Faculty Bibliography

BACKGROUND: Lung cancer accounts for the largest absolute risk of second malignancies among Hodgkin lymphoma (HL) survivors. However, no population-based studies have compared overall survival (OS) between HL survivors who developed nonsmall cell lung cancer (HL-NSCLC) versus patients with first primary NSCLC (NSCLC-1). METHODS: The authors compared the OS of 178,431 patients who had NSCLC-1 and 187 patients who had HL-NSCLC (among 22,648 HL survivors), accounting for sex, race, sociodemographic status, calendar year, and age at NSCLC diagnosis, and NSCLC histology and stage. All patients were reported to the population-based Surveillance, Epidemiology, and End Results Program. Hazard ratios (HRs) were derived from a Cox proportional hazards model. RESULTS: Although the NSCLC stage distribution was similar in both groups (20% localized, 30% regional, and 50% distant), HL survivors experienced significantly inferior stage-specific OS. For patients with localized, regional, and distant stage NSCLC, the HRs (95% confidence interval [CI]) for death among HL survivors were 1.60 (95% CI, 1.08-2.37; P < .0001), 1.67 (95% CI, 1.26-2.22; P = .0004), and 1.31 (95% CI, 1.06-1.61; P = .013), respectively. Among HL-NSCLC patients, significant associations were observed between more advanced NSCLC stage and the following variables: younger age at HL diagnosis (P = .003), younger age at NSCLC diagnosis (P = .048), and longer latency between HL and NSCLC diagnoses (P = .015). CONCLUSIONS: Compared with patients who had de novo NSCLC, HL survivors experienced a significant 30% to 60% decrease in OS after an NSCLC diagnosis. Further research is needed to not only elucidate the clinical-biologic underpinnings of NSCLC after HL, including the influence of previous HL treatment, but also to define the role of lung cancer screening in selected patients.

PURPOSE: The increased risk of breast cancer (BC) among women receiving chest radiotherapy for Hodgkin's lymphoma (HL) is well-established. However, there are no large population-based studies that describe overall survival (OS) and cause-specific survival (CSS) compared with women with first primary BC. METHODS: For 298 HL survivors who developed BC (HL-BC group) and 405,223 women with a first or only BC (BC-1 group), actuarial OS and CSS were compared, accounting for age, BC stage, hormone receptor status, sociodemographic status, radiation for HL, and other variables. All patients were derived from the population-based Surveillance, Epidemiology, and End Results program. RESULTS: OS among patients with HL-BC was significantly inferior that of to patients with BC-1: 15-year OS was 48% versus 69% (P < .0001) for localized BC, and 33% versus 43% (P < .0001) for regional/distant BC. Patients with HL-BC had a significantly increased seven-fold risk (P < .0001) of death from other cancers (ie, not HL or BC) compared with patients with BC-1. Mortality from heart disease among patients with HL-BC with either localized or regional/distant disease was also significantly increased (hazard ratio = 2.22, P = .04; and hazard ratio = 4.28, P = .02, respectively) compared with patients with BC-1. Although 10-year BC-CSS was similar for patients with HL-BC and BC-1 with regional/distant disease, it was inferior for patients with localized BC (82% v 88%, respectively; P = .002). CONCLUSION: Women with HL may survive a subsequent diagnosis of BC, only to experience significant excesses of death from other primary cancers and cardiac disease. Greater awareness of screening for cardiac disease and subsequent primary cancers in patients with HL-BC is warranted

