Association of Breast Cancer Screening Behaviors With Stage at Breast Cancer Diagnosis and Potential for Additive Multi-Cancer Detection via Liquid Biopsy Screening: A Claims-Based Study.

PURPOSE: To evaluate mammography uptake and subsequent breast cancer diagnoses, as well as the prospect of additive cancer detection via a liquid biopsy multi-cancer early detection (MCED) screening test during a routine preventive care exam (PCE). METHODS: Patients with incident breast cancer were identified from five years of longitudinal Blue Health Intelligence® (BHI®) claims data (2014-19) and their screening mammogram and PCE utilization were characterized. Ordinal logistic regression analyses were performed to identify the association of a biennial screening mammogram with stage at diagnosis. Additional screening opportunities for breast cancer during a PCE within two years before diagnosis were identified, and the method extrapolated to all cancers, including those without recommended screening modalities. RESULTS: Claims for biennial screening mammograms and the time from screening to diagnosis were found to be predictors of breast cancer stage at diagnosis. When compared to women who received a screening mammogram proximal to their breast cancer diagnosis (0-4 months), women who were adherent to guidelines but had a longer time window from their screening mammogram to diagnosis (4-24 months) had a 87% increased odds of a later-stage (stages III or IV) breast cancer diagnosis (p-value <0.001), while women with no biennial screening mammogram had a 155% increased odds of a later-stage breast cancer diagnosis (p-value <0.001). This highlights the importance of screening in the earlier detection of breast cancer. Of incident breast cancer cases, 23% had no evidence of a screening mammogram in the two years before diagnosis. However, 49% of these women had a PCE within that time. Thus, an additional 11% of breast cancer cases could have been screened if a MCED test had been available during a PCE. Additionally, MCED tests have the potential to target up to 58% of the top 5 cancers that are the leading causes of cancer death currently without a USPSTF recommended screening modality (prostate, pancreatic, liver, lymphoma, and ovarian cancer). CONCLUSION: The study used claims data to demonstrate the association of cancer screening with cancer stage at diagnosis and demonstrates the unmet potential for a MCED screening test which could be ordered during a PCE.

Associations between cancer and Alzheimer's disease in a U.S. Medicare population.

Several studies have reported bidirectional inverse associations between cancer and Alzheimer's disease (AD). This study evaluates these relationships in a Medicare population. Using Surveillance, Epidemiology, and End Results (SEER) linked to Medicare data, 1992-2005, we evaluated cancer risks following AD in a case-control study of 836,947 cancer cases and 142,869 controls as well as AD risk after cancer in 742,809 cancer patients and a non-cancer group of 420,518. We applied unconditional logistic regression to estimate odds ratios (ORs) and Cox proportional hazards models to estimate hazards ratios (HRs). We also evaluated cancer in relation to automobile injuries as a negative control to explore potential study biases. In the case-control analysis, cancer cases were less likely to have a prior diagnosis of AD than controls (OR = 0.86; 95% CI = 0.81-0.92). Cancer cases were also less likely than controls to have prior injuries from automobile accidents to the same degree (OR = 0.83; 95% CI = 0.78-0.88). In the prospective cohort, there was a lower risk observed in cancer survivors, HR = 0.87 (95% CI = 0.84-0.90). In contrast, there was no association between cancer diagnosis and subsequent automobile accident injuries (HR = 1.03; 95% CI = 0.98-1.07). That cancer risks were similarly reduced after both AD and automobile injuries suggest biases against detecting cancer in persons with unrelated medical conditions. The modestly lower AD risk in cancer survivors may reflect underdiagnosis of AD in those with a serious illness. This study does not support a relationship between cancer and AD.

Calibration and seasonal adjustment for matched case-control studies of vitamin D and cancer.

Vitamin D measurements are influenced by seasonal variation and specific assay used. Motivated by multicenter studies of associations of vitamin D with cancer, we formulated an analytic framework for matched case-control data that accounts for seasonal variation and calibrates to a reference assay. Calibration data were obtained from controls sampled within decile strata of the uncalibrated vitamin D values. Seasonal sine-cosine series were fit to control data. Practical findings included the following: (1) failure to adjust for season and calibrate increased variance, bias, and mean square error and (2) analysis of continuous vitamin D requires a variance adjustment for variation in the calibration estimate. An advantage of the continuous linear risk model is that results are independent of the reference date for seasonal adjustment. (3) For categorical risk models, procedures based on categorizing the seasonally adjusted and calibrated vitamin D have near nominal operating characteristics; estimates of log odds ratios are not robust to choice of seasonal reference date, however. Thus, public health recommendations based on categories of vitamin D should also define the time of year to which they refer. This work supports the use of simple methods for calibration and seasonal adjustment and is informing analytic approaches for the multicenter Vitamin D Pooling Project for Breast and Colorectal Cancer. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Associations between cancer and Parkinson's disease in U.S. elderly adults.

