N-of-1 Design and Its Applications to Personalized Treatment Studies.

N-of-1 trial is a type of clinical trial which has been applied in chronic recurrent conditions that require long-term non-curative treatment. In this type of trials, each patient will be randomly assigned to one of the treatment sequences and repeatedly crossed over two or more treatments of interests. Through this cross-comparing method (cross-over phase), investigator can identify an optimal treatment (medicine or therapy) for the patient and treat the patient with the optimal treatment in an extension phase. This design could efficiently reduce the placebo effect, which is often seen in clinical trials, and maximize the true treatment effect. This type of design has been used in some traditional Chinese medicine (TCM) clinical trials lately. However, it brings some challenges for collecting and analyzing the data. Research on statistical methodology of this type of design is rarely found in the literature. The goal of this research is to discuss the application of the N-of-1 design to personalized treatment studies. We will demonstrate a real study conducted in TCM and present some theoretical and simulation results.

Design and sample size estimation in clinical trials with clustered survival times as the primary endpoint.

Many clinical trials involve the collection of data on the time to occurrence of the same type of multiple events within sample units, in which ordering of events is arbitrary and times are usually correlated. To design a clinical trial with this type of clustered survival times as the primary endpoint, estimating the number of subjects (sampling units) required for a given power to detect a specified treatment difference is an important issue. In this paper we derive a sample size formula for clustered survival data via Lee, Wei and Amato's marginal model. It can be easily used to plan a clinical trial in which clustered survival times are of primary interest. Simulation studies demonstrate that the formula works very well. We also discuss and compare cluster survival time design and single survival time design (for example, time to the first event) in different scenarios.

A sample size adjustment procedure for clinical trials based on conditional power.

When designing clinical trials, researchers often encounter the uncertainty in the treatment effect or variability assumptions. Hence the sample size calculation at the planning stage of a clinical trial may also be questionable. Adjustment of the sample size during the mid-course of a clinical trial has become a popular strategy lately. In this paper we propose a procedure for calculating additional sample size needed based on conditional power, and adjusting the final-stage critical value to protect the overall type-I error rate. Compared to other previous procedures, the proposed procedure uses the definition of the conditional type-I error directly without appealing to an extra special function for it. It has better flexibility in setting up interim decision rules and the final-stage test is a likelihood ratio test.