Development and evaluation of prediction models: pitfalls and solutions (Part...BenVanCalster
Slides for the statistics in practice session for the Biometrisches Kolloquium (organized by the Deutsche Region der Internationalen Biometrischen Gesellschaft), 16 March 2021.
Part I from Maarten van Smeden: https://www.slideshare.net/MaartenvanSmeden/development-and-evaluation-of-prediction-models-pitfalls-and-solutions
Development and evaluation of prediction models: pitfalls and solutions (Part...BenVanCalster
Slides for the statistics in practice session for the Biometrisches Kolloquium (organized by the Deutsche Region der Internationalen Biometrischen Gesellschaft), 16 March 2021.
Part I from Maarten van Smeden: https://www.slideshare.net/MaartenvanSmeden/development-and-evaluation-of-prediction-models-pitfalls-and-solutions
How to establish and evaluate clinical prediction models - StatsworkStats Statswork
A clinical prediction model can be used in various clinical contexts, including screening for asymptomatic illness, forecasting future events such as disease, and assisting doctors in their decision-making and health education. Despite the positive effects of clinical prediction models on practice, prediction modeling is a difficult process that necessitates meticulous statistical analysis and sound clinical judgments. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following always on Time, outstanding customer support, and High-quality Subject Matter Experts.
Read More With Us: https://bit.ly/3dxn32c
Why Statswork?
Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics across Methodologies | Wide Range of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
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Are you interested in learning how to prevent hospital readmissions for your diabetic population? It is a popular belief that measuring blood glucose for your diabetic population is the most predictive variable in determining a hospital readmission for a diabetic. However, many providers of care simply do not perform the test on known diabetic patients. This study takes a look at an advanced analytic method that works within the current healthcare providers workflow to looks to identify the likelihood of a future 30-day unplanned readmission before hospital discharge.
Digital platforms could disrupts how pharma companies plan and excecute clini...Jayanthi Repalli, PhD
Pharmaceutical companies spent millions of dollars every year on clinical trials. They are essential part of finding new drugs. However, the lack of participants is the major cause for the delay of trials. Digital platforms could solve this problem for pharm companies and accelerate new drug development. Hope you find this infographic useful. Feel free to drop a note!
This disclaimer informs readers know that the views, thoughts, and opinions expressed in the presentation belong solely to the author, and not to the author’s employer, organization, committee or other group or individual.
The absence of a gold standard: a measurement error problemMaarten van Smeden
Talk about gold standard problems and solutions in medicine and epidemiology. Invited by the department of infectious disease epidemiology, University Medical Center Utrecht
Meta Analysis of Medical Device Data Applications for Designing Studies and R...NAMSA
Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence discusses what meta analysis is as well as the potential benefits.
Austin Ophthalmology is an open access, peer reviewed, scholarly journal dedicated to publish articles covering all areas of Ophthalmology.
The journal aims to promote latest information and provide a forum for doctors, researchers, physicians, and healthcare professionals to find most recent advances in the areas of Ophthalmology. Austin Ophthalmology accepts research articles, reviews, mini reviews, case reports and rapid communication covering all aspects of Ophthalmology.
Austin Ophthalmology strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Austin Publishing Group also brings universally peer reviewed journals under one roof thereby promoting knowledge sharing.
Developing and validating statistical models for clinical prediction and prog...Evangelos Kritsotakis
Talk on clinical prediction models presented at the Joint Seminar Series in Translational and Clinical Medicine organised by the University of Crete Medical School, the Institute of Molecular Biology and Biotechnology of the Foundation for Research and Technology Hellas (IMBB-FORTH), and the University of Crete Research Center (UCRC), Heraklion [online], Greece, April 7, 2021.
How to establish and evaluate clinical prediction models - StatsworkStats Statswork
A clinical prediction model can be used in various clinical contexts, including screening for asymptomatic illness, forecasting future events such as disease, and assisting doctors in their decision-making and health education. Despite the positive effects of clinical prediction models on practice, prediction modeling is a difficult process that necessitates meticulous statistical analysis and sound clinical judgments. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following always on Time, outstanding customer support, and High-quality Subject Matter Experts.
Read More With Us: https://bit.ly/3dxn32c
Why Statswork?
Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics across Methodologies | Wide Range of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
United Kingdom: 44-1143520021
India: 91-4448137070
WhatsApp: 91-8754446690
Are you interested in learning how to prevent hospital readmissions for your diabetic population? It is a popular belief that measuring blood glucose for your diabetic population is the most predictive variable in determining a hospital readmission for a diabetic. However, many providers of care simply do not perform the test on known diabetic patients. This study takes a look at an advanced analytic method that works within the current healthcare providers workflow to looks to identify the likelihood of a future 30-day unplanned readmission before hospital discharge.
