Journal of Data Science

Clinical risk prediction models are commonly developed in a post-hoc and passive fashion, capitalizing on convenient data from completed clinical trials or retrospective cohorts. Impacts of the models often end at their publication rather than with the patients. The field of clinical risk prediction is rapidly improving in a progressively more transparent data science era. Based on collective experience over the past decade by the Prostate Biopsy Collaborative Group (PBCG), this paper proposes the following four data science-driven strategies for improving clinical risk prediction to the benefit of clinical practice and research. The first proposed strategy is to actively design prospective data collection, monitoring, analysis and validation of risk tools following the same standards as for clinical trials in order to elevate the quality of training data. The second suggestion is to make risk tools and model formulas available online. User-friendly risk tools will bring quantitative information to patients and their clinicians for improved knowledge-based decision-making. As past experience testifies, online tools expedite independent validation, providing helpful information as to whether the tools are generalizable to new populations. The third proposal is to dynamically update and localize risk tools to adapt to changing demographic and clinical landscapes. The fourth strategy is to accommodate systematic missing data patterns across cohorts in order to maximize the statistical power in model training, as well as to accommodate missing information on the end-user side too, in order to maximize utility for the public.

Abstract: The application of linear mixed models or generalized linear mixed models to large databases in which the level 2 units (hospitals) have a wide variety of characteristics is a problem frequently encountered in studies of medical quality. Accurate estimation of model parameters and standard errors requires accounting for the grouping of outcomes within hospitals. Including the hospitals as random effect in the model is a common method of doing so. However in a large, diverse population, the required assump tions are not satisfied, which can lead to inconsistent and biased parameter estimates. One solution is to use cluster analysis with clustering variables distinct from the model covariates to group the hospitals into smaller, more homogeneous groups. The analysis can then be carried out within these groups. We illustrate this analysis using an example of a study of hemoglobin A1c control among diabetic patients in a national database of United States Department of Veterans’ Affairs (VA) hospitals.

Abstract: This paper aims to propose a suitable statistical model for the age distribution of prostate cancer detection. Descriptive studies suggest the onset of prostate cancer after 37 years of age with maximum diagnosis age at around 70 years. The major deficiency of descriptive studies is that the results cannot be generalized for all types of populations usually having non-identical environmental conditions. The proposition follows by checking the suitability of the model through different statistical tools like Akaike Information Criterion, Kolmogorov Smirnov distance, Bayesian Information Criterion and χ2 statistic. The Maximum likelihood estimate of the parameters of the proposed model along with their asymptotic confidence intervals have been obtained for the considered real data set.

Subsampling the data is used in this paper as a learning method about the influence of the data points for drawing inference on the parameters of a fitted logistic regression model. The alternative, alternative regularized, alternative regularized lasso, and alternative regularized ridge estimators are proposed for the parameter estimation of logistic regression models and are then compared with the maximum likelihood estimators. The proposed alternative regularized estimators are obtained by using a tuning parameter but the proposed alternative estimators are not regularized. The proposed alternative regularized lasso estimators are the averaged standard lasso estimators and the alternative regularized ridge estimators are also the averaged standard ridge estimators over subsets of groups where the number of subsets could be smaller than the number of parameters. The values of the tuning parameters are obtained to make the alternative regularized estimators very close to the maximum likelihood estimators and the process is explained with two real data as well as a simulated study. The alternative and alternative regularized estimators always have the closed form expressions in terms of observations that the maximum likelihood estimators do not have. When the maximum likelihood estimators do not have the closed form expressions, the alternative regularized estimators thus obtained provide the approximate closed form expressions for them.