Combining fractional polynomial model building with multiple imputation
Multivariable fractional polynomial (MFP) models are commonly used in medical research. The datasets in which MFP models are applied often contain covariates with missing values. To handle the missing values, we describe methods for combining multiple imputation with MFP modelling, considering in tu...
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| Format: | Online |
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John Wiley and Sons Inc.
2015
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4871237/ |
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pubmed-4871237 |
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pubmed-48712372016-05-18 Combining fractional polynomial model building with multiple imputation Morris, Tim P. White, Ian R. Carpenter, James R. Stanworth, Simon J. Royston, Patrick Research Articles Multivariable fractional polynomial (MFP) models are commonly used in medical research. The datasets in which MFP models are applied often contain covariates with missing values. To handle the missing values, we describe methods for combining multiple imputation with MFP modelling, considering in turn three issues: first, how to impute so that the imputation model does not favour certain fractional polynomial (FP) models over others; second, how to estimate the FP exponents in multiply imputed data; and third, how to choose between models of differing complexity. Two imputation methods are outlined for different settings. For model selection, methods based on Wald‐type statistics and weighted likelihood‐ratio tests are proposed and evaluated in simulation studies. The Wald‐based method is very slightly better at estimating FP exponents. Type I error rates are very similar for both methods, although slightly less well controlled than analysis of complete records; however, there is potential for substantial gains in power over the analysis of complete records. We illustrate the two methods in a dataset from five trauma registries for which a prognostic model has previously been published, contrasting the selected models with that obtained by analysing the complete records only. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. John Wiley and Sons Inc. 2015-06-10 2015-11-10 /pmc/articles/PMC4871237/ /pubmed/26095614 http://dx.doi.org/10.1002/sim.6553 Text en © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Morris, Tim P. White, Ian R. Carpenter, James R. Stanworth, Simon J. Royston, Patrick |
| spellingShingle |
Morris, Tim P. White, Ian R. Carpenter, James R. Stanworth, Simon J. Royston, Patrick Combining fractional polynomial model building with multiple imputation |
| author_facet |
Morris, Tim P. White, Ian R. Carpenter, James R. Stanworth, Simon J. Royston, Patrick |
| author_sort |
Morris, Tim P. |
| title |
Combining fractional polynomial model building with multiple imputation |
| title_short |
Combining fractional polynomial model building with multiple imputation |
| title_full |
Combining fractional polynomial model building with multiple imputation |
| title_fullStr |
Combining fractional polynomial model building with multiple imputation |
| title_full_unstemmed |
Combining fractional polynomial model building with multiple imputation |
| title_sort |
combining fractional polynomial model building with multiple imputation |
| description |
Multivariable fractional polynomial (MFP) models are commonly used in medical research. The datasets in which MFP models are applied often contain covariates with missing values. To handle the missing values, we describe methods for combining multiple imputation with MFP modelling, considering in turn three issues: first, how to impute so that the imputation model does not favour certain fractional polynomial (FP) models over others; second, how to estimate the FP exponents in multiply imputed data; and third, how to choose between models of differing complexity. Two imputation methods are outlined for different settings. For model selection, methods based on Wald‐type statistics and weighted likelihood‐ratio tests are proposed and evaluated in simulation studies. The Wald‐based method is very slightly better at estimating FP exponents. Type I error rates are very similar for both methods, although slightly less well controlled than analysis of complete records; however, there is potential for substantial gains in power over the analysis of complete records. We illustrate the two methods in a dataset from five trauma registries for which a prognostic model has previously been published, contrasting the selected models with that obtained by analysing the complete records only. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. |
| publisher |
John Wiley and Sons Inc. |
| publishDate |
2015 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4871237/ |
| _version_ |
1613580881480384512 |