Design of implementation studies for quality improvement programs: an effectiveness-cost-effectiveness framework.
Translational research applies basic science discoveries in clinical and community settings. Implementation research is often limited by tremendous variability among settings; therefore, generalization of findings may be limited. Adoption of a novel procedure in a community practice is usually a local decision guided by setting-specific knowledge. The conventional statistical framework that aims to produce generalizable knowledge is inappropriate for local quality improvement investigations. We propose an analytic framework based on cost-effectiveness of the implementation study design, taking into account prior knowledge from local experts. When prior knowledge does not indicate a clear preference between the new and standard procedures, local investigation should guide the choice. The proposed approach requires substantially smaller sample sizes than the conventional approach. Sample size formulae and general guidance are provided.
Ken Cheung is with the Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY. Naihua Duan is with the Department of Biostatistics and Department of Psychiatry, Columbia University, and is also with Division of Biostatistics, New York State Psychiatric Institute, New York.
Ken
K
Cheung
Naihua
N
Duan
D003362:Cost-Benefit Analysis; D019317:Evidence-Based Medicine; D006801:Humans; D016730:Program Development / Q000191:economics* / Q000379:methods*; D058996:Quality Improvement; D012107:Research Design; D057170:Translational Research, Biomedical