ArticleBMC medical research methodology2021
Statistical model building: Background "knowledge" based on inappropriate preselection causes misspecification.
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Causal Model Building in the Context of Cardiac Rehabilitation: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Autistic young people's psychological well-being in school.Autism : the international journal of research and practice · 2026Article
- Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning.Plants (Basel, Switzerland) · 2025Article
- Variable selection methods for descriptive modeling.PloS one · 2025Article
- MENTAL HEALTH STATUS AND THIRD PLACES USE AMONG RURAL WORKING-AGE ADULTS IN THE UNITED STATES.Rural society · 2025Article
- Anticholinergic burden and health-related quality of life among adult patients in a resource-limited setting: a cross-sectional study.International journal of clinical pharmacy · 2024Article
- Adaptation and Utilization of a Postmarket Evaluation Model for Digital Contact Tracing Mobile Health Tools in the United States: Observational Cross-sectional Study.JMIR public health and surveillance · 2023Observational
- Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStatistical model building requires selection of variables for a model depending on the model's aim. In descriptive and explanatory models, a common recommendation often met in the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed "background knowledge" truly is. In fact, "known" predictors might be findings from preceding studies which may also have employed inappropriate model building strategies.
methodsWe conducted a simulation study assessing the influence of treating variables as "known predictors" in model building when in fact this knowledge resulting from preceding studies might be insufficient. Within randomly generated preceding study data sets, model building with variable selection was conducted. A variable was subsequently considered as a "known" predictor if a predefined number of preceding studies identified it as relevant.
resultsEven if several preceding studies identified a variable as a "true" predictor, this classification is often false positive. Moreover, variables not identified might still be truly predictive. This especially holds true if the preceding studies employed inappropriate selection methods such as univariable selection.
conclusionsThe source of "background knowledge" should be evaluated with care. Knowledge generated on preceding studies can cause misspecification.
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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.