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Local influence diagnostics for incomplete overdispersed longitudinal counts

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  • Trias Wahyuni Rakhmawati
  • Geert Molenberghs
  • Geert Verbeke
  • Christel Faes

Abstract

We develop local influence diagnostics to detect influential subjects when generalized linear mixed models are fitted to incomplete longitudinal overdispersed count data. The focus is on the influence stemming from the dropout model specification. In particular, the effect of small perturbations around an MAR specification are examined. The method is applied to data from a longitudinal clinical trial in epileptic patients. The effect on models allowing for overdispersion is contrasted with that on models that do not.

Suggested Citation

  • Trias Wahyuni Rakhmawati & Geert Molenberghs & Geert Verbeke & Christel Faes, 2016. "Local influence diagnostics for incomplete overdispersed longitudinal counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1722-1737, July.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1722-1737
    DOI: 10.1080/02664763.2015.1117594
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    References listed on IDEAS

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    1. Geert Molenberghs & Caroline Beunckens & Cristina Sotto & Michael G. Kenward, 2008. "Every missingness not at random model has a missingness at random counterpart with equal fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 371-388, April.
    2. Verbeke, Geert & Lesaffre, Emmanuel, 1997. "The effect of misspecifying the random-effects distribution in linear mixed models for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 23(4), pages 541-556, February.
    3. Ivy Jansen & Geert Molenberghs & Marc Aerts & Herbert Thijs & Kristel Van Steen, 2003. "A Local Influence Approach Applied to Binary Data from a Psychiatric Study," Biometrics, The International Biometric Society, vol. 59(2), pages 410-419, June.
    4. Geert Verbeke & Geert Molenberghs & Herbert Thijs & Emmanuel Lesaffre & Michael G. Kenward, 2001. "Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach," Biometrics, The International Biometric Society, vol. 57(1), pages 7-14, March.
    5. Molenberghs, Geert & Verbeke, Geert & Thijs, Herbert & Lesaffre, Emmanuel & Kenward, Michael G., 2001. "Influence analysis to assess sensitivity of the dropout process," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 93-113, July.
    6. Xiaoyan Shi & Hongtu Zhu & Joseph G. Ibrahim, 2009. "Local Influence for Generalized Linear Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 65(4), pages 1164-1174, December.
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