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2016 all »
art. 4 Patient Centered Hazard Ratio Estimation
Using Principal Strati cation Weights:
Application to the NORCCAP Randomized Trial
of Colorectal Cancer Screening
by Todd MacKenzie, Magnus Loberg and
A. James O'Malley

art. 3 Electronic Health Records to Evaluate
and Account for Non-response Bias: A Survey
of Patients Using Chronic Opioid Therapy
by Susan Shortreed, Michael Von Korff, Stephen
Thielke, Linda LeResche, Kathleen Saunders,
Dori Rosenberg and Judith Turner

art. 2 Large Sparse Optimal Matching
with R package rcbalance
by Samuel Pimentel

art. 1 Review of "Explanation in Causal
Inference: Mediation and Interaction"
(author T.J. Vanderweele)
Book review by Luke Keele

2015 all »
art. 10 Targeted Learning for Pre-Analysis Plans
in Public Health and Health Policy Research
by Sherri Rose

art. 9 Review of "Causal Inference for Statistics,
Social, and Biomedical Sciences"
(authors: G.W. Imbens and D.B. Rubin)
Book review by Fabrizia Mealli

art. 8 Simulation-Extrapolation for Estimating
Means and Causal Effects with Mismeasured
by J.R. Lockwood and Daniel McCaffrey

art. 7 Reprint of "Observational Studies"
by William Cochran followed by comments
by current researchers in observational studies

art. 6 The non-zero mean SIMEX:
Improving estimation in the face of
measurement error
by Nabila Parveen, Erica Moodie
and Bluma Brenner

art. 5 Simultaneous Sensitivity Analysis in Stata:
arsimsens and pairsimsens
by Daniel Lempert

art. 4 The Statistical Modeling of Aging and
Risk of Transition Project: Data Collection and
Harmonization Across 11 Longitudinal Cohort
Studies of Aging, Cognition, and Dementia
by Erin Abner et al.

art. 3 Application of Propensity Scores to a
Continuous Exposure: E ffect of Lead Exposure
in Early Childhood on Reading and
Mathematics Scores
by Michael Elliott, Nanhua Zhang & Dylan Small

art. 2 Evidence of False Positives in Research
Clearinghouses and Influential Journals:
An Application of of P-Curve to Policy Research
by Sean Tanner

art. 1 Two R Packages for Sensitivity Analysis
in Observational Studies
by Paul Rosenbaum

Editorial Board
Dylan Small
University of Pennsylvania

Associate Editors:
Peter Austin
University of Toronto
Anirban Basu
University of Washington
Jake Bowers
University of Illinois
Alan Brookhart
University of North Carolina
Jing Cheng
University of California, San Francisco
Thomas Cook
Northwestern University
Xavier De Luna
Umea University
Beth Ann Griffin
RAND Corporation
Jens Hainmueller
Stanford University
David Harding
University of California, Berkeley
Ben Hansen
University of Michigan
Joseph Hogan
Brown University
Kosuke Imai
Princeton University
Guido Imbens
Stanford University
Luke Keele
Pennsylvania State University
Justin McCrary
University of California, Berkeley
Stephen Morgan
Johns Hopkins University
Paul Rosenbaum
University of Pennsylvania
Jason Roy
University of Pennsylvania
Jasjeet Sekhon
University of California, Berkeley
Michael Sobel
Columbia University
Elizabeth Stuart
Johns Hopkins University
Eric Tchetgen Tchetgen
Harvard University
Mark Van Der Laan
University of California, Berkeley
Advisory Committee
David Banks
Duke University
Marie Davidian
North Carolina State University
Joel Greenhouse
Carnegie Mellon University
M. Elizabeth Halloran
Fred Hutchinson Cancer Research Center
University of Washington
Sharon-Lise Normand
Harvard University

Application of Propensity Scores to a
Continuous Exposure: E ffect of Lead Exposure
in Early Childhood on Reading and
Mathematics Scores
by Michael Elliott, Nanhua Zhang & Dylan Small

Published on 03-10-2015
The estimation of causal eff ects in observational studies is usually limited by the lack of randomization, which can result in di fferent treatment or exposure groups di ffering systematically with respect to characteristics that influence outcomes. To remove such systematic diff erences, methods to "balance" subjects on observed covariates across treatment or exposure levels have been developed over the past three decades. These methods have been primarily developed in settings with binary treatment or exposures. However, in many observational studies, the exposures are continuous instead of being binary or discrete, and are usually considered as doses of treatment. In this manuscript we consider estimating the causal eff ect of early childhood lead exposure on youth academic achievement, where the exposure variable blood lead concentration can take any values that are greater than or equal to 0, using three balancing methods: propensity score analysis, non-bipartite matching, and Bayesian regression trees. We find some evidence that the standard logistic regression analysis controlling for age and socioeconomic confounders used in previous analyses (Zhang et al. (2013)) overstates the e ect of lead exposure on performance on standardized mathematics and reading examinations; however, signifi cant declines remain, including at doses currently below the recommended exposure levels.
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