In health related studies, non-inferiority tests are used to demonstrate that a new treatment is not worse than a currently existing treatment by more than a pre-specified margin. In this paper we have proposed a Bayesian approach and compared it with two other methods available in the literature. We discuss three approaches; a Z-score approach, a generalized p-value approach and a Bayesian approach, to test the non-inferiority hypotheses in two-arm trials for ratio of log-normal means. The log-normal distribution is widely used to describe the positive random variables with positive skewness which is appealing for data arising from studies with small sample sizes. We demonstrate the approaches using data arising from an experimental aging study on cognitive penetrability of posture control. We also examine the suitability of three methods under various sample sizes via simulations. The results from the simulation studies indicate that the generalized p-value and the Bayesian approach reach an agreement approximately and the degree of the agreement increases when the sample sizes increase. However, the Z-score approach can produce unsatisfactory results even under large sample sizes.

The research work was conducted with the objective to find the significant metrics for biometrics deployment in e-banking through an assessment of the concerns, opinions and perceptions of biometric technologists regarding the implementation of biometrics in e-banking. This paper is pursued by collecting information through survey of technologists working with biometrics; the technologists are chosen using snowball sampling. Then the results of the surveys are analyzed to find the significant metrics for the deployment of biometrics technology in e-banking. The study suggests that the overall significant metrics for the deployment of Biometric technology in E-Banking with the biometric technologists perspective are Performance, Circumvention Resistance, Collectability, Size and Comparability, Minimum Operational Limitations, Intrusion Level and Portability.

A multiple testing procedure can be a single-step data-independent procedure, such as Bonferroni’s method, or a data-dependent stepwise procedure such as Hochberg’s step-up method and Hommel’s method. It can be an α-exhaustive, where the maximum type-I error rate under all configurations of null hypotheses equals α, or α-conservative, where the type-I error rate falls below the nominal level. We develop a simple one-step a-exhaustive procedure that can improve power 2%-5% over Hochberg’s and Hommel’s methods in common situations when the test statistics are mutually independent. The method can also be generalized to correlated test statistics. In our method we construct the stopping rules using the product of marginal p-values and control the upper bounds of the kth order terms so that α is exhausted for any configuration of k null hypotheses. Such upper bounds are determined progressively from k = 1 towards k = K, the number of null hypotheses in the problem. The method can be used in different multiple testing problems, including adaptive clinical trial designs.