A combined approach to generate laboratory reference intervals using unbalanced longitudinal data
The interpretation of individual laboratory test results requires the availability of population-based reference intervals. In children, reference interval estimation has to consider frequently the strong age-dependency. Generally, for the construction of reference intervals, a sufficiently large nu...
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| Published in | Journal of Pediatric Endocrinology & Metabolism Vol. 30; no. 7; pp. 767 - 773 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Germany
De Gruyter
26.07.2017
Walter de Gruyter GmbH |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0334-018X 2191-0251 2191-0251 |
| DOI | 10.1515/jpem-2017-0171 |
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| Abstract | The interpretation of individual laboratory test results requires the availability of population-based reference intervals. In children, reference interval estimation has to consider frequently the strong age-dependency. Generally, for the construction of reference intervals, a sufficiently large number of independent measurement values is required. Data selections from hospitals or cohort studies often comprise dependencies violating the independence assumption.
In this article, we propose a combination of LMS-like (mean, M; coefficient of variation, S; skewness, λ or L) and resampling methods to overcome this drawback. The former is recommended by the World Health Organization (WHO) for the construction of continuous reference intervals of anthropometric measurements in children. The approach allows the inclusion of dependent measurements, for example, repeated measurements per subject. It also provides pointwise confidence envelopes as a measure of reliability.
The combination of LMS-type methods and resampling provides a feasible approach to estimate age-dependent percentiles and reference intervals using unbalanced longitudinal data. |
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| AbstractList | The interpretation of individual laboratory test results requires the availability of population-based reference intervals. In children, reference interval estimation has to consider frequently the strong age-dependency. Generally, for the construction of reference intervals, a sufficiently large number of independent measurement values is required. Data selections from hospitals or cohort studies often comprise dependencies violating the independence assumption. Methods: In this article, we propose a combination of LMS-like (mean, M; coefficient of variation, S; skewness, λ or L) and resampling methods to overcome this drawback. The former is recommended by the World Health Organization (WHO) for the construction of continuous reference intervals of anthropometric measurements in children. The approach allows the inclusion of dependent measurements, for example, repeated measurements per subject. It also provides pointwise confidence envelopes as a measure of reliability. Results and conclusions: The combination of LMS-type methods and resampling provides a feasible approach to estimate age-dependent percentiles and reference intervals using unbalanced longitudinal data. The interpretation of individual laboratory test results requires the availability of population-based reference intervals. In children, reference interval estimation has to consider frequently the strong age-dependency. Generally, for the construction of reference intervals, a sufficiently large number of independent measurement values is required. Data selections from hospitals or cohort studies often comprise dependencies violating the independence assumption. In this article, we propose a combination of LMS-like (mean, M; coefficient of variation, S; skewness, λ or L) and resampling methods to overcome this drawback. The former is recommended by the World Health Organization (WHO) for the construction of continuous reference intervals of anthropometric measurements in children. The approach allows the inclusion of dependent measurements, for example, repeated measurements per subject. It also provides pointwise confidence envelopes as a measure of reliability. The combination of LMS-type methods and resampling provides a feasible approach to estimate age-dependent percentiles and reference intervals using unbalanced longitudinal data. The interpretation of individual laboratory test results requires the availability of population-based reference intervals. In children, reference interval estimation has to consider frequently the strong age-dependency. Generally, for the construction of reference intervals, a sufficiently large number of independent measurement values is required. Data selections from hospitals or cohort studies often comprise dependencies violating the independence assumption.BACKGROUNDThe interpretation of individual laboratory test results requires the availability of population-based reference intervals. In children, reference interval estimation has to consider frequently the strong age-dependency. Generally, for the construction of reference intervals, a sufficiently large number of independent measurement values is required. Data selections from hospitals or cohort studies often comprise dependencies violating the independence assumption.In this article, we propose a combination of LMS-like (mean, M; coefficient of variation, S; skewness, λ or L) and resampling methods to overcome this drawback. The former is recommended by the World Health Organization (WHO) for the construction of continuous reference intervals of anthropometric measurements in children. The approach allows the inclusion of dependent measurements, for example, repeated measurements per subject. It also provides pointwise confidence envelopes as a measure of reliability.METHODSIn this article, we propose a combination of LMS-like (mean, M; coefficient of variation, S; skewness, λ or L) and resampling methods to overcome this drawback. The former is recommended by the World Health Organization (WHO) for the construction of continuous reference intervals of anthropometric measurements in children. The approach allows the inclusion of dependent measurements, for example, repeated measurements per subject. It also provides pointwise confidence envelopes as a measure of reliability.The combination of LMS-type methods and resampling provides a feasible approach to estimate age-dependent percentiles and reference intervals using unbalanced longitudinal data.RESULTS AND CONCLUSIONSThe combination of LMS-type methods and resampling provides a feasible approach to estimate age-dependent percentiles and reference intervals using unbalanced longitudinal data. |
| Author | Kirsten, Toralf Kratzsch, Jürgen Vogel, Mandy Kiess, Wieland Engel, Christoph |
| Author_xml | – sequence: 1 givenname: Mandy surname: Vogel fullname: Vogel, Mandy email: mvogel@life.uni-leipzig.de organization: Center of Pediatric Research (CPL), University of Leipzig, Leipzig, Germany – sequence: 2 givenname: Toralf surname: Kirsten fullname: Kirsten, Toralf organization: Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany – sequence: 3 givenname: Jürgen surname: Kratzsch fullname: Kratzsch, Jürgen organization: Institute of Laboratory, Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany – sequence: 4 givenname: Christoph surname: Engel fullname: Engel, Christoph organization: Department of Medical Computer Science, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany – sequence: 5 givenname: Wieland surname: Kiess fullname: Kiess, Wieland organization: Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28586305$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1002/sim.746 10.1038/pr.2016.274 10.1017/S136898001200105X 10.1111/apa.13438 10.1002/sim.1861 10.1111/1467-985X.00045 10.1515/cclm-2012-0684 10.1183/09031936.00080312 10.1191/1471082X06st122oa 10.1007/s10654-016-0216-9 10.1136/adc.2004.050799 10.1016/0009-8981(87)90151-3 10.1016/j.clinbiochem.2016.02.010 10.1007/s10654-013-9774-2 10.3109/17477166.2010.490267 10.1002/sim.2227 10.1515/cclm-2016-0793 10.1136/bmj.c1140 |
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| Keywords | epidemiology biological variation LMS method reference intervals, reference limits |
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| SubjectTerms | Adolescent biological variation Child Child, Preschool Cholesterol, HDL - blood Clinical Laboratory Techniques - standards epidemiology Female Follow-Up Studies Humans Infant Infant, Newborn Laboratories LMS method Longitudinal Studies Male Prospective Studies reference intervals, reference limits Reference Values Reproducibility of Results Statistics as Topic |
| Title | A combined approach to generate laboratory reference intervals using unbalanced longitudinal data |
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