ReviewDeutsches Arzteblatt international2024
One Question, Many Results—Why Epidemiological Studies Yield Heterogeneous Findings. Part 34 of a Series on Evaluation of Scientific Publications.
Review in Deutsches Arzteblatt international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Hypertension in steelworkers: a global systematic review and meta-analysis of prevalence and associated factors.Preventive medicine reports · 2026Review
- The association of insulin resistance and obesity with uterine fibroids in non-diabetic populations: a cross-sectional study.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObservational epidemiological studies often yield different results on the same research question. In this article, we explain how this comes about.
methodsIn this review, which is based on publications retrieved by a selective search in PubMed and the Web of Science, we use information from international publications, simulation studies on sampling error, and a quantitative bias analysis on fictitious data to demonstrate why the results of epidemiological studies are often uncertain, and why it is, therefore, generally necessary to perform more than one study on any particular question.
resultsSampling errors, imprecise measurements, alternative but equally appropriate methods of data analysis, and features of the populations being studied are common reasons why studies on the same question can yield different results. Simulation studies are used to illustrate the fact that effect estimates such as relative risks or odds ratios can deviate markedly from the true value because of sampling error, i.e., by chance alone. Quantitative bias analysis is used to show how strongly effect estimates can be distorted by misclassification of exposures or outcomes. Finally, it is shown through illustrative examples that different but equally appropriate methods of data analysis can lead to divergent study results.
conclusionThe above reasons why epidemiological study results can be heterogeneous are explained in this review. Quantitative bias analyses and sensitivity analyses with alternative data evaluation strategies can help explain divergent results on one and the same question.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.