Following the discussion on the significance of effect sizes, and selecting suitable data entry formats, this next step is to understand different software functionalities through appropriate case studies.
The discussion surrounding meta analysis has effect size at the prime spot. It is the magnitude or size of an effect and usually refers to treatment effect in comprehensive meta analysis (CMA).
The earlier article (Introduction to CMA) gave a brief overview of the various functionalities offered by the CMA software. This article acquaints the user with the procedure of selecting appropriate data entry formats.
Survival analysis is a method under predictive modeling where the dependent variable is time. Therefore, it involves time-to-event prediction modeling. The methodology is that our outcome variable is time until the occurrence of a certain event.
Regression analysis is a statistical tool to study the relationship between variables. These variables are the outcome variable and one or more exposure variables. In other words, regression analysis is an equation which predicts a response from the value of a certain predictor.
Findings in meta-analysis are integrated by the means of effect sizes also known as the currency of meta-analysis. They span the group of ‘indices’. An index is a statistical measure which is a compound representation of the several observations in the study. It gives a general dimension to the phenomenon.
Monte Carlo simulation is an extension of statistical analysis where simulated data is produced. This method uses repeated sampling techniques to generate simulated data.
Variable returns to scale (VRS) is a type of frontier scale used in data envelopment analysis (DEA). It helps to estimate efficiencies whether an increase or decrease in input or outputs does not result in a proportional change in the outputs or inputs respectively (Cooper, Seiford, & Zhu, 2011).