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Clinical Prediction Models

About: The article was published on 2013-02-01 and is currently open access. It has received 435 citations till now.

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Summary

  • The number of articles presenting a neurosurgical prediction model is rapidly increasing [1].
  • Nowadays, with the rise of so many prediction models, the authors should be able to make firm conclusions which model to use on their patients.
  • Clinical prediction models aim to predict an outcome of interest, for example, survival in high-grade glioma (HGG) patients or intraventricular hemorrhage in preterm infants, by combining two or more patient-related variables.
  • The development and evaluation of clinical prediction models involve multiple methodological steps.
  • Utilizing invalid prediction models may jeopardize adequate decisionmaking in their daily clinical practice.

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LETTER TO THE EDITOR
Clinical prediction models
Hendrik-Jan Mijderwijk
1
& Thomas Beez
1
& Daniel nggi
1
& Daan Nieboer
2
Received: 27 February 2020 /Accepted: 6 March 2020
#
The Author(s) 2020
Dear Editor:
The number of articles presenting a neurosurgical prediction
model is rapidly increasing [1]. Although the number of pub-
lications reporting a clinical prediction model in Childs
Nervous System, Journal of Neurosurgery Pediatrics,and
Pediatric Neurosurgery is relatively constant over the years
(Fig. 1a), circa two-thirds of these publications have been
published since 2015 (Fig. 1b). Nowadays, with the rise of
so many prediction models, we should be able to make firm
conclusions which model to use on our patients.
Clinical prediction models aim to predict an outcome of
interest, for example, survival in high-grade glioma (HGG)
patients or intraventricula r hemorrhage in preterm infants, by
combining two or more patient-related variables. The obtained
predictions of these models can then be used for medical and
shared decision-making such as initiating surgical treatment,
and for example for guidance in planning future lifestyle.
The developm ent and evaluation of clinical predi ction
models involve multiple methodological steps. It is well-
known that these steps are often inadequately addressed and/
or inadequately reported in a publication which clearly limits
the usefulness of the presented prediction model. Utilizing
invalid prediction models may jeopardize adequate decision-
making in our daily clinical practice. Therefore, we want to
point out a few crucial aspects of prediction models.
1. Sample sizeIn clinical prediction model studies, the
number of events in the study population defines the ef-
fective sample size. As a rule of thumb, the minimum
required number of 10 events per prognostic variable
considered has been generally accepted; although recent-
ly,moreadvancedcalculationshavebeenproposedwhich
may yield a different ratio. The number of events is the
smaller of the number of patients having the event or not
having the event. Thus, if in a set of 110 HGG patients, 50
HGG patients died during the study period, considering
up to 5 prognostic variables seems reasonable for the de-
velopment of a prediction model. Considering too many
prognostic variables increases the risk of overfitting [2].
Overfitted models show promising results when evaluated
on the patients on which the model was developed but
show disappointing results when applied to other sets of
patients. This point is essential because many pediatric
neurosurgical studies have relatively low effective sample
sizes.
2. ValidationIt is vital to gauge the validity of the predic-
tions provided by a prediction model. As a result of
overfitting, prediction models tend to have a too optimis-
tic predictive performance in terms of discrimination and
calibration [2]. Internal validation aims to quantify this
optimism. These techniques reuse the same set of patients
on which the model was developed. This step is a mini-
mum requirement for publication of a prediction model.
External validation assesses the performance of the pre-
diction model on a different set of patients, for example,
collected at other geographical locations and/or time pe-
riods. External validation is imperative before a clinical
uptake of the proposed model can take place.
3. ReportingGuidelines for reporting prediction models
do exist. The TRIPOD (transparent reporting of a multi-
variable prediction model for individual prognosis or di-
agnosis) guideline together with its e xplanatory paper
provides crucial information for the design, conduction,
and evaluation of a prediction model [3, 4]. It is highly
recommended to adhere to the items of the TRIPOD
checklist for proper and transparent reporting. Often
graphical presentations of the underlying statistical model
are provided in a publication, but (statistical) details are
lacking. For example, when Cox regression is used for
predicting survival, the regression coefficients and the
* Hendrik-Jan Mijderwijk
Hendrik-Jan.Mijderwijk@med.uni-duesseldorf.de
1
Department of Neurosurgery, Heinrich-Heine University Medical
Center, Moorenstraße 5, 40225 Düsseldorf, Germany
2
Department of Public Health, Erasmus MC, University Medical
Center Rotterdam, s Gravendijkwal 230, 3015
CE Rotterdam, The Netherlands
Child's Nervous System
https://doi.org/10.1007/s00381-020-04577-8

