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[Comparison among various software for LMS growth curve fitting methods].

Lin Han, Wenhong Wu, Qiuxia Wei

    Wei Sheng Yan Jiu = Journal of Hygiene Research
    |May 23, 2015
    PubMed
    Summary
    This summary is machine-generated.

    R software is optimal for fitting the skewness-median-coefficient of variation (LMS) growth curves in pediatric populations. This method enhances statistical analysis for child and adolescent health professionals.

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    Area of Science:

    • Pediatric Health
    • Biostatistics
    • Growth Curve Analysis

    Background:

    • Accurate growth monitoring is crucial for child and adolescent health.
    • The skewness-median-coefficient of variation (LMS) method provides a robust framework for growth curve fitting.
    • Evaluating different software for LMS growth curve analysis is essential for practical application.

    Purpose of the Study:

    • To compare various software packages for their efficacy in fitting LMS growth curves.
    • To identify the most suitable statistical method for child and adolescent growth analysis at the grass-roots level.

    Main Methods:

    • Analysis of regular physical examination head circumference data from infants aged 3, 6, 9, and 12 months.
    • Application of statistical software including SAS, R, STATA, and SPSS for LMS growth curve fitting.
    • Evaluation of software based on user convenience, community adoption, interface, results display, and maintenance.

    Main Results:

    • All statistical software yielded consistent calculation outcomes for LMS growth curve fitting.
    • Each software package presented distinct advantages and disadvantages in terms of usability and features.
    • R software demonstrated superior performance and user-friendliness for LMS growth curve fitting compared to SAS, STATA, and SPSS.

    Conclusions:

    • R software offers significant advantages for grass-roots child and adolescent staff performing LMS growth curve analysis.
    • The findings support the optimization of growth curve statistical methods using R for pediatric health professionals.