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Fatigue Sections
Author Bio
Introduction
Fatigue in Medical Illness
Fatigue Defined
Research Questions
Measurement and Assessment
Fatigue Measurement
Related Constructs
Designing Fatigue Surveys
Case Definition
Data Collection
Maximizing Completion
Currently selected section: Designing Intervention Trials
Controlled Trials
Selecting Study Procedures
Issues in Data Analysis
Conclusion




Chapter 9: Fatigue: Issues in Designing Fatigue Intervention Trials
        

You Answered:

Selection CDesign #3 is best because sources of systematic bias can be reduced as diffusion of he intervention is unlikely, and patients are carefully selected and monitored over time.
 

CORRECT

Design #3 addresses the concerns inherent in Design #1, corrects some of the potential sources of bias in Design #2, and favorably increases the period of monitoring. The only patients who will be studied will be those with breast cancer, and treatment diffusion from study patients to comparison patients is eliminated by the collection of comparison data from another site. The trade-offs in these approaches, namely concern about generalizability of the results and concern about the use of different data collectors and pre-existing institutional distinctions, seem worth making given the likelihood of bias otherwise. The sample size is not very large, but there are no data available to inform a power calculation, and the number seems reasonable. Follow-up data are collected at specified time points and the follow-up is 6 months. Like design #2, the recruitment will be done to ensure that there are 50 patients per group with at least 4 months of follow-up. This will again allow both an "intent-to-treat" analysis and a "completer" analysis. The improvements in Design #3, which eliminate the possibility that a different number and timing of outcome assessments will bias the data, suggest that it is most likely to yield interpretable data.

 

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