Evaluation of Exact and Asymptotic Interval Estimators in Logistic Analysis of Matched Case-Control Studies

Biometrics ◽  
1991 ◽  
Vol 47 (4) ◽  
pp. 1311 ◽  
Author(s):  
Stein E. Vollset ◽  
Karim F. Hirji ◽  
Abdelmonem A. Afifi
2009 ◽  
Vol 30 (5) ◽  
pp. 480-483 ◽  
Author(s):  
Elizabeth Cerceo ◽  
Ebbing Lautenbach ◽  
Darren R. Linkin ◽  
Warren B. Bilker ◽  
Ingi Lee

Of 57 case-control studies of antimicrobial resistance, matching was used in 23 (40%). Matched variables differed substantially across studies. Of these 23 matched case-control studies, 12 (52%) justified the use of matching, and 9 (39%) noted the strengths or limitations of this approach. Analysis that accounted for matching was performed in only 52% of the case-control studies.


2017 ◽  
Vol 28 (3) ◽  
pp. 822-834
Author(s):  
Mitchell H Gail ◽  
Sebastien Haneuse

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.


Biometrika ◽  
1990 ◽  
Vol 77 (4) ◽  
pp. 897
Author(s):  
Bryan Langholz ◽  
Duncan Thomas ◽  
Tsunjen Chen ◽  
Phillip Rhodes

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