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Version: 1.0 | ||
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RestoreWorkspace: Default | ||
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AlwaysSaveHistory: Default | ||
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EnableCodeIndexing: Yes | ||
UseSpacesForTab: Yes | ||
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RnwWeave: Sweave | ||
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--- | ||
title: "Personalizing medical decision making" | ||
subtitle: "Recent advances in prediction model research" | ||
author: "Thomas Debray, PhD" | ||
format: revealjs | ||
engine: knitr | ||
--- | ||
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## Prediction | ||
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Estimate something that is yet unknown | ||
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- Presence of a certain disease (<span style="color:green;">diagnosis</span>) | ||
- Future occurrence of a particular event (<span style="color:green;">prognosis</span>) | ||
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![](Picture 1.png){width=100% height=400} | ||
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## Prediction | ||
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Calculate the absolute risk (probability) for distinct individuals | ||
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**Why?** | ||
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- Identify high-risk individuals | ||
- Identify absolute treatment effect | ||
- Target decision making to individuals | ||
- Causal inference | ||
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![](Picture 2.png){width=100% fig-align="right"} | ||
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## How? | ||
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Calculate the absolute risk (probability) for distinct individuals | ||
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**How?** | ||
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Combine information from multiple predictors | ||
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- Subject characteristics (e.g. age, gender) | ||
- History and physical examination results (e.g. blood pressure) | ||
- Imaging results | ||
- (Bio)markers | ||
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(e.g. coronary plaque) | ||
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## Prediction | ||
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Calculate the absolute risk (probability) for distinct individuals | ||
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![](Picture 3.png){width=100%} | ||
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## Prediction | ||
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Develop a multivariable statistical model | ||
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- Need for patient data from large cohort studies | ||
- Many strategies available | ||
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(Regression, decision trees, neural networks) | ||
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![](Picture 4.png){width=100%} | ||
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## Machine Learning | ||
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- Strong focus on prediction and classification | ||
- Combination of data-driven algorithms | ||
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- Nearest Neighbour | ||
- Recursive Partitioning | ||
- Neural Network | ||
- Support Vector Machine | ||
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- Avoidance of modeling assumptions (e.g. additivity, linearity), resulting in high flexibility | ||
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![](Picture 5.png){width=20% height=70 fig-align="right"} | ||
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# Validation | ||
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## Why do we need external validation? | ||
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- The predictive performance of a model estimated on the development data is often too optimistic | ||
- A prognostic model should provide predictions that are valid outside the specific context of the sample that was used for model development | ||
- How a model was derived is of little importance if it performs well. | ||
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## Causes of poor performance | ||
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- Overfitting | ||
- Invalid predictor effects | ||
- Discrepancies in outcome and predictor assessment | ||
- Differences between study characteristics | ||
- Heterogeneity in case-mix variation | ||
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## What is a “good” model? | ||
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![](Picture 6.png){width=100%} | ||
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## What is a “good” model? | ||
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![](Picture 7.png){width=100%} | ||
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## Current shortcomings of validation studies | ||
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**Why do we need big datasets for external validation?** | ||
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- External validation requires sufficient data | ||
- The predictive performance of a model tends to vary across settings, populations and periods | ||
- Multiple external validation studies are needed to fully appreciate the generalizability of a prediction model | ||
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# Meta-analysis | ||
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## The rise of big data sets | ||
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Data increasingly available for thousands or even millions of patients from multiple practices, hospitals, or countries. | ||
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- Meta-analysis of individual participant data (IPD) from multiple studies | ||
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- Observational studies | ||
- Randomized controlled trials | ||
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- Analyses of databases and registry data containing e-health records | ||
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![](Picture 8.png){width=100% fig-align="right"} | ||
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## Individual Participant Data Meta-analyses | ||
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![](Picture 9.png){width=100%} | ||
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## An illustrative example | ||
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**Wynants et al. previously identified >700 prediction models for coronavirus-19** | ||
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We conducted a meta-analysis of participant-level data from 46 914 patients across 18 countries to externally validate the most promising models for predicting short term mortality | ||
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::: {style="font-size: 0.5em"} | ||
- de Jong VMT, Rousset RZ, Antonio-Villa NE, Buenen AG, Van Calster B, Bello-Chavolla OY, et al. Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis. BMJ. 2022 Jul 12;e069881. | ||
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- Wynants L, Van Calster B, Bonten MMJ, Collins GS, Debray TPA, De Vos M, et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ. 2020 Apr 7;369:BMJ Publishing Group Ltd. | ||
::: | ||
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## An illustrative example | ||
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![](Picture 10.png){width=100%} | ||
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## An illustrative example | ||
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![](Picture 11.png){width=100%} | ||
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## Model development using IPD-MA | ||
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Internal-external cross-validation | ||
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![](Picture 12.png){width=100%} | ||
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## Development and validation of ENCALS | ||
