Abstract #302186

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JSM 2003 Abstract #302186
Activity Number: 97
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #302186
Title: Inputation and Parameter Estimation for Bivariate Correlated Longitudinal Data in Clinical Trials
Author(s): Naum M. Khutoryansky*+ and Michael R. Chernick
Companies: Novo Nordisk Pharmaceuticals, Inc. and Novo Nordisk Pharmaceuticals, Inc.
Address: 100 College Rd. West, Princeton, NJ, 08540,
Keywords: imputation ; bivariate data ; longitudinal data
Abstract:

Bivariate models are useful when analyzing longitudinal data of two correlated endpoints. Longitudinal data in clinical trials are often incomplete which can create bias in estimation of their means and variances. The incompleteness is partially caused by withdrawal from trials. In some clinical trials with two endpoints, only one of them is used as a marker for withdrawal. The drop-out mechanism plays an important role in imputation and correct estimation of statistical parameters of bivariate longitudinal data. We compare several methods of parameter estimation for bivariate data including linear mixed models, the multiple imputation methods and incremental methods. The comparison is done on simulated longitudinal data that are generated to be the bivariate Markov processes and resemble data in diabetes clinical trials. The drop-out mechanism depends on individual previously observed values of one of the endpoints.

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