The Association between Oxaliplatin-Induced Peripheral Neuropathy, Sociodemographic, and Clinical Characteristics with Falls in Older Adults with Colorectal Cancer.

Our purpose was to investigate the associations between oxaliplatin-induced peripheral neuropathy (OIPN), sociodemographic, and clinical characteristics of older colorectal cancer patients with falls. The study population consisted of older adults diagnosed with colorectal cancer obtained from the Surveillance, Epidemiology, and End Results database combined with Medicare claims. OIPN was defined using specific (OIPN 1) and broader (OIPN 2) definitions of OIPN, based on diagnosis codes. Extensions of the Cox regression model to accommodate repeated events were used to obtain overall hazard ratios (HR) with 95% confidence intervals and the cumulative hazard of fall. The unadjusted risk of fall for colorectal cancer survivors with vs. without OIPN 1 at 36 months of follow-up was 19.6% vs. 14.3%, respectively. The association of OIPN with time to fall was moderate (OIPN 1, HR = 1.37, 95% CI: 1.04, 1.79) to small (OIPN 2, HR = 1.24, 95% CI: 1.01, 1.53). Memantine, opioids, cannabinoids, prior history of fall, female sex, advanced age and disease stage, chronic liver disease, diabetes, and chronic obstructive pulmonary disease all increased the hazard rate of fall. Incorporating fall prevention in cancer care is essential to minimize morbidity and mortality of this serious event in older colorectal cancer survivors.

The association between sociodemographic, clinical, and potentially preventive therapies with oxaliplatin-induced peripheral neuropathy in colorectal cancer patients.

PURPOSE: The purpose of this retrospective cohort study was to evaluate whether several potentially preventive therapies reduced the rate of oxaliplatin-induced peripheral neuropathy (OIPN) in colorectal cancer patients and to assess the relationship of sociodemographic/clinical factors with OIPN diagnosis. METHODS: Data were obtained from the Surveillance, Epidemiology, and End Results database combined with Medicare claims. Eligible patients were diagnosed with colorectal cancer between 2007 and 2015, ≥ 66 years of age, and treated with oxaliplatin. Two definitions were used to denote diagnosis of OIPN based on diagnosis codes: OIPN 1 (specific definition, drug-induced polyneuropathy) and OIPN 2 (broader definition, additional codes for peripheral neuropathy). Cox regression was used to obtain hazard ratios (HR) with 95% confidence intervals (CI) for the relative rate of OIPN within 2 years of oxaliplatin initiation. RESULTS: There were 4792 subjects available for analysis. At 2 years, the unadjusted cumulative incidence of OIPN 1 was 13.1% and 27.1% for OIPN 2. For both outcomes, no therapies reduced the rate of OIPN diagnosis. The anticonvulsants gabapentin and oxcarbazepine/carbamazepine were associated with an increased rate of OIPN (both definitions) as were increasing cycles of oxaliplatin. Compared to younger patients, those 75-84 years of age experienced a 15% decreased rate of OIPN. For OIPN 2, prior peripheral neuropathy and moderate/severe liver disease were also associated with an increased hazard rate. For OIPN 1, state buy-in health insurance coverage was associated with a decreased hazard rate. CONCLUSION: Additional studies are needed to identify preventive therapeutics for OIPN in cancer patients treated with oxaliplatin.

Characteristics Associated With Nonreceipt of Surveillance Testing and the Relationship With Survival in Stage II and III Colon Cancer.

We investigated characteristics of patients with colon cancer that predicted nonreceipt of posttreatment surveillance testing and the subsequent associations between surveillance status and survival outcomes. This was a retrospective cohort study of the Surveillance, Epidemiology, and End Results database combined with Medicare claims. Patients diagnosed between 2002 and 2009 with disease stages II and III and who were between 66 and 84 years of age were eligible. A minimum of 3 years' follow-up was required, and patients were categorized as having received any surveillance testing (any testing) versus none (no testing). Poisson regression was used to obtain risk ratios with 95% confidence intervals for the relative likelihood of No Testing. Cox models were used to obtain subdistribution hazard ratios with 95% confidence intervals for 5- and 10-year cancer-specific and noncancer deaths. There were 16,009 colon cancer cases analyzed. Patient characteristics that predicted No Testing included older age, Black race, stage III disease, and chemotherapy. Patients in the No Testing group had an increased rate of 10-year cancer death that was greater for patients with stage III disease (subdistribution hazard ratio = 1.79, 95% confidence interval: 1.48, 2.17) than those with stage II disease (subdistribution hazard ratio = 1.41, 95% confidence interval: 1.19, 1.66). Greater efforts are needed to ensure all patients receive the highest quality medical care after diagnosis of colon cancer.

