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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
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

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

Published on 02-01-2016
Background: In observational studies concerning drug use and misuse, persons misusing drugs may be less likely to respond to surveys. However, little is known about differences in drug use and drug misuse risk factors between survey respondents and nonrespondents. Methods: Using electronic health record (EHR) data, we compared respondents and nonrespondents of a telephone survey of middle-aged and older chronic opioid therapy patients to assess predictors of interview nonresponse. We compared general patient characteristics, specific opioid misuse risk factors, and patterns of opioid use associated with increased risk of opioid misuse. Inverse probability weights were calculated to account for nonresponse bias by EHR-measured covariates. EHR-measured covariate distributions for the full sample (nonrespondents and respondents), the unweighted respondent sample, and the inverse probability weighted respondent sample are reported. We present weighted and unweighted prevalence of self-reported opioid misuse risk factors. Results: Among 2489 potentially eligible patients, 1477 (59.3%) completed interviews. Response rates differed with age (45- 54 years, 51.8%; 55-64 years, 58.7%; 65-74 years, 67.9%; and 75 years or older, 59.9%). Tobacco users had lower response rates than did nonusers (53.5% versus 60.9%). Charlson comorbidity score was also related to response rates. Individuals with a Charlson score of 2 had the highest response rate at 65.6%; response rates were lower amoung patients with the lowest (the patients with the fewest health conditions had response rates of 56.7-60.0%) and the highest Charlson scores (patients with the most health conditions had response rates of 52.2-56.0%). These bivariate relationships persisted in adjusted multivariable logistic regression models predicting survey response. Response rates of persons with and without specific opioid misuse risk factors were similar (e.g., 58.7% for persons with substance abuse diagnoses, 59.4% for those without). Opioid use patterns associated with opioid misuse did not predict response rates (e.g., 60.6% versus 59.2% for those receiving versus not receiving opioids from 3 or more physicians outside their primary care clinic). Very few patient characteristics predicted non-response; thus, inverse probability weights accounting for nonresponse had little impact on the distributions of EHR-measured covariates or self-reported measures related to opioid use and misuse. Conclusions: Response rates differed by characteristics that predict nonresponse in general health surveys (age, tobacco use), but did not appear to differ by specific patient or drug use risk factors for prescription opioid misuse among middle- and older-aged chronic opioid therapy patients. When observational studies are conducted in health plan populations, electronic health records may be used to evaluate nonresponse bias and to adjust for variables predicting interview nonresponse, complementing other research uses of EHR data in observational studies.
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