Evidence Based Medicine pills
Evidence Based Medicine pills
Contrary to what it could be supposed, the inclusion of a large number of variables in a linear regression model can be counterproductive to its performance, producing overfitting of the data and decreasing the capacity for generalization. This is known as the curse of multidimensionality.
Visual manipulation of data using poorly designed charts can distort data interpretation. The most common errors, such as missing axes, manipulated scales, and confusing pie charts, are described, which can lead to erroneous conclusions. Learning to detect these errors will allow us to improve our ability to visually analyze and interpret data.
Overfitting occurs when an algorithm over-learns the details of the training data, capturing not only the essence of the relationship between them, but also the random noise that will always be present. This negatively affects its performance and its ability to generalize when we introduce new data, not seen during training.
The Egger’s test is the most popular quantitative method to assess funnel plot asymmetry. It is based on a linear regression model between the effect measurement and the precision of the studies. A non-zero intercept value indicates asymmetry in the funnel plot probably due to a probable publication bias.
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.