arXiv subjects

Laure Wynants

Publications and source records attributed to Laure Wynants.

14 recordsLinked to original sources

arXiv2026

Value-of-Information Analysis for External Validation of Risk Prediction Models in Multicenter Studies and Systematic Reviews

External validation studies have finite sample sizes, creating uncertainty about whether a prediction model's Net Benefit (NB) exceeds default strategies' NB. The expected value of perfect information (EVPI) quantifies consequences of uncertainty. Current EVPI methods focus on single studies, ignoring between-center heterogeneity. We extend EVPI and expected value of partial perfect information (EVPPI) to account for between-cluster heterogeneity in multicenter studies and meta-analyses. We distinguish between the global and local optimal strategy and between observed and unobserved clusters. We define EVPIglobal, EVPIcluster_j, EVPIcluster, and EVPPIcluster,prevalence, implemented in the MetaNB R package, and illustrate them using a systematic review across 36 centers of the ADNEX model for ovarian cancer diagnosis. Assuming one global decision regarding ADNEX adoption, there is no need for further data to confirm ADNEX is superior overall (EVPIglobal 0). Meta-analysis borrows information across observed clusters, resulting in consistent local superiority of ADNEX and nonzero but typically lower EVPIcluster_j than when considering local data alone. There is 0.03 probability default strategies are superior in unobserved centers. Eliminating uncertainty on performance and prevalence in each (EVPIcluster) would gain 1134 net avoided false positives (FP) per year, assuming 350000 tumors annually with 20% malignancies. Determining only local prevalence with certainty (EVPPIcluster, prevalence) would gain net 158 avoided FP per year. EVPI extensions disentangle sources of uncertainty and quantify the need for further validation to determine the global or locally optimal strategy. Considering uncertainty and heterogeneity in clinical utility across clusters is essential to decide whether additional validation studies are warranted.

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arXiv2026

The fundamental problem of risk prediction for individuals: health AI, uncertainty, and personalized medicine

Background and Objective: Clinical prediction models are commonly evaluated regarding performance for a population, although decisions are made for individuals. The classic view relates uncertainty in risk estimates for individuals to sample size (estimation uncertainty) while other sources are model uncertainty (variability in modeling choices) and applicability uncertainty (variability in measurement procedures and between populations). We aim to illustrate the uncertainty of prediction models in estimating individual risks with an ovarian cancer example. Methods: We used real and synthetic data for ovarian cancer diagnosis to train 59400 models with variations in estimation, model, and applicability uncertainty. We then used these models to estimate the probability of ovarian cancer in a fixed test set of 100 patients and evaluate the variability in individual estimates. Results: We show empirically that estimation uncertainty can be strongly dominated by model uncertainty and applicability uncertainty, even for models that perform well at the population level. Estimation uncertainty decreased considerably with increasing training sample size, whereas model and applicability uncertainty remained large. Conclusion: Individual risk estimates are far more uncertain than often assumed. Model uncertainty and applicability uncertainty usually remain invisible when prediction models or algorithms are based on a single study. Predictive algorithms should inform, not dictate, care and support personalization through clinician-patient interaction.

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arXiv2025

Clustered Flexible Calibration Plots For Binary Outcomes Using Random Effects Modeling

Evaluation of clinical prediction models across multiple clusters, whether centers or datasets, is becoming increasingly common. A comprehensive evaluation includes an assessment of the agreement between the estimated risks and the observed outcomes, also known as calibration. Calibration is of utmost importance for clinical decision making with prediction models and it may vary between clusters. We present three approaches to take clustering into account when evaluating calibration. (1) Clustered group calibration (CG-C), (2) two-stage meta-analysis calibration (2MA-C) and (3) mixed model calibration (MIX-C) can obtain flexible calibration plots with random effects modelling and providing confidence and prediction intervals. As a case example, we externally validate a model to estimate the risk that an ovarian tumor is malignant in multiple centers (N = 2489). We also conduct a simulation study and synthetic data study generated from a true clustered dataset to evaluate the methods. In the simulation study MIX-C and 2MA-C (splines) gave estimated curves closest to the true overall curve. In the synthetic data study MIX-C produced cluster specific curves closest to the truth. Coverage of the prediction interval across the plot was best for 2MA-C with splines. We recommend using 2MA-C with splines to estimate the overall curve and the 95% PI and MIX-C for the cluster specific curves, especially when sample size per cluster is limited. We provide ready-to-use code to construct summary flexible calibration curves with confidence and prediction intervals to assess heterogeneity in calibration across datasets or centers.

