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Cancer has traditionally been studied using the disease site of origin as the organizing framework. However, recent advances in molecular genetics have begun to challenge this taxonomy, as detailed molecular profiling of tumors has led to discoveries of subsets of tumors that have profiles that possess distinct clinical and biological characteristics. This is increasingly leading to research that seeks to investigate whether these subtypes of tumors have distinct etiologies. However, research in this field has been opportunistic and anecdotal, typically involving the comparison of distributions of individual risk factors between tumors classified on the basis of candidate tumor characteristics. The purpose of this article is to place this area of investigation within a more general conceptual and analytic framework, with a view to providing more efficient and practical strategies for designing and analyzing epidemiologic studies to investigate etiologic heterogeneity. We propose a formal definition of etiologic heterogeneity and show how classifications of tumor subtypes with larger etiologic heterogeneities inevitably possess greater disease risk predictability overall. We outline analytic strategies for estimating the degree of etiologic heterogeneity among a set of subtypes and for choosing subtypes that optimize the heterogeneity, and we discuss technical challenges that require further methodologic research. We illustrate the ideas by using a pooled case-control study of breast cancer classified by expression patterns of genes known to define distinct tumor subtypes., (Copyright © 2013 John Wiley & Sons, Ltd.)

In constructing predictive models, investigators frequently assess the incremental value of a predictive marker by comparing the ROC curve generated from the predictive model including the new marker with the ROC curve from the model excluding the new marker. Many commentators have noticed empirically that a test of the two ROC areas often produces a non-significant result when a corresponding Wald test from the underlying regression model is significant. A recent article showed using simulations that the widely used ROC area test produces exceptionally conservative test size and extremely low power. In this article, we demonstrate that both the test statistic and its estimated variance are seriously biased when predictions from nested regression models are used as data inputs for the test, and we examine in detail the reasons for these problems. Although it is possible to create a test reference distribution by resampling that removes these biases, Wald or likelihood ratio tests remain the preferred approach for testing the incremental contribution of a new marker., (Copyright © 2012 John Wiley & Sons, Ltd.)

Background: To define the clinicopathologic and prognostic features of patients with human T-cell lymphotropic virus type-1 (HTLV-1)-associated adult T-cell leukemia/lymphoma (ATLL) in North America, standard criteria were used to identify patients with ATLL., Methods: Statistical analyses used included descriptive statistics, Kaplan-Meir survival analysis, and recursive partitioning., Results: Eighty-nine patients were identified between August 1992 and May 2007, including 37 (41.6%) males and 52 (58.4%) females with a median age of 50 years (range, 22-82 years). All but 6 patients had immigrated to the United States from the Caribbean, Latin America, or Africa. The acute subtype predominated (68.5%). The majority of patients received a combination-alkylator-based chemotherapy regimen in the front-line setting (72.6%). The most common regimen was cyclophosphamide, doxorubicin, vincristine, and prednisone at standard doses or attenuated and/or with methotrexate (CHOP-like), which produced an overall response rate of 64.1%. Despite initial responses to therapy, the median overall survival for all subtypes was 24 weeks (range, 0.9-315 weeks). Although the International Prognostic Index and Prognostic Index for peripheral T-cell lymphoma unspecified identified subsets of patients, these models were not completely predictive. A recursive partitioning analysis was performed on the data, which successfully identified 3 prognostic categories based on Eastern Cooperative Oncology Group performance status, stage, age, and calcium level at diagnosis., Conclusions: This series proposed a new prognostic model for patients with HTLV-1-associated ATLL and confirmed a poor outcome for these patients in North America., (Copyright (c) 2010 American Cancer Society.)

When a cancer patient develops a new tumor it is necessary to determine if it is a recurrence (metastasis) of the original cancer, or an entirely new occurrence of the disease. This is accomplished by assessing the histo-pathology of the lesions. However, there are many clinical scenarios in which this pathological diagnosis is difficult. Since each tumor is characterized by a distinct pattern of somatic mutations, a more definitive diagnosis is possible in principle in these difficult clinical scenarios by comparing the two patterns. In this article we develop and evaluate a statistical strategy for this comparison when the data are derived from array copy number data, designed to identify all of the somatic allelic gains and losses across the genome. First a segmentation algorithm is used to estimate the regions of allelic gain and loss. The correlation in these patterns between the two tumors is assessed, and this is complemented with more precise quantitative comparisons of each plausibly clonal mutation within individual chromosome arms. The results are combined to determine a likelihood ratio to distinguish clonal tumor pairs (metastases) from independent second primaries. Our data analyses show that in many cases a strong clonal signal emerges. Sensitivity analyses show that most of the diagnoses are robust when the data are of high quality., (Copyright 2010 John Wiley & Sons, Ltd.)

Background: Fallopian tube cancers are rare neoplasms. These malignancies are thought to behave biologically and clinically like ovarian cancer. The purpose of this study was to compare the clinical behavior and outcome of fallopian tube and ovarian cancer., Methods: The Surveillance, Epidemiology, and End Results database was reviewed to identify women with tumors of the fallopian tube (FT) and ovary (OV) diagnosed between 1988 and 2004. Demographic and clinical data were compared, and the impact of tumor site on survival was analyzed using Cox models and the Kaplan-Meier method., Results: A total of 55,825 patients were identified, 1576 (3%) with FT and 54,249 (97%) with OV cancer. FT patients were more likely to present with early stage tumors (P < .001). Among FT patients, 47% had stage I/II tumors compared with 29% of OV cancers. In an adjusted Cox model of all patients, cancer-specific mortality was 48% lower in FT patients (hazard ratio, 0.52; 95% confidence interval [CI], 0.48-0.56) compared with OV cancer. Among patients with FT tumors, advanced age and stage were independent predictors of decreased survival. When stratified by stage, survival was similar for stage I and II tumors, but stage III and IV FT patients had an improved survival. The 5-year survival for stage III FT cancer was 54% (95% CI, 48%-60%), compared with 30% (95% CI, 29%-31%) for OV., Conclusions: Fallopian tube cancers present earlier and at advanced stage have a better overall survival than primary ovarian malignancies. Future clinical trials should recognize the possible distinct clinical behavior of fallopian tube cancers.

The Lorenz curve is a graphical tool that is widely used to characterize the concentration of a measure in a population, such as wealth. It is frequently the case that the measure of interest used to rank experimental units when estimating the empirical Lorenz curve, and the corresponding Gini coefficient, is subject to random error. This error can result in an incorrect ranking of experimental units which inevitably leads to a curve that exaggerates the degree of concentration (variation) in the population. We consider a specific data configuration with a hierarchical structure where multiple observations are aggregated within experimental units to form the outcome whose distribution is of interest. Within this context, we explore this bias and discuss several widely available statistical methods that have the potential to reduce or remove the bias in the empirical Lorenz curve. The properties of these methods are examined and compared in a simulation study. This work is motivated by a health outcomes application that seeks to assess the concentration of black patient visits among primary care physicians. The methods are illustrated on data from this study.