Methodology Editorials

Although many papers of medical research report on a treatment effect, it is not always clear what is exactly meant by that effect. An estimand is a precise definition of a treatment effect and includes 5 attributes: population, treatment, (outcome) variable, intercurrent events, and summary ...

Selection in clinical research does not necessarily result in selection bias . To understand when selection leads to bias, we discuss collider-conditioning bias, which is a common and often self-inflicted type of selection bias. Collider-conditioning bias may be difficult to recognize, and paying ...

Researchers frequently come across sample size calculations in the scientific literature they read, in projects undertaken by their peers, and likely within their own work. However, despite its ubiquity, calculating a sample size is often perceived as a hurdle and not fully understood. This paper ...

Propensity score methods are popular to control for confounding in observational biomedical studies of risk factors or medical treatments. This paper focused on aspects of propensity score methods that often remain undiscussed, including unmeasured confounding, missing data, variable selection, ...

Directed acyclic graphs (DAGs), or causal diagrams, are graphical representations of causal structures that can be used in medical research to understand and illustrate potential bias, including bias arising from confounding, selection, and misclassification. Further, they provide guidance for ...

For many statistical methods that are commonly used in medical research, it is assumed that observations are independent. However, when this assumption is violated, alternative methods may be needed. In this paper, we describe why an analysis that ignores the dependencies within the data may ...

In this methodology editorial, the problem of censored survival (or time-to-event) data in clinical research is discussed. In case of censored observations, part of the observation time for some participants in a study is unobserved, which means that measures such as an average survival time cannot ...

The medical research literature is abundant with regression analyses that include multiple covariates, so-called multivariable regression models. Despite their common application, the interpretation of their results is not always clear or claimed interpretations are not justified. To outline the ...

Electronic health record (EHR) data not only offer many exciting research opportunities but also come with their own inherent limitations. Researchers may not always realise the challenges associated with the use of EHR data for research, or the fact that using large datasets of ‘real-world data’ ...

There are different ways to quantify the relation between two or more continuous variables. Some researchers use correlation coefficients; others will apply regression methods such as linear regression. In this paper, we show that the choice between correlation and regression is not purely a ...

In almost all medical research, more than a single hypothesis is being tested or more than a single relation is being estimated. Testing multiple hypotheses increases the risk of drawing a false-positive conclusion. We briefly discuss this phenomenon, which is often called multiple testing. Also, ...

Diagnostic accuracy studies are fundamental for the assessment of diagnostic tests. Researchers need to understand the implications of their chosen design, opting for comparative designs where possible. Researchers should analyse test accuracy studies using the appropriate methods, acknowledging ...

Immortal time bias should always be considered in an observational study if exposure status is determined based on a measurement or event that occurs after baseline. This bias can lead to an overestimation of an effect, but also to an underestimation, which is explained. Several approaches are ...

The name of the study should properly reflect the actual conduct and analysis of the study. This short paper provides guidance on how to properly name the study design. The first distinction is between a trial (intervention given to patients to study its effect) and an observational study. For ...

The validity of clinical research is potentially threatened by missing data. Any variable measured in a study can have missing values, including the exposure, the outcome, and confounders. When missing values are ignored in the analysis, only those subjects with complete records will be included in ...

The validity of any biomedical study is potentially affected by measurement error or misclassification. It can affect different variables included in a statistical analysis, such as the exposure, the outcome, and confounders, and can result in an overestimation as well as in an underestimation of ...

The results of observational studies of causal effects are potentially biased due to confounding. Various methods have been proposed to control for confounding in observational studies. Eight basic aspects of confounding adjustment are described, with a focus on correction for confounding through ...

When statistically comparing outcomes between two groups, researchers have to decide whether to use parametric methods, such as the t -test, or non-parametric methods, like the Mann–Whitney test. In endocrinology, for example, many studies compare hormone levels between groups, or at different ...