Designing cost-efficient randomized trials by using flexible recruitment strategies
1 Department of Biostatistics & Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, USA
2 Department of Biostatistics, School of Medicine, Indiana University, 901 West New York Street, Indianapolis, IN 46202, USA
3 Department of Music and Arts Technology, School of Engineering and Technology, Purdue University, Indianapolis, USA
4 Department of Adult Health, School of Nursing, Indiana University, 901 West New York Street, Indianapolis, IN 46202, USA
BMC Medical Research Methodology 2012, 12:106 doi:10.1186/1471-2288-12-106Published: 24 July 2012
Sample size planning for clinical trials is usually based on detecting a target effect size of an intervention or treatment. Explicit incorporation of costs into such planning is considered in this article in the situation where effects of an intervention or treatment may depend on (interact with) baseline severity of the targeted symptom or disease. Because much larger sample sizes are usually required to establish such an interaction effect, investigators frequently conduct studies to establish a marginal effect of the intervention for individuals with a certain level of baseline severity.
We conduct a rigorous investigation on how to determine optimum baseline symptom or disease severity inclusion criteria so that the most cost-efficient design can be used. By using a regression model with an interaction term of treatment by symptom severity, power functions were derived for various levels of baseline symptom severity. Computer algorithms and mathematical optimization were used to determine the most cost-efficient research designs assuming either single- or dual-stage screening procedures.
In the scenarios we considered, impressive cost savings can be achieved by informed selection of baseline symptom severity via the inclusion criteria. Further cost-savings can be achieved if a two stage screening procedure is used and there are some known, relatively inexpensively collected, pre-screening information. The amount of total cost savings are shown to depend on the ratio of the screening and intervention costs. In our investigation, we assumed that: 1) the cost of approaching available subjects for screening is constant, and 2) all variables are normally distributed. There is a need to carry out further investigations with more relaxed assumptions (e.g., skewed data distribution).
As cost becomes a more and more prominent issue in modern clinical trials, cost-saving strategies will become more and more important. Strategies, such as the ones we propose here, can help to minimize costs while maximizing knowledge generation.