Trim-and-fill Trim-and-fill

A seance

The existence of publication bias can alter the results of a meta-analysis. The trim and fill method attempts to calculate an estimate of the effect corrected for bias that may have been introduced by missing studies. The objective is to impute these missing studies and include them in the funnel plot until the asymmetry disappears. Once this extended funnel is achieved, the effect measure is recalculated to obtain an estimate that corrects the effect of small studies.
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Trim-and-fill Trim-and-fill

Behind the scenes

Shapiro-Wilk and Kolmogorov-Smirnov tests are the most commonly used contrast tests to check the goodness of fit to a normal distribution of our data. Its implementation is described step by step and its equivalence in graphical methods such as the theoretical quantile graph and the cumulative density function graph.
Read MoreBehind the scenes
Trim-and-fill Trim-and-fill

The vanishing hitchhiker

The p curve focuses on the significant p values of the primary studies of a meta-analysis and allows us to estimate, in addition to publication bias, whether there may be a real effect after the study, what its magnitude is, and whether there is suspicion of improper practices by researchers to obtain statistically significant values.
Read MoreThe vanishing hitchhiker
Trim-and-fill Trim-and-fill

The tree and the labyrinth

A decision tree is a machine learning model that is used to estimate a target variable based on several input variables. This target variable can be either numerical (regression trees) or nominal (classification trees). The methodology for constructing decision trees for regression and classification is described, as well as their interpretation.
Read MoreThe tree and the labyrinth
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