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How much can we learn about missing data?: an exploration of a clinical trial in psychiatry

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  • Dan Jackson
  • Ian R. White
  • Morven Leese

Abstract

Summary. When a randomized controlled trial has missing outcome data, any analysis is based on untestable assumptions, e.g. that the data are missing at random, or less commonly on other assumptions about the missing data mechanism. Given such assumptions, there is an extensive literature on suitable methods of analysis. However, little is known about what assumptions are appropriate. We use two sources of ancillary data to explore the missing data mechanism in a trial of adherence therapy in patients with schizophrenia: carer‐reported (proxy) outcomes and the number of contact attempts. This requires additional assumptions to be made whose plausibility we discuss. Proxy outcomes are found to be unhelpful in this trial because they are insufficiently associated with patient outcome and because the ancillary assumptions are implausible. The number of attempts required to achieve a follow‐up interview is helpful and suggests that these data are unlikely to depart far from being missing at random. We also perform sensitivity analyses to departures from missingness at random, based on the investigators’ prior beliefs elicited at the start of the trial. Wider use of techniques such as these will help to inform the choice of suitable assumptions for the analysis of randomized controlled trials.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssa:v:173:y:2010:i:3:p:593-612
    DOI: 10.1111/j.1467-985X.2009.00627.x
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    References listed on IDEAS

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    1. 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.
    2. Joseph G. Ibrahim & Stuart R. Lipsitz & Nick Horton, 2001. "Using auxiliary data for parameter estimation with non‐ignorably missing outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 361-373.
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    Cited by:

    1. 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.

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