baseline survival at a given timepoint should be reported
to enable external validation.
We encourage the journal and its readers to critically
review studies on prediction models. Published work does
not always provide sufficient details of the model, al-
though this is necessary to judge the quality of the pre-
diction model. More details of the presented methodolog-
ical concerns and others including the selection of candi-
date prognostic variables and evaluating model perfor-
mance measures have been recently published and illus-
trated with clinical examples specifically for neurosur-
geons [1]. Furthermore, 7 key steps for the development
and evaluation of a neurosurgical clinical prediction mod-
el are tabulated for a quick overview. Pediatric neurosur-
geons should ideally be aware of this methodology as it is
highly con s equ ent ia l t o our daily clinica l pr ac ti ce .
Acknowledgments We thank Maarten F.M. Engel, an information spe-
cialist at Erasmus MC, for performing the literature search.
Funding Information Open Access funding provided by Projekt DEAL.
Compliance with ethical standards
Conflict of interest On behalf of all authors, the corresponding author
states that there is no conflict of interest.
2000 2005 2010 2015 2020
0
10
20
30
40
Year of publication
Number of publications
Total Pediatric
Neurosurgical Clinical
Prediction Models
Clinical Prediction Models
published in Child's Nervous
System
Clinical Prediction Models
published in Journal of
Neurosurgery Pediatrics
Clinical Prediction Models
published in Pediatric
Neurosurgery
2000 2005 2010 2015 2020
0
10
20
30
40
Year of publication
Number of publications
Total Pediatric
Neurosurgical Clinical
Prediction Models
Total Pediatric
Neurosurgical Clinical
Prediction Models
Nervous System, Journal
of Neurosurgery
Pediatrics, and Pediatric
Neurosurgery
a
b
Fig. 1 a, b Time trends of the published number of pediatric neurosurgical
clinical prediction models according to the following systematic searches in
PubMed until December 31, 2019. For the black results: (Prognostic-
index* OR Prognostic-rule* OR Prognostic-model* OR prognostic-scor*
OR prediction-index* OR prediction-rule* OR prediction-model* OR
prediction-scor* OR predictive-index* OR predictive-rule* OR
predictive-model* OR predictive-scor*) AND (Neurosurgery [mh] OR
Neurosurgical Procedures [mh] OR Neurosurg* OR Neurological-surg*)
AND (child[mh] OR Infant [mh] OR Adolescent[mh] OR
Minors[mh] OR Pediatrics[mh] OR Child Health Services[mh] OR
Hospitals, Pediatric[mh] OR Intensive Care Units, Pediatric[Mesh] OR
infan*[tiab] OR newborn*[tiab] OR new born*[tiab] OR baby [tiab] OR
babies [tiab] OR neonat* [tiab] OR perinat*[tia b] OR postnat*[t iab] OR
prematur*[tiab] OR pre-matur*[tiab] OR child [mesh] OR child [tiab] OR
childs [tiab] OR childhood*[tiab] OR children*[tiab] OR kid [tiab] OR
kids [tiab] OR toddler*[tiab] OR adoles*[tiab ] OR teen*[tiab] OR
boy*[tiab] OR girl*[tiab] OR minors*[tiab] OR underag*[tiab] OR under
age*[tiab] OR under aging [tiab] OR under ageing [tiab] OR juvenil*[tiab]
OR youth*[tiab] OR kindergar*[tiab] OR puber*[tiab] OR pubescen*[tiab]
OR prepubescen*[tiab] OR prepuberty*[tiab] OR pediatric*[tiab] OR
peadiatric*[tiab] OR schoolchil d*[tiab] OR preschool*[tiab ] OR
highschool*[tiab] OR suckling*[tiab] OR PICU [tiab] OR N ICU [tiab]
OR PICUs [tiab] OR NICUs [tiab]). For the colored results: (Prognostic-
index* OR Prognostic-rule* OR Prognostic-model* OR prognostic-scor*
OR prediction-index* OR prediction-rule* OR prediction-model* OR
prediction-scor* OR predictive-index* OR predictive-rule* OR
predictive-model* OR predictive-scor*) AND (Neurosurgery [mh] OR
Neurosurgical Procedures [mh] OR Neurosurg* OR Neurological-surg*)
AND (Childs Nerv Syst[Journal] OR Journal of Neurosurgery
Pediatrics[Journal] OR Pediatric Neurosurgery[Journal])
Childs Nerv Syst

Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adap-
tation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, pro-
vide a link to the Creative Commons licence, and indicate if changes were
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credit line to the material. If material is not included in the article's
Creative Commons licence and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain
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licence, visit http://creativecommons.org/licenses/by/4.0/.
References
1. Mijderwijk HJ, Steyerberg EW, Steiger HJ, Fischer I, Kamp MA
(2019) Fundamentals of clinical prediction modeling for the
neurosurgeon. Neurosurgery 85:302311. https://doi.org/10.1093/
neuros/nyz282
2. Steyerberg EW (2019) Clinical prediction models. A practical ap-
proach to development, validation, and updating. Springer, New
York. https://doi.org/10.1007/978-3-030-16399-0
3. Coll ins GS, Reitsma JB, Altman DG, Moons KGM (2015)
Transparent reporting of a multivariable prediction model for indi-
vidual prognosis or diagnosis (TRIPOD): the TRIPOD statement.
BMC Med 13:110. https://doi.org/10.1186/s12916-014-0241-z
4. Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P,
Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS (2015)
Transparent reporting of a multivariable prediction model for indi-
vidual prognosis or diagnosis (TRIPOD): explanation and elabora-
tion. Ann Intern Med 162:W1W73. https://doi.org/10.7326/M14-
0698
PublishersnoteSpringer Nature remains neutral with regard to jurisdic-
tional claims in published maps and institutional affiliations.
Childs Nerv Syst
Citations
More filters
Book
29 Mar 2012
TL;DR: The problem of missing data concepts of MCAR, MAR and MNAR simple solutions that do not (always) work multiple imputation in a nutshell and some dangers, some do's and some don'ts are covered.
Abstract: Basics Introduction The problem of missing data Concepts of MCAR, MAR and MNAR Simple solutions that do not (always) work Multiple imputation in a nutshell Goal of the book What the book does not cover Structure of the book Exercises Multiple imputation Historic overview Incomplete data concepts Why and when multiple imputation works Statistical intervals and tests Evaluation criteria When to use multiple imputation How many imputations? Exercises Univariate missing data How to generate multiple imputations Imputation under the normal linear normal Imputation under non-normal distributions Predictive mean matching Categorical data Other data types Classification and regression trees Multilevel data Non-ignorable methods Exercises Multivariate missing data Missing data pattern Issues in multivariate imputation Monotone data imputation Joint Modeling Fully Conditional Specification FCS and JM Conclusion Exercises Imputation in practice Overview of modeling choices Ignorable or non-ignorable? Model form and predictors Derived variables Algorithmic options Diagnostics Conclusion Exercises Analysis of imputed data What to do with the imputed data? Parameter pooling Statistical tests for multiple imputation Stepwise model selection Conclusion Exercises Case studies Measurement issues Too many columns Sensitivity analysis Correct prevalence estimates from self-reported data Enhancing comparability Exercises Selection issues Correcting for selective drop-out Correcting for non-response Exercises Longitudinal data Long and wide format SE Fireworks Disaster Study Time raster imputation Conclusion Exercises Extensions Conclusion Some dangers, some do's and some don'ts Reporting Other applications Future developments Exercises Appendices: Software R S-Plus Stata SAS SPSS Other software References Author Index Subject Index

2,156 citations

Journal ArticleDOI
01 May 2012-Heart
TL;DR: An overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals, how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies).
Abstract: Clinical prediction models are increasingly used to complement clinical reasoning and decision-making in modern medicine, in general, and in the cardiovascular domain, in particular. To these ends, developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such models by professionals must guide their decision-making, and improve patient outcomes and the cost-effectiveness of care. In the first paper of this series of two companion papers, issues relating to prediction model development, their internal validation, and estimating the added value of a new (bio)marker to existing predictors were discussed. In this second paper, an overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals (external validation studies), how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies). Each step is illustrated with empirical examples from the cardiovascular field.

