Combined-Indications Significance Level of Multiple Related Indications Developed Simultaneously.

BACKGROUND: Typically, regulatory requirements include 2 confirmatory studies, each at a 1-sided .025 significance level, for a medicine to be approved for a specific indication. When the same medicine has been approved in related indications, 1 confirmatory study at a 1-sided .025 significance level could constitute adequate evidence of efficacy for a new indication. METHODS: This article does not contain any studies with human or animal subjects performed by any of the authors. For multiple related indications developed simultaneously to constitute sufficient evidence of clinical efficacy, the combined-studies significance level can be set at the same level as if those indications are developed sequentially. RESULTS: This article establishes possible strategies to develop a few related indications at the same time for marketing registration approval, maintaining a desired combined-studies significance level; for example, 1-sided .0000156 for 2 indications, with 1 option having each indication assessed with 1 confirmatory study at .00395 significance level. CONCLUSION: It is possible to develop a few indications at the same time for marketing registration approval, where the combinedstudies significance level is less stringent than that of the usual paradigm with 2 confirmatory studies each at 1-sided .025 significance level for every indication.

Design of randomized controlled confirmatory trials using historical control data to augment sample size for concurrent controls.

This paper deals with the methods to augment concurrent controls (CC) in a randomized controlled trial with available historical data in clinical studies. In their article, Matching with multiple control groups and adjusting for group differences, Stuart and Rubin proposed a matching method where the primary/local control and the secondary/non-local control are both included in the propensity score estimates. The authors discuss a similar approach taking the CC as the primary and the historical control as the secondary, and find that this approach does not save the sample size of the randomized trial compared to the traditional randomized design without supplementation of historical data. A new matching method that saves sample size is proposed, where propensity scores are estimated without the concurrent randomized control patients. A two-stage design is proposed, which allows one to examine the assumption of the new matching method before a commitment of using the matching method in the second stage. Previous clinical trials data is used as an example to illustrate the feasibility of the proposed methods. Simulation studies have been used to investigate operating characteristics of the proposed method.

Clinical Study Design to Assess Both Short- and Long-term Efficacy in Addition to Group Sequential Test on Safety.

In clinical studies for disorders such as rheumatoid arthritis, type 2 diabetes mellitus, multiple sclerosis, osteoporosis, etc, sometimes the developers need to address safety concerns (eg, cardiovascular risk) in the phase III development, so that a large long-term safety study is needed before registration. This article does not contain any studies with human or animal subjects performed by any of the authors. Aiming for potential regulatory approval with a single confirmatory study, the authors suggest a design that assesses short-term efficacy (eg, signs or symptoms) and long-term efficacy (eg, structure or imaging), as well as safety (eg, major adverse cardiac events), for which a group sequential test is performed applying an alpha spending function. A graphical testing procedure is suggested for the data analysis. The testing procedure controls the family-wise type I error rate. The study may reach all or part of short-term efficacy, long-term efficacy, and/or safety objectives. It is possible to get market approval with a single confirmatory study that assesses short-term efficacy, long-term efficacy, and safety.

Performance of intraclass correlation coefficient (ICC) as a reliability index under various distributions in scale reliability studies.

Many published scale validation studies determine inter-rater reliability using the intra-class correlation coefficient (ICC). However, the use of this statistic must consider its advantages, limitations, and applicability. This paper evaluates how interaction of subject distribution, sample size, and levels of rater disagreement affects ICC and provides an approach for obtaining relevant ICC estimates under suboptimal conditions. Simulation results suggest that for a fixed number of subjects, ICC from the convex distribution is smaller than ICC for the uniform distribution, which in turn is smaller than ICC for the concave distribution. The variance component estimates also show that the dissimilarity of ICC among distributions is attributed to the study design (ie, distribution of subjects) component of subject variability and not the scale quality component of rater error variability. The dependency of ICC on the distribution of subjects makes it difficult to compare results across reliability studies. Hence, it is proposed that reliability studies should be designed using a uniform distribution of subjects because of the standardization it provides for representing objective disagreement. In the absence of uniform distribution, a sampling method is proposed to reduce the non-uniformity. In addition, as expected, high levels of disagreement result in low ICC, and when the type of distribution is fixed, any increase in the number of subjects beyond a moderately large specification such as n = 80 does not have a major impact on ICC.

Fluctuation of serum basal insulin levels following single and multiple dosing of insulin glargine.

BACKGROUND: The large fluctuations in blood concentrations and activity observed with insulin therapies such as NPH insulin or insulin ultralente may result in hyper- or hypoglycemia. METHODS: We compared the fluctuations of these insulins with the long-acting basal insulin analog insulin glargine as a re-analysis of three Phase I studies: (I) glargine with NPH or ultralente [single-dose (0.4 IU/kg), randomized study in healthy volunteers (n = 36)]; (II) glargine or NPH [single-dose (0.3 IU/kg), randomized study in patients with diabetes mellitus Type 1 (DMT1) (n = 20)]; and (III) glargine (tailor-made dose) plus insulin lispro in DMT1 (n = 15 over 11 days). Percent deviation around average serum concentration over 24 h (PF24) was used to determine within-patient fluctuation and mean fluctuation value for each treatment group. RESULTS: Mean PF24 in healthy volunteers (Study I) was significantly lower with glargine (19.8%) than with NPH and ultralente (31.9% and 47.2%, respectively; both P < 0.001 vs. glargine). Similarly, about half the fluctuation observed with NPH (PF24 25.8%) was seen with glargine (PF24 14.2%; P < 0.001) in DMT1 (Study II). In ambulatory DMT1 patients receiving multiple glargine doses, PF24 values demonstrated that the same low fluctuations (PF24 20%) were retained throughout near-maintenance treatment (Study III). CONCLUSIONS: Glargine provided less diurnal fluctuation in serum insulin levels than NPH and ultralente in healthy volunteers and patients with DMT1. This lower fluctuation of glargine over NPH or ultralente can help to reduce hyper- or hypoglycemia risks associated with insulin therapy and accordingly encourage achievement of better blood glucose control.