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4. Commonly misunderstood technical terms


Case-control study

A cohort study is based on the follow up of groups defined at baseline concerning events occurring during follow up. When one group includes cases (defined by the exposure to a studied agent or procedure) and another group controls (unexposed subjects), the study design is often incorrectly described as a case-control study. A case-control study is instead based on comparing cases (diagnosed by a studied disease) with controls concerning their exposure history.

Incidence and risk

The words risk and incidence are often used as synonyms. However, incidence can be defined either as cumulative incidence or incidence density. The first definition is the same as the risk, the number of cases developing a disease during a specified follow up relative to the number of subjects at the start of follow up. The latter definition has the same numerator, the number of cases developing a disease during a specified follow up, but the denominator is different, the sum of person-time at risk. While the denominator of the cumulative incidence is just the number of persons at the start of follow up, the denominator of the incidence density can be calculated by summing each individual's time at risk in terms of days, months or years. In large samples, an approximation can be made by using the average number of persons during follow-up multiplied by the length of follow-up in the same terms.


A statistical model is multivariate based on the assumption of a multivariate probability distribution. An ordinary multiple regression model includes multiple explanatory variables but has only one response variable. Therefore, it is not based on a multivariate probability distribution assumption, and it is usually presented as a multivariable model. A multiple regression model with more than one response variable would be multivariate. It could be described as a multivariate multivariable model if it also included more than one explanatory variable.

Primary endpoint, efficacy, adverse events, etc.

The terminology developed for confirmatory trials is increasingly often used in observational studies, in which they have no clear interpretation. For example, an adverse event is generally known as any untoward medical occurrence with a temporal but not necessarily causal relation to the studied treatment. While treatment-related adverse events, complications or side effects may be registered in observational studies, information on all temporally related adverse events is probably not available. Primary and secondary outcomes usually play essential roles in addressing multiplicity issues, but multiplicity issues are not relevant in observational studies, and while efficacy can be studied in an experiment, effectiveness can be studied in observational studies.

Quartiles and other quantiles

Quartiles are the three points that divide an ordered dataset into four quarts. The first and third of these points have one quart of the dataset on one side of it and three quarts on the other; the remaining second quartile, a.k.a. median, has two quarters of the dataset on each side. The quarts are often incorrectly described as quartiles.