Case-control genome-wide association studies provide a vast amount of genetic information that may be used to investigate secondary phenotypes. We study the situation in which the primary disease is rare and the secondary phenotype and genetic markers are dichotomous. An analysis of the association between a genetic marker and the secondary phenotype based on controls only (CO) is valid, whereas standard methods that also use cases result in biased estimates and highly inflated type I error if there is an interaction between the secondary phenotype and the genetic marker on the risk of the primary disease. Here we present an adaptively weighted (AW) method that combines the case and control data to study the association, while reducing to the CO analysis if there is strong evidence of an interaction. The possibility of such an interaction and the misleading results for standard methods, but not for the AW or CO approaches, are illustrated by data from a case-control study of colorectal adenoma. Simulations and asymptotic theory indicate that the AW method can reduce the mean square error for estimation with a prespecified SNP and increase the power to discover a new association in a genome-wide study, compared to CO analysis. Further experience with genome-wide studies is needed to determine when methods that assume no interaction gain precision and power, thereby can be recommended, and when methods such as the AW or CO approaches are needed to guard against the possibility of nonzero interactions

Identifying regions with the highest and lowest mortality rates and producing the corresponding color-coded maps help epidemiologists identify promising areas for analytic etiological studies. Based on a two-stage Poisson-Gamma model with covariates, we use information on known risk factors, such as smoking prevalence, to adjust mortality rates and reveal residual variation in relative risks that may reflect previously masked etiological associations. In addition to covariate adjustment, we study rankings based on standardized mortality ratios (SMRs), empirical Bayes (EB) estimates, and a posterior percentile ranking (PPR) method and indicate circumstances that warrant the more complex procedures in order to obtain a high probability of correctly classifying the regions with the upper 100gamma% and lower 100gamma% of relative risks for gamma= 0.05, 0.1, and 0.2. We also give analytic approximations to the probabilities of correctly classifying regions in the upper 100gamma% of relative risks for these three ranking methods. Using data on mortality from heart disease, we found that adjustment for smoking prevalence has an important impact on which regions are classified as high and low risk. With such a common disease, all three ranking methods performed comparably. However, for diseases with smaller event counts, such as cancers, and wide variation in event counts among regions, EB and PPR methods outperform ranking based on SMRs

For the well-known Fay-Herriot small area model, standard variance component estimation methods frequently produce zero estimates of the strictly positive model variance. As a consequence, an empirical best linear unbiased predictor of a small area mean, commonly used in the small area estimation, could reduce to a simple regression estimator, which typically has an overshrinking problem. We propose an adjusted maximum likelihood estimator of the model variance that maximizes an adjusted likelihood defined as a product of the model variance and a standard likelihood (e.g., profile or residual likelihood) function. The adjustment factor was suggested earlier by Carl Morris in the context of approximating a hierarchical Bayes solution where the hyperparameters, including the model variance, are assumed to follow a prior distribution. Interestingly, the proposed adjustment does not affect the mean squared error property of the model variance estimator or the corresponding empirical best linear unbiased predictors of the small area means in a higher order asymptotic sense. However, as demonstrated in our simulation study, the proposed adjustment has a considerable advantage in the small sample inference, especially in estimating the shrinkage parameters and in constructing the parametric bootstrap prediction intervals of the small area means, which require the use of a strictly positive consistent model variance estimate

The quality of a production process is often judged by a quality assurance audit, which is essentially a structured system of sampling inspection plan. The defects of sampled products are assessed and compared with a quality standard, which is determined from a tradeoff among manufacturing costs, operating costs and customer needs. In this paper, we propose a new hierarchical Bayes quality measurement plan that assumes an implicit prior for the hyperparameters. The resulting posterior means and variances are obtained adaptively using a parametric bootstrap method. (C) Published in 2009 by John Wiley & Sons, Ltd. $$:

Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information. This method is particularly useful in small area problems. The variability of an EBLUP is traditionally measured by the mean squared prediction error (MSPE), and interval estimates are generally constructed using estimates of the MSPE. Such methods have shortcomings like under-coverage or over-coverage, excessive length and lack of interpretability. We propose a parametric bootstrap approach to estimate the entire distribution of a suitably centered and scaled EBLUP. The bootstrap histogram is highly accurate, and differs from the true EBLUP distribution by only O(d(3)n(-3/2)), where d is the number of parameters and n the number of observations. This result is used to obtain highly accurate prediction intervals. Simulation results demonstrate the superiority of this method over existing techniques of constructing prediction intervals in linear mixed models. $$: