%0 Journal Article %1 Garrido2014 %A Garrido, Melissa M %A Kelley, Amy S %A Paris, Julia %A Roza, Katherine %A Meier, Diane E %A Morrison, R Sean %A Aldridge, Melissa D %D 2014 %J Health services research %K 80andover Adult Aged CancerCareFacilities CancerCareFacilities:statistics&numericaldat ConfoundingFactors(Epidemiology) DataInterpretation Female Humans Male MiddleAged Models Neoplasms Neoplasms:epidemiology NewYork Ohio OutcomeAssessment(HealthCare) OutcomeAssessment(HealthCare):methods PalliativeCare PalliativeCare:statistics&numericaldata Pennsylvania PropensityScore ResearchDesign Statistical TreatmentOutcome Virginia Wisconsin YoungAdult %N 5 %P 1701-20 %R 10.1111/1475-6773.12182 %T Methods for constructing and assessing propensity scores. %U http://www.ncbi.nlm.nih.gov/pubmed/24779867 %V 49 %X 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.

@article{Garrido2014, 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.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Garrido, Melissa M and Kelley, Amy S and Paris, Julia and Roza, Katherine and Meier, Diane E and Morrison, R Sean and Aldridge, Melissa D}, biburl = {https://www.bibsonomy.org/bibtex/2e7fc0acf1bc029b32586ec2c37ca603e/jepcastel}, doi = {10.1111/1475-6773.12182}, interhash = {b88920b8facf6103046b4bcf47a358ae}, intrahash = {e7fc0acf1bc029b32586ec2c37ca603e}, issn = {1475-6773}, journal = {Health services research}, keywords = {80andover Adult Aged CancerCareFacilities CancerCareFacilities:statistics&numericaldat ConfoundingFactors(Epidemiology) DataInterpretation Female Humans Male MiddleAged Models Neoplasms Neoplasms:epidemiology NewYork Ohio OutcomeAssessment(HealthCare) OutcomeAssessment(HealthCare):methods PalliativeCare PalliativeCare:statistics&numericaldata Pennsylvania PropensityScore ResearchDesign Statistical TreatmentOutcome Virginia Wisconsin YoungAdult}, month = {10}, note = {Propensity score; STATA; Introductori; Anàlisi de dades}, number = 5, pages = {1701-20}, pmid = {24779867}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Methods for constructing and assessing propensity scores.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24779867}, volume = 49, year = 2014 }