PQStat - Baza Wiedzy en:statpqpl:metapl

Asymmetry testing

Anonymous (anonymous@undisclosed.example.com) — 2022-03-19T12:36:39+00:00

Asymmetry testing Symmetry in the effects obtained is usually indicative of the absence of publication bias, but it should be kept in mind that many objective factors can disrupt symmetry, e.g., studies with statistically insignificant effects or small studies are often not published, making it much more difficult to reach such results. At the same time, there are no sufficiently comprehensive and universal statistical tools for asymmetry detection. As a result, a significant part of meta-analy…

Summary effect

Anonymous (anonymous@undisclosed.example.com) — 2022-03-19T12:07:39+00:00

Summary effect As a result of the meta-analysis, its most desirable element is to summarize the collected studies, i.e., to report the overall effect, . Such a summary can be done in two ways, by designating a fixed effect or a random effect. *

Heterogeneity testing

Anonymous (anonymous@undisclosed.example.com) — 2022-03-19T12:18:37+00:00

Heterogeneity testing It is difficult to expect every study to end up with exactly the same effect size. Naturally, the results obtained in different papers will be somewhat different. The study of heterogeneity is intended to determine to what extent emerging differences between the results obtained in different papers affect the overall effect constructed in the meta-analysis.

Group comparison

Anonymous (anonymous@undisclosed.example.com) — 2022-03-19T12:47:01+00:00

Group comparison There are situations in which the data collected are of the same effect, performed on the same population, but under slightly different conditions. Suppose that part of the study was performed under condition A and part under condition B. Then it may be interesting to compare the overall effects obtained for each group. Demonstrating differences between overall effects may be the main goal of a meta-analysis, and then it is inadvisable to compound both subgroups simultaneously …

Meta-regression

Anonymous (anonymous@undisclosed.example.com) — 2022-03-19T13:01:00+00:00

Meta-regression Meta-regression analysis is conducted in an analogous manner to the regression analysis described in the section Multiple Regression. In the case of meta-regression, the study objects are the individual studies, their results (e.g., odds ratios, relative risks, differences in means) constitute the dependent variable

Cumulative meta-analysis

Anonymous (anonymous@undisclosed.example.com) — 2022-03-19T12:45:59+00:00

Cumulative meta-analysis The typical purpose of conducting a cumulative meta-analysis is to show how the effect has changed since the last meta-analysis on a topic was conducted/published, or how it has changed over the years. Then chronologically (according to the timeline) more studies are added and the overall effect is calculated each time. Equally important is the cumulative analysis in a study of how the overall effect changes depending on the magnitude of the impact of a selected additio…

Weights of individual studies

Anonymous (anonymous@undisclosed.example.com) — 2022-03-19T12:08:51+00:00

Weights of individual studies The weight of the study depends on the observed variability. For the fixed effect, the variability is due only to sampling error (error within each study) - that is, the size of : For a random effect, variability is due to sampling error (error within each study) - that is, the magnitude of

Introduction

Anonymous (anonymous@undisclosed.example.com) — 2022-03-19T11:58:32+00:00

Introduction The most familiar image associated with meta-analysis is the forest plot showing the results of each study along with a summary. In order for the selected literature to be summarized together, it must be consistent in description and the measures given there must be the same.

Sensitivity testing

Anonymous (anonymous@undisclosed.example.com) — 2022-03-19T12:21:34+00:00

Sensitivity testing The overall effect of a study may change depending on which studies we include and which we exclude from the analysis. It is the responsibility of the researcher to check how sensitive the analysis is to changes in study selection criteria. Checking for sensitivity helps determine the