Digital platforms could disrupts how pharma companies plan and excecute clini...Jayanthi Repalli, PhD
Pharmaceutical companies spent millions of dollars every year on clinical trials. They are essential part of finding new drugs. However, the lack of participants is the major cause for the delay of trials. Digital platforms could solve this problem for pharm companies and accelerate new drug development. Hope you find this infographic useful. Feel free to drop a note!
This disclaimer informs readers know that the views, thoughts, and opinions expressed in the presentation belong solely to the author, and not to the author’s employer, organization, committee or other group or individual.
The absence of a gold standard: a measurement error problemMaarten van Smeden
Talk about gold standard problems and solutions in medicine and epidemiology. Invited by the department of infectious disease epidemiology, University Medical Center Utrecht
Meta Analysis of Medical Device Data Applications for Designing Studies and R...NAMSA
Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence discusses what meta analysis is as well as the potential benefits.
Austin Ophthalmology is an open access, peer reviewed, scholarly journal dedicated to publish articles covering all areas of Ophthalmology.
The journal aims to promote latest information and provide a forum for doctors, researchers, physicians, and healthcare professionals to find most recent advances in the areas of Ophthalmology. Austin Ophthalmology accepts research articles, reviews, mini reviews, case reports and rapid communication covering all aspects of Ophthalmology.
Austin Ophthalmology strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Austin Publishing Group also brings universally peer reviewed journals under one roof thereby promoting knowledge sharing.
Developing and validating statistical models for clinical prediction and prog...Evangelos Kritsotakis
Talk on clinical prediction models presented at the Joint Seminar Series in Translational and Clinical Medicine organised by the University of Crete Medical School, the Institute of Molecular Biology and Biotechnology of the Foundation for Research and Technology Hellas (IMBB-FORTH), and the University of Crete Research Center (UCRC), Heraklion [online], Greece, April 7, 2021.
Prediction, Big Data, and AI: Steyerberg, Basel Nov 1, 2019Ewout Steyerberg
Title"Clinical prediction models in the age of artificial intelligence and big data", presented at the Basel Biometrics Society seminar Nov 1, 2019, Basel, by Ewout Steyerberg, with substantial inout from Maarten van Smeden and Ben van Calster
ISCB 2023 Sources of uncertainty b.pptxBenVanCalster
This talk gives an overview and illustration of various sources of uncertainty when developing clinical risk prediction models: aleatory, approximation, model, modeler, data, and population uncertainty. Presented at the International Conference of Clinical Biostatistics, 29th of August 2023, Milan, Italy.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available dataset containing features of breast tumors was utilized to identify breast tumors using machine learning and deep learning techniques. Various prediction models were constructed, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These models were trained to classify and predict breast tumor cases based on the provided features.
BREAST TUMOR DETECTION USING EFFICIENT MACHINE LEARNING AND DEEP LEARNING TEC...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump
of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among
women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can
affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less
than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early
detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available
dataset containing features of breast tumors was utilized to identify breast tumors using machine learning
and deep learning techniques. Various prediction models were constructed, including logistic regression
(LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB),
Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These
models were trained to classify and predict breast tumor cases based on the provided features.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of machine learning.
Measuring clinical utility: uncertainty in Net BenefitLaure Wynants
Introduction and Objective(s)
The impact of introducing a prediction model in clinical practice to inform clinical decisions on interventions (eg. treat patient vs. do not treat patient) can be quantified by Net Benefit (NB). NB is calculated as TP/N - FP/N * w, where TP is the number of true positives, FP is the number of false positives, and w is a weight reflecting the benefit of a TP and the harm of a FP. NB and decision curves (where NB is plotted for a range of w) are population-level quantities that can tell policymakers whether using a prediction model is better than using alternative strategies (such as treat all or treat none). Nonetheless, the NB estimate itself is uncertain. The objective of this talk is to investigate the origins and measures of NB uncertainty.
Method(s) and Results
Sampling variability and heterogeneity between populations are sources of uncertainty about NB. We will show that despite wide confidence and prediction intervals around NB, the choice of optimal strategy may be unaffected. A first measure of uncertainty is the probability of usefulness. It is the probability that the model is the optimal strategy among competing strategies and can be calculated through a random effects meta-analysis. The probability of usefulness has conceptual links with a second measure, the Net Benefit Value of Information (NB VOI). VOI is a concept borrowed from decision theory that quantifies the expected loss due to not confidently knowing which of competing strategies is the best. The methods will be illustrated with case studies in ovarian cancer diagnosis and prognosis after myocardial infarction.
