This is a project to develop new methods of post-selection inference and trajectory analysis directed towards important public health applications in genomics and epidemiology. The broad objective of the project is to provide new methods of post-selection inference for detecting the presence of signi?cant predictors in high- dimensional screening. These methods will be developed with a view to applications in several areas: drug resistance studies, personalized medicine, growth trajectories, and survival outcomes. Screening large numbers of predictors and assessing their utility in treatment decisions is a challenging problem. Among other things, the project will provide a more powerful alternative to the popular (yet conservative) Bonferroni method of controlling familywise error rates that are a crucial concern for these applications. Work on this topic was initiated in the principal investigator's current R01 grant, but was con?ned to linear regression settings. The signi?canc of the new application is that it will greatly expand the scope of these methods to allow for their much broader application. High-dimensional screening is especially relevant for extracting predictive features of growth trajectories. In addition, the project will develop new screening tests speci?cally for the purpose of comparing survival functions. This will be done in terms of nonparametric tests for stochastic ordering and hazard rate ordering under various censoring and biased sampling scenarios. An empirical likelihood approach (more powerful than the Wald approach) will be used. Post- selection inference issues to be addressed in this setting involve devising a way to calibrate maximally selected empirical likelihood-based test statistics over the follow-up period, and in screening for the presence of signi?cant orderings among multiple groups of subjects. A further objective is to develop new methods for reconstructing growth trajectories from sparse temporal data for use as predictors of health outcomes. This work was also initiated in the principal investigator's current R01 grant, and recently used by the principa investigator to study of the association between autism and dynamical features of growth during early infancy. The renewal will focus on improving these trajectory analysis methods by adjusting for measurement error and prior knowledge about the shape and boundedness of growth trajectories, with a view to applications in two new areas: 1) biosignatures in Finnish prenatal studies of schizophrenia, bipolar disorder and related psychotic disorders, and 2) pregnancy weight gain and long term maternal and child health outcomes.

Public Health Relevance

This relevance of the project to public health is that novel statistical methods for post-selection inference and trajectory analysis will be developed for addressing important questions in genomics and life course epidemiology. In particular, rigorous statistical inference for locating mutations related to malaria drug resistance, and for studying the effect of growth rate trajectories on adult neuropsychological outcomes, will be developed.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Brazhnik, Paul
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Columbia University (N.Y.)
Biostatistics & Other Math Sci
Schools of Public Health
New York
United States
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McKeague, Ian W; Qian, Min (2018) Marginal screening of 2 × 2 tables in large-scale case-control studies. Biometrics :
Wang, Huixia Judy; McKeague, Ian W; Qian, Min (2018) Testing for Marginal Linear Effects in Quantile Regression. J R Stat Soc Series B Stat Methodol 80:433-452
Brown, Alan S; Gyllenberg, David; Hinkka-Yli-Salomäki, Susanna et al. (2017) Altered growth trajectory of head circumference during infancy and schizophrenia in a National Birth Cohort. Schizophr Res 182:115-119
Niemelä, Solja; Sourander, Andre; Surcel, Heljä-Marja et al. (2016) Prenatal Nicotine Exposure and Risk of Schizophrenia Among Offspring in a National Birth Cohort. Am J Psychiatry 173:799-806
Eck, Daniel J; McKeague, Ian W (2016) Central Limit Theorems under additive deformations. Stat Probab Lett 118:156-162
McKeague, Ian W; Levin, Bruce (2016) Convergence of empirical distributions in an interpretation of quantum mechanics. Ann Appl Probab 26:2540-2555
Qian, Min (2016) Comment. J Am Stat Assoc 111:1538-1541
Chang, Hsin-Wen; El Barmi, Hammou; McKeague, Ian W (2016) Tests for stochastic ordering under biased sampling. J Nonparametr Stat 28:659-682
McKeague, Ian W; Qian, Min (2015) An adaptive resampling test for detecting the presence of significant predictors. J Am Stat Assoc 110:1422-1433
McKeague, Ian W (2015) Central limit theorems under special relativity. Stat Probab Lett 99:149-155

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