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The BBB conducts research in statistical theory and methodology relevant to problems under investigation in the areas of maternal and child health. The Branch also provides statistical support in the design and conduct of clinical trials and consults and collaborates with intramural and extramural scientists on statistical and mathematical problems. The Branch also develops quantitative procedures appropriate for application in biomedical and life sciences and supports the statistical and applied mathematics activities of DIPHR.
The Branch’s mission is to:
- Develop original biostatistical and bioinformatics research relevant for the research mission of the Division and Institute.
- Engage in collaborative research with other Division, Institute, and extramural investigators working in research areas relevant for the Division and Institute.
- Provide service to the Division, Institute, NIH, DHHS, and other government agencies via consultation, collaboration, and assistance to advance the scientific discipline of biostatistics and the goals of the Institute.
- Recruit and mentor highly qualified students and trainees at various stages of their careers to position them for professional careers in biostatistical and bioinformatics research.
- BBB investigators proposed innovative new statistical approaches for estimating gene-environment association for longitudinal quantitative trait outcomes. The proposed parametric and non-parametric models account for both linkage disequilibrium and temporal trends, simultaneously. (Fan et al.,Genetic Epidemiology, In Press).
- BBB investigators proposed a biologically valid discrete survival model that combines both the survival and hierarchical models allowing investigators to obtain the distribution of time-to-pregnancy and day-specific probabilities during the fertile window in a single model. The model allows for the consideration of covariate effects at both the cycle and daily level while accounting for daily variation in conception. (Sundaram et al., Biostatistics, 2012). PMID: 21697247
- BBB investigators derived optimality properties of group testing procedures for estimating prevalence of a rare disease whose status is classified with error. Exact ranges of disease prevalence are obtained for which group testing provides more efficient estimation when group size increases. (Liu et al., Biometrika, 2012).
- BBB investigators proposed a latent class model for associating high dimensional environmental exposure data with disease prevalence. This new innovative methodological approach is applied to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis. (Zhang et al, Biostatistics, 2012). PMID: 21908867
- A BBB investigator proposes new statistical methodology to perform covariate adjustment for estimating the area under the ROC curve when some of the gold standard tests are missing. This innovative methodology incorporates both missing at random and missing non-randomly gold standard test results. (Liu and Zhou, Biometrics, 2013). PMID: 3622116
- Researchers developed a joint model for batched or pooled longitudinal data subject to informative dropout. Both a shared random parameter and a pattern mixture model formulation are considered for estimation with the pattern mixture model being shown to be more robust to modeling assumptions, while the shared random parameter model has a more direct interpretation. (Albert and Shih, in press, Statistical Methods in Medical Research, 2012). PMID: 21300625
- Paul Albert, Chief of the Biostatistics & Bioinformatics Branch, is featured in a recent Inside the NICHD interview.