Updating Clinical Prediction Models: An Illustrative Case Study

2021 ◽  
pp. 109-113
Author(s):  
Hendrik-Jan Mijderwijk ◽  
Stefan van Beek ◽  
Daan Nieboer
Keyword(s):  
Prediction Models ◽  
Illustrative Case ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Illustrative Case Study

Dynamic clinical prediction models for discrete time‐to‐event data with competing risks—A case study on the OUTCOMEREA database

2018 ◽  
Vol 61 (3) ◽  
pp. 514-534 ◽  
Author(s):  
Rachel Heyard ◽  
Jean‐François Timsit ◽  
Wafa Ibn Essaied ◽  
Leonhard Held ◽  
Keyword(s):  
Competing Risks ◽  
Discrete Time ◽  
Prediction Models ◽  
Event Data ◽  
Clinical Prediction ◽  
Time To Event ◽  
Clinical Prediction Models ◽  
Time To Event Data

scholarly journals A nonparametric updating method to correct clinical prediction model drift

2019 ◽  
Vol 26 (12) ◽  
pp. 1448-1457 ◽  
Author(s):  
Sharon E Davis ◽  
Robert A Greevy ◽  
Christopher Fonnesbeck ◽  
Thomas A Lasko ◽  
Colin G Walsh ◽  
...  
Keyword(s):  
Prediction Models ◽  
Testing Procedure ◽  
Learning Approaches ◽  
Department Of Veterans Affairs ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Subsequent Performance ◽  
Nonparametric Models ◽  
Population Shift

Abstract Objective Clinical prediction models require updating as performance deteriorates over time. We developed a testing procedure to select updating methods that minimizes overfitting, incorporates uncertainty associated with updating sample sizes, and is applicable to both parametric and nonparametric models. Materials and Methods We describe a procedure to select an updating method for dichotomous outcome models by balancing simplicity against accuracy. We illustrate the test’s properties on simulated scenarios of population shift and 2 models based on Department of Veterans Affairs inpatient admissions. Results In simulations, the test generally recommended no update under no population shift, no update or modest recalibration under case mix shifts, intercept correction under changing outcome rates, and refitting under shifted predictor-outcome associations. The recommended updates provided superior or similar calibration to that achieved with more complex updating. In the case study, however, small update sets lead the test to recommend simpler updates than may have been ideal based on subsequent performance. Discussion Our test’s recommendations highlighted the benefits of simple updating as opposed to systematic refitting in response to performance drift. The complexity of recommended updating methods reflected sample size and magnitude of performance drift, as anticipated. The case study highlights the conservative nature of our test. Conclusions This new test supports data-driven updating of models developed with both biostatistical and machine learning approaches, promoting the transportability and maintenance of a wide array of clinical prediction models and, in turn, a variety of applications relying on modern prediction tools.

scholarly journals Application of clinical prediction modeling in pediatric neurosurgery: a case study

2021 ◽  
Author(s):  
Hendrik-Jan Mijderwijk ◽  
Thomas Beez ◽  
Daniel Hänggi ◽  
Daan Nieboer
Keyword(s):  
Decision Making ◽  
Prediction Models ◽  
Pediatric Neurosurgery ◽  
Medical Decision ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Current Form ◽  
Related Risk ◽  
Decision Making Processes

AbstractThere has been an increasing interest in articles reporting on clinical prediction models in pediatric neurosurgery. Clinical prediction models are mathematical equations that combine patient-related risk factors for the estimation of an individual’s risk of an outcome. If used sensibly, these evidence-based tools may help pediatric neurosurgeons in medical decision-making processes. Furthermore, they may help to communicate anticipated future events of diseases to children and their parents and facilitate shared decision-making accordingly. A basic understanding of this methodology is incumbent when developing or applying a prediction model. This paper addresses this methodology tailored to pediatric neurosurgery. For illustration, we use original pediatric data from our institution to illustrate this methodology with a case study. The developed model is however not externally validated, and clinical impact has not been assessed; therefore, the model cannot be recommended for clinical use in its current form.

scholarly journals A genetic programming approach to development of clinical prediction models: A case study in symptomatic cardiovascular disease

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0202685 ◽  
Author(s):  
Christian A. Bannister ◽  
Julian P. Halcox ◽  
Craig J. Currie ◽  
Alun Preece ◽  
Irena Spasić
Keyword(s):  
Cardiovascular Disease ◽  
Genetic Programming ◽  
Prediction Models ◽  
Programming Approach ◽  
Clinical Prediction ◽  
Clinical Prediction Models

scholarly journals Automatic development of clinical prediction models with genetic programming: A case study in cardiovascular disease

2014 ◽  
Vol 17 (3) ◽  
pp. A200-A201 ◽  
Author(s):  
C.A. Bannister ◽  
C.J. Currie ◽  
A. Preece ◽  
I. Spasic
Keyword(s):  
Cardiovascular Disease ◽  
Genetic Programming ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Clinical Prediction Models

Containerport Investment Appraisal and Risk Analysis: Illustrative Case Study

2002 ◽  
Vol 1782 (1) ◽  
pp. 64-72 ◽  
Author(s):  
Thomas A. Grigalunas ◽  
Young-Tae Chang ◽  
Meifeng Luo
Keyword(s):  
Risk Analysis ◽  
Illustrative Case ◽  
Investment Appraisal ◽  
Illustrative Case Study

Systemic Unsustainability as a Threat to Democracy

2020 ◽  
Author(s):  
Andrea Felicetti
Keyword(s):  
Research Agenda ◽  
Ecological Crisis ◽  
Illustrative Case ◽  
Sustainability Transition ◽  
Ways Of Thinking ◽  
Illustrative Case Study ◽  
New Research

Resilient socioeconomic unsustainability poses a threat to democracy whose importance has yet to be fully acknowledged. As the prospect of sustainability transition wanes, so does perceived legitimacy of institutions. This further limits representative institutions’ ability to take action, making democratic deepening all the more urgent. I investigate this argument through an illustrative case study, the 2017 People’s Climate March. In a context of resilient unsustainability, protesters have little expectation that institutions might address the ecological crisis and this view is likely to spread. New ways of thinking about this problem and a new research agenda are needed.

The Effects of Teaching Reflection Diaries on In-Service High School Teachers in Japan

Relay Journal ◽  
2020 ◽  
pp. 80-99
Author(s):  
Naoya Shibata
Keyword(s):  
High School ◽  
High School Teachers ◽  
Illustrative Case ◽  
Teaching Beliefs ◽  
Experienced Teacher ◽  
Female Teachers ◽  
Self Confidence ◽  
Research Findings ◽  
Illustrative Case Study

Although teaching reflection diaries (TRDs) are prevalent tools for teacher training, TRDs are rarely used in Japanese secondary educational settings. In order to delve into the effects of TRDs on teaching development, this illustrative case study was conducted with two female teachers (one novice, and one experienced) at a Japanese private senior high school. The research findings demonstrated that both in-service teachers perceived TRDs as beneficial tools for understanding their strengths and weaknesses. TRDs and class observations illustrated that the novice teacher raised their self-confidence in teaching and gradually changed their teaching activities. On the other hand, the experienced teacher held firm teaching beliefs based on their successful teaching experiences and were sometimes less willing to experiment with different approaches. However, they changed their teaching approaches when they lost balance between their class preparation and other duties. Accordingly, although teachers’ firm beliefs and successful experiences may sometimes become possible hindrances from using TRDs effectively, TRDs can be useful tools to train and help teachers realise their strengths and weaknesses.

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