What are moderator variables in meta-analysis?
What are moderator variables in meta-analysis?
A moderator is a third variable that conditions the relations between 2 others. Because effect sizes are the relations between 2 variables, any variable that predicts the effect sizes is a moderator.
What is a meta-analysis simple definition?
Meta-analysis is a quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about that body of research. Typically, but not necessarily, the study is based on randomized, controlled clinical trials.
What is meta-regression in meta-analysis?
Meta-regression is defined to be a meta-analysis that uses regression analysis to combine, compare, and synthesize research findings from multiple studies while adjusting for the effects of available covariates on a response variable.
What is the difference between meta regression and subgroup analysis?
A subgroup anal- ysis is performed when the characteristic of interest is a categorical variable (eg, design of the trial as randomized controlled trial or clinical controlled trial). A meta- regression analysis is performed when the characteristic of interest is a metric variable (eg, sample size of the tri- als).
What are categorical variables?
Show page numbers. Categorical variables are qualitative data in which the values are assigned to a set of distinct groups or categories. These groups may consist of alphabetic (e.g., male, female) or numeric labels (e.g., male = 0, female = 1) that do not contain mathematical information beyond the frequency counts related to group membership.
When should meta regression not be used in a meta analysis?
Meta-regression should generally not be considered when there are fewer than ten studies in a meta-analysis. Meta-regressions are similar in essence to simple regressions, in which an outcome variable is predicted according to the values of one or more explanatory variables.
How do I choose the right data for a meta-analysis?
It is important to be familiar with the type of data (e.g. dichotomous, continuous) that result from measurement of an outcome in an individual study, and to choose suitable effect measures for comparing intervention groups. Most meta-analysis methods are variations on a weighted average of the effect estimates from the different studies.
What is the problem of missing data in meta-analysis?
The problem of missing data is one of the numerous practical considerations that must be thought through when undertaking a meta-analysis. In particular, review authors should consider the implications of missing outcome data from individual participants (due to losses to follow-up or exclusions from analysis) (see Section 10.12 ).
