Related Experiment Video
Updated: Jul 20, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Customised birthweight: coefficients for an Australian population and validation of the model
Scott Pain1, Allan M Z Chang, Vicki Flenady
1Centre for Clinical Studies, Mater Health Services, Brisbane, Queensland, Australia. scott.pain@mater.org.au
Insights
This study validates a customised birthweight model for Australian infants, providing robust coefficients to identify abnormal growth. Further research is needed to refine population standards for accurate maternity care.
Area of Science:
- Perinatal Medicine
- Biostatistics
- Public Health
Background:
- Current Australian birthweight references may not accurately identify abnormal infant growth due to unaddressed constitutional factors.
- Previous customised birthweight reference models developed in other regions utilized unvalidated statistical assumptions.
Purpose of the Study:
- To validate the statistical model for customised birthweight estimation in an Australian population.
- To generate a reference set of coefficients for customised birthweight, aiding maternity care and future research.
Main Methods:
- Utilized de-identified data from 42,206 births at Mater Mother's Hospital, Brisbane (1997-2005).
- Excluded multiple pregnancies, preterm births (<37 weeks), stillbirths, and major congenital abnormalities.
- Employed multivariate analysis and a double cross-validation procedure to ensure model robustness.
Main Results:
- Confirmed normal distribution of birthweight for statistical analysis.
- Developed robust coefficients for customised birthweight calculation in the Australian context.
- The validated statistical model demonstrated significant robustness.
Conclusions:
- Empirical data supports the robustness of the customised birthweight model.
- Further research is necessary to delineate normal physiological growth from pathology.
- Future work should focus on defining population segments for optimal customised birthweight standard construction.
Background:
Published birthweight references in Australia do not fully take into account constitutional factors that influence birthweight and therefore may not provide an accurate reference to identify the infant with abnormal growth. Furthermore, studies in other regions that have derived adjusted (customised) birthweight references have applied untested assumptions in the statistical modelling.
Aims:
To validate the customised birthweight model and to produce a reference set of coefficients for estimating a customised birthweight that may be useful for maternity care in Australia and for future research.
Methods:
De-identified data were extracted from the clinical database for all births at the Mater Mother's Hospital, Brisbane, Australia, between January 1997 and June 2005. Births with missing data for the variables under study were excluded. In addition the following were excluded: multiple pregnancies, births less than 37 completed week's gestation, stillbirths, and major congenital abnormalities. Multivariate analysis was undertaken. A double cross-validation procedure was used to validate the model.
Results:
The study of 42,206 births demonstrated that, for statistical purposes, birthweight is normally distributed. Coefficients for the derivation of customised birthweight in an Australian population were developed and the statistical model is demonstrably robust.
Conclusions:
This study provides empirical data as to the robustness of the model to determine customised birthweight. Further research is required to define where normal physiology ends and pathology begins, and which segments of the population should be included in the construction of a customised birthweight standard.
Related Concept Videos
z Scores and Area Under the Curve
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the Guinness...
Estimating Population Mean with Known Standard Deviation
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
Distributions to Estimate Population Parameter
Estimating Population Standard Deviation
Regression Toward the Mean