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Likelihood estimation for a longitudinal negative binomial regression model with missing outcomes

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  • Simon J. Bond
  • Vernon T. Farewell

Abstract

Joint damage in psoriatic arthritis can be measured by clinical and radiological methods, the former being done more frequently during longitudinal follow-up of patients. Motivated by the need to compare findings based on the different methods with different observation patterns, we consider longitudinal data where the outcome variable is a cumulative total of counts that can be unobserved when other, informative, explanatory variables are recorded. We demonstrate how to calculate the likelihood for such data when it is assumed that the increment in the cumulative total follows a discrete distribution with a location parameter that depends on a linear function of explanatory variables. An approach to the incorporation of informative observation is suggested. We present analyses based on an observational database from a psoriatic arthritis clinic. Although the use of the new statistical methodology has relatively little effect in this example, simulation studies indicate that the method can provide substantial improvements in bias and coverage in some situations where there is an important time varying explanatory variable. Copyright Journal compilation (c) 2009 Royal Statistical Society.

Suggested Citation

  • Simon J. Bond & Vernon T. Farewell, 2009. "Likelihood estimation for a longitudinal negative binomial regression model with missing outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(3), pages 369-382.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:3:p:369-382
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    1. Odd O. Aalen & Johan Fosen & Harald Weedon-Fekjær & Ørnulf Borgan & Einar Husebye, 2004. "Dynamic Analysis of Multivariate Failure Time Data," Biometrics, The International Biometric Society, vol. 60(3), pages 764-773, September.
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    1. repec:eee:joecag:v:3:y:2014:i:c:p:44-49 is not listed on IDEAS

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