Biostatistics Funding Looking Good at NIH
This column highlights research activities that may be of interest to ASA members. These brief articles include information about new research solicitations and the federal budget for statistics. Comments or suggestions for future articles may be sent to ASA Research and Graduate Education Manager Keith Crank at firstname.lastname@example.org.
Keith Crank has a BS in mathematics education and an MS in mathematics from Michigan State University and a PhD in statistics from Purdue University. Prior to joining the ASA as research and graduate education manager, he was a program officer at the National Science Foundation, primarily in the probability program.
There is good news coming out of the National Institutes of Health (NIH) regarding funding of biostatistics. The Biostatistical Methods and Research Design Study Section (BMRD) is not going to disappear—at least not in the near future. A decision has been made to continue BMRD with an expanded scope. However, the continuation of BMRD depends on it having a sufficient number of proposals to review. If your research is in an area covered by BMRD, as described here, please submit your proposal for review by this study section.
Last December, I wrote about some misconceptions regarding funding of proposals submitted to BMRD. These misconceptions may have been the main cause of the decline in the number of proposals submitted to BMRD in recent years. I doubt that my December article completely changed people’s opinion of BMRD. So I invite you to come to this year’s JSM session on NIH that I have organized. You can raise issues directly with some of the people involved in BMRD, as well as program directors from NCI and NIGMS.
I want to thank Marie Davidian (NCSU) and Michelle Dunn (NCI) for their efforts to keep BMRD from being folded into another study section. They deserve much of the credit for keeping BMRD alive.
Here is the latest information from the NIH web site about BMRD:
The Biostatistical Methods and Research Design (BMRD) Study Section reviews applications that seek to advance statistical and mathematical techniques and technologies applicable to the design and analysis of data from biomedical, behavioral, and social science research. Emphasis is on the promotion of quantitative methods to aid in the design, analysis, and interpretation of clinical, genomic, and population-based research studies. This includes analytic software development, novel applications, and secondary data analyses utilizing existing database resources.
Specific areas covered by BMRD
High-dimensional data methods such as those arising from genomic technologies, proteomics, sequencing, and imaging studies; development and applications of methods for data mining and statistical machine learning; statistical methods for high throughput data; biomarker identification
Novel analyses of existing data sets
Innovative application of existing, or development of new, statistical and computational methodologies; application of methods in substantially new areas of application; innovative, nonroutine data analysis strategies, including combinations of existing methods rather than de novo development of new methods; development and evaluation of novel analytic tools to address new questions within existing data sets
Development and innovative application of randomized trial designs; sample size determination; design issues for experimental and observational studies; methods to improve study design efficiencies; methods for survey sample design; methods for comparative effectiveness studies
Data collection and measurement
Development and adaption of methods to estimate and improve data precision, reliability, and validity; methods to estimate and adjust for bias, measurement error, confounding, sampling and nonsampling error; psychometric methods
Data analysis and modeling
Development of statistical theory, analytic methods and models, computational tools, and algorithms for the analysis and interpretation of data from clinical studies, randomized trials, observational studies, epidemiological studies, human genetic association studies, environmental studies, complex surveys, large databases, and registries; methods to handle data features and anomalies such as correlation, clustering, and missing data; risk prediction and forecasting methods; causal modeling