Open Access. Powered by Scholars. Published by Universities.®
Longitudinal Data Analysis and Time Series Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Longitudinal Data Analysis and Time Series
"%Qls Sas Macro: A Sas Macro For Analysis Of Longitudinal Data Using Quasi-Least Squares"., Hanjoo Kim, Justine Shults
"%Qls Sas Macro: A Sas Macro For Analysis Of Longitudinal Data Using Quasi-Least Squares"., Hanjoo Kim, Justine Shults
UPenn Biostatistics Working Papers
Quasi-least squares (QLS) is an alternative computational approach for estimation of the correlation parameter in the framework of generalized estimating equations (GEE). QLS overcomes some limitations of GEE that were discussed in Crowder (Biometrika 82 (1995) 407-410). In addition, it allows for easier implementation of some correlation structures that are not available for GEE. We describe a user written SAS macro called %QLS, and demonstrate application of our macro using a clinical trial example for the comparison of two treatments for a common toenail infection. %QLS also computes the lower and upper boundaries of the correlation parameter for analysis of …
On The Designation Of The Patterned Associations For Longitudinal Bernoulli Data: Weight Matrix Versus True Correlation Structure?, Hanjoo Kim, Joseph M. Hilbe, Justine Shults
On The Designation Of The Patterned Associations For Longitudinal Bernoulli Data: Weight Matrix Versus True Correlation Structure?, Hanjoo Kim, Joseph M. Hilbe, Justine Shults
UPenn Biostatistics Working Papers
Due to potential violation of standard constraints for the correlation for binary data, it has been argued recently that the working correlation matrix should be viewed as a weight matrix that should not be confused with the true correlation structure. We propose two arguments to support our view to the contrary for the first-order autoregressive AR(1) correlation matrix. First, we prove that the standard constraints are not unduly restrictive for the AR(1) structure that is plausible for longitudinal data; furthermore, for the logit link function the upper boundary value only depends on the regression parameter and the change in covariate …
On The Violation Of Bounds For The Correlation In Generalized Estimating Equation Analyses Of Binary Data From Longitudinal Trials, Justine Shults, Wenguang Sun, Xin Tu, Jay Amsterdam
On The Violation Of Bounds For The Correlation In Generalized Estimating Equation Analyses Of Binary Data From Longitudinal Trials, Justine Shults, Wenguang Sun, Xin Tu, Jay Amsterdam
UPenn Biostatistics Working Papers
It is well-known that the correlation among binary outcomes is constrained by the marginal means, yet approaches such as generalized estimating equations (GEE) do not check that the constraints for the correlations are satisfied. We explore this issue for Markovian dependence in the context of a GEE analysis of a clinical trial that compares Venlafaxine with Lithium in the prevention of major depressive episode. We obtain simplified expressions for the constraints for the logistic model and the equicorrelated and first-order autoregressive correlation structures. We then obtain the limiting values of the GEE and quasi-least squares (QLS) estimates of the correlation …
Use Of Unbiased Estimating Equations To Estimate Correlation In Generalized Estimating Equation Analysis Of Longitudinal Trials, Wenguang Sun, Justine Shults, Mary Leonard
Use Of Unbiased Estimating Equations To Estimate Correlation In Generalized Estimating Equation Analysis Of Longitudinal Trials, Wenguang Sun, Justine Shults, Mary Leonard
UPenn Biostatistics Working Papers
In a recent publication, Wang and Carey (Journal of the American Statistical Association, 99, pp. 845-853, 2004) presented a new approach for estimation of the correlation parameters in the framework of generalized estimating equations (GEE). They considered correlated continuous, binary and count data with a generalized Markov correlation structure that includes the first-order autoregressive AR(1) and Markov structures as special cases. They made detailed comparisons with pseudo-likelihood (PL) and the first stage of quasi-least squares (QLS), a two-stage approach in the framework of generalized estimating equations (GEE). In this note we extend their comparisons for the second (bias corrected) stage …