TY - JOUR
T1 - Use of partitioned GMM marginal regression model with time-dependent covariates
T2 - Analysis of Chinese Longitudinal Healthy Longevity Study
AU - Vazquez-Arreola, Elsa
AU - Xue, Dan
AU - Wilson, Jeffrey R.
N1 - Publisher Copyright:
© 2020 The Author(s).
PY - 2020/5/24
Y1 - 2020/5/24
N2 - Background: Elderly population's health is a major concern for most industrial nations. National health surveys provide a measure of the state of elderly health. One such survey is the Chinese Longitudinal Healthy Longevity Survey. It collects data on risk factors and outcomes on the elderly. We examine these longitudinal survey data to determine the changes in health and to identify risk factors as they impact health outcomes including the elderly's ability to do a physical check. Methods: We use a Partitioned GMM logistic regression model to identify risk factors. The model also accounts for the correlation between lagged time-dependent covariates and the outcomes. It addresses present and past measures of time-dependent covariates on simultaneous outcomes. The relation produces additional regression coefficients as byproduct of the Partitioned model, identifying the immediate, delayed effects (lag - 1), further delayed (lag-2), etc. Therefore, the model presents the opportunity for decision makers to monitor the covariate over time. This technique is particularly useful in healthcare and health related research. We use the Chinese Longitudinal Health Longevity Survey data to identify those risk factors and to display the utility of the model. Results: We found that one's ability to make own decisions, frequently consuming vegetables, exercise frequently, one's ability to transfer without assistance, having visual difficulties and being able to pick book from floor while standing had varying effects of significance on one's health and ability to complete physical checks as they get older. Conclusions: The partitioning of the covariates as immediate effect, delayed effect or further delayed effect are important measures in a declining population.
AB - Background: Elderly population's health is a major concern for most industrial nations. National health surveys provide a measure of the state of elderly health. One such survey is the Chinese Longitudinal Healthy Longevity Survey. It collects data on risk factors and outcomes on the elderly. We examine these longitudinal survey data to determine the changes in health and to identify risk factors as they impact health outcomes including the elderly's ability to do a physical check. Methods: We use a Partitioned GMM logistic regression model to identify risk factors. The model also accounts for the correlation between lagged time-dependent covariates and the outcomes. It addresses present and past measures of time-dependent covariates on simultaneous outcomes. The relation produces additional regression coefficients as byproduct of the Partitioned model, identifying the immediate, delayed effects (lag - 1), further delayed (lag-2), etc. Therefore, the model presents the opportunity for decision makers to monitor the covariate over time. This technique is particularly useful in healthcare and health related research. We use the Chinese Longitudinal Health Longevity Survey data to identify those risk factors and to display the utility of the model. Results: We found that one's ability to make own decisions, frequently consuming vegetables, exercise frequently, one's ability to transfer without assistance, having visual difficulties and being able to pick book from floor while standing had varying effects of significance on one's health and ability to complete physical checks as they get older. Conclusions: The partitioning of the covariates as immediate effect, delayed effect or further delayed effect are important measures in a declining population.
KW - Correlated data
KW - Generalized linear models
KW - Partitioned coefficients
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U2 - 10.1186/s12874-020-01003-0
DO - 10.1186/s12874-020-01003-0
M3 - Article
C2 - 32448318
AN - SCOPUS:85085362984
SN - 1471-2288
VL - 20
JO - BMC Medical Research Methodology
JF - BMC Medical Research Methodology
IS - 1
M1 - 128
ER -