Meta-analysis: Key features, potentials and misunderstandings

Diabetes mellitus: pathophysiological changes and therap

Response to: Letter on immunoassay measurement errors

Diabetes mellitus: pathophysiological changes and therap

Measurement error in clinical research, yes it matters

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.Diabetes mellitus: pathophysiological changes and therap

Missing data: the impact of what is not there

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

When observational studies can give wrong answers: the potential of immortal time bias

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.Clinical epidemiolog

Methodology for the endocrinologist: basic aspects of confounding adjustment

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

How measurements affected by medication use are reported and handled in observational research: a literature review

Purpose In epidemiological research, measurements affected by medication, for example, blood pressure lowered by antihypertensives, are common. Different ways of handling medication are required depending on the research questions and whether the affected measurement is the exposure, the outcome, or a confounder. This study aimed to review handling of medication use in observational research. Methods PubMed was searched for etiological studies published between 2015 and 2019 in 15 high-ranked journals from cardiology, diabetes, and epidemiology. We selected studies that analyzed blood pressure, glucose, or lipid measurements (whether exposure, outcome or confounder) by linear or logistic regression. Two reviewers independently recorded how medication use was handled and assessed whether the methods used were in accordance with the research aim. We reported the methods used per variable category (exposure, outcome, confounder). Results A total of 127 articles were included. Most studies did not perform any method to account for medication use (exposure 58%, outcome 53%, and confounder 45%). Restriction (exposure 22%, outcome 23%, and confounders 10%), or adjusting for medication use using a binary indicator were also used frequently (exposure: 18%, outcome: 19%, confounder: 45%). No advanced methods were applied. In 60% of studies, the methods' validity could not be judged due to ambiguous reporting of the research aim. Invalid approaches were used in 28% of the studies, mostly when the affected variable was the outcome (36%). Conclusion Many studies ambiguously stated the research aim and used invalid methods to handle medication use. Researchers should consider a valid methodological approach based on their research question.Clinical epidemiolog

Exploratory analyses in aetiologic research and considerations for assessment of credibility: mini-review of literature

OBJECTIVETo provide considerations for reporting and interpretation that can improve assessment of the credibility of exploratory analyses in aetiologic research.DESIGNMini-review of the literature and account of exploratory research principles.SETTINGThis study focuses on a particular type of causal research, namely aetiologic studies, which investigate the causal effect of one or multiple risk factors on a particular health outcome or disease. The mini review included aetiologic research articles published in four epidemiology journals in the first issue of 2021: American Journal of Epidemiology, Epidemiology, European Journal of Epidemiology, and International Journal of Epidemiology, specifically focusing on observational studies of causal risk factors of diseases.MAIN OUTCOME MEASURESNumber of exposure-outcome associations reported, grouped by type of analysis (main, sensitivity, and additional).RESULTSThe journal articles reported many exposure-outcome associations: a mean number of 33 (range 1-120) exposure-outcome associations for the primary analysis, 30 (0-336) for sensitivity analyses, and 163 (0-1467) for additional analyses. Six considerations were discussed that are important in assessing the credibility of exploratory analyses: research problem, protocol, statistical criteria, interpretation of findings, completeness of reporting, and effect of exploratory findings on future causal research.CONCLUSIONSBased on this mini-review, exploratory analyses in aetiologic research were not always reported properly. Six considerations for reporting of exploratory analyses in aetiologic research were provided to stimulate a discussion about their preferred handling and reporting. Researchers should take responsibility for the results of exploratory analyses by clearly reporting their exploratory nature and specifying which findings should be investigated in future research and how.Clinical epidemiolog

Helicobacter pylori infection is not correlated with subclinical thrombocytopenia: A cross-sectional study

Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Multiple testing: when 's many too much?

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, methods to mitigate the risk of false-positive conclusions are discussed.Clinical epidemiolog