Bias in the estimation of cumulative viremia in cohort studies of HIV-infected individuals

TL;DR

This study demonstrates that infrequent sampling in HIV studies causes significant upward bias in estimating cumulative viremia, emphasizing the need for frequent measurements for accurate assessment.

Contribution

It quantifies the bias introduced by sampling frequency in estimating cumulative viremia and highlights the importance of sampling strategy in HIV cohort studies.

Findings

Fewer sampling points lead to overestimation of cumulative viremia.

Sampling bias varies with viral load dynamics over time.

Frequent sampling reduces bias and improves estimate accuracy.

Abstract

Purpose: The use of cumulative measures of exposure to raised HIV viral load (viremia copy-years) is an increasingly common in HIV prevention and treatment epidemiology due to the high biological plausibility. We sought to estimate the magnitude and direction of bias in a cumulative measure of viremia caused by different frequency of sampling and duration of follow-up. Methods: We simulated longitudinal viral load measures and reanalysed cohort study datasets with longitudinal viral load measurements under different sampling strategies to estimate cumulative viremia. Results: In both simulated and observed data, estimates of cumulative viremia by the trapezoidal rule show systematic upward bias when there are fewer sampling time points and/or increased duration between sampling time points, compared to estimation of full time series. Absolute values of cumulative viremia vary appreciably by the patterns of viral load over time, even after adjustment for total duration of follow up. Conclusions: Sampling bias due to differential frequency of sampling appears extensive and of meaningful magnitude in measures of cumulative viremia. Cumulative measures of viremia should be used only in studies with sufficient frequency of viral load measures and always as relative measures.