Evidence Based Medicine pills
Evidence Based Medicine pills
The central limit theorem states that if we take a sufficiently large number of random samples from the same population and calculate the mean for each sample, the distribution of those means will tend to follow a normal distribution, regardless of the original distribution of the data. This allows for the safe application of many statistical analyses, such as estimating confidence intervals and hypothesis testing.
The post analyzes the problem of class imbalance in biomedical models and how overall accuracy can become useless when the minority class is the clinically relevant one. It explains which evaluation metrics are most appropriate and outlines the main strategies to handle imbalance, such as oversampling (SMOTE, ADASYN), selective undersampling (Tomek links), and ensemble methods that stabilize performance in low-prevalence scenarios.
The multiple imputation by chained equations (MICE) technique is based on a predictive algorithm that iteratively imputes missing data for a variable based on the values present in the other variables of the dataset. To do this, it is important to ensure that the presence of the missing data does not depend on the variable itself but rather is due to chance or its relationship with other variables.
Occasionally, among the primary studies in a meta-analysis, there may be some that, without estimating an excessively large or small effect, can have a significant influence on the overall estimate of the study, thus compromising its robustness. These are referred to as influential studies, or more commonly, influencers. The distinction between these and outlier or extreme studies is described, as well as the most commonly used methods for detecting them.
The fixed-effect model (singular) is used to combine the primary studies in a meta-analysis when it is assumed that all studies estimate the same population effect. The fixed-effects model (plural), also called mixed-effects model, is useful for subgroup analysis within a meta-analysis, combining aspects of the fixed-effect and the random-effects models.