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**Prognosis of amyotrophic lateral disease** | ||
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14 cohort studies (specialized ALS centres) | ||
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- N = 190 to 1,936 per study (total N = 11,475) | ||
- Median follow-up: 97.5 months | ||
- Composite endpoint | ||
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(Non-invasive ventilation for more than 23h/day, or death) | ||
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## Development and validation of ENCALS | ||
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![](Picture 13.png){width=100%} | ||
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## Simple versus complex modelling | ||
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::: {style="font-size: 0.8em"} | ||
- Prediction of heart failure | ||
- A cohort of 871,687 individuals from 225 general practices | ||
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(43,987 events) | ||
- Candidate predictors: <span style="color:green;"> *age*, *sex*, *current smoking*, *ethnicity* </span>(CE, Caucasian ethnicity), index of multiple deprivation (IMD), body mass index (BMI), creatinine level (CL), and total cholesterol (TC). | ||
- Implementation of internal-external cross-validation to develop simple and complex Cox regression models | ||
::: | ||
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::: {style="font-size: 0.5em"} | ||
Takada T, Nijman S, Denaxas S, Snell KIE, Uijl A, Nguyen TL, et al. Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets. J Clin Epidemiol. 2021 Apr 6;137:83–91. | ||
::: | ||
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## Simple versus complex modelling | ||
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Development and validation of several prognostic models | ||
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1. Cox regression model with four predictors (*) as linear terms | ||
2. Cox regression model with eight predictors; including a RCS with three knots for all continuous predictor variables, and interaction terms between all possible combinations of two variables. Estimation involves a ridge penalty term. | ||
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![](Picture 14.png){width=100%} | ||
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## Developing generalizable prediction models | ||
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Stepwise estimation procedure | ||
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::: {style="font-size: 0.7em"} | ||
- Fitting of a pre-specified GLM in each study | ||
- Evaluation of performance using IECV | ||
- Loss = f(overall performance in hold-out studies, between-study variation) | ||
- Expand (or reduce) model until the overall loss no longer decreases | ||
- Implementation in “metamisc” | ||
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https://CRAN.R-project.org/package=metamisc | ||
::: | ||
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![](Picture 15.png){width=100%} | ||
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## Reporting guidelines | ||
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![](Picture 16.png){width=100%} | ||
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# Treatment effect modelling | ||
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## Background | ||
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- Causal treatment effects are estimated at the population level in randomised controlled trials (RCTs) | ||
- However, clinical decision is often to be made at the individual level in practice. | ||
- Individualized absolute treatment effects provide a natural starting point to engage in shared decision making | ||
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::: {style="font-size: 0.5em"} | ||
Nguyen TL, Collins GS, Landais P, Le Manach Y. Counterfactual clinical prediction models could help to infer individualized treatment effects in randomized controlled trials-An illustration with the International Stroke Trial. J Clin Epidemiol. 2020 May 25;125:47–56. | ||
::: | ||
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## Background | ||
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Requirements | ||
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- Move to the absolute risk scale | ||
- Adjust for individual patient characteristics, including | ||
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- Prognostic variables | ||
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predicting outcome risk on reference treatment | ||
- Treatment variables | ||
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with potential for effect modification | ||
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- Consider counterfactual outcomes | ||
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## Background | ||
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An example: **The SYNTAX score II** | ||
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“*The SYNTAX score II is a clinical tool that combines clinical variables with the anatomical SYNTAX score, providing expected 4-year mortality for both coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) — thus recommending either PCI only, CABG only or equipoise in treatment based on long-term mortality*.” | ||
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::: {style="font-size: 0.5em"} | ||
DOI: 10.21037/acs.2018.07.02 | ||
::: | ||
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## Background | ||
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![](Picture 17.png){width=100%} | ||
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## Background | ||
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![](Picture 18.png){width=100%} | ||
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## Methods for treatment effect modelling | ||
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![](Picture 19.png){width=100%} | ||
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## Simulation study (1 interaction) | ||
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![](Picture 20.png){width=100%} | ||
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## Empirical example | ||
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- RCT with 1:1 allocation ratio (N = 512) | ||
- Population: clinically diagnosed acute otitis media (AOM) in children 6 months to 5 years of age | ||
- Intervention: amoxicillin | ||
- Outcome: fever or ear pain was after 3 days’ follow-up | ||
- Baseline data on: treatment received, sex, presence of recurrent AOM, fever, bilateral occurrence, ear pain, presence of a runny nose, cough, tympanic membrane abnormality, and age | ||
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## Empirical example | ||
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![](Picture 21.png){width=100%} | ||
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## Main findings | ||
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- Small RCTs | ||
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- Hard to improve beyond risk-magnification | ||
- However, the price to pay to allow for treatment-covariate interactions was small when using both shrinkage and selection, especially for the hierarchical group lasso | ||
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- Large RCTs | ||
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- Shrinkage and selection still needed | ||
- Allowing for all interactions was beneficial | ||
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project: | ||
title: "Copenhagen2023" | ||
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editor: visual | ||
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slides/Workshop clinical trials/Workshop clinical trials.Rproj
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Version: 1.0 | ||
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RestoreWorkspace: Default | ||
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AlwaysSaveHistory: Default | ||
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EnableCodeIndexing: Yes | ||
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RnwWeave: Sweave | ||
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