A Frailty-Model-Based Approach to Estimating the Age-Dependent Penetrance Function of Candidate Genes Using Population-Based Case-Control Study Designs: An Application to Data on the BRCA1 Gene.

Summary. The population-based case-control study design is perhaps one of, if not the most, commonly used designs for investigating the genetic and environmental contributions to disease risk in epidemiological studies. Ages at onset and disease status of family members are routinely and systematically collected from the participants in this design. Considering age at onset in relatives as an outcome, this article is focused on using the family history information to obtain the hazard function, i.e., age-dependent penetrance function, of candidate genes from case-control studies. A frailty-model-based approach is proposed to accommodate the shared risk among family members that is not accounted for by observed risk factors. This approach is further extended to accommodate missing genotypes in family members and a two-phase case-control sampling design. Simulation results show that the proposed method performs well in realistic settings. Finally, a population-based two-phase case-control breast cancer study of the BRCA1 gene is used to illustrate the method

Tailored Bayes: a risk modeling framework under unequal misclassification costs.

Risk prediction models are a crucial tool in healthcare. Risk prediction models with a binary outcome (i.e., binary classification models) are often constructed using methodology which assumes the costs of different classification errors are equal. In many healthcare applications, this assumption is not valid, and the differences between misclassification costs can be quite large. For instance, in a diagnostic setting, the cost of misdiagnosing a person with a life-threatening disease as healthy may be larger than the cost of misdiagnosing a healthy person as a patient. In this article, we present Tailored Bayes (TB), a novel Bayesian inference framework which "tailors" model fitting to optimize predictive performance with respect to unbalanced misclassification costs. We use simulation studies to showcase when TB is expected to outperform standard Bayesian methods in the context of logistic regression. We then apply TB to three real-world applications, a cardiac surgery, a breast cancer prognostication task, and a breast cancer tumor classification task and demonstrate the improvement in predictive performance over standard methods

ADDITIONAL HEALTHCARE EXPENDITURES OF DEPRESSION FOR ELDERLY CANCER PATIENTS WITH DEPRESSION

The risk of depression is high for cancer patients and a large portion of cancer patients are age 65 and over. Both depression and cancer are economically burdensome and depression is associated with healthcare expenditure increase for elderly patients. However, whether comorbid depression affects healthcare expenditures in elderly cancer patients from payers’ and patients’ perspectives is largely unknown. Objective: To investigate whether depression is associated with higher healthcare expenditure among elderly cancer patients from both payers’ and patients’ perspectives and, and determine whether depression is associated with higher probability of having high out-of-cost burden. Methods: From the Medicare Current Beneficiary Survey (MCBS)-Medicare database, we identified breast, lung and prostate cancer patients aged 65 years or older who were newly diagnosed between 2007 and 2012 using Medicare claims. Presence of depression was based on self-reports from the surveys. Healthcare expenditures included expenditures incurred in the cancer diagnosis year and the subsequent calendar year. High out-of-cost burden was referred to as out-of-pocket cost as over 10% of respondent’s income. For the analyses of healthcare expenditures, generalized linear models (GLM) and two-part models were used to examine the impact of depression on healthcare expenditures when controlling for all other covariates assessed in the study. We stratified the analyses by healthcare service types and payers. For the analyses of high out-of-pocket cost burden, logistic regression was used to estimate whether depression was associated with higher probability of having high out-of-pocket cost burden. Results: Of the 710 elderly breast, lung and prostate cancer patients identified, 128 (18%) reported depression. The results revealed that elderly cancer patients with depression had $11,454 higher overall total healthcare expenditures. From Medicare’s perspective, elderly patients with depression incurred$8,280 higher expenditures, $4,327 higher medical provider expenditures and$870 higher expenditures on other services. They were also more likely to use inpatient services and other services. From the patients’ perspective, they had higher healthcare expenditures, medical provider expenditures and other expenditures ($1,270,$654 and $465, respectively). For high out-of-pocket cost burden, although the unadjusted result was significant, the adjusted result was not. Conclusions: Elderly patients with depression had significantly higher healthcare expenditures from the payers’ perspective. Although they did not have higher out-of-pocket cost burden, they did have higher healthcare expenditures from patients’ perspectives and over different expenditure types. These findings provide compelling evidence for policy makers, physicians and researchers to develop guidelines for and conduct studies of depression screening, diagnosis and treatment for geriatric cancer populations

An Interval-Censored Proportional Hazards Model

We fit a Cox proportional hazards (PH) model to interval-censored survival data by first subdividing each individual\u27s failure interval into non-overlapping sub-intervals. Using the set of all interval endpoints in the data set, those that fall into the individual\u27s interval are then used as the cut points for the sub-intervals. Each sub-interval has an accompanying weight calculated from a parametric Weibull model based on the current parameter estimates. A weighted PH model is then fit with multiple lines of observations corresponding to the sub-intervals for each individual, where the lower end of each sub-interval is used as the observed failure time with the accompanying weights incorporated. Right-censored observations are handled in the usual manner. We iterate between estimating the baseline Weibull distribution and fitting the weighted PH model until the regression parameters of interest converge. The regression parameter estimates are fixed as an offset when we update the estimates of the Weibull distribution and recalculate the weights. Our approach is similar to Satten et al.\u27s (1998) method for interval-censored survival analysis that used imputed failure times generated from a parametric model in a PH model. Simulation results demonstrate apparently unbiased parameter estimation for the correctly specified Weibull model and little to no bias for a mis-specified log-logistic model. Breast cosmetic deterioration data and ICU hyperlactemia data are analyzed