Billheimer, D., Gerner, E. W., McLaren, C. E., & LaFleur, B. (2014). Combined benefit of prediction and treatment: a criterion for evaluating clinical prediction models. Cancer informatics, 13(Suppl 2), 93-103.
Clinical treatment decisions rely on prognostic evaluation of a patient's future health outcomes. Thus, predictive models under different treatment options are key factors for making good decisions. While many criteria exist for judging the statistical quality of a prediction model, few are available to measure its clinical utility. As a consequence, we may find that the addition of a clinical covariate or biomarker improves the statistical quality of the model, but has little effect on its clinical usefulness. We focus on the setting where a treatment decision may reduce a patient's risk of a poor outcome, but also comes at a cost; this may be monetary, inconvenience, or the potential side effects. This setting is exemplified by cancer chemoprevention, or the use of statins to reduce the risk of cardiovascular disease. We propose a novel approach to assessing a prediction model using a formal decision analytic framework. We combine the predictive model's ability to discriminate good from poor outcome with the net benefit afforded by treatment. In this framework, reduced risk is balanced against the cost of treatment. The relative cost-benefit of treatment provides a useful index to assist patient decisions. This index also identifies the relevant clinical risk regions where predictive improvement is needed. Our approach is illustrated using data from a colorectal adenoma chemoprevention trial.
Gomez-Rubio, P., Roberge, J., Arendell, L., Harris, R., O'Rourke, M., Chen, Z., Cantu-Soto, E., Meza-Montenegro, M., Billheimer, D., Lu, Z., & Klimecki, W. (2011). Association between body mass index and arsenic methylation efficiency in adult women from southwest U.S. and northwest Mexico. Toxicology Applied Pharmacology, 252(2), 176-182.
BIO5 Collaborators
Dean Billheimer, Walter Klimecki
Lafleur, B., Lee, W., Billheimer, D., Lockhart, C., Liu, J., & Merchant, N. (2011). Statistical methods for assays with limits of detection: Serum bile acid as a differentiator between patients with normal colons, adenomas, and colore. J Carcinog, 10, 12.
Sanders, M. E., Dias, E. C., Xu, B. J., Mobley, J. A., Billheimer, D., Roder, H., Grigorieva, J., Dowsett, M., Arteaga, C. L., & Caprioli, R. M. (2008). Differentiating Proteomic Biomarkers in Breast Cancer by Laser Capture Microdissection and MALDI MS. Journal of Proteome Research, 7(4), 1500-1507.
PMID: 18386930;PMCID: PMC2738605;Abstract:
We assessed proteomic patterns in breast cancer using MALDI MS and laser capture microdissected cells. Protein and peptide expression in invasive mammary carcinoma versus normal mammary epithelium and estrogen-receptor positive versus estrogen-receptor negative tumors were compared. Biomarker candidates were identified by statistical analysis and classifiers were developed and validated in blinded test sets. Several of the mlz features used in the classifiers were identified by LC-MS/MS and two were confirmed by immunohistochemistry. © 2008 American Chemical Society.
Gomez-Rubio, P., Gomez-Rubio, P., Klimentidis, Y., Klimentidis, Y., Cantu-Soto, E., Cantu-Soto, E., Meza-Montenegro, M., Meza-Montenegro, M., Billheimer, D., Billheimer, D., Lu, Z., Lu, Z., Chen, Z., Chen, Z., Klimecki, W., & Klimecki, W. (2012). Indigenous American Ancestry is Associated with Arsenic Methylation Efficiency in an Admixed Population of Northwest Mexico. J Toxicol Environ Health A, 75(1), 36-49.
BIO5 Collaborators
Dean Billheimer, Walter Klimecki
