Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=25641057
&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215
The impact of covariate adjustment at randomization and analysis for binary
outcomes: understanding differences between superiority and
noninferiority?trials
#MMPMID25641057
Nicholas K
; Yeatts SD
; Zhao W
; Ciolino J
; Borg K
; Durkalski V
Stat Med
2015[May]; 34
(11
): 1834-40
PMID25641057
show ga
The question of when to adjust for important prognostic covariates often arises
in the design of clinical trials, and there remain various opinions on whether to
adjust during both randomization and analysis, at randomization alone, or at
analysis alone. Furthermore, little is known about the impact of covariate
adjustment in the context of noninferiority (NI) designs. The current
simulation-based research explores this issue in the NI setting, as compared with
the typical superiority setting, by assessing the differential impact on power,
type I error, and bias in the treatment estimate as well as its standard error,
in the context of logistic regression under both simple and covariate adjusted
permuted block randomization algorithms. In both the superiority and NI settings,
failure to adjust for covariates that influence outcome in the analysis phase,
regardless of prior adjustment at randomization, results in treatment estimates
that are biased toward zero, with standard errors that are deflated. However, as
no treatment difference is approached under the null hypothesis in superiority
and under the alternative in NI, this results in decreased power and nominal or
conservative (deflated) type I error in the context of superiority but inflated
power and type I error under NI. Results from the simulation study suggest that,
regardless of the use of the covariate in randomization, it is appropriate to
adjust for important prognostic covariates in analysis, as this yields nearly
unbiased estimates of treatment as well as nominal type I error.
free
free
free
