Baseline characteristics and statistical power in randomized  controlled trials: selection, prognostic targeting, or covariate  adjustment?
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.
Roozenbeek
Bob
B
Department of Public Health, Center for Medical Decision Making, Erasmus MC, Rotterdam, The Netherlands.
Maas
Andrew I R
AI
Lingsma
Hester F
HF
Butcher
Isabella
I
Lu
Juan
J
Marmarou
Anthony
A
McHugh
Gillian S
GS
Weir
James
J
Murray
Gordon D
GD
Steyerberg
Ewout W
EW
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