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.

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.

Many diagnostic tests are based on statistical models that predict the probability that a given subject will be positive for that test. Although the ROC curve evaluates the overall performance of the test, the choice of the probability threshold to differentiate between positives and negatives will condition the performance of the test in a given clinical scenario.

The binomial distribution is used when we want to calculate the probability of obtaining a certain number of successes in a series of Bernoulli trials, assuming we already know the probability of success in each trial. In contrast, the beta distribution is used in the opposite situation: we have observed a given number of successes and failures, and we want to estimate how likely each possible value of the success probability is. In other words, it allows us to update our beliefs about that probability based on the data we have collected.