ArticleInternational journal of epidemiology2021
Reflection on modern methods: generalized linear models for prognosis and intervention-theory, practice and implications for machine learning.
Article in International journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The identification of cognitive impairment in Parkinson's disease using biofluids, neuroimaging, and artificial intelligence.Frontiers in neuroscience · 2024Pooled it
- Explainable Machine Learning Predictive Models for Surgical Site Infections: Scoping Review.Journal of medical Internet research · 2026Article
- Cross-Cultural Adaptation and Psychometric Testing of the Italian Barriers to Nursing Research Participation (I-BNPRQ).Healthcare (Basel, Switzerland) · 2026Article
- Article
- District-Level Dengue Early Warning Prediction System in Bangladesh Using Hybrid Explainable AI and Bayesian Deep Learning.Tropical medicine and infectious disease · 2026Article
- Predicting health-related quality of life two years post-diagnosis across seven cancer types: using machine learning to identify vulnerable patients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Predictors of postoperative recovery using self-reported HRQoL among children undergoing elective surgery.Paediatrics & child health · 2026Article
- Leveraging multivariate approaches to advance the science of early-life adversity.Child abuse & neglect · 2025Review
- Improved intrahospital transport time via proximity-based staff assignments.Journal of the American Medical Informatics Association : JAMIA · 2025Observational
- On the Uses and Abuses of Regression Models: A Call for Reform of Statistical Practice and Teaching.Statistics in medicine · 2025Article
- Simulating hierarchical data to assess the utility of ecological versus multilevel analyses in obtaining individual-level causal effects.BMC medical research methodology · 2025Article
- Use of Stacked Proportional Bar Graphs ("Grotta Bars") in Observational Neurology Research: A Meta-Research Study.Neurology · 2025Article
- Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor.NPJ digital medicine · 2024Article
- Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care.BMC medical informatics and decision making · 2024Article
- Human Substantia Nigra Neurons Encode Reward Expectations.bioRxiv : the preprint server for biology · 2024Article
- Trends in Lower Extremity Artery Disease Repair Incidence, Comorbidity, and Mortality: A Danish Nationwide Cohort Study, 1996-2018.Vascular health and risk management · 2024Article
- Polygenic Propensity for Longevity, APOE-ε4 Status, Dementia Diagnosis, and Risk for Cause-Specific Mortality: A Large Population-Based Longitudinal Study of Older Adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
- The shared genetic architecture of smoking behaviours and psychiatric disorders: evidence from a population-based longitudinal study in England.BMC genomic data · 2023Article
- Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting.Arthritis research & therapy · 2023Article
- Impact of special educational needs provision on hospital utilisation, school attainment and absences for children in English primary schools stratified by gestational age at birth: A target trial emulation study protocol.NIHR open research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Nevertheless, these two concepts are often conflated in practice. We use the framework of generalized linear models (GLMs) to illustrate that predictive and causal queries require distinct processes for their application and subsequent interpretation of results. In particular, we identify five primary ways in which GLMs for prediction differ from GLMs for causal inference: (i) the covariates that should be considered for inclusion in (and possibly exclusion from) the model; (ii) how a suitable set of covariates to include in the model is determined; (iii) which covariates are ultimately selected and what functional form (i.e. parameterization) they take; (iv) how the model is evaluated; and (v) how the model is interpreted. We outline some of the potential consequences of failing to acknowledge and respect these differences, and additionally consider the implications for machine learning (ML) methods. We then conclude with three recommendations that we hope will help ensure that both prediction and causal modelling are used appropriately and to greatest effect in health research.
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.