Ordinal outcomes arise frequently in clinical studies when each subject is assigned to a category and the categories have a natural order. Classification rules for ordinal outcomes may be developed with commonly used regression models such as the full continuation ratio (CR) model (fCR), which allows the covariate effects to differ across all continuation ratios, and the CR model with a proportional odds structure (pCR), which assumes the covariate effects to be constant across all continuation ratios. For settings where the covariate effects differ between some continuation ratios but not all, fitting either fCR or pCR may lead to suboptimal prediction performance. In addition, these standard models do not allow for nonlinear covariate effects. In this article, we propose a sparse CR kernel machine (KM) regression method for ordinal outcomes where we use the KM framework to incorporate nonlinearity and impose sparsity on the overall differences between the covariate effects of continuation ratios to control for overfitting. In addition, we provide data driven rule to select an optimal kernel to maximize the prediction accuracy. Simulation results show that our proposed procedures perform well under both linear and nonlinear settings, especially when the true underlying model is in-between fCR and pCR models. We apply our procedures to develop a prediction model for levels of anti-CCP among rheumatoid arthritis patients and demonstrate the advantage of our method over other commonly used methods.
Accurate risk prediction models are needed to identify different risk groups for individualized prevention and treatment strategies. In the Nurses’ Health Study, to examine the effects of several biomarkers and genetic markers on the risk of rheumatoid arthritis (RA), a three-phase nested case-control (NCC) design was conducted, in which two sequential NCC subcohorts were formed with one nested within the other, and one set of new markers measured on each of the subcohorts. One objective of the study is to evaluate clinical values of novel biomarkers in improving upon existing risk models because of potential cost associated with assaying biomarkers. In this paper, we develop robust statistical procedures for constructing risk prediction models for RA and estimating the incremental value (IncV) of new markers based on three-phase NCC studies. Our method also takes into account possible time-varying effects of biomarkers in risk modeling, which allows us to more robustly assess the biomarker utility and address the question of whether a marker is better suited for short-term or long-term risk prediction. The proposed procedures are shown to perform well in finite samples via simulation studies.
Background: Dietary supplement use is widespread in the United States. Although it has been suggested in both in vitro and small in vivo human studies that chromium has potentially beneficial effects in type 2 diabetes (T2D), chromium supplementation in diabetes has not been investigated at the population level.
Objective: The objective of this study was to examine the use and potential benefits of chromium supplementation in T2D by examining NHANES data.
Methods: An individual was defined as having diabetes if he or she had a glycated hemoglobin (HbA1c) value of ≥6.5%, or reported having been diagnosed with diabetes. Data on all consumed dietary supplements from the NHANES database were analyzed, with the OR of having diabetes as the main outcome of interest based on chromium supplement use.
Results: The NHANES for the years 1999–2010 included information on 62,160 individuals. After filtering the database for the required covariates (gender, ethnicity, socioeconomic status, body mass index, diabetes diagnosis, supplement usage, and laboratory HbA1c values), and when restricted to adults, the study cohort included 28,539 people. A total of 58.3% of people reported consuming a dietary supplement in the previous 30 d, 28.8% reported consuming a dietary supplement that contained chromium, and 0.7% consumed supplements that had “chromium” in the title. Compared with nonusers, the odds of having T2D (HbA1c ≥6.5%) were lower in persons who consumed chromium-containing supplements within the previous 30 d than in those who did not (OR: 0.73; 95% CI: 0.62, 0.86; P = 0.001). Supplement use alone (without chromium) did not influence the odds of having T2D (OR: 0.89; 95% CI: 0.77, 1.03; P = 0.11).
Conclusions: Over one-half the adult US population consumes nutritional supplements, and over one-quarter consumes supplemental chromium. The odds of having T2D were lower in those who, in the previous 30 d, had consumed supplements containing chromium. Given the magnitude of exposure, studies on safety and efficacy are warranted.