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Supplemental material, sj-pdf-1-ctj-10.1177_17407745211028872 for Stratified randomization for platform trials with differing experimental arm eligibility by Subodh Selukar, Susanne May, Dave Law and Megan Othus in Clinical Trials

Roughly 1 in 20 people in the United States suffers from temporomandibular disorders (TMD), a set of disorders with symptoms ranging from mild jaw pain to chronic, debilitating pain. Prior research has indicated that patients with TMD may have more conditions comorbid to TMD (such as back pain or fibromyalgia), and experts have called for further work to increase awareness of a possible relationship between comorbid conditions and pain sensitivity. Using data from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) project's case-control study, we address this problem. We used regression methods to determine if the number of painful comorbid conditions was associated with 33 different pain measures. We also adjusted for possible biases due to the sampling design using Inverse Probability Weighting (IPW) and also for multiple testing using Bonferroni adjustment. After these adjustments, we found that pressure pain measures were significantly associated with the number of comorbid conditions, but that in general other measures of pain sensitivity were not. Also, no association was detected when looking at cases versus controls individually. The surprising results that pain type had differential significance and that case status did not raises questions that warrant further research (alternatively, this may be due to decreased sample size to detect a difference between cases and controls). Despite this, there did exist conclusive results to suggest that, at least for pressure pain measures, there is a relationship between the number of comorbid conditions and pain sensitivity.

Alzheimer's Disease is a neurological disorder chiefly present in the elderly that affects functions of the brain such as memory and logic, eventually resulting in death. There is no known cure to Alzheimer's and evidence points to the possibility of a genetic link. This study analyzes microarray data from patients with Alzheimer's disease and disease-free patients in order to evaluate and determine differential gene expression patterns between the two groups. The statistical problem stemming from this data involves many predictor variables with a small sample size, preventing the use of classical statistical approaches from being effective. We turn to a novel three-step approach: first, we screen the genes in order to keep only the genes marginally related to the outcome (presence of Alzheimer's); second, we implemented a sparse sufficient dimension reduction to retain only predictors relevant to the outcome; lastly, we perform a hierarchical clustering method to group genes that exhibit mutual dependence. We adapted this methodology from Adragni et. al and expand on their work by optimizing the existing R code with parallel capabilities in order to enhance performance speed. Thus, our results reflect both an analysis of the microarray data and a performance study of the modified code.