RISKS, IMPACTS, AND MITIGATION OF MISSING EPRO DATA ON CLINICAL TRIALS

Author(s)

Holzbaur E, Ross J
Almac Clinical Technologies, Souderton, PA, USA

OBJECTIVES: Although missing and incomplete responses in ePROs can be minimized through risk assessment and mitigation plans, missing data can have varying implications on clinical trials.  This conceptual paper assesses the impact of missing ePRO data to the trial, taking into account the phase of the trial and intended use of the data. METHODS: Common uses for data gathered via ePRO instruments and diaries are reviewed.  An assessment of the impact of different levels of missing data and associated risks with analyzing data is also performed.   RESULTS: Data gathered via ePRO are frequently used to support primary/secondary trial endpoints.  They are also commonly used for exploratory purposes, allowing sponsors to gather preliminary information to guide the planning of future trials.  Types of data collected may include study medication usage for study drug reconciliation reasons, symptom presence or severity to determine eligibility for trial participation, and responses over time to indicate improvement or worsening of the symptom/disease.  Each data use is assessed for risks to the analyzability of the data associated with different levels of missing data.   For example, in projects with ePRO responses used to support primary/secondary endpoints overall project risk is low when compliance rates are high (e.g. 90-100%).  As compliance rates drop to <80%, bias introduced in the results increases, quality of the data decreases, and risks that the data may not be able to be used in the analysis rises.  Impacts could include a need to recruit additional patients or that the trial may need to be re-run. CONCLUSIONS: Impacts of missing data on clinical trial analysis vary depending on the intended use of the data.  It is important to understand the impact of missing data to the project so that an appropriate plan can be decided upon and included in the protocol.

Conference/Value in Health Info

2014-05, ISPOR 2014, Palais des Congres de Montreal

Value in Health, Vol. 17, No. 3 (May 2014)

Code

PRM135

Topic

Methodological & Statistical Research

Topic Subcategory

Confounding, Selection Bias Correction, Causal Inference

Disease

Multiple Diseases

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