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Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.

Publication ,  Journal Article
Lunceford, JK; Davidian, M
Published in: Stat Med
October 15, 2004

Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use.

Duke Scholars

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Published In

Stat Med

DOI

ISSN

0277-6715

Publication Date

October 15, 2004

Volume

23

Issue

19

Start / End Page

2937 / 2960

Location

England

Related Subject Headings

  • Treatment Outcome
  • Statistics & Probability
  • Regression Analysis
  • Monte Carlo Method
  • Humans
  • Data Interpretation, Statistical
  • Computer Simulation
  • 4905 Statistics
  • 4202 Epidemiology
  • 1117 Public Health and Health Services
 

Citation

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Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med, 23(19), 2937–2960. https://doi.org/10.1002/sim.1903
Lunceford, Jared K., and Marie Davidian. “Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.Stat Med 23, no. 19 (October 15, 2004): 2937–60. https://doi.org/10.1002/sim.1903.
Lunceford, Jared K., and Marie Davidian. “Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.Stat Med, vol. 23, no. 19, Oct. 2004, pp. 2937–60. Pubmed, doi:10.1002/sim.1903.
Journal cover image

Published In

Stat Med

DOI

ISSN

0277-6715

Publication Date

October 15, 2004

Volume

23

Issue

19

Start / End Page

2937 / 2960

Location

England

Related Subject Headings

  • Treatment Outcome
  • Statistics & Probability
  • Regression Analysis
  • Monte Carlo Method
  • Humans
  • Data Interpretation, Statistical
  • Computer Simulation
  • 4905 Statistics
  • 4202 Epidemiology
  • 1117 Public Health and Health Services