Tests of Effect in 1: J Matched Case-Control Studies Based on a Modified Likelihood Approach

Biometrika ◽  
1990 ◽  
Vol 77 (4) ◽  
pp. 897
Author(s):  
Bryan Langholz ◽  
Duncan Thomas ◽  
Tsunjen Chen ◽  
Phillip Rhodes
Keyword(s):  
Case Control ◽  
Case Control Studies ◽  
Matched Case ◽  
Likelihood Approach ◽  
Modified Likelihood

Tests of effect in 1: J matched case-control studies based on a modified likelihood approach

Biometrika ◽  
1990 ◽  
Vol 77 (4) ◽  
pp. 897-900 ◽  
Author(s):  
BRYAN LANGHOLZ ◽  
DUNCAN THOMAS ◽  
TSUNJEN CHEN ◽  
PHILLIP RHODES
Keyword(s):  
Case Control ◽  
Case Control Studies ◽  
Matched Case ◽  
Likelihood Approach ◽  
Modified Likelihood

scholarly journals A Geometric Approach to Nonlinear Regression Diagnostics with Applications to matched Case-Control Studies

1984 ◽  
Vol 12 (3) ◽  
pp. 816-826 ◽  
Author(s):  
Suresh H. Moolgavkar ◽  
Edward D. Lustbader ◽  
David J. Venzon
Keyword(s):  
Nonlinear Regression ◽  
Geometric Approach ◽  
Case Control ◽  
Regression Diagnostics ◽  
Case Control Studies ◽  
Matched Case

scholarly journals Empirical and weighted conditional likelihoods for matched case-control studies with missing covariates

2013 ◽  
Vol 119 ◽  
pp. 185-199 ◽  
Author(s):  
Tianqing Liu ◽  
Xiaohui Yuan ◽  
Zhaohai Li ◽  
Yuanzhang Li
Keyword(s):  
Case Control ◽  
Missing Covariates ◽  
Case Control Studies ◽  
Matched Case

Algorithm AS 162: Multivariate Conditional Logistic Analysis of Stratum- Matched Case-Control Studies

1981 ◽  
Vol 30 (2) ◽  
pp. 190 ◽  
Author(s):  
P. G. Smith ◽  
M. C. Pike ◽  
A. P. Hill ◽  
N. E. Breslow ◽  
N. E. Day
Keyword(s):  
Case Control ◽  
Case Control Studies ◽  
Logistic Analysis ◽  
Matched Case

scholarly journals Role of Matching in Case-Control Studies of Antimicrobial Resistance

2009 ◽  
Vol 30 (5) ◽  
pp. 480-483 ◽  
Author(s):  
Elizabeth Cerceo ◽  
Ebbing Lautenbach ◽  
Darren R. Linkin ◽  
Warren B. Bilker ◽  
Ingi Lee
Keyword(s):  
Antimicrobial Resistance ◽  
Case Control ◽  
Case Control Studies ◽  
Matched Case

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.

scholarly journals Estimating the receiver operating characteristic curve in matched case control studies

2018 ◽  
Vol 38 (3) ◽  
pp. 437-451 ◽  
Author(s):  
Hui Xu ◽  
Jing Qian ◽  
Nina P. Paynter ◽  
Xuehong Zhang ◽  
Brian W. Whitcomb ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Case Control Studies ◽  
Matched Case ◽  
Operating Characteristic Curve ◽  
Receiver Operating

scholarly journals Nested Case-Control Studies With Multiple Measurements: Estimating Marginal Relationships

2021 ◽  
Author(s):  
Joshua N. Sampson ◽  
Paul S. Albert ◽  
Mark P. Purdue
Keyword(s):  
Conditional Logistic Regression ◽  
Case Control ◽  
Case Control Studies ◽  
Robust Variance ◽  
Non Hodgkin Lymphoma ◽  
Multiple Measurements ◽  
Matched Case ◽  
Nested Case Control ◽  
Standard Software ◽  
Single Time Point

Abstract Background: We consider the analysis of nested, matched, case-control studies that have multiple biomarker measurements per individual. We propose a simple approach for estimating the marginal relationship between a biomarker measured at a single time point and the risk of an event. We know of no other standard software package that can perform such analyses while explicitly accounting for the matching. Results: We propose an application of conditional logistic regression (CLR) that can include all measurements and uses a robust variance estimator. We compare our approach to other methods such as performing CLR with only the first measurement, CLR with an average of all measurements, and Generalized Estimating Equations. In simulations, our approach is significantly more powerful than CLR with one measurement or an average of all measurements, and has similar to power to GEE but correctly accounts for the matching. We then apply our approach to the CLUE cohort to show that an increased level of the immune marker sCD27 is associated with non‐Hodgkin lymphoma (NHL) and, by evaluating the strength of the association as a function of time until diagnosis, that the an increased level is likely an effect of the disease as opposed to a cause of the disease. The approach can be implemented by the R function clogitRV available at https://github.com/sampsonj74/clogitRV.Conclusion: We offered an approach and software for analyzing matched case-control studies with multiple measurements. We demonstrated that these methods are accurate, precise, and statistically powerful.

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