Conclusions
Uncertainty in NB can be large. The probability of usefulness from a random-effects meta-analysis reflects heterogeneity in clinical utility across populations, while the NB VOI can be used to determine whether more validation data from a certain population is needed.
Dichotomania and other challenges for the collaborating biostatisticianLaure Wynants
Conference presentation at ISCB 41 in the session
"Biostatistical inference in practice: moving beyond false
dichotomies"
A comment in Nature, signed by over 800 researchers, called for the scientific community to “retire statistical significance”. The responses included a call to halt the use of the term „statistically significant”, and changes in journal’s author guidelines. The leading discourse among statisticians is that inadequate statistical training of clinical researchers and publishing practices are to blame for the misuse of statistical testing. In this presentation, we search our collective conscience by reviewing ethical guidelines for statisticians in light of the p-value crisis, examine what this implies for us when conducting analyses in collaborative work and teaching, and whether the ATOM (accept uncertainty; be thoughtful, open and modest) principles can guide us.
Integrated ACO selected for the NAACOS Innovation ShowcaseEric Weaver
Integrated ACO has been recognized as an ACO Innovation Leader in Data and Analytics. My company was chosen from a competitive field of applicants to present its innovation solution at the National Association of ACOs (NAACOS) Spring 2015 Conference in Baltimore, MD on April 2, 2015. Our in-house development of a predictive model for Congestive Heart Failure hospital admissions was recognized as one of the best in the country.
Similar to A plea for good methodology when developing clinical prediction models (20)
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
A plea for good methodology when developing clinical prediction models
1. A plea for good methodology:
the strengths and limitations of
approaches to developing prediction
models in obstetrics and gynecology
Ben Van Calster
Department of Development and Regeneration, KU Leuven (B)
Department of Biomedical Data Sciences, LUMC (NL)
Research Ethics Committee, University Hospitals Leuven (B)
Epi-Centre, KU Leuven (B)
Glasgow/Leuven, October 16th 2020
3. To explain or to predict?
DESCRIBE / EXPLAIN
• Study independent associations / predictors / risk factors
• Key: effect size per variable
• Not prediction modeling!
PREDICT
• Obtain a system that gives predictions (risk estimates)
• Aim is the use in NEW patients: it should work ‘tomorrow’, not now
• Key: quality of the predictions
3
4. Strengths of prediction models
• Help in (shared) clinical decision making
• Objectify predictions
• Patient counseling
• Effect on clinical workflow and outcomes
GOOD METHODOLOGY AND
GOOD REPORTING ARE ESSENTIAL!
4
Beam and Kohane. JAMA 2018;319:1317-8.
5. Get the objective right
5
Riley. Nature 2019;572:27-9.
Cronin & Vickers. Urology 2010;76:1298-301.
6. Get the objective right
• Is there a real clinical need for a new model?
• For which outcome, and for which management decision?
• When during the clinical workflow should the prediction be made?
• Does this match with the timing of the predictors?
• Do you have/can you collect data that is (really) fit for purpose?
6
8. Too many models, too few validations
• 1060 models predicting outcomes after CVD (1990-2015) (Wessler et al, 2017)
• 363 models predicting CVD (Damen et al, 2016)
• 231 models related to Covid-19 (Wynants et al, 2020; living syst review)
ObGyn related:
• 263 models in obstetrics (Kleinrouweler et al, 2016)
• 116 models to diagnose ovarian malignancy (Kaijser et al, 2014)
Perhaps academic CVs need help, but patients need help more
8
Thanks to @GSCollins
Wessler et al. Diagn Progn Res 2017;1:20. Damen et al. BMJ 2016;353:i2416. Wynants et al. BMJ 2020;369:m1328.
Kleinrouweler et al. AJOG 2016;214:79-90. Kaijser et al. Hum Reprod Update 2014;20:229-62.
9. Models in obstetrics
Only 23 of 263 models (9%) have been externally validated!
9
Kleinrouweler et al. AJOG 2016;214:79-90.
10. Knowledge is power (1)
Avoid dichotomization of continuous predictor variables
• Biologically implausible
• Deletes information, worse predictions (AUC ) (Collins 2016; Steyerberg 2018)
• Only clinical decisions should be binary
10
Collins et al. Stat Med 2016;35:4124-35.
Steyerberg et al. J Clin Epidemiol 2018;98:133-43.
Butts & Ng. Statistical and methodological myths and urban legends, p361-86. Routledge/Taylor & Francis, 2009.
11. Knowledge is power (2)
Use available knowledge, do not always ask the data!
11
Good & Hardin. Common errors in statistics (and how to avoid them). Wiley, 2006.
