Determining relative importance of variables in developing and validating predictive models
<p>Abstract</p> <p>Background</p> <p>Multiple regression models are used in a wide range of scientific disciplines and automated model selection procedures are frequently used to identify independent predictors. However, determination of relative importance of potential...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2009-09-01
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| Series: | BMC Medical Research Methodology |
| Online Access: | http://www.biomedcentral.com/1471-2288/9/64 |
| _version_ | 1811266773640544256 |
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| author | Atenafu Eshetu G Beyene Joseph Hamid Jemila S To Teresa Sung Lillian |
| author_facet | Atenafu Eshetu G Beyene Joseph Hamid Jemila S To Teresa Sung Lillian |
| author_sort | Atenafu Eshetu G |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Multiple regression models are used in a wide range of scientific disciplines and automated model selection procedures are frequently used to identify independent predictors. However, determination of relative importance of potential predictors and validating the fitted models for their stability, predictive accuracy and generalizability are often overlooked or not done thoroughly.</p> <p>Methods</p> <p>Using a case study aimed at predicting children with acute lymphoblastic leukemia (ALL) who are at low risk of Tumor Lysis Syndrome (TLS), we propose and compare two strategies, bootstrapping and random split of data, for ordering potential predictors according to their relative importance with respect to model stability and generalizability. We also propose an approach based on relative increase in percentage of explained variation and area under the Receiver Operating Characteristic (ROC) curve for developing models where variables from our ordered list enter the model according to their importance. An additional data set aimed at identifying predictors of prostate cancer penetration is also used for illustrative purposes.</p> <p>Results</p> <p>Age is chosen to be the most important predictor of TLS. It is selected 100% of the time using the bootstrapping approach. Using the random split method, it is selected 99% of the time in the training data and is significant (at 5% level) 98% of the time in the validation data set. This indicates that age is a stable predictor of TLS with good generalizability. The second most important variable is white blood cell count (WBC). Our methods also identified an important predictor of TLS that was otherwise omitted if relying on any of the automated model selection procedures alone. A group at low risk of TLS consists of children younger than 10 years of age, without T-cell immunophenotype, whose baseline WBC is < 20 × 10<sup>9</sup>/L and palpable spleen is < 2 cm. For the prostate cancer data set, the Gleason score and digital rectal exam are identified to be the most important indicators of whether tumor has penetrated the prostate capsule.</p> <p>Conclusion</p> <p>Our model selection procedures based on bootstrap re-sampling and repeated random split techniques can be used to assess the strength of evidence that a variable is truly an independent and reproducible predictor. Our methods, therefore, can be used for developing stable and reproducible models with good performances. Moreover, our methods can serve as a good tool for validating a predictive model. Previous biological and clinical studies support the findings based on our selection and validation strategies. However, extensive simulations may be required to assess the performance of our methods under different scenarios as well as check their sensitivity to a random fluctuation in the data.</p> |
| first_indexed | 2024-04-12T20:49:22Z |
| format | Article |
| id | doaj.art-843c1b6a260741d0bd8099a9643cab31 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-04-12T20:49:22Z |
| publishDate | 2009-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-843c1b6a260741d0bd8099a9643cab312022-12-22T03:17:10ZengBMCBMC Medical Research Methodology1471-22882009-09-01916410.1186/1471-2288-9-64Determining relative importance of variables in developing and validating predictive modelsAtenafu Eshetu GBeyene JosephHamid Jemila STo TeresaSung Lillian<p>Abstract</p> <p>Background</p> <p>Multiple regression models are used in a wide range of scientific disciplines and automated model selection procedures are frequently used to identify independent predictors. However, determination of relative importance of potential predictors and validating the fitted models for their stability, predictive accuracy and generalizability are often overlooked or not done thoroughly.</p> <p>Methods</p> <p>Using a case study aimed at predicting children with acute lymphoblastic leukemia (ALL) who are at low risk of Tumor Lysis Syndrome (TLS), we propose and compare two strategies, bootstrapping and random split of data, for ordering potential predictors according to their relative importance with respect to model stability and generalizability. We also propose an approach based on relative increase in percentage of explained variation and area under the Receiver Operating Characteristic (ROC) curve for developing models where variables from our ordered list enter the model according to their importance. An additional data set aimed at identifying predictors of prostate cancer penetration is also used for illustrative purposes.</p> <p>Results</p> <p>Age is chosen to be the most important predictor of TLS. It is selected 100% of the time using the bootstrapping approach. Using the random split method, it is selected 99% of the time in the training data and is significant (at 5% level) 98% of the time in the validation data set. This indicates that age is a stable predictor of TLS with good generalizability. The second most important variable is white blood cell count (WBC). Our methods also identified an important predictor of TLS that was otherwise omitted if relying on any of the automated model selection procedures alone. A group at low risk of TLS consists of children younger than 10 years of age, without T-cell immunophenotype, whose baseline WBC is < 20 × 10<sup>9</sup>/L and palpable spleen is < 2 cm. For the prostate cancer data set, the Gleason score and digital rectal exam are identified to be the most important indicators of whether tumor has penetrated the prostate capsule.</p> <p>Conclusion</p> <p>Our model selection procedures based on bootstrap re-sampling and repeated random split techniques can be used to assess the strength of evidence that a variable is truly an independent and reproducible predictor. Our methods, therefore, can be used for developing stable and reproducible models with good performances. Moreover, our methods can serve as a good tool for validating a predictive model. Previous biological and clinical studies support the findings based on our selection and validation strategies. However, extensive simulations may be required to assess the performance of our methods under different scenarios as well as check their sensitivity to a random fluctuation in the data.</p>http://www.biomedcentral.com/1471-2288/9/64 |
| spellingShingle | Atenafu Eshetu G Beyene Joseph Hamid Jemila S To Teresa Sung Lillian Determining relative importance of variables in developing and validating predictive models BMC Medical Research Methodology |
| title | Determining relative importance of variables in developing and validating predictive models |
| title_full | Determining relative importance of variables in developing and validating predictive models |
| title_fullStr | Determining relative importance of variables in developing and validating predictive models |
| title_full_unstemmed | Determining relative importance of variables in developing and validating predictive models |
| title_short | Determining relative importance of variables in developing and validating predictive models |
| title_sort | determining relative importance of variables in developing and validating predictive models |
| url | http://www.biomedcentral.com/1471-2288/9/64 |
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