Contents
When to combine effect size estimates in meta-analysis?
Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs When a meta-analysis on results from experimental studies is conducted, differences in the study design must be taken into consideration.
How are effect measures used to compare outcomes?
There are several different ways of comparing outcome data between two intervention groups (‘effect measures’) for each data type. For example, dichotomous outcomes can be compared between intervention groups using a risk ratio, an odds ratio, a risk difference or a number needed to treat.
When are continuous outcomes are measured using different scales?
Method: the first four trials in table 1 provide the mean, standard deviation, and sample size for each study arm; however, the trials used two different scales. Data from each trial are standardized by dividing the difference in means by the pooled standard deviation (pooled from the intervention and control groups).
What are two approaches to meta-analysis of time to event outcomes?
Two approaches to meta-analysis of time-to-event outcomes are readily available to Cochrane Review authors. The choice of which to use will depend on the type of data that have been extracted from the primary studies, or obtained from re-analysis of individual participant data.
Which is better effect size or effect size?
By specifying an effect size, which is the minimum difference that is worth research attention, researcher could design a study with optimal power rather than wasting resources on trivial effects. The larger the effect size (the difference between the null and alternative means) is, the greater the power of a test is.
The previous lesson mentioned that power is a function of effect size, alpha level and sample size. What is effect size? How is it related to power analysis? How can we determine effect size? Answers to these questions are provided below.