%0 Journal Article %1 Stuermer2014 %A Stürmer, T %A Wyss, R %A Glynn, R J %A Brookhart, M A %D 2014 %J Journal of internal medicine %K AgeFactors Combination Comorbidity ComparativeEffectivenessResearch ComparativeEffectivenessResearch:methods ComparativeEffectivenessResearch:standards ConfoundingFactors(Epidemiology) DrugTherapy EpidemiologicResearchDesign Humans OutcomeAssessment(HealthCare) OutcomeAssessment(HealthCare):methods OutcomeAssessment(HealthCare):standards OutcomeAssessment(HealthCare):statistics&num Pharmacoepidemiology Pharmacoepidemiology:methods PropensityScore SexFactors %N 6 %P 570-80 %R 10.1111/joim.12197 %T Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs. %U http://www.ncbi.nlm.nih.gov/pubmed/24520806 %V 275 %X Treatment effects, especially when comparing two or more therapeutic alternatives as in comparative effectiveness research, are likely to be heterogeneous across age, gender, co-morbidities and co-medications. Propensity scores (PSs), an alternative to multivariable outcome models to control for measured confounding, have specific advantages in the presence of heterogeneous treatment effects. Implementing PSs using matching or weighting allows us to estimate different overall treatment effects in differently defined populations. Heterogeneous treatment effects can also be due to unmeasured confounding concentrated in those treated contrary to prediction. Sensitivity analyses based on PSs can help to assess such unmeasured confounding. PSs should be considered a primary or secondary analytic strategy in nonexperimental medical research, including pharmacoepidemiology and nonexperimental comparative effectiveness research.

@article{Stuermer2014, abstract = {Treatment effects, especially when comparing two or more therapeutic alternatives as in comparative effectiveness research, are likely to be heterogeneous across age, gender, co-morbidities and co-medications. Propensity scores (PSs), an alternative to multivariable outcome models to control for measured confounding, have specific advantages in the presence of heterogeneous treatment effects. Implementing PSs using matching or weighting allows us to estimate different overall treatment effects in differently defined populations. Heterogeneous treatment effects can also be due to unmeasured confounding concentrated in those treated contrary to prediction. Sensitivity analyses based on PSs can help to assess such unmeasured confounding. PSs should be considered a primary or secondary analytic strategy in nonexperimental medical research, including pharmacoepidemiology and nonexperimental comparative effectiveness research.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Stürmer, T and Wyss, R and Glynn, R J and Brookhart, M A}, biburl = {https://www.bibsonomy.org/bibtex/24fc5dcbb6a4f07072c1ebe08565b4045/jepcastel}, doi = {10.1111/joim.12197}, interhash = {f2a229b10aefb7be55d40fd782c93e06}, intrahash = {4fc5dcbb6a4f07072c1ebe08565b4045}, issn = {1365-2796}, journal = {Journal of internal medicine}, keywords = {AgeFactors Combination Comorbidity ComparativeEffectivenessResearch ComparativeEffectivenessResearch:methods ComparativeEffectivenessResearch:standards ConfoundingFactors(Epidemiology) DrugTherapy EpidemiologicResearchDesign Humans OutcomeAssessment(HealthCare) OutcomeAssessment(HealthCare):methods OutcomeAssessment(HealthCare):standards OutcomeAssessment(HealthCare):statistics&num Pharmacoepidemiology Pharmacoepidemiology:methods PropensityScore SexFactors}, month = {6}, note = {Propensity score; Introductori}, number = 6, pages = {570-80}, pmid = {24520806}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24520806}, volume = 275, year = 2014 }