Extrapolation before imputation reduces bias when imputing censored covariates

Modeling symptom progression to identify informative subjects for a new Huntington's disease clinical trial is problematic since time to diagnosis, a key covariate, can be heavily censored. Imputation is an appealing strategy where censored covariates are replaced with their conditional means, but existing methods saw over 200% bias under heavy censoring. Calculating these conditional means well requires estimating and then integrating over the survival function of the censored covariate from the censored value to infinity. To estimate the survival function flexibly, existing methods use the semiparametric Cox model with Breslow's estimator, leaving the integrand for the conditional means (the estimated survival function) undefined beyond the observed data. The integral is then estimated up to the largest observed covariate value, and this approximation can cut off the tail of the survival function and lead to severe bias, particularly under heavy censoring. We propose a hybrid approach that splices together the semiparametric survival estimator with a parametric extension, making it possible to approximate the integral up to infinity. In simulation studies, our proposed approach of extrapolation then imputation substantially reduces the bias seen with existing imputation methods, even when the parametric extension was misspecified. We further demonstrate how imputing with corrected conditional means helps to prioritize patients for future clinical trials.Comment: 16 pages main text (incl. 2 tables and 3 figures); Supplemental Materials, R code, and R package available on GitHub (linked in main text

The `Why' behind including `Y' in your imputation model

Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommmendation always holds and why this is sometimes true. We examine deterministic imputation (i.e., single imputation where the imputed values are treated as fixed) and stochastic imputation (i.e., single imputation with a random value or multiple imputation) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Moreover, we dispel common misconceptions about deterministic imputation models and demonstrate why the outcome should not be included in these models. This paper aims to bridge the gap between imputation in theory and in practice, providing mathematical derivations to explain common statistical recommendations. We offer a better understanding of the considerations involved in imputing missing covariates and emphasize when it is necessary to include the outcome variable in the imputation model

It takes more than a machine: A pilot feasibility study of point-of-care HIV-1 viral load testing at a lower-level health center in rural western Uganda

Barriers continue to limit access to viral load (VL) monitoring across sub-Saharan Africa adversely impacting control of the HIV epidemic. The objective of this study was to determine whether the systems and processes required to realize the potential of rapid molecular technology are available at a prototypical lower-level (i.e., level III) health center in rural Uganda. In this open-label pilot study, participants underwent parallel VL testing at both the central laboratory (i.e., standard of care) and on-site using the GeneXpert HIV-1 assay. The primary outcome was the number of VL tests completed each clinic day. Secondary outcomes included the number of days from sample collection to receipt of result at clinic and the number of days from sample collection to patient receipt of the result. From August 2020 to July 2021, we enrolled a total of 242 participants. The median number of daily tests performed on the Xpert platform was 4, (IQR = 2–7). Time from sample collection to result was 51 days (IQR = 45–62) for samples sent to the central laboratory and 0 days (IQR = 0–0.25) for the Xpert assay conducted at the health center. However, few participants elected to receive results by one of the expedited options, which contributed to similar time-to-patient between testing approaches (89 versus 84 days, p = 0.07). Implementation of a rapid, near point-of-care VL assay at a lower-level health center in rural Uganda appears feasible, but interventions to promote rapid clinical response and influence patient preferences about result receipt require further study. Trial registration: ClinicalTrials.gov Identifier: NCT04517825, Registered 18 August 2020. Available at: https://clinicaltrials.gov/ct2/show/NCT04517825

The impact of earthquakes in Latin America on the continuity of HIV care: A retrospective observational cohort study

Objectives: As earthquakes occur frequently in Latin America and can cause significant disruptions in HIV care, we sought to analyze patterns of HIV care for adults at Latin American clinical sites experiencing a significant earthquake within the past two decades. Study design: Retrospective clinical cohort study. Methods: Adults receiving HIV care at sites experiencing at least a “moderate intensity” (Modified Mercalli scale) earthquake in the Caribbean, Central and South America network for HIV epidemiology (CCASAnet) contributed data from 2003 to 2017. Interrupted Time Series models were fit with discontinuities at site-specific earthquake dates (Sept. 16, 2015 in Chile; Apr. 18, 2014 and Sept. 19, 2017 in Mexico; and Aug. 15, 2007 in Peru) to assess clinical visit, CD4 measure, viral load lab, and ART initiation rates 3- and 6-months after versus before earthquakes. Results: Comparing post-to pre-earthquake periods, there was a sharp drop in median visit (incidence rate ratio [IRR] = 0.79, 95% confidence interval [CI]: 0.68–0.91) and viral load lab (IRR = 0.78, 95% CI: 0.62–0.99) rates per week, using a 3-month window. CD4 measurement rates also decreased (IRR = 0.43; 95% CI: 0.37–0.51), though only using a 6-month window. Conclusions: Given that earthquakes occur frequently in Latin America, disaster preparedness plans must be more broadly implemented to avoid disruptions in HIV care and attendant poor outcomes