ArticleDiagnostic and prognostic research2025
A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models for individual risk-part 1: binary outcomes.
Article in Diagnostic and prognostic research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for sarcopenia in maintenance hemodialysis patients: a systematic review and meta-analysis.BMC nephrology · 2025Pooled it
- Critical appraisal of fairness metrics for artificial intelligence-based clinical prediction models: a scoping review.The Lancet. Digital health · 2026Article
- A general sample size framework for developing or updating a predictive algorithm: with application to clinical prediction models.BMC medical research methodology · 2026Article
- A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models - part 3: continuous outcomes.Diagnostic and prognostic research · 2026Article
- Development and validation of a clinical prediction model for postcontrast acute kidney injury in patients with postoperative acute kidney injury of acute Stanford type A aortic dissection.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
14 authors.
Funding
Abstract
backgroundWhen using a dataset to develop or update a clinical prediction model, small sample sizes increase concerns of overfitting, instability, poor predictive performance and a lack of fairness. For models estimating the risk of a binary outcome, previous research has outlined sample size calculations that target low overfitting and a precise overall risk estimate. However, more guidance is needed for targeting precise and fair individual-level risk estimates.
methodsWe propose a decomposition of Fisher's information matrix to help examine sample sizes required for developing or updating a model, aiming for precise and fair individual-level risk estimates. We outline a five-step process for use before data collection or when an existing dataset or pilot study is available. It requires researchers to specify the overall risk in the target population, the (anticipated) distribution of key predictors in the model and an assumed 'core model' either specified directly (i.e. a logistic regression equation is provided) or based on a specified C-statistic and relative effects of (standardised) predictors.
resultsWe produce closed-form solutions that decompose the variance of an individual's risk estimate into the Fisher's unit information matrix, predictor values and the total sample size. This allows researchers to quickly calculate and examine the anticipated precision of individual-level predictions and classifications for specified sample sizes. The information can be presented to key stakeholders (e.g. health professionals, patients, grant funders) to inform target sample sizes for prospective data collection or whether an existing dataset is sufficient. Our proposal is implemented in our new software module pmstabilityss. We provide two real examples and emphasise the importance of clinical context, including any risk thresholds for decision making and fairness checks.
conclusionsOur approach helps researchers examine potential sample sizes required to target precise and fair individual-level predictions when developing or updating prediction models for binary outcomes.
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.