Mixed models for longitudinal left-censored repeated measures

TL;DR

This paper reviews likelihood-based methods for analyzing longitudinal left-censored data, demonstrating their implementation in SAS and comparing their performance, with an application to HIV viral load measurements.

Contribution

It introduces practical implementation of two likelihood-based methods for left-censored longitudinal data analysis using SAS and compares their effectiveness.

Findings

Likelihood-based methods effectively handle left-censoring in longitudinal data.

SAS Proc NLMIXED can be used to fit these models.

Application to HIV data illustrates the methods' utility.

Abstract

Longitudinal studies could be complicated by left-censored repeated measures. For example, in Human Immunodeficiency Virus infection, there is a detection limit of the assay used to quantify the plasma viral load. Simple imputation of the limit of the detection or of half of this limit for left-censored measures biases estimations and their standard errors. In this paper, we review two likelihood-based methods proposed to handle left-censoring of the outcome in linear mixed model. We show how to fit these models using SAS Proc NLMIXED and we compare this tool with other programs. Indications and limitations of the programs are discussed and an example in the field of HIV infection is shown.