Predicting chance of disease : calculation using prediction rules
A prediction rule is a statistical model that can be used to predict the presence or absence of a disease based on a limited number of tests or predictive factors. One of the mathematical methods used to formulate prediction rules is a logistic regression analysis of patient data. The discriminatory power of a model is visualizable using box-whisker plots and ROC curves; calibration plots show the match between the predicted chance and the observed frequency of a disease. These graphs are used to assess whether a model adequately reproduces reality. On publication of prediction rules it is important that the regression function is written out and that the chances of a disease on the basis of diagnostic scores are displayed in a histogram. For the practical significance of the model, it is also important to know how often the predicted low, medium or high probabilities of a disease do actually occur in comparison with the advance chance of occurrence.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2014 |
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| Erschienen: |
2014 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:158 |
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| Enthalten in: |
Nederlands tijdschrift voor geneeskunde - 158(2014) vom: 12., Seite A7041 |
| Sprache: |
Niederländisch |
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| Weiterer Titel: |
Ziektekansen voorspellen: rekenen met predictieregels |
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| Beteiligte Personen: |
Verbeek, Anna J M [VerfasserIn] |
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| Themen: |
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| Anmerkungen: |
Date Completed 30.03.2015 Date Revised 02.12.2018 published: Print Citation Status MEDLINE |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM240903528 |
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| 246 | 3 | 3 | |a Ziektekansen voorspellen: rekenen met predictieregels |
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| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a A prediction rule is a statistical model that can be used to predict the presence or absence of a disease based on a limited number of tests or predictive factors. One of the mathematical methods used to formulate prediction rules is a logistic regression analysis of patient data. The discriminatory power of a model is visualizable using box-whisker plots and ROC curves; calibration plots show the match between the predicted chance and the observed frequency of a disease. These graphs are used to assess whether a model adequately reproduces reality. On publication of prediction rules it is important that the regression function is written out and that the chances of a disease on the basis of diagnostic scores are displayed in a histogram. For the practical significance of the model, it is also important to know how often the predicted low, medium or high probabilities of a disease do actually occur in comparison with the advance chance of occurrence | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
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| 700 | 1 | |a van Dijck, Jos A A M |e verfasserin |4 aut | |
| 700 | 1 | |a Verbeek, André L M |e verfasserin |4 aut | |
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