title: Estimating causal effects using prior information on nontrial treatments.
abstract: BACKGROUND
  Departures from randomized treatments complicate the analysis of many randomized controlled trials. Intention-to-treat analysis estimates the effect of being allocated to treatment. It is now possible to estimate the effect of receiving treatment without assuming comparability of groups defined by actual treatment. However, the methodology is largely confined to trials where the only treatment changes were switches to other trial treatments.
  
  
  OBJECTIVE
  To propose a method for comparing the effects of receiving trial treatments in an active-controlled clinical trial where some participants received nontrial treatments and others received no treatment at all, and to illustrate the method in the PENTA 5 trial in HIV-infected children.
  
  
  METHODS
  We combine the instrumental variables approach, which forms unbiased estimating equations based on the randomization but does not fully identify the contrasts of trial treatment effects, with prior information about the distribution of possible effects of nontrial treatments and of one trial treatment; we do not need to use prior information about the comparisons of trial treatments. Prior information in PENTA 5 was elicited from the investigators.
  
  
  RESULTS
  In PENTA 5, the prior information suggested that all treatments were beneficial, but there was uncertainty about the degree of benefit. Allowing for this prior information changed point estimates and increased standard errors compared with ignoring nontrial treatments.
  
  
  CONCLUSIONS
  The method depends on the correct specification of the causal effect of treatment: in PENTA 5, this assumed a linear effect of dose and no interactions between treatments. This specification is hard to check from the data but can be explored in sensitivity analyses. Prior information would be better derived from the literature whenever possible.
  
  
  CONCLUSIONS
  The use of partial prior information gives a way to adjust for complex patterns of departures from randomized treatments. It should be useful in all trials where nontrial treatments are used and in active-controlled trials where trial treatments are not universally used.
authors.affiliation: MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK.
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Bond
authors.forename: Ian R
authors.initials: IR
authors.lastname: White
mesh_terms: D001499:Bayes Theorem; D015984:Causality; D003627:Data Interpretation, Statistical; D004359:Drug Therapy, Combination; D015658:HIV Infections / Q000628:therapy*; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012107:Research Design; D013223:Statistics as Topic; D011795:Surveys and Questionnaires; D016896:Treatment Outcome
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