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Dirichlet process mixture models for the analysis of repeated attempt designs

Author

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  • Michael J. Daniels
  • Minji Lee
  • Wei Feng

Abstract

In longitudinal studies, it is not uncommon to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful provides useful information for the purposes of assessing missing data assumptions. This is because measurements from subjects who provide the data after numerous failed attempts may differ from those who provide the measurement after fewer attempts. Previous models for these designs were parametric and/or did not allow sensitivity analysis. For the former, there are always concerns about model misspecification and for the latter, sensitivity analysis is essential when conducting inference in the presence of missing data. Here, we propose a new approach which minimizes issues with model misspecification by using Bayesian nonparametrics for the observed data distribution. We also introduce a novel approach for identification and sensitivity analysis. We re‐analyze the repeated attempts data from a clinical trial involving patients with severe mental illness and conduct simulations to better understand the properties of our approach.

Suggested Citation

  • Michael J. Daniels & Minji Lee & Wei Feng, 2023. "Dirichlet process mixture models for the analysis of repeated attempt designs," Biometrics, The International Biometric Society, vol. 79(4), pages 3907-3915, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3907-3915
    DOI: 10.1111/biom.13894
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    References listed on IDEAS

    as
    1. Dan Jackson & Ian R. White & Morven Leese, 2010. "How much can we learn about missing data?: an exploration of a clinical trial in psychiatry," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(3), pages 593-612, July.
    2. Angela M. Wood & Ian R. White & Matthew Hotopf, 2006. "Using number of failed contact attempts to adjust for non‐ignorable non‐response," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 525-542, July.
    3. Jason Roy & Michael J. Daniels, 2008. "A General Class of Pattern Mixture Models for Nonignorable Dropout with Many Possible Dropout Times," Biometrics, The International Biometric Society, vol. 64(2), pages 538-545, June.
    4. Jason Roy & Kirsten J. Lum & Bret Zeldow & Jordan D. Dworkin & Vincent Lo Re & Michael J. Daniels, 2018. "Bayesian nonparametric generative models for causal inference with missing at random covariates," Biometrics, The International Biometric Society, vol. 74(4), pages 1193-1202, December.
    5. Antonio R. Linero & Michael J. Daniels, 2015. "A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies With Nonignorable Missingness With Application to an Acute Schizophrenia Clinical Trial," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 45-55, March.
    6. Michael J. Daniels & Joseph W. Hogan, 2000. "Reparameterizing the Pattern Mixture Model for Sensitivity Analyses Under Informative Dropout," Biometrics, The International Biometric Society, vol. 56(4), pages 1241-1248, December.
    7. repec:bla:biomet:v:71:y:2015:i:4:p:1160-1167 is not listed on IDEAS
    8. Jason Roy, 2003. "Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model," Biometrics, The International Biometric Society, vol. 59(4), pages 829-836, December.
    9. Haiqun Lin & Charles E. McCulloch & Robert A. Rosenheck, 2004. "Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal Studies," Biometrics, The International Biometric Society, vol. 60(2), pages 295-305, June.
    Full references (including those not matched with items on IDEAS)

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