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Design, statistical analysis and sample size calculation of a phase IIb/III study of linagliptin versus voglibose and placebo.

ABSTRACT: Many patients with diabetes mellitus (DM) require a combination of antidiabetic drugs with complementary mechanisms of action to lower their hemoglobin A1c levels to achieve therapeutic targets and reduce the risk of cardiovascular complications. Linagliptin is a novel member of the dipeptidyl peptidase-4 (DPP-4) inhibitor class of antidiabetic drugs. DPP-4 inhibitors increase incretin (glucagon-like peptide-1 and gastric inhibitory polypeptide) levels, inhibit glucagon release and, more importantly, increase insulin secretion and inhibit gastric emptying. Currently, phase III clinical studies with linagliptin are underway to evaluate its clinical efficacy and safety. Linagliptin is expected to be one of the most appropriate therapies for Japanese patients with DM, as deficient insulin secretion is a greater concern than insulin resistance in this population. The number of patients with DM in Japan is increasing and this trend is predicted to continue. Several antidiabetic drugs are currently marketed in Japan; however there is no information describing the effective dose of linagliptin for Japanese patients with DM.This prospective, randomized, double-blind study will compare linagliptin with placebo over a 12-week period. The study has also been designed to evaluate the safety and efficacy of linagliptin by comparing it with another antidiabetic, voglibose, over a 26-week treatment period. Four treatment groups have been established for these comparisons. A phase IIb/III combined study design has been utilized for this purpose and the approach for calculating sample size is described.This is the first phase IIb/III study to examine the long-term safety and efficacy of linagliptin in diabetes patients in the Japanese population.

SUBMITTER: Horie Y

PROVIDER: S-EPMC2743680 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

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Design, statistical analysis and sample size calculation of a phase IIb/III study of linagliptin versus voglibose and placebo.

Horie Yoshiharu Y Hayashi Naoyuki N Dugi Klaus K Takeuchi Masahiro M

Trials 20090905

<h4>Background</h4>Many patients with diabetes mellitus (DM) require a combination of antidiabetic drugs with complementary mechanisms of action to lower their hemoglobin A1c levels to achieve therapeutic targets and reduce the risk of cardiovascular complications. Linagliptin is a novel member of the dipeptidyl peptidase-4 (DPP-4) inhibitor class of antidiabetic drugs. DPP-4 inhibitors increase incretin (glucagon-like peptide-1 and gastric inhibitory polypeptide) levels, inhibit glucagon releas ...[more]

PMID: 19732457

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Project description:BackgroundMulti-centre randomized controlled clinical trials play an important role in modern evidence-based medicine. Advantages of collecting data from more than one site are numerous, including accelerated recruitment and increased generalisability of results. Mixed models can be applied to account for potential clustering in the data, in particular when many small centres contribute patients to the study. Previously proposed methods on sample size calculation for mixed models only considered balanced treatment allocations which is an unlikely outcome in practice if block randomisation with reasonable choices of block length is used.MethodsWe propose a sample size determination procedure for multi-centre trials comparing two treatment groups for a continuous outcome, modelling centre differences using random effects and allowing for arbitrary sample sizes. It is assumed that block randomisation with fixed block length is used at each study site for subject allocation. Simulations are used to assess operation characteristics such as power of the sample size approach. The proposed method is illustrated by an example in disease management systems.ResultsA sample size formula as well as a lower and upper boundary for the required overall sample size are given. We demonstrate the superiority of the new sample size formula over the conventional approach of ignoring the multi-centre structure and show the influence of parameters such as block length or centre heterogeneity. The application of the procedure on the example data shows that large blocks require larger sample sizes, if centre heterogeneity is present.ConclusionUnbalanced treatment allocation can result in substantial power loss when centre heterogeneity is present but not considered at the planning stage. When only few patients by centre will be recruited, one has to weigh the risk of imbalance between treatment groups due to large blocks and the risk of unblinding due to small blocks. The proposed approach should be considered when planning multi-centre trials.

Project description:Inference on disease dynamics is typically performed using case reporting time series of symptomatic disease. The inferred dynamics will vary depending on the reporting patterns and surveillance system for the disease in question, and the inference will miss mild or underreported epidemics. To eliminate the variation introduced by differing reporting patterns and to capture asymptomatic or subclinical infection, inferential methods can be applied to serological data sets instead of case reporting data. To reconstruct complete disease dynamics, one would need to collect a serological time series. In the statistical analysis presented here, we consider a particular kind of serological time series with repeated, periodic collections of population-representative serum. We refer to this study design as a serial seroepidemiology (SSE) design, and we base the analysis on our epidemiological knowledge of influenza. We consider a study duration of three to four years, during which a single antigenic type of influenza would be circulating, and we evaluate our ability to reconstruct disease dynamics based on serological data alone. We show that the processes of reinfection, antibody generation, and antibody waning confound each other and are not always statistically identifiable, especially when dynamics resemble a non-oscillating endemic equilibrium behavior. We introduce some constraints to partially resolve this confounding, and we show that transmission rates and basic reproduction numbers can be accurately estimated in SSE study designs. Seasonal forcing is more difficult to identify as serology-based studies only detect oscillations in antibody titers of recovered individuals, and these oscillations are typically weaker than those observed for infected individuals. To accurately estimate the magnitude and timing of seasonal forcing, serum samples should be collected every two months and 200 or more samples should be included in each collection; this sample size estimate is sensitive to the antibody waning rate and the assumed level of seasonal forcing.

Dataset Information

Design, statistical analysis and sample size calculation of a phase IIb/III study of linagliptin versus voglibose and placebo.

Publications

Design, statistical analysis and sample size calculation of a phase IIb/III study of linagliptin versus voglibose and placebo.

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