The Impact of Selection Bias Due to Increasing Response Rates among Population Controls in Occupational Case-Control Studies

2012 ◽  
Vol 185 (1) ◽  
pp. 104-106 ◽  
Author(s):  
Matthias Möhner
Keyword(s):  
Selection Bias ◽  
Response Rates ◽  
Case Control ◽  
Case Control Studies ◽  
The Impact ◽  
Population Controls

The Impact of Selection Bias Due to Increasing Response Rates among Population Controls in Occupational Case-Control Studies

2012 ◽  
Vol 185 (1) ◽  
pp. 106-107 ◽  
Author(s):  
Ann Olsson ◽  
Roel Vermeulen ◽  
Hans Kromhout ◽  
Susan Peters ◽  
Per Gustavsson ◽  
...  
Keyword(s):  
Selection Bias ◽  
Response Rates ◽  
Case Control ◽  
Case Control Studies ◽  
The Impact ◽  
Population Controls

Quantifying the Impact of Selection Bias Caused by Nonparticipation in a Case–Control Study of Mobile Phone Use

2009 ◽  
Vol 19 (1) ◽  
pp. 33-41.e1 ◽  
Author(s):  
Martine Vrijheid ◽  
Lesley Richardson ◽  
Bruce K. Armstrong ◽  
Anssi Auvinen ◽  
Gabriele Berg ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Selection Bias ◽  
Case Control Study ◽  
Case Control ◽  
Mobile Phone Use ◽  
The Impact ◽  
Control Study

Response rates in case-control studies of cancer by era of fieldwork and by characteristics of study design

2018 ◽  
Vol 28 (6) ◽  
pp. 385-391 ◽  
Author(s):  
Mengting Xu ◽  
Lesley Richardson ◽  
Sally Campbell ◽  
Javier Pintos ◽  
Jack Siemiatycki
Keyword(s):  
Study Design ◽  
Response Rates ◽  
Case Control ◽  
Case Control Studies

Response rates among control subjects in case-control studies☆

1995 ◽  
Vol 5 (3) ◽  
pp. 245-249 ◽  
Author(s):  
Martha L. Slattery ◽  
Sandra L. Edwards ◽  
Bette J. Caan ◽  
Richard A. Kerber ◽  
John D. Potter
Keyword(s):  
Response Rates ◽  
Case Control ◽  
Case Control Studies ◽  
Control Subjects

Selection Bias and the Use of Controls with Malformations in Case-Control Studies of Birth Defects

Epidemiology ◽  
1999 ◽  
Vol 10 (3) ◽  
pp. 238-241 ◽  
Author(s):  
Susan Lieff ◽  
Andrew F. Olshan ◽  
Martha Werler ◽  
David A. Savitz ◽  
Allen A. Mitchell
Keyword(s):  
Selection Bias ◽  
Birth Defects ◽  
Case Control ◽  
Case Control Studies

scholarly journals Evaluating and comparing biomarkers with respect to the area under the receiver operating characteristics curve in two-phase case–control studies

Biostatistics ◽  
2016 ◽  
Vol 17 (3) ◽  
pp. 499-522 ◽  
Author(s):  
Ying Huang
Keyword(s):  
Case Control ◽  
Receiver Operating Characteristics ◽  
Biased Sampling ◽  
Second Phase ◽  
Operating Characteristics ◽  
Case Control Studies ◽  
Two Phase ◽  
Receiver Operating Characteristics Curve ◽  
The Impact ◽  
Receiver Operating

Abstract Two-phase sampling design, where biomarkers are subsampled from a phase-one cohort sample representative of the target population, has become the gold standard in biomarker evaluation. Many two-phase case–control studies involve biased sampling of cases and/or controls in the second phase. For example, controls are often frequency-matched to cases with respect to other covariates. Ignoring biased sampling of cases and/or controls can lead to biased inference regarding biomarkers' classification accuracy. Considering the problems of estimating and comparing the area under the receiver operating characteristics curve (AUC) for a binary disease outcome, the impact of biased sampling of cases and/or controls on inference and the strategy to efficiently account for the sampling scheme have not been well studied. In this project, we investigate the inverse-probability-weighted method to adjust for biased sampling in estimating and comparing AUC. Asymptotic properties of the estimator and its inference procedure are developed for both Bernoulli sampling and finite-population stratified sampling. In simulation studies, the weighted estimators provide valid inference for estimation and hypothesis testing, while the standard empirical estimators can generate invalid inference. We demonstrate the use of the analytical variance formula for optimizing sampling schemes in biomarker study design and the application of the proposed AUC estimators to examples in HIV vaccine research and prostate cancer research.

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