title: Evaluation of propensity scores, disease risk scores, and regression in confounder adjustment for the safety of emerging treatment with group sequential monitoring.
abstract: OBJECTIVE
  The objective of this study was to evaluate regression, matching, and stratification on propensity score (PS) or disease risk score (DRS) in a setting of sequential analyses where statistical hypotheses are tested multiple times.
  
  
  METHODS
  In a setting of sequential analyses, we simulated incident users and binary outcomes with different confounding strength, outcome incidence, and the adoption rate of treatment. We compared Type I error rate, empirical power, and time to signal using the following confounder adjustments: (i) regression; (ii) treatment matching (1:1 or 1:4) on PS or DRS; and (iii) stratification on PS or DRS. We estimated PS and DRS using lookwise and cumulative methods (all data up to the current look). We applied these confounder adjustments in examining the association between non-steroidal anti-inflammatory drugs and bleeding.
  
  
  RESULTS
  Propensity score and DRS methods had similar empirical power and time to signal. However, DRS methods yielded Type I error rates up to 17% for 1:4 matching and 15.3% for stratification methods when treatment and outcome were common and confounding strength with treatment was stronger. When treatment and outcome were not common, stratification on PS and DRS and regression yielded 8-10% Type I error rates and inflated empirical power. However, when outcome and treatment were common, both regression and stratification on PS outperformed other matching methods with Type I error rates close to 5%.
  
  
  CONCLUSIONS
  We suggest regression and stratification on PS when the outcomes and/or treatment is common and use of matching on PS with higher ratios when outcome or treatment is rare or moderately rare.
authors.affiliation: Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA.
authors.forename: Stanley
authors.initials: S
authors.lastname: Xu
authors.affiliation: Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA.
authors.forename: Susan
authors.initials: S
authors.lastname: Shetterly
authors.affiliation: Biostatistics Unit, Group Health Research Institute, Seattle, WA, USA.
authors.forename: Andrea J
authors.initials: AJ
authors.lastname: Cook
authors.affiliation: Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA.
authors.forename: Marsha A
authors.initials: MA
authors.lastname: Raebel
authors.affiliation: Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA.
authors.forename: Sunali
authors.initials: S
authors.lastname: Goonesekera
authors.affiliation: Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Azadeh
authors.initials: A
authors.lastname: Shoaibi
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Jason
authors.initials: J
authors.lastname: Roy
authors.affiliation: Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
authors.forename: Bruce
authors.initials: B
authors.lastname: Fireman
mesh_terms: D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000009:adverse effects* / Q000627:therapeutic use; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D004059:Diffusion of Innovation; D005260:Female; D006470:Hemorrhage / Q000453:epidemiology / Q000209:etiology*; D006801:Humans; D008297:Male; D057216:Propensity Score; D012044:Regression Analysis
chemical_list: D000894:Anti-Inflammatory Agents, Non-Steroidal
keywords: disease risk score; group sequential analyses; matching; pharmacoepidemiology; propensity score; stratification