Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners

被引：453
作者：
Desai, Rishi J. [1 ,2 ]
Franklin, Jessica M. [1 ,2 ]
机构：
[1] Brigham & Womens Hosp, Div Pharmacoepidemiol & Pharmacoecon, 1620 Tremont St, Boston, MA 02120 USA
[2] Harvard Med Sch, 1620 Tremont St, Boston, MA 02120 USA
来源：
BMJ-BRITISH MEDICAL JOURNAL | 2019年 / 367卷
关键词：
CAUSAL INFERENCE; LOGISTIC-REGRESSION; PERFORMANCE; SELECTION; SETTINGS; EXPOSURE; OUTCOMES; COMPARE; MODELS; RISK;
D O I：
10.1136/bmj.l5657
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
This report aims to provide methodological guidance to help practitioners select the most appropriate weighting method based on propensity scores for their analysis out of many available options (eg, inverse probability treatment weights, standardised mortality ratio weights, fine stratification weights, overlap weights, and matching weights), and outlines recommendations for transparent reporting of studies using weighting based on the propensity scores.
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页数：10
相关论文
共 34 条
[1]   Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies [J].
Austin, Peter C. ;
Stuart, Elizabeth A. .
STATISTICS IN MEDICINE, 2015, 34 (28) :3661-3679
[2]   The performance of different propensity score methods for estimating marginal hazard ratios [J].
Austin, Peter C. .
STATISTICS IN MEDICINE, 2013, 32 (16) :2837-2849
[3]   Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research [J].
Austin, Peter C. .
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2009, 38 (06) :1228-1234
[4]   Propensity Score Methods for Confounding Control in Nonexperimental Research [J].
Brookhart, M. Alan ;
Wyss, Richard ;
Layton, J. Bradley ;
Stuerner, Til .
CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES, 2013, 6 (05) :604-611
[5]   Constructing inverse probability weights for marginal structural models [J].
Cole, Stephen R. ;
Hernan, Miguel A. .
AMERICAN JOURNAL OF EPIDEMIOLOGY, 2008, 168 (06) :656-664
[6]  
Desai RJ, 2019, PHARMACOEPIDEM DR S, V28, P19
[7]   Extension of Disease Risk Score-Based Confounding Adjustments for Multiple Outcomes of Interest: An Empirical Evaluation [J].
Desai, Rishi J. ;
Wyss, Richard ;
Jin, Yinzhu ;
Bohn, Justin ;
Toh, Sengwee ;
Cosgrove, Austin ;
Kennedy, Adee ;
Kim, Jessica ;
Kim, Clara ;
Ouellet-Hellstrom, Rita ;
Karami, Sara ;
Major, Jacqueline M. ;
Niman, Aaron ;
Wang, Shirley, V ;
Gagne, Joshua J. .
AMERICAN JOURNAL OF EPIDEMIOLOGY, 2018, 187 (11) :2439-2448
[8]   A Propensity-score-based Fine Stratification Approach for Confounding Adjustment When Exposure Is Infrequent [J].
Desai, Rishi J. ;
Rothman, Kenneth J. ;
Bateman, Brian T. ;
Hernandez-Diaz, Sonia ;
Huybrechts, Krista F. .
EPIDEMIOLOGY, 2017, 28 (02) :249-257
[9]   Comparing the performance of propensity score methods in healthcare database studies with rare outcomes [J].
Franklin, Jessica M. ;
Eddings, Wesley ;
Austin, Peter C. ;
Stuart, Elizabeth A. ;
Schneeweiss, Sebastian .
STATISTICS IN MEDICINE, 2017, 36 (12) :1946-1963
[10]   Metrics for covariate balance in cohort studies of causal effects [J].
Franklin, Jessica M. ;
Rassen, Jeremy A. ;
Ackermann, Diana ;
Bartels, Dorothee B. ;
Schneeweiss, Sebastian .
STATISTICS IN MEDICINE, 2014, 33 (10) :1685-1699
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