Background The objective of this article is to evaluate the pharmacokinetics, efficacy and safety of lixisenatide (subcutaneous injection) in elderly (≥65 years old) and very elderly (≥75 years old) patients with type 2 diabetes mellitus.

During a new drug development process, it is desirable to timely detect potential safety signals. For this purpose, repeated meta-analyses may be performed sequentially on accumulating safety data. Moreover, if the amount of safety data from the originally planned program is not enough to ensure adequate power to test a specific hypothesis (e.g., the noninferiority hypothesis of an event of interest), the total sample size may be increased by adding new studies to the program. Without appropriate adjustment, it is well known that the type I error rate will be inflated because of repeated analyses and sample size adjustment. In this paper, we discuss potential issues associated with adaptive and repeated cumulative meta-analyses of safety data conducted during a drug development process. We consider both frequentist and Bayesian approaches. A new drug development example is used to demonstrate the application of the methods.

The Food and Drug Administration (FDA) guidance for evaluating cardiovascular (CV) risk in new antidiabetic therapies to treat type 2 diabetes released in December 2008 recommends that sponsors conduct appropriate data analysis to rule out CV safety concerns for drugs treating type 2 diabetes. CV trials of antidiabetic drugs and drugs of other indications for chronic conditions are usually large-scale/long-term trials and can be designed as adaptive noninferiority/superiority trials. In these trials, treatment effect may not manifest immediately after patients take study medication and there will be a dilution in treatment effect after treatment discontinuation. These factors should be taken into account for more precise planning of study sample size and timeline. In this paper, we first derive closed-form formulas for the number of events and total exposure as functions of many other trial parameters. We then outline some considerations for the design of an adaptive noninferiority/superiority CV trial based on ideas of other authors. We also use an example to illustrate the application of the methods.

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