Clique-Finding for Heterogeneity and Multidimensionality in Biomarker Epidemiology Research: The CHAMBER Algorithm

Commonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the "CHAMBER" algorithm).This algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for disease based on inherited genotype. We use a set-covering algorithm to identify optimal cliques and a Boolean function that identifies etiologically heterogeneous groups of individuals. We evaluated this approach using simulated case-control genotype-disease associations involving two- and four-gene patterns. The CHAMBER algorithm correctly identified these simulated etiologies. We also used two population-based case-control studies of breast and endometrial cancer in African American and Caucasian women considering data on genotypes involved in steroid hormone metabolism. We identified novel patterns in both cancer sites that involved genes that sulfate or glucuronidate estrogens or catecholestrogens. These associations were consistent with the hypothesized biological functions of these genes. We also identified cliques representing the joint effect of multiple candidate genes in all groups, suggesting the existence of biologically plausible combinations of hormone metabolism genes in both breast and endometrial cancer in both races.The CHAMBER algorithm may have utility in exploring the multifactorial etiology and etiologic heterogeneity in complex disease

Genomic variation in myeloma: design, content, and initial application of the Bank On A Cure SNP Panel to detect associations with progression-free survival

<p>Abstract</p> <p>Background</p> <p>We have engaged in an international program designated the <it>Bank On A Cure</it>, which has established DNA banks from multiple cooperative and institutional clinical trials, and a platform for examining the association of genetic variations with disease risk and outcomes in multiple myeloma.</p> <p>We describe the development and content of a novel custom SNP panel that contains 3404 SNPs in 983 genes, representing cellular functions and pathways that may influence disease severity at diagnosis, toxicity, progression or other treatment outcomes. A systematic search of national databases was used to identify non-synonymous coding SNPs and SNPs within transcriptional regulatory regions. To explore SNP associations with PFS we compared SNP profiles of short term (less than 1 year, <it>n </it>= 70) versus long term progression-free survivors (greater than 3 years, <it>n </it>= 73) in two phase III clinical trials.</p> <p>Results</p> <p>Quality controls were established, demonstrating an accurate and robust screening panel for genetic variations, and some initial racial comparisons of allelic variation were done. A variety of analytical approaches, including machine learning tools for data mining and recursive partitioning analyses, demonstrated predictive value of the SNP panel in survival. While the entire SNP panel showed genotype predictive association with PFS, some SNP subsets were identified within drug response, cellular signaling and cell cycle genes.</p> <p>Conclusion</p> <p>A targeted gene approach was undertaken to develop an SNP panel that can test for associations with clinical outcomes in myeloma. The initial analysis provided some predictive power, demonstrating that genetic variations in the myeloma patient population may influence PFS.</p

Acupuncture and chiropractic care for chronic pain in an integrated health plan: a mixed methods study

<p>Abstract</p> <p>Background</p> <p>Substantial recent research examines the efficacy of many types of complementary and alternative (CAM) therapies. However, outcomes associated with the "real-world" use of CAM has been largely overlooked, despite calls for CAM therapies to be studied in the manner in which they are practiced. Americans seek CAM treatments far more often for chronic musculoskeletal pain (CMP) than for any other condition. Among CAM treatments for CMP, acupuncture and chiropractic (A/C) care are among those with the highest acceptance by physician groups and the best evidence to support their use. Further, recent alarming increases in delivery of opioid treatment and surgical interventions for chronic pain--despite their high costs, potential adverse effects, and modest efficacy--suggests the need to evaluate real world outcomes associated with promising non-pharmacological/non-surgical CAM treatments for CMP, which are often well accepted by patients and increasingly used in the community.</p> <p>Methods/Design</p> <p>This multi-phase, mixed methods study will: (1) conduct a retrospective study using information from electronic medical records (EMRs) of a large HMO to identify unique clusters of patients with CMP (e.g., those with differing demographics, histories of pain condition, use of allopathic and CAM health services, and comorbidity profiles) that may be associated with different propensities for A/C utilization and/or differential outcomes associated with such care; (2) use qualitative interviews to explore allopathic providers' recommendations for A/C and patients' decisions to pursue and retain CAM care; and (3) prospectively evaluate health services/costs and broader clinical and functional outcomes associated with the receipt of A/C relative to carefully matched comparison participants receiving traditional CMP services. Sensitivity analyses will compare methods relying solely on EMR-derived data versus analyses supplementing EMR data with conventionally collected patient and clinician data.</p> <p>Discussion</p> <p>Successful completion of these aggregate aims will provide an evaluation of outcomes associated with the real-world use of A/C services. The trio of retrospective, qualitative, and prospective study will also provide a clearer understanding of the decision-making processes behind the use of A/C for CMP and a transportable methodology that can be applied to other health care settings, CAM treatments, and clinical populations.</p> <p>Trial registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01345409">NCT01345409</a></p

Willingness to Pay for Diagnostic Certainty Comparing Patients, Physicians, and Managed Care Executives

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75302/1/j.1525-1497.1999.00313.x.pd