920 citations


Cites methods from "Clinical Prediction Models"

  • ...Methods for prediction model updating Several methods for updating prediction models have been proposed and evaluated (table 1).(3) 11 12 Most often, differences are seen in the outcome or event frequency between the development and new validation sample....

    [...]

Journal ArticleDOI
TL;DR: Improvements in methodology and reporting are needed for studies that compare modeling algorithms for clinical prediction modeling in the literature and found no evidence of superior performance of ML over LR.

885 citations


Cites background or methods from "Clinical Prediction Models"

  • ...The traditional approach to develop these models involves the use of regression models, for example, logistic regression (LR) to predict disease presence (diagnosis) or disease outcomes (prognosis) [1]....

    [...]

  • ...Another limitation is that many studies had a fairly limited number of events per considered predictor, a common problem despite repeated warnings [1,17,99,103,109]....

    [...]

Journal ArticleDOI
TL;DR: The validation methods are applicable to a wide range of prognostic studies and provide researchers with a toolkit for external validation of a published Cox model.
Abstract: A prognostic model should not enter clinical practice unless it has been demonstrated that it performs a useful role. External validation denotes evaluation of model performance in a sample independent of that used to develop the model. Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature. Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample. Validating Cox models is not straightforward because event probabilities are estimated relative to an unspecified baseline function. We describe statistical approaches to external validation of a published Cox model according to the level of published information, specifically (1) the prognostic index only, (2) the prognostic index together with Kaplan-Meier curves for risk groups, and (3) the first two plus the baseline survival curve (the estimated survival function at the mean prognostic index across the sample). The most challenging task, requiring level 3 information, is assessing calibration, for which we suggest a method of approximating the baseline survival function. We apply the methods to two comparable datasets in primary breast cancer, treating one as derivation and the other as validation sample. Results are presented for discrimination and calibration. We demonstrate plots of survival probabilities that can assist model evaluation. Our validation methods are applicable to a wide range of prognostic studies and provide researchers with a toolkit for external validation of a published Cox model.

662 citations


Cites background from "Clinical Prediction Models"

  • ...For logistic regression models, for instance, there are several well-recognised approaches [4-9]....

    [...]

Journal ArticleDOI
TL;DR: Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated to optimize the utility of predictive analytics for shared decision-making and patient counseling.
Abstract: The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention. Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice. Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.

635 citations


Cites background or methods from "Clinical Prediction Models"

  • ...The concepts explained in this section are illustrated in Additional file 1, with the validation of the Risk of Ovarian Malignancy Algorithm (ROMA) for the diagnosis of ovarian malignancy in women with an ovarian tumor selected for surgical removal [28]; further details can be found elsewhere [1, 4, 29]....

    [...]

  • ...When we find poorly calibrated predictions at validation, algorithm updating should be considered to provide more accurate predictions for new patients from the validation setting [1, 20]....

    [...]

  • ...miscalibration, and suffers from low statistical power [1, 4]....

    [...]

