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Amplifying and Quantifying HIV-1 RNA in HIV Infected Individuals with Viral Loads Below the Limit of Detection by Standard Clinical Assays
Published on: September 26, 2011
Modelling HIV-RNA viral load in vertically infected children
Linsay Gray1, Mario Cortina-Borja, Marie-Louise Newell
1Centre for Paediatric Epidemiology and Biostatistics, Institute of Child Health, University College London, UK. L.Gray@ich.ucl.ac.uk
Insights
Understanding human immunodeficiency virus (HIV) RNA viral load patterns in children requires accounting for repeated measures. Simple methods for handling censored data, like using the mid-point, are sufficient for modeling viral load dynamics over age.
Area of Science:
- Virology
- Biostatistics
- Pediatric Infectious Diseases
Background:
- Human immunodeficiency virus (HIV) RNA viral load is a key marker for disease progression.
- Understanding viral load dynamics in vertically infected children is crucial for disease management.
- Assay detection limits can result in left-censored viral load measurements.
Purpose of the Study:
- To determine if complex statistical methods are necessary to model HIV RNA viral load patterns in children.
- To assess the impact of repeated measures and data censoring on viral load modeling.
- To compare different statistical approaches for analyzing longitudinal HIV RNA viral load data.
Main Methods:
- Utilized fractional polynomials for modeling viral load dynamics over age.
- Compared complex methods (EM algorithm, Gibbs sampler) with simpler alternatives.
- Investigated approaches for handling left-censored data, including cut-off values and mid-point imputation.
- Employed linear mixed-effects and ordinary least squares models.
Main Results:
- Fractional polynomials significantly outperformed conventional models for viral load dynamics.
- Accounting for repeated measures was necessary to improve statistical power for model selection.
- Simple methods for handling censored data, such as mid-point imputation, did not negatively impact model fit.
- Complex methodologies were not essential for identifying the optimal viral load model.
Conclusions:
- Complex statistical methods are not always necessary for modeling HIV RNA viral load in children.
- Fractional polynomials offer a superior approach to modeling viral load dynamics over age.
- Appropriate handling of repeated measures and censored data is important, but simpler imputation methods suffice.
Abstract:
Human immunodeficiency virus (HIV) ribo-nucleic acid (RNA) viral load is a measure of actively replicating virus and is used as a marker of disease progression. For a thorough understanding of the dynamics of the evolution of the virus in the early life of HIV-1 vertically infected children, it is important to elucidate the pattern of HIV-RNA viral load over age. An aspect of assay systems used in the quantification of RNA viral load is that they measure values above particular cut-off values for detection, below which the assays used are not sufficiently sensitive. In this way, measurements are potentially left-censored. Recent adult studies suggest that to adequately model RNA pattern over age, it is necessary to account for within-subject correlation, due to repeated measures, and censoring. The aim of this study, therefore, was to establish whether it is necessary to use complex methods to allow for repeated measures within individuals and censoring of the HIV-RNA viral load in children enrolled in a cohort study. The approach involved the identification of an appropriate model for the basic pattern of RNA viral load by age and subsequent assessment of various estimation procedures accounting for repeated measures and censoring in different ways. Methods developed by Hughes involving the expectation-maximization (EM) algorithm and the Gibbs sampler were taken as the benchmark for comparison of simpler alternatives. Other approaches considered involve linear mixed-effects and ordinary least squares in which censoring is dealt with informally by taking the cut-off value as absolute or taking the mid-point between cut-off and zero. Fractional polynomials provided a substantially superior approach for modelling the dynamics of viral load over age compared to conventional polynomials or change-point models. Allowing for repeated measures was necessary to improve the power of the likelihood ratio tests required to establish the final model, but methods beyond taking the mid-point for censored values did not further improve the fit. Although Hughes' methodology is the best approach, its implementation is not necessary for the identification of the optimal model.
