The analysis of binary longitudinal data with time-dependent covariates

2012 ◽  
Vol 31 (10) ◽  
pp. 931-948 ◽  
Author(s):  
Matthew W. Guerra ◽  
Justine Shults ◽  
Jay Amsterdam ◽  
Thomas Ten-Have
Keyword(s):  
Longitudinal Data ◽  
Time Dependent ◽  
Binary Longitudinal Data ◽  
Time Dependent Covariates

scholarly journals Marginal quantile regression for longitudinal data analysis in the presence of time-dependent covariates

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
I-Chen Chen ◽  
Philip M. Westgate
Keyword(s):  
Parameter Estimation ◽  
Longitudinal Data ◽  
Quantile Regression ◽  
Mean Squared Error ◽  
Time Dependent ◽  
Regression Methods ◽  
Squared Error ◽  
Highly Correlated ◽  
Time Dependent Covariates ◽  
Independence Structure

AbstractWhen observations are correlated, modeling the within-subject correlation structure using quantile regression for longitudinal data can be difficult unless a working independence structure is utilized. Although this approach ensures consistent estimators of the regression coefficients, it may result in less efficient regression parameter estimation when data are highly correlated. Therefore, several marginal quantile regression methods have been proposed to improve parameter estimation. In a longitudinal study some of the covariates may change their values over time, and the topic of time-dependent covariate has not been explored in the marginal quantile literature. As a result, we propose an approach for marginal quantile regression in the presence of time-dependent covariates, which includes a strategy to select a working type of time-dependency. In this manuscript, we demonstrate that our proposed method has the potential to improve power relative to the independence estimating equations approach due to the reduction of mean squared error.

A novel approach to selecting classification types for time-dependent covariates in the marginal analysis of longitudinal data

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3176-3186 ◽  
Author(s):  
I-Chen Chen ◽  
Philip M Westgate
Keyword(s):  
Longitudinal Data ◽  
Hypothesis Testing ◽  
Estimating Equations ◽  
Time Dependent ◽  
Parameter Estimates ◽  
Regression Parameter ◽  
Marginal Analysis ◽  
Moment Conditions ◽  
Testing Procedures ◽  
Time Dependent Covariates

Generalized estimating equations are routinely utilized for the marginal analysis of longitudinal data. In order to obtain consistent regression parameter estimates, these estimating equations must be unbiased. However, when certain types of time-dependent covariates are presented, these equations can be biased unless the working independence structure is used. Unfortunately, regression parameter estimation can be very inefficient with this structure because not all valid moment conditions are incorporated within the corresponding equations. Therefore, approaches have been proposed to utilize all valid moment conditions. However, these approaches assume that the data analyst knows the type of time-dependent covariate, although this likely is not the case in practice. Whereas hypothesis testing has been used to determine covariate type, we propose a novel strategy to select a working covariate type in order to avoid potentially high type II error rates with these hypothesis testing procedures. Parameter estimates resulting from our proposed method are consistent and have overall improved mean squared error relative to hypothesis testing approaches. Existing and proposed methods are compared in a simulation study and application example.

scholarly journals Application of Seemingly Unrelated Regression in Medical Data with Intermittently Observed Time-Dependent Covariates

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Sareh Keshavarzi ◽  
Seyyed Mohammad Taghi Ayatollahi ◽  
Najaf Zare ◽  
Maryam Pakfetrat
Keyword(s):  
Longitudinal Data ◽  
Observation Time ◽  
Seemingly Unrelated Regression ◽  
Time Dependent ◽  
Simulation Studies ◽  
Mixed Effect ◽  
Unrelated Regression ◽  
Effect Regression ◽  
Time Dependent Covariates ◽  
Observation Times

Background. In many studies with longitudinal data, time-dependent covariates can only be measured intermittently (not at all observation times), and this presents difficulties for standard statistical analyses. This situation is common in medical studies, and methods that deal with this challenge would be useful.Methods. In this study, we performed the seemingly unrelated regression (SUR) based models, with respect to each observation time in longitudinal data with intermittently observed time-dependent covariates and further compared these models with mixed-effect regression models (MRMs) under three classic imputation procedures. Simulation studies were performed to compare the sample size properties of the estimated coefficients for different modeling choices.Results. In general, the proposed models in the presence of intermittently observed time-dependent covariates showed a good performance. However, when we considered only the observed values of the covariate without any imputations, the resulted biases were greater. The performances of the proposed SUR-based models in comparison with MRM using classic imputation methods were nearly similar with approximately equal amounts of bias and MSE.Conclusion. The simulation study suggests that the SUR-based models work as efficiently as MRM in the case of intermittently observed time-dependent covariates. Thus, it can be used as an alternative to MRM.

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