Advanced Issues in Propensity Scores: Longitudinal and Missing Data

Kevin A. Kupzyk, Sarah J. Beal

Research output: Contribution to journalArticle

Abstract

In order to investigate causality in situations where random assignment is not possible, propensity scores can be used in regression adjustment, stratification, inverse-probability treatment weighting, or matching. The basic concepts behind propensity scores have been extensively described. When data are longitudinal or missing, the estimation and use of propensity scores become a challenge. Traditional methods of propensity score estimation delete cases listwise. Missing data estimation, by multiple imputation, can be used to alleviate problems due to missing values, if performed correctly. Longitudinal studies are another situation where propensity score use may be difficult because of attrition and needing to account for data when propensities may vary over time. This article discusses the issues of missing data and longitudinal designs in the context of propensity scores. The syntax, datasets, and output used for these examples are available on http://jea.sagepub.com/content/early/recent for readers to download and follow.

Original languageEnglish (US)
Pages (from-to)59-84
Number of pages26
JournalJournal of Early Adolescence
Volume37
Issue number1
DOIs
StatePublished - Jan 1 2016

Fingerprint

Propensity Score
basic concept
weighting
causality
Causality
syntax
Longitudinal Studies
longitudinal study
regression
Values

Keywords

  • education
  • health promotion
  • problem/risky/antisocial behaviors
  • tobacco use/smoking

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Social Sciences (miscellaneous)
  • Sociology and Political Science
  • Life-span and Life-course Studies

Cite this

Advanced Issues in Propensity Scores : Longitudinal and Missing Data. / Kupzyk, Kevin A.; Beal, Sarah J.

In: Journal of Early Adolescence, Vol. 37, No. 1, 01.01.2016, p. 59-84.

Research output: Contribution to journalArticle

@article{8fdd3a3a7dcb4eb89e7dee9f87fd5044,
title = "Advanced Issues in Propensity Scores: Longitudinal and Missing Data",
abstract = "In order to investigate causality in situations where random assignment is not possible, propensity scores can be used in regression adjustment, stratification, inverse-probability treatment weighting, or matching. The basic concepts behind propensity scores have been extensively described. When data are longitudinal or missing, the estimation and use of propensity scores become a challenge. Traditional methods of propensity score estimation delete cases listwise. Missing data estimation, by multiple imputation, can be used to alleviate problems due to missing values, if performed correctly. Longitudinal studies are another situation where propensity score use may be difficult because of attrition and needing to account for data when propensities may vary over time. This article discusses the issues of missing data and longitudinal designs in the context of propensity scores. The syntax, datasets, and output used for these examples are available on http://jea.sagepub.com/content/early/recent for readers to download and follow.",
keywords = "education, health promotion, problem/risky/antisocial behaviors, tobacco use/smoking",
author = "Kupzyk, {Kevin A.} and Beal, {Sarah J.}",
year = "2016",
month = "1",
day = "1",
doi = "10.1177/0272431616636229",
language = "English (US)",
volume = "37",
pages = "59--84",
journal = "Journal of Early Adolescence",
issn = "0272-4316",
publisher = "SAGE Publications Inc.",
number = "1",

}

TY - JOUR

T1 - Advanced Issues in Propensity Scores

T2 - Longitudinal and Missing Data

AU - Kupzyk, Kevin A.

AU - Beal, Sarah J.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - In order to investigate causality in situations where random assignment is not possible, propensity scores can be used in regression adjustment, stratification, inverse-probability treatment weighting, or matching. The basic concepts behind propensity scores have been extensively described. When data are longitudinal or missing, the estimation and use of propensity scores become a challenge. Traditional methods of propensity score estimation delete cases listwise. Missing data estimation, by multiple imputation, can be used to alleviate problems due to missing values, if performed correctly. Longitudinal studies are another situation where propensity score use may be difficult because of attrition and needing to account for data when propensities may vary over time. This article discusses the issues of missing data and longitudinal designs in the context of propensity scores. The syntax, datasets, and output used for these examples are available on http://jea.sagepub.com/content/early/recent for readers to download and follow.

AB - In order to investigate causality in situations where random assignment is not possible, propensity scores can be used in regression adjustment, stratification, inverse-probability treatment weighting, or matching. The basic concepts behind propensity scores have been extensively described. When data are longitudinal or missing, the estimation and use of propensity scores become a challenge. Traditional methods of propensity score estimation delete cases listwise. Missing data estimation, by multiple imputation, can be used to alleviate problems due to missing values, if performed correctly. Longitudinal studies are another situation where propensity score use may be difficult because of attrition and needing to account for data when propensities may vary over time. This article discusses the issues of missing data and longitudinal designs in the context of propensity scores. The syntax, datasets, and output used for these examples are available on http://jea.sagepub.com/content/early/recent for readers to download and follow.

KW - education

KW - health promotion

KW - problem/risky/antisocial behaviors

KW - tobacco use/smoking

UR - http://www.scopus.com/inward/record.url?scp=85003918734&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85003918734&partnerID=8YFLogxK

U2 - 10.1177/0272431616636229

DO - 10.1177/0272431616636229

M3 - Article

AN - SCOPUS:85003918734

VL - 37

SP - 59

EP - 84

JO - Journal of Early Adolescence

JF - Journal of Early Adolescence

SN - 0272-4316

IS - 1

ER -