| Autor: |
Shortreed, Susan M., Laber, Eric, Scott Stroup, T., Pineau, Joelle, Murphy, Susan A. |
| Zdroj: |
Statistics in Medicine; Oct2014, Vol. 33 Issue 24, p4202-4214, 13p |
| Abstrakt: |
Sequential multiple assignment randomized trials (SMARTs) are increasingly being used to inform clinical and intervention science. In a SMART, each patient is repeatedly randomized over time. Each randomization occurs at a critical decision point in the treatment course. These critical decision points often correspond to milestones in the disease process or other changes in a patient's health status. Thus, the timing and number of randomizations may vary across patients and depend on evolving patient-specific information. This presents unique challenges when analyzing data from a SMART in the presence of missing data. This paper presents the first comprehensive discussion of missing data issues typical of SMART studies: we describe five specific challenges and propose a flexible imputation strategy to facilitate valid statistical estimation and inference using incomplete data from a SMART. To illustrate these contributions, we consider data from the Clinical Antipsychotic Trial of Intervention and Effectiveness, one of the most well-known SMARTs to date. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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