Handling Overlapping Asymmetric Data Sets—A Twice Penalized P-Spline Approach
<b>Aims:</b> Overlapping asymmetric data sets are where a large cohort of observations have a small amount of information recorded, and within this group there exists a smaller cohort which have extensive further information available. Missing imputation is unwise if cohort size differs...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/12/5/777 |
| _version_ | 1797264166055051264 |
|---|---|
| author | Matthew McTeer Robin Henderson Quentin M. Anstee Paolo Missier |
| author_facet | Matthew McTeer Robin Henderson Quentin M. Anstee Paolo Missier |
| author_sort | Matthew McTeer |
| collection | DOAJ |
| description | <b>Aims:</b> Overlapping asymmetric data sets are where a large cohort of observations have a small amount of information recorded, and within this group there exists a smaller cohort which have extensive further information available. Missing imputation is unwise if cohort size differs substantially; therefore, we aim to develop a way of modelling the smaller cohort whilst considering the larger. <b>Methods:</b> Through considering traditionally once penalized P-Spline approximations, we create a second penalty term through observing discrepancies in the marginal value of covariates that exist in both cohorts. Our now twice penalized P-Spline is designed to firstly prevent over/under-fitting of the smaller cohort and secondly to consider the larger cohort. <b>Results:</b> Through a series of data simulations, penalty parameter tunings, and model adaptations, our twice penalized model offers up to a 58% and 46% improvement in model fit upon a continuous and binary response, respectively, against existing B-Spline and once penalized P-Spline methods. Applying our model to an individual’s risk of developing steatohepatitis, we report an over 65% improvement over existing methods. <b>Conclusions:</b> We propose a twice penalized P-Spline method which can vastly improve the model fit of overlapping asymmetric data sets upon a common predictive endpoint, without the need for missing data imputation. |
| first_indexed | 2024-04-25T00:24:34Z |
| format | Article |
| id | doaj.art-936066c9cf9a4e22994fca26259b2eff |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-04-25T00:24:34Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-936066c9cf9a4e22994fca26259b2eff2024-03-12T16:50:19ZengMDPI AGMathematics2227-73902024-03-0112577710.3390/math12050777Handling Overlapping Asymmetric Data Sets—A Twice Penalized P-Spline ApproachMatthew McTeer0Robin Henderson1Quentin M. Anstee2Paolo Missier3School of Computing, Faculty of Science, Agriculture & Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UKSchool of Mathematics, Statistics and Physics, Faculty of Science, Agriculture & Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UKTranslational & Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UKSchool of Computer Science, University of Birmingham, Birmingham B15 2TT, UK<b>Aims:</b> Overlapping asymmetric data sets are where a large cohort of observations have a small amount of information recorded, and within this group there exists a smaller cohort which have extensive further information available. Missing imputation is unwise if cohort size differs substantially; therefore, we aim to develop a way of modelling the smaller cohort whilst considering the larger. <b>Methods:</b> Through considering traditionally once penalized P-Spline approximations, we create a second penalty term through observing discrepancies in the marginal value of covariates that exist in both cohorts. Our now twice penalized P-Spline is designed to firstly prevent over/under-fitting of the smaller cohort and secondly to consider the larger cohort. <b>Results:</b> Through a series of data simulations, penalty parameter tunings, and model adaptations, our twice penalized model offers up to a 58% and 46% improvement in model fit upon a continuous and binary response, respectively, against existing B-Spline and once penalized P-Spline methods. Applying our model to an individual’s risk of developing steatohepatitis, we report an over 65% improvement over existing methods. <b>Conclusions:</b> We propose a twice penalized P-Spline method which can vastly improve the model fit of overlapping asymmetric data sets upon a common predictive endpoint, without the need for missing data imputation.https://www.mdpi.com/2227-7390/12/5/777P-Splinepenalized regressionsmoothingasymmetric dataB-Splinenon-Parametric |
| spellingShingle | Matthew McTeer Robin Henderson Quentin M. Anstee Paolo Missier Handling Overlapping Asymmetric Data Sets—A Twice Penalized P-Spline Approach Mathematics P-Spline penalized regression smoothing asymmetric data B-Spline non-Parametric |
| title | Handling Overlapping Asymmetric Data Sets—A Twice Penalized P-Spline Approach |
| title_full | Handling Overlapping Asymmetric Data Sets—A Twice Penalized P-Spline Approach |
| title_fullStr | Handling Overlapping Asymmetric Data Sets—A Twice Penalized P-Spline Approach |
| title_full_unstemmed | Handling Overlapping Asymmetric Data Sets—A Twice Penalized P-Spline Approach |
| title_short | Handling Overlapping Asymmetric Data Sets—A Twice Penalized P-Spline Approach |
| title_sort | handling overlapping asymmetric data sets a twice penalized p spline approach |
| topic | P-Spline penalized regression smoothing asymmetric data B-Spline non-Parametric |
| url | https://www.mdpi.com/2227-7390/12/5/777 |
| work_keys_str_mv | AT matthewmcteer handlingoverlappingasymmetricdatasetsatwicepenalizedpsplineapproach AT robinhenderson handlingoverlappingasymmetricdatasetsatwicepenalizedpsplineapproach AT quentinmanstee handlingoverlappingasymmetricdatasetsatwicepenalizedpsplineapproach AT paolomissier handlingoverlappingasymmetricdatasetsatwicepenalizedpsplineapproach |