title: Baseline characteristics and statistical power in randomized  controlled trials: selection, prognostic targeting, or covariate  adjustment?
abstract: OBJECTIVE
  Heterogeneity of patients is a common problem in randomized controlled trials (RCTs) in various fields of clinical research. We aimed to investigate the potential benefits of different approaches for dealing with heterogeneity in a case study on traumatic brain injury (TBI).
  
  
  METHODS
  Statistical modeling studies in three surveys and six randomized controlled trials.
  
  
  METHODS
  Individual patient data (n = 8033) from the IMPACT database.
  
  
  METHODS
  We investigated the statistical power and efficiency of randomized controlled trials (RCTs) in relation to (1) selection according to baseline characteristics, (2) prognostic targeting (i.e., excluding those with a relatively extreme prognosis), and (3) covariate-adjusted analysis. Statistical power was expressed as the required sample size for obtaining 80% power and efficiency as the relative change in study duration, reflecting both gains in power and adverse effects on recruitment. Uniform and targeted treatment effects were simulated for 6 month unfavorable outcome.
  
  
  RESULTS
  For a uniform treatment effect, selection resulted ina sample size reduction of 33% in the surveys and 5% in the RCTs, but decreased recruitment by 65% and 41%, respectively. Hence, the relative study duration was prolonged (surveys: 95%; RCTs: 60%). Prognostic targeting resulted in sample size reductions of 28% and 17%, and increased relative study duration by 5% in surveys and 11% in the RCTs. Covariate adjustment reduced sample sizes by 30% and 16%, respectively, and did not affect recruitment. For a targeted treatment effect, the sample size reductions by selection (surveys: 47%; RCTs: 20%) and prognostic targeting (surveys: 49%; RCTs: 41%) were larger and adverse effects on recruitment smaller.
  
  
  CONCLUSIONS
  The benefits of selection and prognostic targeting in terms of statistical power are reversed by adverse effects on recruitment. Covariate adjusted analysis in a broadly selected group of patients is advisable if a uniform treatment effect is assumed, since there is no decrease in recruitment.
authors.affiliation: Department of Public Health, Center for Medical Decision Making, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: Bob
authors.initials: B
authors.lastname: Roozenbeek
authors.forename: Andrew I R
authors.initials: AI
authors.lastname: Maas
authors.forename: Hester F
authors.initials: HF
authors.lastname: Lingsma
authors.forename: Isabella
authors.initials: I
authors.lastname: Butcher
authors.forename: Juan
authors.initials: J
authors.lastname: Lu
authors.forename: Anthony
authors.initials: A
authors.lastname: Marmarou
authors.forename: Gillian S
authors.initials: GS
authors.lastname: McHugh
authors.forename: James
authors.initials: J
authors.lastname: Weir
authors.forename: Gordon D
authors.initials: GD
authors.lastname: Murray
authors.forename: Ewout W
authors.initials: EW
authors.lastname: Steyerberg
mesh_terms: D000328:Adult; D000367:Age Factors; D000704:Analysis of Variance; D015982:Bias; D001930:Brain Injuries / Q000401:mortality / Q000628:therapy; D003198:Computer Simulation; D003422:Critical Care / Q000379:methods / Q000706:statistics & numerical data*; D005260:Female; D015600:Glasgow Coma Scale; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D008875:Middle Aged; D015337:Multicenter Studies as Topic; D009460:Neurologic Examination / Q000706:statistics & numerical data; D018579:Patient Selection; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012025:Reflex, Pupillary; D018570:Risk Assessment / Q000706:statistics & numerical data; D018401:Sample Size; D055815:Young Adult