BACKGROUND: Several studies suggest that cancer is reduced before and after a Parkinson's disease (PD) diagnosis. However, determining relationships among diseases of ageing is challenging due to possible biases in ascertaining disease. This study evaluates the PD and cancer relationship, addressing potential biases. METHODS: Using Surveillance, Epidemiology, and End Results-Medicare linked data (1992-2005) of adults ≥ 65 years, we assessed PD risk after cancer comparing PD in 743 779 cancer patients with PD in a non-cancer group (n = 419 432) in prospective cohort analyses. We also conducted a case-control study of 836 947 cancer cases and 142 869 controls to assess cancer following PD. We applied Cox proportional hazards models to estimate hazards ratios (HRs) for PD after cancer and unconditional logistic regression to estimate odds ratios (ORs) for PD preceding cancer, controlling for physician visits and other factors. To explore biases in ascertaining cancer, we examined relationships between cancer and automobile accident injuries, which we expected to be null. RESULTS: No association was observed between cancer and subsequent PD [HR = 0.97; 95% confidence interval (CI) = 0.92-1.01] nor between cancer and subsequent automobile injuries (HR = 1.03; 95% CI = 0.98-1.07). One site, lung cancer, was associated with subsequent reduced PD, which may reflect confounding by smoking. In the case-control analysis, PD was associated with reduced subsequent cancer, overall (OR = 0.77; 95% CI = 0.71-0.82) and for several cancer sites. However, the automobile injury/ subsequent cancer association was similar (OR = 0.83; 95% CI = 0.78-0.88), suggesting a cancer detection bias after serious health outcomes. CONCLUSIONS: In totality, our data do not support a biological relationship between PD and cancer.

The risk of amyotrophic lateral sclerosis after cancer in U.S. elderly adults: a population-based prospective study.

Although epidemiologic studies have examined the risk of amyotrophic lateral sclerosis (ALS) in relation to cancer, none have been large population-based studies using incident ALS and adjusting for medical surveillance. Addressing those limitations, all first primary cancer cases from the Surveillance, Epidemiology and End Results (SEER) Program (1992-2005), linked to Medicare claims data were used. Cases were followed from cancer diagnosis until the earliest date of ALS diagnosis, a break in Medicare claims data, death, age 85 or December 31, 2005. A comparison group from a 5% random Medicare sample in the SEER areas who were cancer-free and censored as above, or until a cancer diagnosis were selected. ALS outcomes were derived from medical claims. The proportional hazards models to estimate ALS hazard ratios (HRs), using age as the time scale, adjusting for sex, race and physician visits, and stratifying the baseline hazard on birth year and SEER registry were used. A total of 303 ALS cases were ascertained in cancer patients (2,154,062 person-years) compared with 246 ALS cases (2,467,634 person-years) in the reference population. There was no overall relationship between cancer and ALS (HR = 0.99; 95% CI = 0.81-1.22), nor by gender or race. Except for an elevated ALS risk in the first year after a leukemia diagnosis, the relationship between site-specific cancers and ALS was null after correcting for multiple comparisons. Having a cancer diagnosis was not associated with an overall risk of incident ALS. The short-term ALS risk after leukemia may reflect screening or reporting errors.

Strategies for developing prediction models from genome-wide association studies.

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with complex human diseases. However, risk prediction models based on them have limited discriminatory accuracy. It has been suggested that including many such SNPs can improve predictive performance. Here, we studied various aspects of model building to improve discriminatory accuracy, as measured by the area under the receiver operating characteristic curve (AUC), including: (1) How well does a one-phase procedure that selects SNPs and estimates odds ratios on the same data perform? (2) How should training data be allocated between SNP selection (Phase 1) and estimation (Phase 2) in a two-phase procedure? (3) Should SNP selection be based on P-value thresholding or ranking P-values? (4) How many SNPs should be selected? and (5) Is multivariate estimation preferred to univariate estimation in the presence of linkage disequilibrium (LD)? We used realistic estimates of the distributions of genetic effect sizes, allele frequencies, and LD patterns based on GWAS data for Crohn's disease and prostate cancer. Theory and simulations were used to estimate AUC. Empirical risk models based on 10,000 cases and controls had considerably lower AUC than theoretically achievable. The most critical aspect of prediction model building was initial SNP selection. The single-phase procedure achieved higher AUC than the two-phase procedure. Multivariate estimation did not perform as well as univariate (marginal) estimation. For complex diseases and samples of 10,000 or fewer cases and controls, one should limit the number of SNPs to tens or hundreds.

Predicting treatment efficacy via quantitative magnetic resonance imaging: a Bayesian joint model.

The prognosis for patients with high grade gliomas is poor, with a median survival of 1 year. Treatment efficacy assessment is typically unavailable until 5-6 months post diagnosis. Investigators hypothesize that quantitative magnetic resonance imaging can assess treatment efficacy 3 weeks after therapy starts, thereby allowing salvage treatments to begin earlier. The purpose of this work is to build a predictive model of treatment efficacy by using quantitative magnetic resonance imaging data and to assess its performance. The outcome is 1-year survival status. We propose a joint, two-stage Bayesian model. In stage I, we smooth the image data with a multivariate spatiotemporal pairwise difference prior. We propose four summary statistics that are functionals of posterior parameters from the first-stage model. In stage II, these statistics enter a generalized non-linear model as predictors of survival status. We use the probit link and a multivariate adaptive regression spline basis. Gibbs sampling and reversible jump Markov chain Monte Carlo methods are applied iteratively between the two stages to estimate the posterior distribution. Through both simulation studies and model performance comparisons we find that we can achieve higher overall correct classification rates by accounting for the spatiotemporal correlation in the images and by allowing for a more complex and flexible decision boundary provided by the generalized non-linear model.

Identification of differentially expressed spatial clusters using humoral response microarray data.

The antibody microarray is a powerful chip-based technology for profiling hundreds of proteins simultaneously and is used increasingly nowadays. To study humoral response in pancreatic cancers, Patwa et al. (2007) developed a two-dimensional liquid separation technique and built a two-dimensional antibody microarray. However, identifying differential expression regions on the antibody microarray requires the use of appropriate statistical methods to fairly assess the large amounts of data generated. In this paper, we propose a permutation-based test using spatial information of the two-dimensional antibody microarray. By borrowing strength from the neighboring differentially expressed spots, we are able to detect the differential expression region with very high power controlling type I error at 0.05 in our simulation studies. We also apply the proposed methodology to a real microarray dataset.