Lifecourse epidemiology is the study of chronic disease risk associated with the long? term effects of physical, metabolic, behavioral, social and psychosocial exposures occurring throughout the life. Current statistical methods for risk factor trajectories fail with multiple outcomes, inconsistent timing or multiple risk factor trajectories, or can fail to replicate. A multidisciplinary team of biostatisticians and lifecourse epidemiologists proposes to address the gaps. We build on the strength of our previous R01, during which we published 22 peer?reviewed journal articles, gave 20 invited talks and 1 webinar, and built free, point?and? click, power and sample size software, used more than 500 times each month, by more than 350 unique scientists. The examples in the proposal come from a funded study of the primordial causes of diabetes and obesity, which will use maternal gestational blood pressure trajectory as a predictor of future childhood obesity. Better design and analysis tools will allow lifecourse epidemiologists to better answer crucial questions about the importance of risk factor trajectories in the etiology of diseases. We have four aims. 1) Develop novel data analysis methods for lifecourse epidemiology studies that assess the association between one or more outcomes and trajectories of risk factors, examine the direct and mediated effects of risk factor trajectories, and determine critical periods within risk factor trajectories. 2) Derive new sample size methods for lifecourse studies involving risk factor trajectories. 3) Extend our widely used free, open?source, point?and?click sample size software for studies of risk factor trajectories to allow scientists to use the new sample size methods. 4) Widely disseminate the new techniques and free software through articles and seminars. Leverage our NIH funding to extend our short courses and massive open online course (1R25GM111901?01), add chapters to a book?in?progress (NLM 1G13LM011879?01), and produce more tutorials for our power and sample size website, currently visited by over 350 scientists each month (NIDCR 1 R01 DE02083201A1). While the research was inspired by a study of fetal programming, the results apply broadly to any study in which hypotheses about relating risk factor trajectories to outcomes are of interest. Risk factor trajectories are increasingly being examined for diseases as diverse as obesity, cardiovascular disease, stroke, diabetes, and cancer. Finally, extending our widely used power and sample size software to most classes of mixed models will bring accurate, easy?to?use, point?and?click study design to hundreds more NIH supported scientists.

Public Health Relevance

Lifecourse epidemiology is the study of chronic disease risk associated with the long?term effects of exposures occurring throughout the life course. When repeated measurements of risk factors over time are used to predict disease outcomes, the complex study designs require new data and sample size methods and software. We propose to create new methods and software for risk factor trajectories, and broadly disseminate them to a wide audience of scientists.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM121081-07
Application #
9518993
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Brazhnik, Paul
Project Start
2010-12-09
Project End
2020-06-30
Budget Start
2018-07-01
Budget End
2019-06-30
Support Year
7
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Colorado Denver
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
041096314
City
Aurora
State
CO
Country
United States
Zip Code
80045
Litt, J S; Alaimo, K; Buchenau, M et al. (2018) Rationale and design for the community activation for prevention study (CAPs): A randomized controlled trial of community gardening. Contemp Clin Trials 68:72-78
Zhang, Xinrui; Muller, Keith E; Goodenow, Maureen M et al. (2018) Internal pilot design for balanced repeated measures. Stat Med 37:375-389
Livingston III, MPH, Melvin D; Cannell, Brad; Muller, Keith et al. (2018) Comparing methods of misclassification correction for studies of adolescent alcohol use. Am J Drug Alcohol Abuse 44:160-166
Sauder, Katherine A; Kaar, Jill L; Starling, Anne P et al. (2017) Predictors of Infant Body Composition at 5 Months of Age: The Healthy Start Study. J Pediatr 183:94-99.e1
Sauder, K A; Hockett, C W; Ringham, B M et al. (2017) Fetal overnutrition and offspring insulin resistance and ?-cell function: the Exploring Perinatal Outcomes among Children (EPOCH) study. Diabet Med 34:1392-1399
Starling, A P; Shapiro, A L B; Sauder, K A et al. (2017) Blood pressure during pregnancy, neonatal size and altered body composition: the Healthy Start study. J Perinatol 37:502-506
Moore, B F; Sauder, K A; Starling, A P et al. (2017) Exposure to secondhand smoke, exclusive breastfeeding and infant adiposity at age 5 months in the Healthy Start study. Pediatr Obes 12 Suppl 1:111-119
Sauder, Katherine A; Koeppen, Hallie J; Shapiro, Allison L B et al. (2017) Prenatal Vitamin D Intake, Cord Blood 25-Hydroxyvitamin D, and Offspring Body Composition: The Healthy Start Study. Nutrients 9:
Perng, Wei; Ringham, Brandy M; Glueck, Deborah H et al. (2017) An observational cohort study of weight- and length-derived anthropometric indicators with body composition at birth and 5 mo: the Healthy Start study. Am J Clin Nutr 106:559-567
Shapiro, Allison L B; Sauder, Katherine A; Tregellas, Jason R et al. (2017) Exposure to maternal diabetes in utero and offspring eating behavior: The EPOCH study. Appetite 116:610-615

Showing the most recent 10 out of 12 publications