References
More filters
Book
29 Mar 2012
TL;DR: The problem of missing data concepts of MCAR, MAR and MNAR simple solutions that do not (always) work multiple imputation in a nutshell and some dangers, some do's and some don'ts are covered.
Abstract: Basics Introduction The problem of missing data Concepts of MCAR, MAR and MNAR Simple solutions that do not (always) work Multiple imputation in a nutshell Goal of the book What the book does not cover Structure of the book Exercises Multiple imputation Historic overview Incomplete data concepts Why and when multiple imputation works Statistical intervals and tests Evaluation criteria When to use multiple imputation How many imputations? Exercises Univariate missing data How to generate multiple imputations Imputation under the normal linear normal Imputation under non-normal distributions Predictive mean matching Categorical data Other data types Classification and regression trees Multilevel data Non-ignorable methods Exercises Multivariate missing data Missing data pattern Issues in multivariate imputation Monotone data imputation Joint Modeling Fully Conditional Specification FCS and JM Conclusion Exercises Imputation in practice Overview of modeling choices Ignorable or non-ignorable? Model form and predictors Derived variables Algorithmic options Diagnostics Conclusion Exercises Analysis of imputed data What to do with the imputed data? Parameter pooling Statistical tests for multiple imputation Stepwise model selection Conclusion Exercises Case studies Measurement issues Too many columns Sensitivity analysis Correct prevalence estimates from self-reported data Enhancing comparability Exercises Selection issues Correcting for selective drop-out Correcting for non-response Exercises Longitudinal data Long and wide format SE Fireworks Disaster Study Time raster imputation Conclusion Exercises Extensions Conclusion Some dangers, some do's and some don'ts Reporting Other applications Future developments Exercises Appendices: Software R S-Plus Stata SAS SPSS Other software References Author Index Subject Index

2,156 citations

Journal ArticleDOI
01 May 2012-Heart
TL;DR: An overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals, how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies).
Abstract: Clinical prediction models are increasingly used to complement clinical reasoning and decision-making in modern medicine, in general, and in the cardiovascular domain, in particular. To these ends, developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such models by professionals must guide their decision-making, and improve patient outcomes and the cost-effectiveness of care. In the first paper of this series of two companion papers, issues relating to prediction model development, their internal validation, and estimating the added value of a new (bio)marker to existing predictors were discussed. In this second paper, an overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals (external validation studies), how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies). Each step is illustrated with empirical examples from the cardiovascular field.

920 citations

Journal ArticleDOI
TL;DR: Improvements in methodology and reporting are needed for studies that compare modeling algorithms for clinical prediction modeling in the literature and found no evidence of superior performance of ML over LR.

885 citations

Journal ArticleDOI
TL;DR: The validation methods are applicable to a wide range of prognostic studies and provide researchers with a toolkit for external validation of a published Cox model.
Abstract: A prognostic model should not enter clinical practice unless it has been demonstrated that it performs a useful role. External validation denotes evaluation of model performance in a sample independent of that used to develop the model. Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature. Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample. Validating Cox models is not straightforward because event probabilities are estimated relative to an unspecified baseline function. We describe statistical approaches to external validation of a published Cox model according to the level of published information, specifically (1) the prognostic index only, (2) the prognostic index together with Kaplan-Meier curves for risk groups, and (3) the first two plus the baseline survival curve (the estimated survival function at the mean prognostic index across the sample). The most challenging task, requiring level 3 information, is assessing calibration, for which we suggest a method of approximating the baseline survival function. We apply the methods to two comparable datasets in primary breast cancer, treating one as derivation and the other as validation sample. Results are presented for discrimination and calibration. We demonstrate plots of survival probabilities that can assist model evaluation. Our validation methods are applicable to a wide range of prognostic studies and provide researchers with a toolkit for external validation of a published Cox model.

662 citations

Journal ArticleDOI
TL;DR: Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated to optimize the utility of predictive analytics for shared decision-making and patient counseling.
Abstract: The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention. Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice. Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.

635 citations

Frequently Asked Questions (7)
Q1. What are the contributions in "Clinical prediction models" ?

The number of articles presenting a neurosurgical prediction model is rapidly increasing [ 1 ]. Although the number of publications reporting a clinical prediction model in Child ’ s Nervous System, Journal of Neurosurgery Pediatrics, and Pediatric Neurosurgery is relatively constant over the years ( Fig. 1a ), circa two-thirds of these publications have been published since 2015 ( Fig. 1b ). 

Clinical prediction models aim to predict an outcome of interest, for example, survival in high-grade glioma (HGG) patients or intraventricular hemorrhage in preterm infants, by combining two or more patient-related variables. 

The obtained predictions of these models can then be used for medical and shared decision-making such as initiating surgical treatment, and for example for guidance in planning future lifestyle. 

As a result of overfitting, prediction models tend to have a too optimistic predictive performance in terms of discrimination and calibration [2]. 

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 

with the rise of so many prediction models, the authors should be able to make firm conclusions which model to use on their patients. 

If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.