Sensitivity Analysis Tools for Clinical Trials with Missing Data [Methods Study], 2013-2018 (ICPSR 39492)

Version Date: Sep 15, 2025

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Principal Investigator(s):
Daniel O. Scharfstein, Johns Hopkins University

https://doi.org/10.3886/ICPSR39492.v1

Version V1

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Summary

Clinical trials study the effects of medical treatments, like how safe they are and how well they work. But most clinical trials don't get all the data they need from patients. Patients may not answer all questions on a survey, or they may drop out of a study after it has started. The missing data can affect researchers' ability to detect the effects of treatments.

To address the problem of missing data, researchers can make different guesses based on why and how data are missing. Then they can look at results for each guess. If results based on different guesses are similar, researchers can have more confidence that the study results are accurate. In this study, the research team created new methods to do these tests and developed software that runs these tests.

To access the sensitivity analysis methods and software, please visit the MissingDataMatters website.

Citation

Scharfstein, Daniel O. Sensitivity Analysis Tools for Clinical Trials with Missing Data [Methods Study], 2013-2018. Inter-university Consortium for Political and Social Research [distributor], 2025-09-15. https://doi.org/10.3886/ICPSR39492.v1

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Funding

Patient-Centered Outcomes Research Institute (PCORI) (ME-1303-6016)

Subject Terms

Geographic Coverage

Distributor(s)

Inter-university Consortium for Political and Social Research
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Time Period(s)

2013 -- 2018
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Study Purpose

To develop and test new statistical methods and software for global sensitivity analyses in clinical trials with missing data. The study aims were the following:

  1. create unified and coherent methods for global sensitivity analysis of clinical trials with monotone and non-monotone missing data;
  2. develop free, open source and reproducible software in SAS and R to implement the methods;
  3. demonstrate the methods and software using clinical trial data with patient-centered outcomes
  4. disseminate the methods and software

Study Design

Sensitivity analyses allow researchers to have greater confidence in their results if their study has missing data. These analyses involve fitting a series of models using varying assumptions and then evaluating how these assumptions would influence treatment effect estimates. If results are similar across different models, researchers have more confidence that results are robust and not seriously biased by missing data. Currently few sensitivity analysis methods can address either

  • Monotone missing data, or data that are missing after first missed assessment
  • Non-monotone missing data, or data that are missing at one assessment but observed at a later assessment

In this study, the research team wanted to develop and test new statistical methods and software for global sensitivity analyses in clinical trials with monotone and non-monotone missing data.

An advisory panel of 15 experts in biostatistics, software development, evidence-based medicine, and patient-reported outcomes helped address technical issues when creating the software.

Data Source

Clinical trials with missing patient-reported outcomes data: The Quetiapine Bipolar trial

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Original Release Date

2025-09-15

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Notes

  • The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.