Pioglitazone for secondary prevention after ischemic stroke and transient ischemic attack: Rationale and design of the Insulin Resistance Intervention after Stroke Trial

Background: Recurrent vascular events remain a major source of morbidity and mortality after stroke or transient ischemic attack (TIA). The IRIS Trial is evaluating an approach to secondary prevention based on the established association between insulin resistance and increased risk for ischemic vascular events. Specifically, IRIS will test the effectiveness of pioglitazone, an insulin-sensitizing drug of the thiazolidinedione class, for reducing the risk for stroke and myocardial infarction (MI) among insulin resistant, nondiabetic patients with a recent ischemic stroke or TIA. Design: Eligible patients for IRIS must have had insulin resistance defined by a Homeostasis Model Assessment-Insulin Resistance \u3e3.0 without meeting criteria for diabetes. Within 6 months of the index stroke or TIA, patients were randomly assigned to pioglitazone (titrated from 15 to 45 mg/d) or matching placebo and followed for up to 5 years. The primary outcome is time to stroke or MI. Secondary outcomes include time to stroke alone, acute coronary syndrome, diabetes, cognitive decline, and all-cause mortality. Enrollment of 3,876 participants from 179 sites in 7 countries was completed in January 2013. Participant follow-up will continue until July 2015. Summary: The IRIS Trial will determine whether treatment with pioglitazone improves cardiovascular outcomes of nondiabetic, insulin-resistant patients with stroke or TIA. Results are expected in early 2016

Best Practices for Biostatistical Consultation and Collaboration in Academic Health Centers

Given the increasing level and scope of biostatistics expertise needed at academic health centers today, we developed best practices guidelines for biostatistics units to be more effective in providing biostatistical support to their institutions, and in fostering an environment in which unit members can thrive professionally. Our recommendations focus on the key areas of: 1) funding sources and mechanisms; 2) providing and prioritizing access to biostatistical resources; and 3) interacting with investigators. We recommend that the leadership of biostatistics units negotiate for sufficient long-term infrastructure support to ensure stability and continuity of funding for personnel, align project budgets closely with actual level of biostatistical effort, devise and consistently apply strategies for prioritizing and tracking effort on studies, and clearly stipulate with investigators prior to project initiation policies regarding funding, lead time, and authorship

American Thyroid Association Design and Feasibility of a Prospective Randomized Controlled Trial of Prophylactic Central Lymph Node Dissection for Papillary Thyroid Carcinoma

Background: The role of prophylactic central lymph node dissection in papillary thyroid cancer (PTC) is controversial in patients who have no pre- or intraoperative evidence of nodal metastasis (clinically N0; cN0). The controversy relates to its unproven role in reducing recurrence rates while possibly increasing morbidity (permanent hypoparathyroidism and unintentional recurrent laryngeal nerve injury). Methods and Results: We examined the design and feasibility of a multi-institutional prospective randomized controlled trial of prophylactic central lymph node dissection in cN0 PTC. Assuming a 7-year study with 4 years of enrollment, 5 years of average follow-up, a recurrence rate of 10% after 7 years, a 25% relative reduction in the rate of the primary endpoint (newly identified structural disease; i.e., persistent, recurrent, or distant metastatic disease) with central lymph node dissection and an annual dropout rate of 3%, a total of 5840 patients would have to be included in the study to achieve at least 80% statistical power. Similarly, given the low rates of morbidity, several thousands of patients would need to be included to identify a significant difference in rates of permanent hypoparathyroidism and unintentional recurrent laryngeal nerve injury. Conclusion: Given the low rates of both newly identified structural disease and morbidity after surgery for cN0 PTC, prohibitively large sample sizes would be required for sufficient statistical power to demonstrate significant differences in outcomes. Thus, a prospective randomized controlled trial of prophylactic central lymph node dissection in cN0 PTC is not readily feasible.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98487/1/thy%2E2011%2E0317.pd

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses

The search for stable prognostic models in multiple imputed data sets

<p>Abstract</p> <p>Background</p> <p>In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition.</p> <p>Methods</p> <p>Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping.</p> <p>Results</p> <p>Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model.</p> <p>Conclusion</p> <p>In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability.</p

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine