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Rolf H.h. Groenwold and Olaf M Dekkers

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Olaf M Dekkers and Rolf H.h. Groenwold

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 illustrated that can be used to avoid immortal time bias in the analysis phase of the study; a time-dependent analysis to avoid immortal time bias optimizes the use of available information.

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Rolf H H Groenwold and Olaf M Dekkers

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 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.

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Rolf H H Groenwold and Olaf M Dekkers

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 analysis. This may lead to biased results and loss of power. We explain why missing data may lead to bias and discuss a commonly used classification of missing data.

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Rolf H H Groenwold and Olaf M Dekkers

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 covariate adjustment using regression analysis. These aspects should be considered when planning an observational study of causal effects or when assessing the validity of the results of such a study.

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Olaf M Dekkers and Rolf H H Groenwold

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 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.