Selection of Optimal Prostate Specific Antigen Cutoffs for Early Detection of Prostate Cancer: Receiver Operating Characteristic Curves

Background:Early detection of prostate cancer is a possible means of decreasing the mortality and increasing the quality of life. Objective :To determine whether the prostate specific antigen (PSA), abnormal DRE, family history, age, and prostate volume could increase the specificity and sensitivity of screening for prostate cancer. Methods :We included 92 patients with PSA > 4 ng/ml between January and December 2011 in Sardjito Hospital. Patients received prostate biopsy due to having abnormal serum prostate specific antigen (PSA) level. The relationship between the possibility of prostate cancer and the following variables were evaluated including: age; PSA level, prostate volume, DRE finding and family history. By using chi-square analysis, multiple logistic regression and receiver operating characteristic (ROC) curve were drawn based on the predictive scoring equation to predict the possibility of prostate cancer. All analyses were performed with SPSS, version 18.0. Results:We analyzed 92 patients with PSA > 4 ng/ml. It showed the relationship between the possibility of prostate cancer and the following variables, including : age (p < 0.001), PSA level (p < 0,001), DRE finding (p < 0.001) family history (p < 0,001) except prostate volume (p = 0.398). Using a predictive equation, P = 1/(1-e-X), where X= -3,821 +1.846 (if DRE positive) + 2,488 ( if family history positive ) + 1.718 ( when PSA > 10 ) + 1.414 ( when age > 68), followed by receiver-operating characteristic curve analysis, it showed the sensitivity 90,4% and specificity 85 % in predicting the possibility of prostate cancer. Conclusion: Age, DRE finding, PSA and family history are factors associated prostate cancer. They can be used as independent predictor to predict prostate cancer. Key words: Logistic regression, early detection,prostate cancer

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A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

Statistical Methods in Medical Research ◽

10.1177/0962280221995956 ◽

2021 ◽

pp. 096228022199595

Author(s):

Yalda Zarnegarnia ◽

Shari Messinger

Keyword(s):

Receiver Operating Characteristic Curve ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Characteristic Curve ◽

Case Control ◽

Paired Data ◽

Characteristic Curves ◽

Receiver Operating Characteristic Curves ◽

Operating Characteristic Curve ◽

Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

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