Evidence Based Medicine pills
Evidence Based Medicine pills
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.
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.
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.
When multiple hypothesis tests are performed, the probability of committing a type 1 error increases, increasing the risk of detecting false positive effects.
The false discovery rate allows to limit the probability of type 1 error when the number of contrasts is very high, also allowing to control the risk of making type 2 errors and failing to detect true positives.
Cochran's Q is a widely used measure to detect heterogeneity between primary studies in a meta-analysis. Its statistical properties and hypothesis testing are reviewed. Finally, other measures calculated from this value are described, such as the I2 statistic and the H2 statistic, frequently used to quantify the intensity of heterogeneity between studies.