Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Multidimensional longitudinal data: estimating a treatment effect from continuous, discrete, or time to event response variables.
AU - Gray, S. M.
AU - Brookmeyer, R.
PY - 2000
Y1 - 2000
N2 - Multidimensional data arise when a number of different response variables are required to measure the outcome of interest. Examples of such outcomes include quality of life, cognitive ability, and health status. The goal of this article is to develop a methodology to estimate a treatment effect from multidimensional data that have been collected longitudinally using continuous, discrete, or time-to-event responses or a mixture of these types of responses. A transformation of the time scale that does not depend on the units of the response variables is used to capture the effect of treatment. This allows information about the treatment effect to be combined across response variables of different types. The model is specified using a pair of regression models for the first two moments, and generalized estimating equations are used for parameter estimation. The methodology is applied to quality-of-life data from an AIDS clinical trial and health status data from an Alzheimer's disease study.
AB - Multidimensional data arise when a number of different response variables are required to measure the outcome of interest. Examples of such outcomes include quality of life, cognitive ability, and health status. The goal of this article is to develop a methodology to estimate a treatment effect from multidimensional data that have been collected longitudinally using continuous, discrete, or time-to-event responses or a mixture of these types of responses. A transformation of the time scale that does not depend on the units of the response variables is used to capture the effect of treatment. This allows information about the treatment effect to be combined across response variables of different types. The model is specified using a pair of regression models for the first two moments, and generalized estimating equations are used for parameter estimation. The methodology is applied to quality-of-life data from an AIDS clinical trial and health status data from an Alzheimer's disease study.
KW - Acceleration
KW - Alzheimer's disease
KW - Generalized estimating equations
KW - Longitudinal data analysis
KW - Multidimensional data
KW - Quality of life.
M3 - Journal article
VL - 95
SP - 396
EP - 406
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 1537-274X
IS - 450
ER -