Accounting for Uncertainty During a Pandemic

We discuss several issues of statistical design, data collection, analysis, communication, and decision making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature; rather, we use examples to illustrate statistical points that we think are important.Comment: 16 page

Effect of population trends in body mass index on prostate cancer incidence and mortality in the United States.

Concurrent with increasing prostate cancer incidence and declining prostate cancer mortality in the United States, the prevalence of obesity has been increasing steadily. Several studies have reported that obesity is associated with increased risk of high-grade prostate cancer and prostate cancer mortality, and it is thus likely that the increase in obesity has increased the burden of prostate cancer. In this study, we assess the potential effect of increasing obesity on prostate cancer incidence and mortality. We first estimate obesity-associated relative risks of low- and high-grade prostate cancer using data from the Prostate Cancer Prevention Trial. Then, using obesity prevalence data from the National Health and Nutrition Examination Survey and prostate cancer incidence data from the Surveillance, Epidemiology, and End Results program, we convert annual grade-specific prostate cancer incidence rates into incidence rates conditional on weight category. Next, we combine the conditional incidence rates with the 1980 prevalence rates for each weight category to project annual grade-specific incidence under 1980 obesity levels. We use a simulation model based on observed survival and mortality data to translate the effects of obesity trends on prostate cancer incidence into effects on disease-specific mortality. The predicted increase in obesity prevalence since 1980 increased high-grade prostate cancer incidence by 15.5% and prostate cancer mortality by between 7.0% (under identical survival for obese and nonobese cases) and 23.0% (under different survival for obese and nonobese cases) in 2002. We conclude that increasing obesity prevalence since 1980 has partially obscured declines in prostate cancer mortality

Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study.

BACKGROUND: There is a need for automated approaches to incorporate information on cancer recurrence events into population-based cancer registries. OBJECTIVE: The aim of this study is to determine the accuracy of a novel data mining algorithm to extract information from linked registry and medical claims data on the occurrence and timing of second breast cancer events (SBCE). METHODS: We used supervised data from 3092 stage I and II breast cancer cases (with 394 recurrences), diagnosed between 1993 and 2006 inclusive, of patients at Kaiser Permanente Washington and cases in the Puget Sound Cancer Surveillance System. Our goal was to classify each month after primary treatment as pre- versus post-SBCE. The prediction feature set for a given month consisted of registry variables on disease and patient characteristics related to the primary breast cancer event, as well as features based on monthly counts of diagnosis and procedure codes for the current, prior, and future months. A month was classified as post-SBCE if the predicted probability exceeded a probability threshold (PT); the predicted time of the SBCE was taken to be the month of maximum increase in the predicted probability between adjacent months. RESULTS: The Kaplan-Meier net probability of SBCE was 0.25 at 14 years. The month-level receiver operating characteristic curve on test data (20% of the data set) had an area under the curve of 0.986. The person-level predictions (at a monthly PT of 0.5) had a sensitivity of 0.89, a specificity of 0.98, a positive predictive value of 0.85, and a negative predictive value of 0.98. The corresponding median difference between the observed and predicted months of recurrence was 0 and the mean difference was 0.04 months. CONCLUSIONS: Data mining of medical claims holds promise for the streamlining of cancer registry operations to feasibly collect information about second breast cancer events

Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics

Background: Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer. Methodology/Principal Findings: Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer. Conclusions/Significance: Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy

Welfare conditionality in lived experience : aggregating qualitative longitudinal research

Punitive welfare conditionality, combining tough sanctions with minimal self-directed support, is a defining feature of contemporary UK working age social security provision. This approach has been justified by policy makers on the basis that it will increase the numbers in paid employment, and thereby offering savings for the public purse that are also beneficial for individuals who are expected to be healthier and better off financially as a result. In this article, we aggregate two qualitative longitudinal studies (Welfare Conditionality, 2014-17; and Lived Experience, 2011-16) that document lived experiences of claiming benefits and using back-to-work support services. In both studies and over time, we find, contrary to policy expectations, that coercion, including sanctions, was usually experienced as unnecessary and harmful and that poverty was prevalent, both in and out of work, tended to worsen and pushed many close to destitution. Conditionality governed encounters with employment services and, perversely, appeared to impede, rather than support, transitions into employment for participants in both studies. In this way, we propose Combined Study Qualitative Longitudinal Research as a new methodological approach for investigating if ‘shared typical’ aspects of lived experiences of welfare conditionality can be identified

Circulating Fatty Acids and Prostate Cancer Risk: Individual Participant Meta-Analysis of Prospective Studies

Background: Individual studies have suggested that some circulating fatty acids are associated with prostate cancer risk, but have not been large enough to provide precise estimates of associations, particularly by stage and grade of disease. Methods: Principal investigators of prospective studies on circulating fatty acids and prostate cancer were invited to collaborate. Investigators provided individual participant data on circulating fatty acids (weight percent) and other characteristics of prostate cancer cases and controls. Prostate cancer risk by study-specific fifths of 14 fatty acids was estimated using multivariable-adjusted conditional logistic regression. All statistical tests were two-sided. Results: Five thousand and ninety-eight case patients and 6649 control patients from seven studies with an average follow-up of 5.1 (SD = 3.3) years were included. Stearic acid (18:0) was inversely associated with total prostate cancer (odds ratio [OR] Q5 vs Q1 = 0.88, 95% confidence interval [CI] = 0.78 to 1.00, P trend = .043). Prostate cancer risk was, respectively, 14% and 16% greater in the highest fifth of eicosapentaenoic acid (20:5n-3) (OR = 1.14, 95% CI = 1.01 to 1.29, P trend = .001) and docosapentaenoic acid (22:5n-3) (OR = 1.16, 95% CI = 1.02 to 1.33, P trend = .003), but in each case there was heterogeneity between studies (P = .022 and P < .001, respectively). There was heterogeneity in the association between docosapentaenoic acid and prostate cancer by grade of disease (P = .006); the association was statistically significant for low-grade disease but not high-grade disease. The remaining 11 fatty acids were not statistically associated with total prostate cancer risk. Conclusion: There was no strong evidence that circulating fatty acids are important predictors of prostate cancer risk. It is not clear whether the modest associations of stearic, eicosapentaenoic, and docosapentaenoic acid are causal