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<TD BGCOLOR="#CCCCFF" ALIGN="LEFT"><a name="skipnav"></A><DIV CLASS="main"><font face="arial,sans-serif" size="3" color="#000000"><B>Areas of Research</B>
<P>
<UL>
<LI><A HREF="#MetaAnalytic">The Meta-Analytic Research Group</A></LI>
<LI><A HREF="#GeoSpace">The Geographic and Spatial Analysis Collaborative (GSAC)</A></LI>
<LI><A HREF="#Predictive">The Clinical Predictive Instruments Research Group</A></LI>
<LI><A HREF="#Nof1">The N-of-1 Studies Group</A></LI>
<LI><A HREF="#Statistical">The Statistical Genetics Group</A></LI>
</UL>
<P>
<A NAME="MetaAnalytic"></A>
<B>The Meta-Analytic Research Group</B> have been involved in the primary development of meta-analysis and related study design methodologies. Meta-analysis is the compilation of a quantitative summary of effects across scientific studies. Standard techniques combine study estimates into a single number summary, but when treatment effects differ markedly across a set of studies, a single pooled number may not provide a useful summary of the evidence. Instead, regression analysis may be used to attribute variations in treatment efficacy to design and population characteristics differing across studies. BRC investigators have developed and used these methods to evaluate the relationship between the rate of events in the control group and treatment efficacy in clinical trials, have compared meta-regression and individual patient regression for detecting treatment interaction and have developed protocols for putting together databases of individual patient records.
<P>
Currently, the group is involved with a project to review all published meta-analyses of diagnostic tests in the clinical literature and to apply existing and develop new techniques of analysis to them. Work is also currently being done on methods for assessing the existence and impact of publication bias on these approaches. Many of these methods are now being implemented in a meta-analysis computer package that is being written to facilitate the development of evidence reports on specific topics. This package not only incorporates the non-Bayesian and Bayesian analytic methods developed by the group, but also is intended to include the ability to electronically screen abstracts, extract data from articles onto an electronic form, transfer these data to a database for analysis and generate tabular and graphical reports.
<P>
<A NAME="GeoSpace"></A>
<B>The Geographic and Spatial Analysis Collaborative (GSAC)</B> 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>
<A NAME="Predictive"></A>
<B>The Clinical Predictive Instruments Research Group</B> 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>
<A NAME="Nof1"></A>
<B>The N-of-1 Studies Group</B> 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>
<A NAME="Statistical"></A>
<B>The Statistical Genetics Group</B> explores the association between genetic information and medical events and outcomes. The group is currently involved with the Framingham association study, a fetal DNA study, osteoarthritis association study and a nephroloby association study.
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