Jeff Y Yang1, Michael Webster-Clark1, Jennifer L Lund1, Robert S Sandler2, Evan S Dellon3, Til Stürmer1. 1. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA. 2. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA; Center for Gastrointestinal Biology and Disease, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA. 3. Center for Gastrointestinal Biology and Disease, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA.
Abstract
BACKGROUND AND AIMS: Confounding is a major concern in nonexperimental studies of endoscopic interventions and can lead to biased estimates of the effects of treatment. Propensity score methods, which are commonly used in the pharmacoepidemiology literature, can effectively control for baseline confounding by balancing measured baseline confounders and risk factors and creating comparable populations of treated and untreated patients. METHODS: We propose the following 5-step checklist to guide the use and evaluation of propensity score methods: (1) select covariates, (2) assess "Table 1" balance in risk factors before propensity score implementation, (3) estimate and implement the propensity score in the study cohort, (4) reassess "Table 1" balance in risk factors after propensity score implementation, and (5) critically evaluate differences between matched and unmatched patients after propensity score implementation. We then applied this checklist to an endoscopy example using a study cohort of 411 adults with newly diagnosed eosinophilic esophagitis (EoE), some of whom were treated with esophageal dilation. RESULTS: We identified 156 patients, aged 18 and older, who were treated with esophageal dilation, and 255 patients who were nondilated. We successfully matched 148 (95%) dilated patients to nondilated patients who had a propensity score within 0.1, based on patient age, sex, race, self-reported food allergy, and presence of narrowing at baseline endoscopy. Crude imbalances were observed before propensity score matching in several baseline covariates, including age, sex, and narrowing; however, propensity score matching was successful in achieving balance across all measured covariates. CONCLUSIONS: We provide an introduction to propensity score methods, including a straightforward checklist for implementing propensity score methods in nonexperimental studies of treatment effectiveness. Moreover, we demonstrate the advantage of using "Table 1" as a simple but effective diagnostic tool for evaluating the success of propensity score methods in an applied example of esophageal dilation in EoE.
BACKGROUND AND AIMS: Confounding is a major concern in nonexperimental studies of endoscopic interventions and can lead to biased estimates of the effects of treatment. Propensity score methods, which are commonly used in the pharmacoepidemiology literature, can effectively control for baseline confounding by balancing measured baseline confounders and risk factors and creating comparable populations of treated and untreated patients. METHODS: We propose the following 5-step checklist to guide the use and evaluation of propensity score methods: (1) select covariates, (2) assess "Table 1" balance in risk factors before propensity score implementation, (3) estimate and implement the propensity score in the study cohort, (4) reassess "Table 1" balance in risk factors after propensity score implementation, and (5) critically evaluate differences between matched and unmatched patients after propensity score implementation. We then applied this checklist to an endoscopy example using a study cohort of 411 adults with newly diagnosed eosinophilic esophagitis (EoE), some of whom were treated with esophageal dilation. RESULTS: We identified 156 patients, aged 18 and older, who were treated with esophageal dilation, and 255 patients who were nondilated. We successfully matched 148 (95%) dilated patients to nondilated patients who had a propensity score within 0.1, based on patient age, sex, race, self-reported food allergy, and presence of narrowing at baseline endoscopy. Crude imbalances were observed before propensity score matching in several baseline covariates, including age, sex, and narrowing; however, propensity score matching was successful in achieving balance across all measured covariates. CONCLUSIONS: We provide an introduction to propensity score methods, including a straightforward checklist for implementing propensity score methods in nonexperimental studies of treatment effectiveness. Moreover, we demonstrate the advantage of using "Table 1" as a simple but effective diagnostic tool for evaluating the success of propensity score methods in an applied example of esophageal dilation in EoE.
Authors: Soko Setoguchi; Sebastian Schneeweiss; M Alan Brookhart; Robert J Glynn; E Francis Cook Journal: Pharmacoepidemiol Drug Saf Date: 2008-06 Impact factor: 2.890
Authors: Evan S Dellon; Wood B Gibbs; Karen J Fritchie; Tara C Rubinas; Lindsay A Wilson; John T Woosley; Nicholas J Shaheen Journal: Clin Gastroenterol Hepatol Date: 2009-09-03 Impact factor: 11.382