This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Monsurul Hoq
Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, 48 Flemington Road, Parkville, Victoria - 3052, Australia
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: Reference intervals (RIs), which are used as an assessment tool in laboratory medicine, change with age for most biomarkers in children. Addressing this, RIs that vary continuously with age have been developed using a range of curve-fitting approaches. The choice of statistical method may be important as different methods may produce substantially different RIs. Hence, we developed a simulation study to investigate the performance of statistical methods for estimating continuous paediatric RIs.
Methods: We compared four methods for estimating age-varying RIs. These were Cole’s LMS, the Generalised Additive Model for Location Scale and Shape (GAMLSS), Royston’s method based on fractional polynomials and exponential transformation, and a new method applying quantile regression using power variables in age selected by fractional polynomial regression for the mean. Data were generated using hypothetical true curves based on five biomarkers with varying complexity of association with age, i.e. linear or nonlinear, constant or nonconstant variation across age, and for four sample sizes (100, 200, 400 and 1000). Root mean square error (RMSE) was used as the primary performance measure for comparison.
Results: Regression-based parametric methods performed better in most scenarios. Royston’s and the new method performed consistently well in all scenarios for sample sizes of at least 400, while the new method had the smallest average RMSE in scenarios with nonconstant variation across age.
Conclusions: We recommend methods based on flexible parametric models for estimating continuous paediatric RIs, irrespective of the complexity of the association between biomarkers and age, for at least 400 samples.
Keywords
Biomarker, Continuous, Reference intervals, Simulations, Statistics
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The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementalDocument1StataandRCodesHoqetalcomparisonofstatisticalmethodforestimatingRIs.docx
Supplementary document 1: Stata and R codes of all four statistical methods applied in estimating age-specific paediatric RIs 1 A: Stata code for Cole’s LMS method 1 B: Stata code for Royston’s method 1 C: State code for Hoq et al’s method 1 D: R code for GAMLSS
- SupplementaryFigures1to8HoqetalcomparisonofstatisticalmethodsforestimatingRIs.docx
Supplementary Figures 1 to 5: Average estimated and true RIs by age, sex and method for five scenarios Figure S-1: Average estimated and true RIs by age, sex, sample size and method for scenario 1 Figure S-2: Average estimated and true RIs by age, sex, sample size and method for scenario 2 Figure S-3: Average estimated and true RIs by age, sex, sample size and method for scenario 3 Figure S-4: Average estimated and true RIs by age, sex, sample size and method for scenario 4 Figure S-5: Average estimated and true RIs by age, sex, sample size and method for scenario 5 Supplementary Figures 6 – 8: Root mean square error by age, sex and method for three scenarios Figure S-6: Root Mead Square Error by age, sex, sample size and method for scenario 3 Figure S-7: Root Mead Square Error by age, sex, sample size and method for scenario 4 Figure S-8: Root Mead Square Error by age, sex, sample size and method for scenario 5
- SupplementaryTable1and2HoqetalcomparisonofstatisticalmethodsforestimatingRIs.docx
Supplementary table 1 and 2 Table S-1: Average coverage across integer age and sex for Hoq et al and Royston’s methods, for five scenarios and four sample sizes Table S-2: Average 95% confidence interval width for lower and upper reference limits for Hoq et al and Royston’s methods across integer age and sex, for five scenarios and four sample sizes
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Monsurul Hoq
Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, 48 Flemington Road, Parkville, Victoria - 3052, Australia
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 16 Jan, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epidemiology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Reference intervals (RIs), which are used as an assessment tool in laboratory medicine, change with age for most biomarkers in children. Addressing this, RIs that vary continuously with age have been developed using a range of curve-fitting approaches. The choice of statistical method may be important as different methods may produce substantially different RIs. Hence, we developed a simulation study to investigate the performance of statistical methods for estimating continuous paediatric RIs.
Methods: We compared four methods for estimating age-varying RIs. These were Cole’s LMS, the Generalised Additive Model for Location Scale and Shape (GAMLSS), Royston’s method based on fractional polynomials and exponential transformation, and a new method applying quantile regression using power variables in age selected by fractional polynomial regression for the mean. Data were generated using hypothetical true curves based on five biomarkers with varying complexity of association with age, i.e. linear or nonlinear, constant or nonconstant variation across age, and for four sample sizes (100, 200, 400 and 1000). Root mean square error (RMSE) was used as the primary performance measure for comparison.
Results: Regression-based parametric methods performed better in most scenarios. Royston’s and the new method performed consistently well in all scenarios for sample sizes of at least 400, while the new method had the smallest average RMSE in scenarios with nonconstant variation across age.
Conclusions: We recommend methods based on flexible parametric models for estimating continuous paediatric RIs, irrespective of the complexity of the association between biomarkers and age, for at least 400 samples.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementalDocument1StataandRCodesHoqetalcomparisonofstatisticalmethodforestimatingRIs.docx
Supplementary document 1: Stata and R codes of all four statistical methods applied in estimating age-specific paediatric RIs 1 A: Stata code for Cole’s LMS method 1 B: Stata code for Royston’s method 1 C: State code for Hoq et al’s method 1 D: R code for GAMLSS
- SupplementaryFigures1to8HoqetalcomparisonofstatisticalmethodsforestimatingRIs.docx
Supplementary Figures 1 to 5: Average estimated and true RIs by age, sex and method for five scenarios Figure S-1: Average estimated and true RIs by age, sex, sample size and method for scenario 1 Figure S-2: Average estimated and true RIs by age, sex, sample size and method for scenario 2 Figure S-3: Average estimated and true RIs by age, sex, sample size and method for scenario 3 Figure S-4: Average estimated and true RIs by age, sex, sample size and method for scenario 4 Figure S-5: Average estimated and true RIs by age, sex, sample size and method for scenario 5 Supplementary Figures 6 – 8: Root mean square error by age, sex and method for three scenarios Figure S-6: Root Mead Square Error by age, sex, sample size and method for scenario 3 Figure S-7: Root Mead Square Error by age, sex, sample size and method for scenario 4 Figure S-8: Root Mead Square Error by age, sex, sample size and method for scenario 5
- SupplementaryTable1and2HoqetalcomparisonofstatisticalmethodsforestimatingRIs.docx
Supplementary table 1 and 2 Table S-1: Average coverage across integer age and sex for Hoq et al and Royston’s methods, for five scenarios and four sample sizes Table S-2: Average 95% confidence interval width for lower and upper reference limits for Hoq et al and Royston’s methods across integer age and sex, for five scenarios and four sample sizes
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