title: Estimating effects of nursing intervention via propensity score analysis.
abstract: 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.
  
  
  OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division of Biostatistics, Mayo Clinic, Rochester, Minnesota 55905, USA. Qin.Rui@mayo.edu
authors.forename: Rui
authors.initials: R
authors.lastname: Qin
authors.forename: Marita G
authors.initials: MG
authors.lastname: Titler
authors.forename: Leah L
authors.initials: LL
authors.lastname: Shever
authors.forename: Taikyoung
authors.initials: T
authors.lastname: Kim
mesh_terms: D000704:Analysis of Variance; D015982:Bias; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D006801:Humans; D007902:Length of Stay; D015403:Nursing Evaluation Research / Q000379:methods*; D017063:Outcome Assessment, Health Care; D010146:Pain / Q000451:nursing; D012044:Regression Analysis; D012107:Research Design