title: Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling.
abstract: OBJECTIVE
  Clinical trials are unlikely to ever be launched for many comparative effectiveness research (CER) questions. Inferences from hypothetical randomized trials may however be emulated with marginal structural modeling (MSM) using observational data, but success in adjusting for time-dependent confounding and selection bias typically relies on parametric modeling assumptions. If these assumptions are violated, inferences from MSM may be inaccurate. In this article, we motivate the application of a data-adaptive estimation approach called super learning (SL) to avoid reliance on arbitrary parametric assumptions in CER.
  
  
  METHODS
  Using the electronic health records data from adults with new-onset type 2 diabetes, we implemented MSM with inverse probability weighting (IPW) estimation to evaluate the effect of three oral antidiabetic therapies on the worsening of glomerular filtration rate.
  
  
  RESULTS
  Inferences from IPW estimation were noticeably sensitive to the parametric assumptions about the associations between both the exposure and censoring processes and the main suspected source of confounding, that is, time-dependent measurements of hemoglobin A1c. SL was successfully implemented to harness flexible confounding and selection bias adjustment from existing machine learning algorithms.
  
  
  CONCLUSIONS
  Erroneous IPW inference about clinical effectiveness because of arbitrary and incorrect modeling decisions may be avoided with SL.
authors.affiliation: Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612, USA. romain.s.neugebauer@kp.org
authors.forename: Romain
authors.initials: R
authors.lastname: Neugebauer
authors.forename: Bruce
authors.initials: B
authors.lastname: Fireman
authors.forename: Jason A
authors.initials: JA
authors.lastname: Roy
authors.forename: Marsha A
authors.initials: MA
authors.lastname: Raebel
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Nichols
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: O'Connor
mesh_terms: D000328:Adult; D000368:Aged; D015331:Cohort Studies; D057186:Comparative Effectiveness Research / Q000706:statistics & numerical data; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy / Q000503:physiopathology; D018450:Disease Progression; D004359:Drug Therapy, Combination; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005919:Glomerular Filtration Rate / Q000187:drug effects; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000627:therapeutic use; D008687:Metformin / Q000008:administration & dosage / Q000627:therapeutic use; D008875:Middle Aged; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D015983:Selection Bias; D013453:Sulfonylurea Compounds / Q000008:administration & dosage / Q000627:therapeutic use; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome; D055815:Young Adult
chemical_list: D007004:Hypoglycemic Agents; D013453:Sulfonylurea Compounds; D008687:Metformin
keywords: Comparative effectiveness research; Inverse probability weighting; Marginal structural model; Selection bias; Super learning; Time-dependent confounding