SynthesisJournal of clinical epidemiology2023
Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review.
Synthesis in Journal of clinical epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses 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
37 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Quality and performance of machine learning versus logistic regression for predicting IVIG resistance in Kawasaki disease: a PROBAST+AI systematic comparison.BMC medical research methodology · 2026Pooled it
- Preoperative gamma-glutamyltransferase-to-lymphocyte ratio and prognosis after curative hepatectomy for hepatocellular carcinoma: a systematic review and meta-analysis.BMC gastroenterology · 2026Pooled it
- Risk prediction models for depression in patients with coronary heart disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2024Pooled it
- Derivation and Validation of Charlson, Elixhauser and RxRisk Cost-Based Comorbidity Indices: A Register-Based Study of 350,000 Individuals Over 11 Years.PharmacoEconomics · 2026Article
- Development and internal validation of an interpretable machine learning prognostic prediction model for postoperative delirium after esophagectomy.Journal of thoracic disease · 2026Article
- Temperature patterns in suspected pediatric acute appendicitis.Pediatric surgery international · 2026Article
- Comparison of Troponin in a Prehospital Blood Sample and Risk Scores.Diagnostics (Basel, Switzerland) · 2026Article
- Integrating lipometabolic and adiposity indices to enhance risk stratification for metabolic dysfunction-associated steatotic liver disease in type 2 diabetes: clinical utility and interplay between triglyceride-glucose body mass index and low-density lipoprotein cholesterol.Cardiovascular diabetology · 2026Article
- PhenoNMF: A novel multi-layer matrix factorization framework for age-stratified comprehensive phenotypic similarity analysis.BMC medical informatics and decision making · 2026Article
- Determinants of Diabetes Remission and Relapse following Sleeve Gastrectomy and Gastric Bypass: A 2- and 5-Year Follow-Up Study.Obesity facts · 2026Article
- Development and validation of an interpretable machine learning model for predicting portal vein system thrombosis following splenectomy in Wilson's disease.Clinical and experimental medicine · 2026Article
- Machine learning-based prediction of meniscal tears in ACL reconstruction using BMI, time to surgery, injury mechanism, and Tegner activity score: A temporally validated decision tool.Journal of experimental orthopaedics · 2026Article
- Low Skeletal Muscle Mass as a Key Predictor of Postoperative Diet Outcomes in Circumferential Hypopharyngeal Cancer Reconstruction.Laryngoscope investigative otolaryngology · 2026Article
- Machine learning-based prediction of myocardial ischemia-reperfusion injury in patients with ST-segment elevation myocardial infarction: the STEMI-MIRI prediction model.BMC cardiovascular disorders · 2026Article
- Artificial Intelligence driven prediction of multiple outcomes in older adults with coronary heart disease.The Gerontologist · 2026Article
- The Association Between Periconceptional Consumption of Ultra-Processed Food and the Incidence of Adverse Pregnancy Outcomes.Nutrients · 2026Article
- Predictive value of baseline levels and early dynamic changes of ctDNA for 28-day mortality in ICU patients with advanced lung cancer: a prospective single-center cohort study.Frontiers in molecular biosciences · 2026Article
- Influence of Continuous Predictor Modelling Methods on Prediction Stability in Clinical Prediction Model Development: An Empirical Comparison Using Real Clinical Data.Clinical epidemiology · 2026Article
- Using interpretable machine learning model to predict lymph node metastasis in patients with localized prostate cancer.Frontiers in oncology · 2026Article
- Incidence and independent risk factors for postoperative delirium in ICU patients after cardiopulmonary bypass cardiac surgery: a retrospective cohort study.Frontiers in cardiovascular medicine · 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
7 authors.
Funding
Abstract
BACKGROUND AND
objectivesWhen developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model.
methodsWe searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020.
resultsIn total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%).
conclusionThough widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model.
Indexed as
Identifiers
What Socratic 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 Socratic graph.