Methods for Constructing and Assessing Propensity Scores

Authors

  • Melissa M. Garrido Ph.D.,

    Corresponding author
    1. GRECC, James J Peters VA Medical Center, Bronx, NY
    2. Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
    • Address correspondence to Melissa M. Garrido, Ph.D., GRECC, James J Peters VA Medical Center, Bronx, NY; Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; 130 W. Kingsbridge Road, Room 4A-17, Bronx, NY 10468; e-mail: melissa.garrido@mssm.edu.

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  • Amy S. Kelley M.D., M.S.H.S.,

    1. GRECC, James J Peters VA Medical Center, Bronx, NY
    2. Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
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  • Julia Paris B.A.,

    1. Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
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  • Katherine Roza B.A.,

    1. Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
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  • Diane E. Meier M.D.,

    1. Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
    2. Center to Advance Palliative Care, New York, NY
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  • R. Sean Morrison M.D.,

    1. GRECC, James J Peters VA Medical Center, Bronx, NY
    2. Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
    3. National Palliative Care Research Center, Hertzberg Palliative Care Institute, New York, NY
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  • Melissa D. Aldridge Ph.D., M.B.A.

    1. GRECC, James J Peters VA Medical Center, Bronx, NY
    2. Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
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Abstract

Objectives

To model the steps involved in preparing for and carrying out propensity score analyses by providing step-by-step guidance and Stata code applied to an empirical dataset.

Study Design

Guidance, Stata code, and empirical examples are given to illustrate (1) the process of choosing variables to include in the propensity score; (2) balance of propensity score across treatment and comparison groups; (3) balance of covariates across treatment and comparison groups within blocks of the propensity score; (4) choice of matching and weighting strategies; (5) balance of covariates after matching or weighting the sample; and (6) interpretation of treatment effect estimates.

Empirical Application

We use data from the Palliative Care for Cancer Patients (PC4C) study, a multisite observational study of the effect of inpatient palliative care on patient health outcomes and health services use, to illustrate the development and use of a propensity score.

Conclusions

Propensity scores are one useful tool for accounting for observed differences between treated and comparison groups. Careful testing of propensity scores is required before using them to estimate treatment effects.

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