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arXiv2025

Comparing methods for handling missing data in electronic health records for dynamic risk prediction of central-line associated bloodstream infection

Electronic health records (EHR) often contain varying levels of missing data. This study compared different imputation strategies to identify the most suitable approach for predicting central line-associated bloodstream infection (CLABSI) in the presence of competing risks using EHR data. We analyzed 30862 catheter episodes at University Hospitals Leuven (2012-2013) to predict 7-day CLABSI risk using a landmark cause-specific supermodel, accounting for competing risks of hospital discharge and death. Imputation methods included simple methods (median/mode, last observation carried forward), multiple imputation, regression-based and mixed-effects models leveraging longitudinal structure, and random forest imputation to capture interactions and non-linearities. Missing indicators were also assessed alone and in combination with other imputation methods. Model performance was evaluated dynamically at daily landmarks up to 14 days post-catheter placement. The missing indicator approach showed the highest discriminative ability, achieving a mean AUROC of up to 0.782 and superior overall performance based on the scaled Brier score. Combining missing indicators with other methods slightly improved performance, with the mixed model approach combined with missing indicators achieving the highest AUROC (0.783) at day 4, and the missForestPredict approach combined with missing indicators yielding the best scaled Brier scores at earlier landmarks. This suggests that in EHR data, the presence or absence of information may hold valuable insights for patient risk prediction. However, the use of missing indicators requires caution, as shifts in EHR data over time can alter missing data patterns, potentially impacting model transportability.

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arXiv2025

Bayesian sample size calculations for external validation studies of risk prediction models

Contemporary sample size calculations for external validation of risk prediction models require users to specify fixed values of assumed model performance metrics alongside target precision levels (e.g., 95% CI widths). However, due to the finite samples of previous studies, our knowledge of true model performance in the target population is uncertain, and so choosing fixed values represents an incomplete picture. As well, for net benefit (NB) as a measure of clinical utility, the relevance of conventional precision-based inference is doubtful. In this work, we propose a general Bayesian framework for multi-criteria sample size considerations for prediction models for binary outcomes. For statistical metrics of performance (e.g., discrimination and calibration), we propose sample size rules that target desired expected precision or desired assurance probability that the precision criteria will be satisfied. For NB, we propose rules based on Optimality Assurance (the probability that the planned study correctly identifies the optimal strategy) and Value of Information (VoI) analysis. We showcase these developments in a case study on the validation of a risk prediction model for deterioration of hospitalized COVID-19 patients. Compared to the conventional sample size calculation methods, a Bayesian approach requires explicit quantification of uncertainty around model performance, and thereby enables flexible sample size rules based on expected precision, assurance probabilities, and VoI. In our case study, calculations based on VoI for NB suggest considerably lower sample sizes are needed than when focusing on precision of calibration metrics.

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arXiv2025

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide

Dynamic predictive modelling using electronic health record (EHR) data has gained significant attention in recent years. The reliability and trustworthiness of such models depend heavily on the quality of the underlying data, which is, in part, determined by the stages preceding the model development: data extraction from EHR systems and data preparation. In this article, we identified over forty challenges encountered during these stages and provide actionable recommendations for addressing them. These challenges are organized into four categories: cohort definition, outcome definition, feature engineering, and data cleaning. This comprehensive list serves as a practical guide for data extraction engineers and researchers, promoting best practices and improving the quality and real-world applicability of dynamic prediction models in clinical settings.

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arXiv2025

A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes

When developing a clinical prediction model, the sample size of the development dataset is a key consideration. Small sample sizes lead to greater concerns of overfitting, instability, poor performance and lack of fairness. Previous research has outlined minimum sample size calculations to minimise overfitting and precisely estimate the overall risk. However even when meeting these criteria, the uncertainty (instability) in individual-level risk estimates may be considerable. In this article we propose how to examine and calculate the sample size required for developing a model with acceptably precise individual-level risk estimates to inform decisions and improve fairness. We outline a five-step process to be used before data collection or when an existing dataset 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 specified C-statistic and relative effects of (standardised) predictors. We produce closed-form solutions that decompose the variance of an individual's risk estimate into Fisher's unit information matrix, predictor values and total sample size; this allows researchers to quickly calculate and examine individual-level uncertainty interval widths and classification instability for specified sample sizes. Such information can be presented to key stakeholders (e.g., health professionals, patients, funders) using prediction and classification instability plots to help identify the (target) sample size required to improve trust, reliability and fairness in individual predictions. Our proposal is implemented in software module pmstabilityss. We provide real examples and emphasise the importance of clinical context including any risk thresholds for decision making.

