title: Estimating effects from randomized trials with discontinuations: the need for intent-to-treat design and G-estimation.
abstract: BACKGROUND
  Randomized trials provide pivotal evidence for evaluation and approval of therapies. Nonetheless, such trials are often plagued by noncompliance, especially in the form of premature discontinuation of treatment. While intent-to-treat (ITT) analysis can provide valid tests of no-effect hypotheses, some trials may make ITT analysis impossible by ceasing follow-up when patients go off assigned treatment. Furthermore, estimates based on ITT, on-treatment, or per-protocol comparisons can seriously understate harm or benefit.
  
  
  OBJECTIVE
  To show how g-estimation based on randomization status is a natural generalization of ITT null testing to estimating efficacy from trials with important discontinuation or noncompliance.
  
  
  METHODS
  We contrast with an analysis of the effect of a tiotropium inhaler on the occurrence of chronic obstructive pulmonary disease (COPD) events in a six-month double-blind placebo-controlled trial of 1829 patients with good but imperfect compliance.
  
  
  RESULTS
  The covariate-adjusted point estimates, 95% confidence limits (CL), and null P-values comparing expected COPD event times in placebo versus tiotropium patients were: ITT, 1.21, CL = 1.02, 1.43, P = 0.027; on-treatment, 1.27, CL = 1.06, 1.52, P = 0.009; per-protocol, 1.36, CL = 1.13, 1.63, P = 0.001; and g-estimation, 1.31, CL = 1.03,1.72, P = 0.027. Thus g-estimation preserved the ITT test of the null, but exhibited more uncertainty about the size of the tiotropium effect than the other methods. In particular, it allowed for a much larger potential effect than did ITT analysis, but produced a much larger null P than exhibited by per-protocol analysis.
  
  
  CONCLUSIONS
  Like ITT analysis, g-estimation requires all patients be followed to the end of the trial protocol, regardless of whether they comply with the protocol. Like on-treatment and per-protocol analyses, it also requires accurate compliance information be recorded.
  
  
  CONCLUSIONS
  G-estimation should become a standard procedure for the analysis of trials with noncompliance. Software to do so is available in major packages, and the procedure is easily coded for other packages.
authors.affiliation: Epidemiology and Statistics, University of California, Los Angeles, CA, USA. lesdomes@ucla.edu
authors.forename: Sander
authors.initials: S
authors.lastname: Greenland
authors.forename: Stephan
authors.initials: S
authors.lastname: Lanes
authors.forename: Michele
authors.initials: M
authors.lastname: Jara
mesh_terms: D001993:Bronchodilator Agents / Q000627:therapeutic use; D003627:Data Interpretation, Statistical; D004311:Double-Blind Method; D006801:Humans; D009330:Nebulizers and Vaporizers; D010349:Patient Compliance; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D029424:Pulmonary Disease, Chronic Obstructive / Q000188:drug therapy; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data*; D012602:Scopolamine Derivatives / Q000627:therapeutic use; D000069447:Tiotropium Bromide
chemical_list: D001993:Bronchodilator Agents; D012602:Scopolamine Derivatives; D000069447:Tiotropium Bromide