Investigating heterogeneity in meta-analysis of studies with rare events

In many meta-analyses, the variable of interest is frequently a count outcome reported in an intervention and a control group. Single- or double-zero studies are often observed in this type of data. Given this setting, the well-known Cochran’s Q statistic for testing homogeneity becomes undefined. In this paper, we propose two statistics for testing homogeneity of the risk ratio, particularly for application in the case of rare events in meta-analysis. The first one is a chi-square type statistic. It is constructed based on information of the conditional probability of the number of events in the treatment group given the total number of events. The second one is a likelihood ratio statistic, derived from the logistic regression models allowing fixed and random effects for the risk ratio. Both proposed statistics are well defined even in the situation of single-zero studies. In a simulation study, the proposed tests show a performance better than the traditional test in terms of type I error and power of the test under common and rare event situations. However, as the performance of the two newly proposed tests is still unsatisfactory in the very rare events setting, we suggest a bootstrap approach that does not rely on asymptotic distributional theory and it is shown that the bootstrap approach performs well in terms of type I error. Furthermore, a number of empirical meta-analyses are used to illustrate the methods.

Statistical Inference on the Basis of Sequential Order Statistics under a Linear Trend for Conditional Proportional Hazard Rates

This paper deals with systems consisting of independent and heterogeneous exponential components. Since failures of components may change lifetimes of surviving components because of load sharing, a linear trend for conditionally proportional hazard rates is considered. Estimates of parameters, both point and interval estimates, are derived on the basis of observed component failures for s(≥ 2) systems. Fisher information matrix of the available data is also obtained which can be used for studying asymptotic behaviour of estimates. The generalized likelihood ratio test is implemented for testing homogeneity of s systems. Illustrative examples are also given.

The effect of behavioral rehearsal technique through group counseling for overcoming social anxiety of students

This research examined the influence of group counseling using behavioral rehearsal techniques to overcome students’ social anxiety. This research used the quantitative method with a quasi-experimental design namely Matching-Only Design. The study involved 30 students for the control group and 30 students for the experimental group selected through purposive sampling. Data were collected using a questionnaire that had been validated. They were then analyzed using normality testing, homogeneity, data nominality testing, gain score and hypothesis testing. The findings of this research indicated that group counseling through behavioral rehearsal techniques could overcome the problem of social anxiety experienced by students. In the pretest, the research showed that the social anxiety level of students in the experimental group was in the high category, and the control group was in the medium category. While the posttests of both experimental and control groups were in the very low category. Behavioral rehearsal techniques should be informational and practical in the development of knowledge, especially in dealing with the problems of social anxiety.