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arXiv2024

Performance evaluation of predictive AI models to support medical decisions: Overview and guidance

A myriad of measures to illustrate performance of predictive artificial intelligence (AI) models have been proposed in the literature. Selecting appropriate performance measures is essential for predictive AI models that are developed to be used in medical practice, because poorly performing models may harm patients and lead to increased costs. We aim to assess the merits of classic and contemporary performance measures when validating predictive AI models for use in medical practice. We focus on models with a binary outcome. We discuss 32 performance measures covering five performance domains (discrimination, calibration, overall, classification, and clinical utility) along with accompanying graphical assessments. The first four domains cover statistical performance, the fifth domain covers decision-analytic performance. We explain why two key characteristics are important when selecting which performance measures to assess: (1) whether the measure's expected value is optimized when it is calculated using the correct probabilities (i.e., a "proper" measure), and (2) whether they reflect either purely statistical performance or decision-analytic performance by properly considering misclassification costs. Seventeen measures exhibit both characteristics, fourteen measures exhibited one characteristic, and one measure possessed neither characteristic (the F1 measure). All classification measures (such as classification accuracy and F1) are improper for clinically relevant decision thresholds other than 0.5 or the prevalence. We recommend the following measures and plots as essential to report: AUROC, calibration plot, a clinical utility measure such as net benefit with decision curve analysis, and a plot with probability distributions per outcome category.

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arXiv2024

The continuous net benefit: Assessing the clinical utility of prediction models when informing a continuum of decisions

Clinical prognostic models help inform decision-making by estimating a patient's risk of experiencing an outcome in the future. The net benefit is increasingly being used to assess the clinical utility of models. By calculating an appropriately weighted average of the true and false positives of a model, the net benefit assesses the value added by a binary decision policy obtained when thresholding a model. Although such 'treat or not' decisions are common, prognostic models are also often used to tailor and personalise the care of patients, which implicitly involves the consideration of multiple interventions at different risk thresholds. We extend the net benefit to consider multiple decision thresholds simultaneously, by taking a weighted area under a rescaled version of the net benefit curve, deriving the continuous net benefit. In addition to the consideration of a continuum of interventions, we also show how the continuous net benefit can be used for populations with a range of optimal thresholds for a single treatment, due to individual variations in expected treatment benefit or harm, highlighting limitations of current proposed methods that calculate the area under the decision curve. We showcase the continuous net benefit through two examples of cardiovascular preventive care, comparing two modelling choices using the continuous net benefit. The continuous net benefit informs researchers of the clinical utility of models during selection, development, and validation, and helps decision makers understand their usefulness, improving their viability towards implementation.

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arXiv2024

The expected value of sample information calculations for external validation of risk prediction models

In designing external validation studies of clinical prediction models, contemporary sample size calculation methods are based on the frequentist inferential paradigm. One of the widely reported metrics of model performance is net benefit (NB), and the relevance of conventional inference around NB as a measure of clinical utility is doubtful. Value of Information methodology quantifies the consequences of uncertainty in terms of its impact on clinical utility of decisions. We introduce the expected value of sample information (EVSI) for validation as the expected gain in NB from conducting an external validation study of a given size. We propose algorithms for EVSI computation, and in a case study demonstrate how EVSI changes as a function of the amount of current information and future study's sample size. Value of Information methodology provides a decision-theoretic lens to the process of planning a validation study of a risk prediction model and can complement conventional methods when designing such studies.

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arXiv2024

missForestPredict -- Missing data imputation for prediction settings

Prediction models are used to predict an outcome based on input variables. Missing data in input variables often occurs at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction settings. The algorithm iteratively imputes variables using random forests until a convergence criterion (unified for continuous and categorical variables and based on the out-of-bag error) is met. The imputation models are saved for each variable and iteration and can be applied later to new observations at prediction time. The missForestPredict package offers extended error monitoring, control over variables used in the imputation and custom initialization. This allows users to tailor the imputation to their specific needs. The missForestPredict algorithm is compared to mean/mode imputation, linear regression imputation, mice, k-nearest neighbours, bagging, miceRanger and IterativeImputer on eight simulated datasets with simulated missingness (48 scenarios) and eight large public datasets using different prediction models. missForestPredict provides competitive results in prediction settings within short computation times.

