Clinical Study Design

Study design is vital to lead valid scientific discovery. Randomized clinical trial is a typical experimental design in translational sciences that results in causal inference on the efficacy of an investigational drug or device for treating a disease. Randomized clinical trial has become a standard practice in pharmaceutical industry for seeking approval from Food and Drug Administration on any newly developed drug or device. Biostatistics plays an essential role in conducting Randomized clinical trial with respect to calculating sample size to achieve the study power, to develop statistical analysis plan  to assess the efficacy of investigational treatment, and to monitor the trial safety data.

Recent advances in medical research and public health often involve comparative effectiveness research, for which the conventional randomized clinical trial at personal level cannot be practically implemented. Clinical trials, at the community or health facility level, have become a norm in study design. Cluster randomized clinical trial is a typical pragmatic trial that is widely adopted in this type of research project and requires the development of new Biostatistical methods for specific cluster randomized clinical trials, for example, stepped-wedge design.

Our Research:

• Wichman, C., Smith, L., and Yu, F. (2021). A framework for clinical and translational research in the era of rigor and reproducibility. Journal of Clinical and Translation Science. 5(1).
• Yu, F., Hein, NA., Bagenda, DS. (2020), Preventing HIV and HSV-2 through knowledge and attitudes: A replication study of a multicomponent community-based intervention in Zimbabwe, Plos One. DOI: 10.1371/journal.pone.0226237.
• Smith, LM., Hein, NA., Bagenda D. (2019) Cash transfers and HIV/HSV-2 prevalence: A replication of a cluster randomized trial in Malawi. PLoS One. 14(1). doi: 10.1371/journal.pone.0210405.
• Li, J., Zhang, Y., Myers, LJ. And Bravata, DW. (2019) Power calculation in stepped-wedge cluster randomized trial with reduced intervention sustainability effect. Journal Biopharmaceutical Statistics. 29(4): 553-674.
• Zhang, Y. and Clarke, W. (2010). A Flexible Futility Monitoring Method with Time-Varying Conditional Power Boundary. Journal of the Society for Clinical Trials. 7: 209-218.