“Bypassing the brain to
compute by reflex is a
sure recipe for disaster”
12. Knowledge is power (3)
Explain how and when predictors are measured, standardize where
reasonably possible
- Units; e.g. progesterone in ng/ml or nmol/L
- How tumor volume or diameter is calculated
- What is meant by ‘hormonal therapy use’ (Which? When?)
- Smoking
- BMI: measured vs self-reported
If measurement varies across studies, model performance deteriorates
(Luijken, 2019; Luijken, 2020)
12
Luijken et al. Stat Med 2019;38:3444-59.
Luijken et al. J Clin Epidemiol 2020;119:7-18.
13. Knowledge is power (4): sample size
You think of buying a Porsche.
But if you do not want to pay for it,
you may get this.
The same applies for developing risk models.
13
14. The currency is sample size
The more complicated (or ‘fancy’) the modeling strategy,
the more you have to pay with sample size.
(counterfeit money does not help: we need good quality data)
In this respect, avoid train-test split, this reduces sample size for model
development: you’re burning your money
14
15. The currency is sample size
Many have heard of the “10 events per variable” rule
1. Often incorrect use: This is not about 10 patients per variable in the final model!
2. This is outdated, 10 EPV is often not enough. See new procedure (BMJ 2020).
3. Flexible algorithms are data hungry, EPV>>10 may be needed (van der Ploeg 2014).
15
Van der Ploeg et al. BMC Med Res Methodol 2014;14:137
Riley et al. BMJ 2020;368:m441.
16. Knowledge is power (5): missing data
Usually, “empty cells” are “full of information”!
Using only complete cases
- decreases sample size (less money)
- typically leaves a non-representative sample (biased risk estimates)
Presence of a test can be more predictive than the test result! See EHR data.
16
Agniel et al. BMJ 2018;360:k1479.
17. Model validation: assess calibration!
Key elements of model performance:
discrimination between patients with and without the event
calibration (correctness) of risk estimates
17
DISCRIMINATION
When it rained, was the
estimated chance of rain
higher (on average)?
CALIBRATION
For days with 80% estimated
chance of rain, did it rain on
8 out of 10 days?
18. Calibration: the Achilles heel
18
Van Calster & Vickers. Med Decis Making 2015;35:162-9.
Van Calster et al. BMC Med 2019;17:230.
Miscalibration: estimated risk is inaccurate
Patient and clinician are misinformed, may lead to inappropriate decisions
(Van Calster & Vickers, 2015)
19. Performance depends on place and time
One external validation in one hospital does not tell much about a model!
“There is no such thing as a validated model”
Study heterogeneity
19
Van Calster et al. BMJ 2020;370:m2614.
20. P-values and significance testing
Very small role in prediction modeling
- Focus is on robust predictions
- Focus is on precision of the performance estimates (e.g. AUC, calibration)
- Focus is on quantifying heterogeneity
- Focus is on qualitative difference between populations
- Focus is on a priori selection of predictors
- further data-driven selection can be based on p-values; high alpha recommended
(Steyerberg & Van Calster, 2020)
20
Steyerberg & Van Calster. Eur J Clin Invest 2020;50:e13229.
21. Machine learning popularity
21
“Typical machine learning algorithms are highly flexible
So will uncover associations we could not find before
Hence better predictions and management decisions”
→ One of the master keys, with guaranteed success!
23. Poor methodology and reporting is common
23
Christodoulou et al (2019) – 71 studies:
- What was done about missing data? 100% poor or unclear
- How was performance validated? 68% unclear or biased approach
- Was calibration of risk estimates studied? 79% not at all
- Prognostic models: time horizon often ignored completely
Kleinrouweler et al (2016) – 263 models:
- Was calibration studied? 82% not at all
- Was the model fully presented so people can use it? Not for 38% of models
- Was the clinical use discussed? Not for 89% of models
FOLLOW TRIPOD GUIDELINES FOR REPORTING!
www.tripod-statement.org
Christodoulou et al. J Clin Epidemiol 2019;110:12-22.
Kleinrouweler et al. AJOG 2016;214:79-90.
Moons et al. Ann Intern Med 2015;162:w1-73.
24. The harm of poor methodology
24
Steyerberg et al. J Clin Epidemiol 2018;98:133-43.
25. Resources on prediction modeling
25
Involve a statistician with knowledge of prediction modeling!
Steyerberg EW. Clinical prediction models (2nd ed). Springer, 2019.
Riley RD et al. Prognosis research in healthcare. OUP, 2019.
Moons KGM et al. Transparent reporting of a multivariable prediction model for individual
prognosis and diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med
2015;162:W1-73.
Wynants L et al. Key steps and common pitfalls in developing and validating risk models.
BJOG 2017; 2017;124:423-432.
Prognosisresearch.com (newly launched website)