ReviewDeutsches Arzteblatt international2026
Missing Values in Empirical Research: Theory and Practice. Part 39 of a Series on the Evaluation of Scientific Publications.
Review in Deutsches Arzteblatt international, 2026. 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.
- Missing Values Below the Detection Limit.Deutsches Arzteblatt international · 2026Article
- In Reply.Deutsches Arzteblatt international · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundValues that are missing because of drop-outs and other reasons are a major challenge in clinical and epidemiological studies. If not dealt with appropriately, missing values can impair the validity of study findings, distort effect estimates, and reduce statistical power. In this article, we present statistical methods of dealing with missing values in the assessment of scientific publications and compare their suitability for minimizing distortion and improving the precision of estimates.
methodsA variety of methods of dealing with missing values are presented and discussed on the basis of publications retrieved by a selective search, as well as examples from the authors' personal experience.
resultsWhen reading a scientific article, one should ascertain how missing values are dealt with, what assumptions are made, and what methods are applied. The underlying mechanisms-missing completely at random [MCAR], missing at random [MAR], and missing not at random [MNAR]-determine the choice of suitable analytic methods. The exclusion of incomplete observations causes distortion except in the case of an MCAR mechanism. Simple imputation methods, such as mean or regression imputation, generally lead to an underestimate of variance, because they neglect uncertainties. In contrast, in the case of an MAR mechanism, multiple imputation yields reliable results, as it replaces missing values multiple times and thereby takes proper account of estimation uncertainties.
conclusionMultiple imputation is an effective method to minimize distortion caused by missing values, but it requires a meticulous examination of the underlying assumptions and of the results.
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.