Developing clinical prediction models when adhering to minimum sample size recommendations: the importance of quantifying bootstrap variability in tuning parameters and predictive performance
Recent minimum sample size formula (Riley et al.) for developing clinical prediction models help ensure that development datasets are of sufficient size to minimise overfitting. While these criteria are known to avoid excessive overfitting on average, the extent of variability in overfitting at reco...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
SAGE Publications
2021
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| _version_ | 1797065305396084736 |
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| author | Martin, GP Riley, RD Collins, GS Sperrin, M |
| author_facet | Martin, GP Riley, RD Collins, GS Sperrin, M |
| author_sort | Martin, GP |
| collection | OXFORD |
| description | Recent minimum sample size formula (Riley et al.) for developing clinical prediction models help ensure that development datasets are of sufficient size to minimise overfitting. While these criteria are known to avoid excessive overfitting on average, the extent of variability in overfitting at recommended sample sizes is unknown. We investigated this through a simulation study and empirical example to develop logistic regression clinical prediction models using unpenalised maximum likelihood estimation, and various post-estimation shrinkage or penalisation methods. While the mean calibration slope was close to the ideal value of one for all methods, penalisation further reduced the level of overfitting, on average, compared to unpenalised methods. This came at the cost of higher variability in predictive performance for penalisation methods in external data. We recommend that penalisation methods are used in data that meet, or surpass, minimum sample size requirements to further mitigate overfitting, and that the variability in predictive performance and any tuning parameters should always be examined as part of the model development process, since this provides additional information over average (optimism-adjusted) performance alone. Lower variability would give reassurance that the developed clinical prediction model will perform well in new individuals from the same population as was used for model development. |
| first_indexed | 2024-03-06T21:26:45Z |
| format | Journal article |
| id | oxford-uuid:435cab35-5db9-4d3e-81ea-a8a5bd1a1b9b |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:26:45Z |
| publishDate | 2021 |
| publisher | SAGE Publications |
| record_format | dspace |
| spelling | oxford-uuid:435cab35-5db9-4d3e-81ea-a8a5bd1a1b9b2022-03-26T14:54:54ZDeveloping clinical prediction models when adhering to minimum sample size recommendations: the importance of quantifying bootstrap variability in tuning parameters and predictive performanceJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:435cab35-5db9-4d3e-81ea-a8a5bd1a1b9bEnglishSymplectic ElementsSAGE Publications2021Martin, GPRiley, RDCollins, GSSperrin, MRecent minimum sample size formula (Riley et al.) for developing clinical prediction models help ensure that development datasets are of sufficient size to minimise overfitting. While these criteria are known to avoid excessive overfitting on average, the extent of variability in overfitting at recommended sample sizes is unknown. We investigated this through a simulation study and empirical example to develop logistic regression clinical prediction models using unpenalised maximum likelihood estimation, and various post-estimation shrinkage or penalisation methods. While the mean calibration slope was close to the ideal value of one for all methods, penalisation further reduced the level of overfitting, on average, compared to unpenalised methods. This came at the cost of higher variability in predictive performance for penalisation methods in external data. We recommend that penalisation methods are used in data that meet, or surpass, minimum sample size requirements to further mitigate overfitting, and that the variability in predictive performance and any tuning parameters should always be examined as part of the model development process, since this provides additional information over average (optimism-adjusted) performance alone. Lower variability would give reassurance that the developed clinical prediction model will perform well in new individuals from the same population as was used for model development. |
| spellingShingle | Martin, GP Riley, RD Collins, GS Sperrin, M Developing clinical prediction models when adhering to minimum sample size recommendations: the importance of quantifying bootstrap variability in tuning parameters and predictive performance |
| title | Developing clinical prediction models when adhering to minimum sample size recommendations: the importance of quantifying bootstrap variability in tuning parameters and predictive performance |
| title_full | Developing clinical prediction models when adhering to minimum sample size recommendations: the importance of quantifying bootstrap variability in tuning parameters and predictive performance |
| title_fullStr | Developing clinical prediction models when adhering to minimum sample size recommendations: the importance of quantifying bootstrap variability in tuning parameters and predictive performance |
| title_full_unstemmed | Developing clinical prediction models when adhering to minimum sample size recommendations: the importance of quantifying bootstrap variability in tuning parameters and predictive performance |
| title_short | Developing clinical prediction models when adhering to minimum sample size recommendations: the importance of quantifying bootstrap variability in tuning parameters and predictive performance |
| title_sort | developing clinical prediction models when adhering to minimum sample size recommendations the importance of quantifying bootstrap variability in tuning parameters and predictive performance |
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