Using data augmentation to facilitate conduct of phase I–II clinical trials with delayed outcomes

Ick Hoon Jin, Suyu Liu, Peter F. Thall, Ying Yuan

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63 Citations (Scopus)


A practical impediment in adaptive clinical trials is that outcomes must be observed soon enough to apply decision rules to choose treatments for new patients. For example, if outcomes take up to six weeks to evaluate and the accrual rate is one patient per week, on average three new patients will be accrued while waiting to evaluate the outcomes of the previous three patients. The question is how to treat the new patients. This logistical problem persists throughout the trial. Various ad hoc practical solutions are used, none entirely satisfactory. We focus on this problem in phase I–II clinical trials that use binary toxicity and efficacy, defined in terms of event times, to choose doses adaptively for successive cohorts. We propose a general approach to this problem that treats late-onset outcomes as missing data, uses data augmentation to impute missing outcomes from posterior predictive distributions computed from partial follow-up times and complete outcome data, and applies the design’s decision rules using the completed data. We illustrate the method with two cancer trials conducted using a phase I–II design based on efficacy–toxicity trade-offs, including a computer stimulation study. Supplementary materials for this article are available online.

Original languageEnglish
Pages (from-to)525-536
Number of pages12
JournalJournal of the American Statistical Association
Issue number506
Publication statusPublished - 2014 Feb 18

Bibliographical note

Funding Information:
Ick Hoon Jin (E-mail: and Suyu Liu (E-mail: are Postdoctoral Fellows, Peter F. Thall is Professor (E-mail:, and Ying Yuan is Associate Professor (E-mail:, Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX 77030-4009. This research was supported by NIH/NCI Cancer Center Support Grant CA016672 36. Yuan and Jin acknowledge support from NIH grant R01 CA154591. Peter Thall’s research was supported by NIH/NCI grant R01 CA 83932.

Publisher Copyright:
© 2014 American Statistical Association.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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