The medical research literature is abundant with regression analyses that include multiple covariates, so-called multivariable regression models. Despite their common application, the... Show moreThe 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 distinctions between different interpretations, we describe several possible research objectives for which a multivariable regression analysis might be an appropriate way of analyzing the data. In addition, we describe caveats in the interpretation of results of multivariable regression analysis. Show less
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... Show moreElectronic 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' does not necessarily provide valuable real-world evidence. This article discusses some of the main differences between EHR data and data collected primarily for research purposes, and the challenges encountered when using EHR data for research. It also offers suggestions on how to deal with these challenges based on worked-out examples. It therefore serves as a quick guide for researchers interested in either reading or performing EHR-based research. Show less
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... Show moreThere 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 statistical one but largely depends on the research aims. Importantly, one should always inspect the data before using either of the two methods. Show less
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... Show moreThe 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 observational studies, it should further be decided whether it is cross-sectional or whether follow-up time is taken into account (cohort or case-control study). The distinction prospective-retrospective has two disadvantages: prospective is often seen as marker of higher quality, which is not necessarily true; there is no unifying definition that makes a proper distinction between retrospective and prospective possible. Show less
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,... Show moreThe 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 relation under investigation. We discuss various aspects of measurement error and argue that often an in-depth discussion is needed to appropriately assess the quality and validity of a study. Show less
