The goal of this project is to develop innovative statistical methods for causal inference in observational studies that can handle time-varying confounders, censoring by death, and missing data in addition to selection bias, and to answer important clinical questions on management of the Amyotrophic Lateral Sclerosis (ALS) disease. The existing studies on addressing these questions have several limitations including their small to moderate sample sizes and the use of limited clinical data and statistical methods that did not adequately address complicating issues including selection bias commonly encountered in observational studies. This study will use the data from the Emory ALS Registry with several notable strengths including a large sample size of over 1,700 patients with long-term follow-ups and collection of extensive clinical information at each clinic visit since 1997. A a result, the Emory ALS Registry is uniquely suited for answering important clinical questions. The analysis of the ALS Registry presents several challenges including time-varying confounders, censoring by death, and missing data. The existing statistical methods cannot be applied directly to address all these issues. These considerations lead to three specific aims: 1) develop a new propensity score for balancing time-varying covariates in observational studies, the propensity process, and develop statistical methods for estimating causal effects based on the propensity process; 2) develop statistical methods for assessing the survivor average causal effects (SACE) in the presence of missing data in observational studies with time-varying covariates; and 3) perform systematic evaluation of the proposed methods in Aims 1 and 2 through extensive simulations and perform analysis of the Emory ALS Registry data. Progress in all aims will be guided by and evaluated on the Emory ALS Registry, and by extensive simulation studies. The proposed methods will enable us to answer important clinical questions, e.g., assessing the effects of procedures including the percutaneous endoscopic gastrostomy (PEG) and the non-invasive positive pressure ventilation (NIPPV) on patient outcomes. The proposed methods are general and promise similar benefits to a wide range of observational studies and registries, since similar data structures and analytical issues are often encountered in these types of studies.

Public Health Relevance

The goal of this study is to develop innovative statistical methods for causal inference in observational studies that can handle time-varying confounders, censoring by death, and missing data. We will apply the proposed methods to the Emory Amyotrophic Lateral Sclerosis (ALS) Registry to answer important clinical questions such as assessing the effects of procedures including the percutaneous endoscopic gastrostomy (PEG) and the non-invasive positive pressure ventilation (NIPPV) on patient outcomes. The proposed methods promise similar benefits to a wide range of observational studies and registries, since similar data structures and complicating issues are often encountered in these types of studies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21NS091630-01
Application #
8870561
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Gubitz, Amelie
Project Start
2015-02-01
Project End
2017-01-31
Budget Start
2015-02-01
Budget End
2016-01-31
Support Year
1
Fiscal Year
2015
Total Cost
$193,729
Indirect Cost
$68,729
Name
Emory University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
066469933
City
Atlanta
State
GA
Country
United States
Zip Code
30322
Safo, Sandra E; Li, Shuzhao; Long, Qi (2018) Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information. Biometrics 74:300-312
Yang, Yihan; Long, Qi; Jackson, Sandra L et al. (2018) Nurse Practitioners, Physician Assistants, and Physicians Are Comparable in Managing the First Five Years of Diabetes. Am J Med 131:276-283.e2
Min, Eun Jeong; Safo, Sandra E; Long, Qi (2018) Penalized Co-Inertia Analysis with Applications to -Omics Data. Bioinformatics :
Pellegrini, Kathryn L; Sanda, Martin G; Patil, Dattatraya et al. (2017) Evaluation of a 24-gene signature for prognosis of metastatic events and prostate cancer-specific mortality. BJU Int 119:961-967
Hu, Yi-Juan; Schmidt, Amand F; Dudbridge, Frank et al. (2017) Impact of Selection Bias on Estimation of Subsequent Event Risk. Circ Cardiovasc Genet 10:
Zhao, Yize; Long, Qi (2017) Variable Selection in the Presence of Missing Data: Imputation-based Methods. Wiley Interdiscip Rev Comput Stat 9:
Jackson, Sandra L; Safo, Sandra; Staimez, Lisa R et al. (2017) Reduced Cardiovascular Disease Incidence With a National Lifestyle Change Program. Am J Prev Med 52:459-468
Johnson, Brent A; Long, Qi; Huang, Yijian et al. (2016) Model selection and inference for censored lifetime medical expenditures. Biometrics 72:731-41
Hsu, Chiu-Hsieh; He, Yulei; Li, Yisheng et al. (2016) Doubly robust multiple imputation using kernel-based techniques. Biom J 58:588-606
Mishra-Kalyani, Pallavi S; Johnson, Brent A; Glass, Jonathan D et al. (2016) Estimating the palliative effect of percutaneous endoscopic gastrostomy in an observational registry using principal stratification and generalized propensity scores. Sci Rep 6:33431

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