Statistical measures, such as the Higgins H test (I²) and τ², can also be used to identify heterogeneity. Heterogeneity can also be identified graphically by analysing whether P < 0.10 (or 0.05), indicating the presence of heterogeneity, and whether there is a large x0 statistic in relation to its degree of freedom. Therefore, it may not be useful when heterogeneity is inevitable. However, it may lack power in meta-analyses with few studies and may identify clinically irrelevant differences. Cochran’s Q-test (heterogeneity test x²) is a commonly used statistical test to detect heterogeneity. Heterogeneity can serve as an important statistical tool when interpreting the forest plot, which is a graphical display of the MA results. However, it is important to understand that heterogeneity is not just a theoretical concept but also has practical implications that should be considered in the analysis. It is appropriate when the studies are diverse and may present variations. In contrast, the random effects model assumes that the treatment effect varies between studies, estimates the average of the effects distribution, and weights by the intra-study and between-study variability. It is appropriate when the studies are similar and do not present significant differences. The fixed effects model assumes that all studies measure the same treatment effect and estimates this unique effect. Generally, two approaches are considered: the fixed effects model and the random effects model. When the time until the event occurs is analysed, but not all individuals in the study experience the event (censored data).Īfter defining the type of data and how their effect measures will be interpreted, the next step is to determine the appropriate statistical analysis to be applied, which will depend on the characteristics of the studies. Given through the count of the number of events that the individual experiences. When the result is given several ordered categories, and these are scored/summed. Where the outcome for each person presents a numerical response (measurement or quantity). Where the outcome for each person only has two possible responses Effect Measures and Type of Data, translated from Higgins et al. There are also ordinal data, counts of rare events, and time-to-event data. Most MAs usually handle dichotomous or continuous data, but they are not the only types of data that exist. Therefore, it is necessary to first review what data is being worked with. Some studies may be fundamental for the systematic review (SR), but due to their characteristics, they may not be able to be included in the MA being conducted.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |