title: Improving participant selection in disease management programmes: insights gained from propensity score stratification.
abstract: While the randomized controlled trial (RCT) remains the gold-standard study design for evaluating treatment effect, outcomes researchers turn to powerful quasi-experimental designs when only observational studies can be conducted. Within these designs, propensity score matching is one of the most popular to evaluate disease management (DM) programme effectiveness. Given that DM programmes generally have a much smaller number of participants than non-participants in the population, propensity score matching will typically result in all or nearly all participants finding successful matches, while most of the non-participants in the population remain unmatched and thereby excluded from the analysis. By excluding data from the unmatched population, the effect of non-treatment in the remaining population with the disease is not captured. In the present study, we examine changes in hospitalization rates stratified by propensity score quintiles across the entire population allowing us to gain insight as to how well the programme chose its participants, or if the programme could have been effective on those individuals not explicitly targeted for the intervention. These data indicate the presence of regression to the mean, and suggest that the DM programme may be overly limited to only the highest strata when there is evidence of a potential benefit for those in all the lower strata as well.
authors.affiliation: Oregon Health & Science University, School of Medicine, Portland, OR, USA. alinden@lindenconsulting.org
authors.forename: Ariel
authors.initials: A
authors.lastname: Linden
authors.forename: John L
authors.initials: JL
authors.lastname: Adams
mesh_terms: D000368:Aged; D000704:Analysis of Variance; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D019468:Disease Management; D005260:Female; D006333:Heart Failure / Q000628:therapy; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D016018:Least-Squares Analysis; D016015:Logistic Models; D008297:Male; D009922:Oregon; D017063:Outcome Assessment, Health Care / Q000458:organization & administration*; D018579:Patient Selection; D011336:Probability; D015397:Program Evaluation / Q000379:methods*; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D018401:Sample Size; D015983:Selection Bias; D017749:Total Quality Management / Q000458:organization & administration