A comparative investigation of methods for longitudinal data with limits of detection through a case study.

The statistical analysis of continuous longitudinal data may be complicated since
quantitative levels of bioassay cannot always be determined. Values beyond the
limits of detection (LOD) in the assays may not be observed and thus censored,
rendering complexity to the analysis of such data. This article examines how both
left-censoring and right censoring of HIV-1 plasma RNA measurements, collected
for the study on AIDS-related Non-Hodgkin's lymphoma (AR-NHL) in East Africa,
affects the quantification of viral load and explores the natural history of
viral load measurements over time in AR-NHL patients receiving anticancer
chemotherapy. Data analyses using Monte Carlo EM algorithm (MCEM) are compared to
analyses where the LOD or LOD/2 (left censoring) value is substituted for the
censored observations, and also to other methods such as multiple imputation, and
maximum likelihood estimation for censored data (generalized Tobit regression).
Simulations are used to explore the sensitivity of the results to changes in the
model parameters. In conclusion, the antiretroviral treatment was associated with
a significant decrease in viral load after controlling the effects of other
covariates. A simulation study with finite sample size shows MCEM is the least
biased method and the estimates are least sensitive to the censoring mechanism.

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