Some further results on incorporating risk factor information in assessing the dependence between paired failure times arising from case-control family studies: an application to prostate cancer

2002 ◽  
Vol 21 (6) ◽  
pp. 863-876 ◽  
Author(s):  
Li Hsu ◽  
Ross L. Prentice ◽  
Janet L. Stanford
Keyword(s):  
Prostate Cancer ◽  
Risk Factor ◽  
Family Studies ◽  
Case Control ◽  
Failure Times ◽  
Control Family ◽  
Risk Factor Information

Analysis of Age of Onset Data from Case-Control Family Studies

Biometrics ◽  
1998 ◽  
Vol 54 (3) ◽  
pp. 1030 ◽  
Author(s):  
Hongzhe Li ◽  
Ping Yang ◽  
Ann G. Schwartz
Keyword(s):  
Age Of Onset ◽  
Family Studies ◽  
Case Control ◽  
Control Family

Semiparametric Estimation of Marginal Hazard Function from Case-Control Family Studies

Biometrics ◽  
2004 ◽  
Vol 60 (4) ◽  
pp. 936-944 ◽  
Author(s):  
Li Hsu ◽  
Lu Chen ◽  
Malka Gorfine ◽  
Kathleen Malone
Keyword(s):  
Hazard Function ◽  
Family Studies ◽  
Semiparametric Estimation ◽  
Case Control ◽  
Control Family

scholarly journals Biases Introduced by Choosing Controls to Match Risk Factors of Cases in Biomarker Research

2012 ◽  
Vol 58 (8) ◽  
pp. 1242-1251 ◽  
Author(s):  
Margaret Sullivan Pepe ◽  
Jing Fan ◽  
Christopher W Seymour ◽  
Christopher Li ◽  
Ying Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Roc Curves ◽  
Case Control ◽  
Prediction Performance ◽  
Case Control Studies ◽  
Matched Design ◽  
Highly Correlated ◽  
Biomarker Research ◽  
Risk Factor Information

Abstract BACKGROUND Selecting controls that match cases on risk factors for the outcome is a pervasive practice in biomarker research studies. Such matching, however, biases estimates of biomarker prediction performance. The magnitudes of these biases are unknown. METHODS We examined the prediction performance of biomarkers and improvements in prediction gained by adding biomarkers to risk factor information. Data simulated from bivariate normal statistical models and data from a study to identify critically ill patients were used. We compared true performance with that estimated from case control studies that do or do not use matching. ROC curves were used to quantify performance. We propose a new statistical method to estimate prediction performance from matched studies for which data on the matching factors are available for subjects in the population. RESULTS Performance estimated with standard analyses can be grossly biased by matching, especially when biomarkers are highly correlated with matching risk factors. In our studies, the performance of the biomarker alone was underestimated whereas the improvement in performance gained by adding the marker to risk factors was overestimated by 2–10-fold. We found examples for which the relative ranking of 2 biomarkers for prediction was inappropriately reversed by use of a matched design. The new approach to estimation corrected for bias in matched studies. CONCLUSIONS To properly gauge prediction performance in the population or the improvement gained by adding a biomarker to known risk factors, matched case control studies must be supplemented with risk factor information from the population and must be analyzed with nonstandard statistical methods.

Milk Consumption Is a Risk Factor for Prostate Cancer: Meta-Analysis of Case-Control Studies

2004 ◽  
Vol 48 (1) ◽  
pp. 22-27 ◽  
Author(s):  
Li-Qiang Qin ◽  
Jia-Ying Xu ◽  
Pei-Yu Wang ◽  
Takashi Kaneko ◽  
Kazuhiko Hoshi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factor ◽  
Meta Analysis ◽  
Case Control ◽  
Milk Consumption ◽  
Case Control Studies

Colorectal cancer is a risk factor for prostate cancer: a case control study

2005 ◽  
Vol 23 (16_suppl) ◽  
pp. 3546-3546
Author(s):  
H. Bejjanki ◽  
V. Khurana ◽  
G. Caldito ◽  
C. Fort ◽  
R. Kochhar
Keyword(s):  
Prostate Cancer ◽  
Colorectal Cancer ◽  
Risk Factor ◽  
Case Control Study ◽  
Case Control ◽  
Control Study
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