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Full-Text Articles in Physical Sciences and Mathematics
Causal Mediation In A Survival Setting With Time-Dependent Mediators, Wenjing Zheng, Mark J. Van Der Laan
Causal Mediation In A Survival Setting With Time-Dependent Mediators, Wenjing Zheng, Mark J. Van Der Laan
Wenjing Zheng
The effect of an expsore on an outcome of interest is often mediated by intermediate variables. The goal of causal mediation analysis is to evaluate the role of these intermediate variables (mediators) in the causal effect of the exposure on the outcome. In this paper, we consider causal mediation of a baseline exposure on a survival (or time-to-event) outcome, when the mediator is time-dependent. The challenge in this setting lies in that the event process takes places jointly with the mediator process; in particular, the length of the mediator history depends on the survival time. As a result, we argue …
A Targeted Confounder Selection Strategy For Propensity Score Estimation, Susan Gruber
A Targeted Confounder Selection Strategy For Propensity Score Estimation, Susan Gruber
Susan Gruber
These slides provide an introduction to data-adaptive propensity score estimation, and the collaborative targeted maximum likelihood estimator (C-TMLE) of van der Laan and Gruber. The notation has been greatly simplified, which makes the work accessible to a more general audience, but loses a little in the translation.
Methods For Evaluating Prediction Performance Of Biomarkers And Tests, Margaret S. Pepe Phd, Holly Janes Phd
Methods For Evaluating Prediction Performance Of Biomarkers And Tests, Margaret S. Pepe Phd, Holly Janes Phd
Margaret S Pepe PhD
This chapter describes and critiques methods for evaluating the performance of markers to predict risk of a current or future clinical outcome. We consider three criteria that are important for evaluating a risk model: calibration, benefit for decision making and accurate classification. We also describe and discuss a variety of summary measures in common use for quantifying predictive information such as the area under the ROC curve and R-squared. The roles and problems with recently proposed risk reclassification approaches are discussed in detail.
Loss Function Based Ranking In Two-Stage, Hierarchical Models, Rongheng Lin, Thomas A. Louis, Susan M. Paddock, Greg Ridgeway
Loss Function Based Ranking In Two-Stage, Hierarchical Models, Rongheng Lin, Thomas A. Louis, Susan M. Paddock, Greg Ridgeway
Rongheng Lin
Several authors have studied the performance of optimal, squared error loss (SEL) estimated ranks. Though these are effective, in many applications interest focuses on identifying the relatively good (e.g., in the upper 10%) or relatively poor performers. We construct loss functions that address this goal and evaluate candidate rank estimates, some of which optimize specific loss functions. We study performance for a fully parametric hierarchical model with a Gaussian prior and Gaussian sampling distributions, evaluating performance for several loss functions. Results show that though SEL-optimal ranks and percentiles do not specifically focus on classifying with respect to a percentile cut …
Asymptotic Theory For Cross-Validated Targeted Maximum Likelihood Estimation, Wenjing Zheng, Mark J. Van Der Laan
Asymptotic Theory For Cross-Validated Targeted Maximum Likelihood Estimation, Wenjing Zheng, Mark J. Van Der Laan
Wenjing Zheng
We consider a targeted maximum likelihood estimator of a path-wise differentiable parameter of the data generating distribution in a semi-parametric model based on observing n independent and identically distributed observations. The targeted maximum likelihood estimator (TMLE) uses V-fold sample splitting for the initial estimator in order to make the TMLE maximally robust in its bias reduction step. We prove a general theorem that states asymptotic efficiency (and thereby regularity) of the targeted maximum likelihood estimator when the initial estimator is consistent and a second order term converges to zero in probability at a rate faster than the square root of …
Semiparametric Analysis Of Recurrent Events: Artificial Censoring, Truncation, Pairwise Estimation And Inference, Debashis Ghosh
Semiparametric Analysis Of Recurrent Events: Artificial Censoring, Truncation, Pairwise Estimation And Inference, Debashis Ghosh
Debashis Ghosh
The analysis of recurrent failure time data from longitudinal studies can be complicated by the presence of dependent censoring. There has been a substantive literature that has developed based on an artificial censoring device. We explore in this article the connection between this class of methods with truncated data structures. In addition, a new procedure is developed for estimation and inference in a joint model for recurrent events and dependent censoring. Estimation proceeds using a mixed U-statistic based estimating function approach. New resampling-based methods for variance estimation and model checking are also described. The methods are illustrated by application to …
Individualized Treatment Rules: Generating Candidate Clinical Trials, Maya Petersen, Steven G. Deeks, Mark J. Van Der Laan
Individualized Treatment Rules: Generating Candidate Clinical Trials, Maya Petersen, Steven G. Deeks, Mark J. Van Der Laan
Maya Petersen
Individualized treatment rules, or rules for altering treatments over time in response to changes in individual covariates, are of primary importance in the practice of clinical medicine. Several statistical methods aim to estimate the rule, termed an optimal dynamic treatment regime, which will result in the best expected outcome in a population. In this article, we discuss estimation of an alternative type of dynamic regime—the statically optimal treatment rule. History-adjusted marginal structural models (HA-MSM) estimate individualized treatment rules that assign, at each time point, the first action of the future static treatment plan that optimizes expected outcome given a patient’s …
A Note On Empirical Likelihood Inference Of Residual Life Regression, Ying Qing Chen, Yichuan Zhao
A Note On Empirical Likelihood Inference Of Residual Life Regression, Ying Qing Chen, Yichuan Zhao
Yichuan Zhao
Mean residual life function, or life expectancy, is an important function to characterize distribution of residual life. The proportional mean residual life model by Oakes and Dasu (1990) is a regression tool to study the association between life expectancy and its associated covariates. Although semiparametric inference procedures have been proposed in the literature, the accuracy of such procedures may be low when the censoring proportion is relatively large. In this paper, the semiparametric inference procedures are studied with an empirical likelihood ratio method. An empirical likelihood confidence region is constructed for the regression parameters. The proposed method is further compared …