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Estimating Effects of Nursing Intervention via Propensity Score Analysis

Qin, Rui; Titler, Marita G.; Shever, Leah L.; Kim, Taikyoung

doi: 10.1097/NNR.0b013e31818c66f6

Background: Lack of randomization of nursing intervention in outcome effectiveness studies may lead to imbalanced covariates. Consequently, estimation of nursing intervention effect can be biased as in other observational studies. Propensity score analysis is an effective statistical method to reduce such bias and further derive causal effects in observational studies.

Objectives: The objective of this study was to illustrate the use of propensity score analysis in quantitative nursing research through an example of pain management effect on length of hospital stay.

Methods: Propensity scores are generated through a regression model treating the nursing intervention as the dependent variable and all confounding covariates as predictor variables. Then, propensity scores are used to adjust for this nonrandomized assignment of nursing intervention through three approaches: regression covariance adjustment, stratification, and matching in the predictive outcome model for nursing intervention.

Results: Propensity score analysis reduces the confounding covariates into a single variable of propensity score. After stratification and matching on propensity scores, observed covariates between nursing intervention groups are more balanced within each stratum or in the matched samples. The likelihood of receiving pain management is accounted for in the outcome model through the propensity scores. Both regression covariance adjustment and matching methods report a significant pain management effect on length of hospital stay in this example. The pain management effect can be regarded as causal when the strongly ignorable treatment assignment assumption holds.

Discussion: Propensity score analysis provides an alternative statistical approach to the classical multivariate regression, stratification, and matching techniques for examining the effects of nursing intervention with a large number of confounding covariates in the background. It can be used to derive causal effects of nursing intervention in observational studies under certain circumstances.

Rui Qin, PhD, is Research Associate, Division of Biostatistics, Mayo Clinic, Rochester, Minnesota.

Marita G. Titler, PhD, RN, FAAN, is Senior Assistant Director, UIHC, and Director; and Leah L. Shever, PhD, RN, is Advanced Practice Nurse, Research, Quality and Outcomes Management, Department of Nursing Services and Patient Care, University of Iowa Hospitals and Clinics.

Taikyoung Kim, MS, is Database Manager, Nursing Interventions and Outcomes Effectiveness in 3 Older Populations, The University of Iowa College of Nursing.

Editor's Note: Additional information provided by the authors expanding this article is on the Editor's Web site at

Accepted for publication June 30, 2008.

This research was supported by a grant from the National Institutes of Health (principal investigator: Titler; NINR R01 NR05331).

Corresponding author: Rui Qin, PhD, Division of Biostatistics, Mayo Clinic, Rochester, MN 55905 (e-mail:

© 2008 Lippincott Williams & Wilkins, Inc.