Using modified approaches on marginal regression analysis of longitudinal data with time-dependent covariates.
Quadratic inference functions (QIFs) and estimating equations using the conjugate gradient method (CGM) for fitting marginal models to longitudinal data show appealing features in improving the efficiency without making assumptions on the correlation structure. However, our simulation study shows that both methods produce biased and inefficient estimates of regression parameters when time-dependent covariates are present. In this paper, we extend both the QIF and CGM methods for fitting marginal models to longitudinal data with time-dependent covariates. The idea is to restrict the moment conditions to the ones that are only valid to certain types of time-dependent covariates. Our simulations show that efficiency on estimating regression parameters is achieved using modified approaches. Furthermore, we apply the modified approach to anthropometric screening data to evaluate the association between body mass index and morbidity in children in the Philippines.
Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, U.S.A.
Yi
Y
Zhou
John
J
Lefante
Janet
J
Rice
Shande
S
Chen
D056808:Biostatistics; D015992:Body Mass Index; D002648:Child; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D009017:Morbidity; D015999:Multivariate Analysis; D010679:Philippines / Q000453:epidemiology; D012044:Regression Analysis; D013997:Time Factors
estimating equations; moment conditions; quadratic inference function; restricted matrix