Journal of Data Science

Neuroimaging technology has received considerable attention in recent years. One of the key problems in the imaging data analysis is the heterogeneity among individual subjects. In particular, the relationship between the imaging biomarkers and the clinical outcomes may vary across different individuals. Popular existing statistical methodologies such as the functional linear regression and high dimensional linear regression can be inadequate because the homogeneous regression relationship is assumed for all subjects. In this paper, we propose the Subject-Specific Scalar-on-Image Regression (S3IR) model to handle heterogeneous populations. Specifically, we utilize a binary subject-specific masking image to capture the heterogeneous sparsity among individuals. The proposed S3IR model incorporates the spatial structure of the imaging data and is able to achieve both local smoothness and subject-specific sparsity of the estimated regression coefficients. Furthermore, we design an EM-type adaptive algorithm to estimate the model coefficients. Simulation studies are presented to show the superior performance of our proposed method over some existing ones in handling heterogeneity. Finally, we apply the S3IR model to analyze data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results show that our model can effectively identify interpretable and significant disease-related regions and improve prediction performance of the cognitive scores.

Abstract: Good inference for the random effects in a linear mixed-effects model is important because of their role in decision making. For example, estimates of the random effects may be used to make decisions about the quality of medical providers such as hospitals, surgeons, etc. Standard methods assume that the random effects are normally distributed, but this may be problematic because inferences are sensitive to this assumption and to the composition of the study sample. We investigate whether using a Dirichlet process prior instead of a normal prior for the random effects is effective in reducing the dependence of inferences on the study sample. Specifically, we compare the two models, normal and Dirichlet process, emphasizing inferences for extrema. Our main finding is that using the Dirichlet process prior provides inferences that are substantially more robust to the composition of the study sample.

Abstract: In the absence of definitive trials on the safety and efficacy of drugs, a systematic and careful synthesis of available data may provide critical information to help decision making by policy makers, medical professionals, patients and other stakeholders. However, uncritical and unbalanced use of pooled data to inform decision about important healthcare issues may have consequences that adversely impact public health, stifle innovation, and con found medical science. In this paper, we highlight current methodological issues and discuss advantages and disadvantages of alternative meta-analytic techniques. It is argued that results from pooled data analysis would have maximal reliability and usefulness in decision making if used in a holistic framework that includes presentation of data in light of all available knowledge and effective collaboration among academia, industry, regulatory bodies and other stakeholders.