A note on the presentation of matched case-control data

1992 ◽  
Vol 11 (5) ◽  
pp. 617-620 ◽  
Author(s):  
Peter Sasieni
Keyword(s):  
Case Control ◽  
Control Data ◽  
Matched Case

Matched Case-Control Data Analysis with Selection Bias

Biometrics ◽  
2001 ◽  
Vol 57 (4) ◽  
pp. 1106-1112 ◽  
Author(s):  
I-Feng Lin ◽  
Myunghee Cho Paik
Keyword(s):  
Data Analysis ◽  
Selection Bias ◽  
Case Control ◽  
Control Data ◽  
Matched Case

Analysing matched or paired data

2017 ◽  
Author(s):  
Janet L. Peacock ◽  
Sally M. Kerry ◽  
Raymond R. Balise
Keyword(s):  
Confidence Intervals ◽  
Case Control ◽  
Paired Data ◽  
Control Data ◽  
Data Transformations ◽  
Use Of Data ◽  
Cohort Data ◽  
Matched Case

Chapter 8 covers analysing matched or paired data, and includes the paired t test, non-Normal data, matched case-control data, and cohort data. It describes the use of data transformations and how results are interpreted when data are paired. It includes how to calculate 95% confidence intervals for estimates. The chapter includes analyses using Stata, SAS, SPSS, and R.

scholarly journals Point-source modelling using matched case-control data

Biostatistics ◽  
2000 ◽  
Vol 1 (1) ◽  
pp. 89-105 ◽  
Author(s):  
Peter J. Diggle ◽  
Sara E. Morris ◽  
Jon C. Wakefield
Keyword(s):  
Point Source ◽  
Case Control ◽  
Control Data ◽  
Matched Case

Estimation of Spatial Variation in Risk Using Matched Case-control Data

2002 ◽  
Vol 44 (8) ◽  
pp. 936-945 ◽  
Author(s):  
Mikala F. Jarner ◽  
Peter Diggle ◽  
Amanda G. Chetwynd
Keyword(s):  
Spatial Variation ◽  
Case Control ◽  
Control Data ◽  
Matched Case

scholarly journals Matched Forest: supervised learning for high-dimensional matched case–control studies

2019 ◽  
Author(s):  
Nooshin Shomal Zadeh ◽  
Sangdi Lin ◽  
George C Runger
Keyword(s):  
Variable Selection ◽  
Case Control ◽  
Supplementary Information ◽  
High Dimensional ◽  
Control Analysis ◽  
Case Control Studies ◽  
Control Data ◽  
Matched Case ◽  
Case Control Analysis ◽  
And Control

Abstract Motivation Matched case–control analysis is widely used in biomedical studies to identify exposure variables associated with health conditions. The matching is used to improve the efficiency. Existing variable selection methods for matched case–control studies are challenged in high-dimensional settings where interactions among variables are also important. We describe a quite different method for high-dimensional matched case–control data, based on the potential outcome model, which is not only flexible regarding the number of matching and exposure variables but also able to detect interaction effects. Results We present Matched Forest (MF), an algorithm for variable selection in matched case–control data. The method preserves the case and control values in each instance but transforms the matched case–control data with added counterfactuals. A modified variable importance score from a supervised learner is used to detect important variables. The method is conceptually simple and can be applied with widely available software tools. Simulation studies show the effectiveness of MF in identifying important variables. MF is also applied to data from the biomedical domain and its performance is compared with alternative approaches. Availability and implementation R code for implementing MF is available at https://github.com/NooshinSh/Matched_Forest. Supplementary information Supplementary data are available at Bioinformatics online.

REGRESSION ANALYSIS OF MATCHED CASE-CONTROL DATA

1982 ◽  
Vol 115 (3) ◽  
pp. 444-452 ◽  
Author(s):  
ROBERT F. WOOLSON ◽  
PETER A. LACHENBRUCH
Keyword(s):  
Regression Analysis ◽  
Case Control ◽  
Control Data ◽  
Matched Case

Analysing matched or paired data

2013 ◽  
Author(s):  
Janet L. Peacock ◽  
Sally M. Kerry
Keyword(s):  
Case Control ◽  
T Test ◽  
Paired Data ◽  
Control Data ◽  
Cohort Data ◽  
Matched Case ◽  
Paired T Test

Chapter 8 covers analysing matched or paired data, and includes the paired t-test, non-normal data, matched case–control data, cohort data, and further reading.

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