Physical Sciences and Mathematics Commons^™

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 …

Hierarchical Vector Auto-Regressive Models And Their Applications To Multi-Subject Effective Connectivity, Cristina Gorrostieta, Mark Fiecas, Hernando Ombao, Erin Burke, Steven Cramer

Mark Fiecas

Designing The Search Trial: Ph250b In Practice, Laura Balzer

Laura B. Balzer

No abstract provided.

Estimating Effects On Rare Outcomes: Knowledge Is Power, Laura B. Balzer, Mark J. Van Der Laan

Laura B. Balzer

Many of the secondary outcomes in observational studies and randomized trials are rare. Methods for estimating causal effects and associations with rare outcomes, however, are limited, and this represents a missed opportunity for investigation. In this article, we construct a new targeted minimum loss-based estimator (TMLE) for the effect of an exposure or treatment on a rare outcome. We focus on the causal risk difference and statistical models incorporating bounds on the conditional risk of the outcome, given the exposure and covariates. By construction, the proposed estimator constrains the predicted outcomes to respect this model knowledge. Theoretically, this bounding provides …

On The Exact Size Of Multiple Comparison Tests, Chris Lloyd

Chris J. Lloyd

No abstract provided.

On The Size Accuracy Of Combination Tests, Chris Lloyd

Chris J. Lloyd

One element of the analysis of adaptive clinical trials is combining the evidence from several (often two) stages. When the endpoint is binary, standard single stage tests statistics do not control size well. Yet the combined test might not be valid if the single stage tests are not. The purpose of this paper is to numerically and theoretically examine the extent to which combining basic tests statistics mitigates or magnifies the size violation of the final test.

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.

An Overview Of Targeted Maximum Likelihood Estimation, Susan Gruber

Susan Gruber

These slides provide an introduction to targeted maximum likelihood estimation in a point treatment setting.

Bayesian Methods For Expression-Based Integration, Elizabeth M. Jennings, Jeffrey S. Morris, Raymond J. Carroll, Ganiraju C. Manyam, Veera Baladandayuthapani

Jeffrey S. Morris

We propose methods to integrate data across several genomic platforms using a hierarchical Bayesian analysis framework that incorporates the biological relationships among the platforms to identify genes whose expression is related to clinical outcomes in cancer. This integrated approach combines information across all platforms, leading to increased statistical power in finding these predictive genes, and further provides mechanistic information about the manner in which the gene affects the outcome. We demonstrate the advantages of the shrinkage estimation used by this approach through a simulation, and finally, we apply our method to a Glioblastoma Multiforme dataset and identify several genes potentially …

A Case-Control Study Of Physical Activity Patterns And Risk Of Non-Fatal Myocardial Infarction, Jian Gong, Hannia Campos, Mark Fiecas, Stephen Mcgarvey, Robert Goldberg, Caroline Richardson, Ana Baylin

Mark Fiecas

Background The interactive effects of different types of physical activity on cardiovascular disease (CVD) risk have not been fully considered in previous studies. We aimed to identify physical activity patterns that take into account combinations of physical activities and examine the association between derived physical activity patterns and risk of acute myocardial infarction (AMI). Methods We examined the relationship between physical activity patterns, identified by principal component analysis (PCA), and AMI risk in a case-control study of myocardial infarction in Costa Rica (N=4172), 1994-2004. The component scores derived from PCA and total METS were used in natural cubic spline models …

Varying-Smoother Models For Functional Responses, Philip T. Reiss, Lei Huang, Huaihou Chen, Stan Colcombe

Philip T. Reiss

This paper studies estimation of a smooth function f(x,v) when we are given functional responses of the form f(x, ·) + error, but scientific interest centers on the collection of functions f(·,v) for different v. The motivation comes from studies of human brain development, in which x denotes age whereas v refers to brain locations. Analogously to varying-coefficient models, in which the mean response is linear in x, the “varying-smoother” models that we consider exhibit nonlinear dependence on x that varies smoothly with v. We discuss three approaches to estimating varying-smoother models: (a) methods that employ a tensor product penalty; …

Progression From New Methicillin-Resistant Staphylococcus Aureus Colonisation To Infection: An Observational Study In A Hospital Cohort, Michelle Nd Balm, Andrew A. Lover, Sharon Salmon, Paul A. Tambyah, Dale A. Fisher

Andrew Lover

Quantifying Effect Of Geographic Location On Epidemiology Of Plasmodium Vivax Malaria, Andrew A. Lover, Richard J. Coker

Andrew Lover

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.