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arXiv2024

Comparison of static and dynamic random forests models for EHR data in the presence of competing risks: predicting central line-associated bloodstream infection

Prognostic outcomes related to hospital admissions typically do not suffer from censoring, and can be modeled either categorically or as time-to-event. Competing events are common but often ignored. We compared the performance of random forest (RF) models to predict the risk of central line-associated bloodstream infections (CLABSI) using different outcome operationalizations. We included data from 27478 admissions to the University Hospitals Leuven, covering 30862 catheter episodes (970 CLABSI, 1466 deaths and 28426 discharges) to build static and dynamic RF models for binary (CLABSI vs no CLABSI), multinomial (CLABSI, discharge, death or no event), survival (time to CLABSI) and competing risks (time to CLABSI, discharge or death) outcomes to predict the 7-day CLABSI risk. We evaluated model performance across 100 train/test splits. Performance of binary, multinomial and competing risks models was similar: AUROC was 0.74 for baseline predictions, rose to 0.78 for predictions at day 5 in the catheter episode, and decreased thereafter. Survival models overestimated the risk of CLABSI (E:O ratios between 1.2 and 1.6), and had AUROCs about 0.01 lower than other models. Binary and multinomial models had lowest computation times. Models including multiple outcome events (multinomial and competing risks) display a different internal structure compared to binary and survival models. In the absence of censoring, complex modelling choices do not considerably improve the predictive performance compared to a binary model for CLABSI prediction in our studied settings. Survival models censoring the competing events at their time of occurrence should be avoided.

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arXiv2024

A comparison of regression models for static and dynamic prediction of a prognostic outcome during admission in electronic health care records

Objective Hospitals register information in the electronic health records (EHR) continuously until discharge or death. As such, there is no censoring for in-hospital outcomes. We aimed to compare different dynamic regression modeling approaches to predict central line-associated bloodstream infections (CLABSI) in EHR while accounting for competing events precluding CLABSI. Materials and Methods We analyzed data from 30,862 catheter episodes at University Hospitals Leuven from 2012 and 2013 to predict 7-day risk of CLABSI. Competing events are discharge and death. Static models at catheter onset included logistic, multinomial logistic, Cox, cause-specific hazard, and Fine-Gray regression. Dynamic models updated predictions daily up to 30 days after catheter onset (i.e. landmarks 0 to 30 days), and included landmark supermodel extensions of the static models, separate Fine-Gray models per landmark time, and regularized multi-task learning (RMTL). Model performance was assessed using 100 random 2:1 train-test splits. Results The Cox model performed worst of all static models in terms of area under the receiver operating characteristic curve (AUC) and calibration. Dynamic landmark supermodels reached peak AUCs between 0.741-0.747 at landmark 5. The Cox landmark supermodel had the worst AUCs (<=0.731) and calibration up to landmark 7. Separate Fine-Gray models per landmark performed worst for later landmarks, when the number of patients at risk was low. Discussion and Conclusion Categorical and time-to-event approaches had similar performance in the static and dynamic settings, except Cox models. Ignoring competing risks caused problems for risk prediction in the time-to-event framework (Cox), but not in the categorical framework (logistic regression).

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arXiv2022

Value of Information Analysis for External Validation of Risk Prediction Models

Background: Before being used to inform patient care, a risk prediction model needs to be validated in a representative sample from the target population. The finite size of the validation sample entails that there is uncertainty with respect to estimates of model performance. We apply value-of-information methodology as a framework to quantify the consequence of such uncertainty in terms of NB. Methods: We define the Expected Value of Perfect Information (EVPI) for model validation as the expected loss in NB due to not confidently knowing which of the alternative decisions confers the highest NB at a given risk threshold. We propose methods for EVPI calculations based on Bayesian or ordinary bootstrapping of NBs, as well as an asymptotic approach supported by the central limit theorem. We conducted brief simulation studies to compare the performance of these methods, and used subsets of data from an international clinical trial for predicting mortality after myocardial infarction as a case study. Results: The three computation methods generated similar EVPI values in simulation studies. In the case study, at the pre-specified threshold of 0.02, the best decision with current information would be to use the model, with an expected incremental NB of 0.0020 over treating all. At this threshold, EVPI was 0.0005 (a relative EVPI of 25%). When scaled to the annual number of heart attacks in the US, this corresponds to a loss of 400 true positives, or extra 19,600 false positives (unnecessary treatments) per year, indicating the value of further model validation. As expected, the validation EVPI generally declined with larger samples. Conclusion: Value-of-information methods can be applied to the NB calculated during external validation of clinical prediction models to provide a decision-theoretic perspective to the consequences of uncertainty.

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