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2017 all »
art. 2 Review of "Observation and Experiment"
(author Paul Rosenbaum)
Book review by Dylan Small

art. 1 Study Protocol for the Evaluation of a
Vocational Rehabilitation
by Philip Fowler, Xavier de Luna, Per Johansson,
Petra Ornstein, Sofia Bill and Peje Bengtsson

2016 all »
art. 9 Reprint "Regression-Discontinuity Analysis:
An Alternative to the Ex-Post Facto Experiment"
by Donald Thistlewaite and Donald Campbell
followed by comments by
Peter Aronow, Nicole Basta, M. Elizabeth Halloran;
Matias Cattaneo and Gonzalo Vazquez-Bare;
Guido Imbens;
Alessandra Mattei and Fabrizia Mealli;
Jasjeet Sekhon and Rocío Titiunik;
and Vivian Wong and Coady Wing

art. 8 Assessing the Dose-Response Relationship
Between Maternal Use of Inhaled Corticosteroids
Therapy and Birth Weight: A Generalized
Propensity Score Approach
by Mariia Samoilenko, Lucie Blais, Benoît
Cossette, Amélie Forget, & Geneviève Lefebvre

art. 7 Review of "Causality in a Social World"
(author Guanglei Hong)
Book review by Ken Frank, Guan Kung Saw
and Ran Xu

art. 6 An Interim Sample Size Recalculation
for Observational Studies
by Sergey Tarima, Peng He, Tao Wang
and Aniko Szabo

art. 5 Cohort Restriction Based on Prior
Enrollment: Examining Potential Biases in
Estimating Cancer and Mortality Risk
by Susan Shortreed, Eric Johnson, Carolyn Rutter,
Aruna Kamineni, Karen Wernli & Jessica Chubak

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
Covariates
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

Editorial Board
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
Ben Hansen
University of Michigan
David Harding
University of California, Berkeley
Joseph Hogan
Brown University
Kosuke Imai
Princeton University
Guido Imbens
Stanford University
Luke Keele
Georgetown University
Genevieve Lefebvre
Universite du Quebec a Montreal
Stephen Morgan
Johns Hopkins University
Paul Rosenbaum
University of Pennsylvania
Jason Roy
University of Pennsylvania
Jas Sekhon
University of California, Berkeley
Susan Shortreed
Group Health Research Institute
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

Cohort Restriction Based on Prior
Enrollment: Examining Potential Biases in
Estimating Cancer and Mortality Risk
by Susan Shortreed, Eric Johnson, Carolyn Rutter,
Aruna Kamineni, Karen Wernli & Jessica Chubak

Published on 09-26-2016
Electronic health records and administrative databases provide rich, longitudinal data for health-related research. These data cover large, diverse populations creating excellent research opportunities, but have limitations. In particular, information is available only for individuals who are enrolled in a particular health system; thus, studies often exclude individual's with short enrollment history. Such cohort restriction may cause selection bias in absolute risk estimates for the full enrollee population. We use hazard ratios (HRs) to estimate the association between length of prior enrollment and cancer and all-cause mortality risk. HRs different from one indicate restricted cohorts would produce biased risk estimates for the full enrollee population. Our study sample included 170,708 enrollees of a Western Washington healthcare delivery system. Unadjusted models found individuals with 10 or more years of prior enrollment had higher risk of cancer and death compared to those with less than 5 years prior enrollment (HRs ranged from 1.29-3.01). Age- and sex-adjusted models accounted for much of this difference (HRs: 0.93-1.24). Models adjusting for additional covariates had similar results (HRs: 0.91-1.14). After evaluating potential selection bias, we conclude that, in this setting, age- and sex-standardizing risk estimates can remove most of the bias due to lengthy, prior-enrollment cohort restrictions. Before generalizing estimates based on a selected sample of patients meeting prior enrollment criteria, researchers should assess the potential for selection bias.
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Shortreed_prior_enrollment_for_publication.pdf
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