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<B>Core Research Groups</B><BR>
<B>Biostatistics Research Center</B><BR>
<B>Division of Clinical Care Research</B><BR>
<P>
The Meta-Analytic Research Group have been involved in the primary development of meta-analysis and related study design methodologies. These methods allow for the combining of multiple clinical studies to provide a summary of treatment or intervention impact. Staff have also explored the impact of heterogeneity on statistical significance in meta-analysis, the effect of the control rate as a predictor of treatment efficacy in meta-analysis of clinical trials, issues in comparisons between meta-analyses and large trials, and the relationship between study design, results and reporting of randomized clinical trials. Work is also currently being done on methods for assessing the existence and impact of publication bias on these approaches.
<P>
The Geographic and Spatial Analysis Collaborative (GSAC) uses geographic information systems (GIS) and spatial analytic methods to explore associations between spatially and temporally related data. This research may include investigations of geographical locations in public health research or in clinical research anatomic sites or regions of the brain, the eye, or other body parts or organs. The spatial analytic methods related to these spatial data range from simple mapping and pattern detection of disease incidence rates to complicated statistical modeling of geographical or medical imaging data. The application of newer statistical methods to these types of data allow for approaches that properly incorporate the spatial or temporal structure in the analyses and can provide important insights into the underlying structure related to a disease or medical condition.
<P>
The Clinical Predictive Instruments Research Group explores methods for the development and validation of statistical prediction models for clinical outcomes. These mathematical models use clinically relevant data to accurately summarize and predict the probability of a medical event in order to assist with clinical decision-making or risk-adjusted evaluations. Working with Dr. Harry Selker the BRC staff have helped develop and validate numerous prediction models for clinical use and decision-making. In addition, with funding from AHRQ and the NLM, the performance of different modeling algorithms, including artificial neural networks and classification trees, and the factors that impact on transportability of these models has been extensively compared. Methods for measuring model performance for binary outcomes have also been developed and assessed.
<P>
The N-of-1 Studies Group has developed and applied a method for effectiveness research that uses patient-focused N-of-1 trials and then combines these trials' results to obtain population estimates of treatment effectiveness to aid in treatment decision-making for an individual patient. The N-of-1 studies use repeated trials on individual patients to optimize treatment for that patient. The new procedure extends this by inclusion of a population estimate across multiple N-of-1 studies. This approach has been applied prospectively to compare estimates of treatment effectiveness of a combination therapy versus a single therapy in the treatment of patients with Fibromyalgia.
<P>
The Statistical Genetics Research Group ......
<P>
The Environmental Statistics Collaborative . . . .
<P>
The Ophthalmologic Statistics Research Group works in the development and application of novel statistical methodologies related to ophthalmology . . . . .
<P>
The Survey Design Group . . .
<P> </P>
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