Surveillance Colonoscopy in Older Stage I Colon Cancer Patients and the Association With Colon Cancer-Specific Mortality.

OBJECTIVES: Guideline-issuing groups differ regarding the recommendation that patients with stage I colon cancer receive surveillance colonoscopy after cancer-directed surgery. This observational comparative effectiveness study was conducted to evaluate the association between surveillance colonoscopy and colon cancer-specific mortality in early stage patients. METHODS: This was a retrospective cohort study of the Surveillance, Epidemiology, and End Results database combined with Medicare claims. Surveillance colonoscopy was assessed as a time-varying exposure up to 5 years after cancer-directed surgery with the following groups: no colonoscopy, one colonoscopy, and ≥ 2 colonoscopies. Inverse probability of treatment weighting was used to balance covariates. The time-dependent Cox regression model was used to obtain inverse probability of treatment weighting-adjusted hazard ratios (HRs), with 95% confidence intervals (CIs) for 5- and 10-year colon cancer, other cancer, and noncancer causes of death. RESULTS: There were 8,783 colon cancer cases available for analysis. Overall, compared with patients who received one colonoscopy, the no colonoscopy group experienced an increased rate of 10-year colon cancer-specific mortality (HR = 1.63; 95% CI 1.31-2.04) and noncancer death (HR = 1.36; 95% CI 1.25-1.49). Receipt of ≥ 2 colonoscopies was associated with a decreased rate of 10-year colon cancer-specific death (HR = 0.60; 95% CI 0.45-0.79), other cancer death (HR = 0.68; 95% CI 0.53-0.88), and noncancer death (HR = 0.69; 95% CI 0.62-0.76). Five-year cause-specific HRs were similar to 10-year estimates. DISCUSSION: These results support efforts to ensure that stage I patients undergo surveillance colonoscopy after cancer-directed surgery to facilitate early detection of new and recurrent neoplastic lesions.

The association between post-treatment surveillance testing and survival in stage II and III colon cancer patients: An observational comparative effectiveness study.

BACKGROUND: The best strategy for surveillance testing in stage II and III colon cancer patients following curative treatment is unknown. Previous randomized controlled trials have suffered from design limitations and yielded conflicting evidence. This observational comparative effectiveness research study was conducted to provide new evidence on the relationship between post-treatment surveillance testing and survival by overcoming the limitations of previous clinical trials. METHODS: This was a retrospective cohort study of the Surveillance, Epidemiology, and End Results database combined with Medicare claims (SEER-Medicare). Stage II and III colon cancer patients diagnosed from 2002 to 2009 and between 66 to 84 years of age were eligible. Adherence to surveillance testing guidelines-including carcinoembryonic antigen, computed tomography, and colonoscopy-was assessed for each year of follow-up and overall for up to three years post-treatment. Patients were categorized as More Adherent and Less Adherent according to testing guidelines. Patients who received no surveillance testing were excluded. The primary outcome was 5-year cancer-specific survival; 5-year overall survival was the secondary outcome. Inverse probability of treatment weighting (IPTW) using generalized boosted models was employed to balance covariates between the two surveillance groups. IPTW-adjusted survival curves comparing the two groups were performed by the Kaplan-Meier method. Weighted Cox regression was used to obtain hazard ratios (HRs) with 95% confidence intervals (CIs) for the relative risk of death for the Less Adherent group versus the More Adherent group. RESULTS: There were 17,860 stage II and III colon cancer cases available for analysis. Compared to More Adherent patients, Less Adherent patients experienced slightly better 5-year cancer-specific survival (HR = 0.83, 95% CI 0.76-0.90) and worse 5-year noncancer-specific survival (HR = 1.61, 95% CI 1.43-1.82) for years 2 to 5 of follow-up. There was no difference between the groups in overall survival (HR = 1.04, 95% CI 0.98-1.10). CONCLUSIONS: More surveillance testing did not improve 5-year cancer-specific survival compared to less testing and there was no difference between the groups in overall survival. The results of this study support a risk-stratified, shared decision-making surveillance strategy to optimize clinical and patient-centered outcomes for colon cancer patients in the survivorship phase of care.

Post-treatment surveillance testing of patients with colorectal cancer and the association with survival: protocol for a retrospective cohort study of the Surveillance, Epidemiology, and End Results (SEER)-Medicare database.

INTRODUCTION: Although the colorectal cancer (CRC) mortality rate has significantly improved over the past several decades, many patients will have a recurrence following curative treatment. Despite this high risk of recurrence, adherence to CRC surveillance testing guidelines is poor which increases cancer-related morbidity and potentially, mortality. Several randomised controlled trials (RCTs) with varying surveillance strategies have yielded conflicting evidence regarding the survival benefit associated with surveillance testing. However, due to differences in study protocols and limitations of sample size and length of follow-up, the RCT may not be the best study design to evaluate this relationship. An observational comparative effectiveness research study can overcome the sample size/follow-up limitations of RCT designs while assessing real-world variability in receipt of surveillance testing to provide much needed evidence on this important clinical issue. The gap in knowledge that this study will address concerns whether adherence to National Comprehensive Cancer Network CRC surveillance guidelines improves survival. METHODS AND ANALYSIS: Patients with colon and rectal cancer aged 66-84 years, who have been diagnosed between 2002 and 2008 and have been included in the Surveillance, Epidemiology, and End Results-Medicare database, are eligible for this retrospective cohort study. To minimise bias, patients had to survive at least 12 months following the completion of treatment. Adherence to surveillance testing up to 5 years post-treatment will be assessed in each year of follow-up and overall. Binomial regression will be used to assess the association between patients' characteristics and adherence. Survival analysis will be conducted to assess the association between adherence and 5-year survival. ETHICS AND DISSEMINATION: This study was approved by the National Cancer Institute and the Institutional Review Board of the University of Central Florida. The results of this study will be disseminated by publishing in the peer-reviewed scientific literature, presentation at national/international scientific conferences and posting through social media.

Confidence intervals for ranks of age-adjusted rates across states or counties.

Health indices provide information to the general public on the health condition of the community. They can also be used to inform the government's policy making, to evaluate the effect of a current policy or healthcare program, or for program planning and priority setting. It is a common practice that the health indices across different geographic units are ranked and the ranks are reported as fixed values. We argue that the ranks should be viewed as random and hence should be accompanied by an indication of precision (i.e., the confidence intervals). A technical difficulty in doing so is how to account for the dependence among the ranks in the construction of confidence intervals. In this paper, we propose a novel Monte Carlo method for constructing the individual and simultaneous confidence intervals of ranks for age-adjusted rates. The proposed method uses as input age-specific counts (of cases of disease or deaths) and their associated populations. We have further extended it to the case in which only the age-adjusted rates and confidence intervals are available. Finally, we demonstrate the proposed method to analyze US age-adjusted cancer incidence rates and mortality rates for cancer and other diseases by states and counties within a state using a website that will be publicly available. The results show that for rare or relatively rare disease (especially at the county level), ranks are essentially meaningless because of their large variability, while for more common disease in larger geographic units, ranks can be effectively utilized.

Next generation analytic tools for large scale genetic epidemiology studies of complex diseases.

Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.

Estimating the proportion of equivalently expressed genes in microarray data based on transformed test statistics.

In microarray data analysis, false discovery rate (FDR) is now widely accepted as the control criterion to account for multiple hypothesis testing. The proportion of equivalently expressed genes (pi(0)) is a key component to be estimated in the estimation of FDR. Some commonly used pi(0) estimators (BUM, SPLOSH, QVALUE, and LBE ) are all based on p-values, and they are essentially upper bounds of pi(0). The simulations we carried out show that these four methods significantly overestimate the true pi(0) when differentially expressed genes and equivalently expressed genes are not well separated. To solve this problem, we first introduce a novel way of transforming the test statistics to make them symmetric about 0. Then we propose a pi(0) estimator based on the transformed test statistics using the symmetry assumption. Real data application and simulation both show that the pi(0) estimate from our method is less conservative than BUM, SPLOSH, QVALUE, and LBE in most of the cases. Simulation results also show that our estimator always has the least mean squared error among these five methods.

The t-mixture model approach for detecting differentially expressed genes in microarrays.

The finite mixture model approach has attracted much attention in analyzing microarray data due to its robustness to the excessive variability which is common in the microarray data. Pan (2003) proposed to use the normal mixture model method (MMM) to estimate the distribution of a test statistic and its null distribution. However, considering the fact that the test statistic is often of t-type, our studies find that the rejection region from MMM is often significantly larger than the correct rejection region, resulting an inflated type I error. This motivates us to propose the t-mixture model (TMM) approach. In this paper, we demonstrate that TMM provides significantly more accurate control of the probability of making type I errors (hence of the familywise error rate) than MMM. Finally, TMM is applied to the well-known leukemia data of Golub et al. (1999). The results are compared with those obtained from MMM.