimation, from a Bayesian or a classical perspective, is not straightforward. In this article the Hastings-Metropolis algorithm is used for learning about the posterior distribution. An analysis of concentration data collected after the administration of the antiarrhythmic drug quinidine is presented. The data consist of 361 measurements on a total of 136 patients. Nine covariates are also available for each individual. These covariates are a mixture of discrete and continuous measurements. Some of the covariates are constant within an individual during the course of the study, whereas others change. A covariate model is constructed, and the sensitivity of the inferences to distributional assumptions is examined. The importance of assessing the appropriateness of modeling assumptions is emphasized and extensive model checking is carried out for the quinidine data using graphical diagnostics. [ABSTRACT FROM AUTHOR]