Statistical Communications in Infectious Diseases

Published by De Gruyter

Volume 12 Issue s1 - Special Issue: Statistical Challenges and Opportunities in HIV/AIDS Research in the Era of Getting-to-Zero HIV Infections; Guest Editors: Misrak Gezmu, C. Jason Liang

September 2020

Issue of Statistical Communications in Infectious Diseases

Contents
Journal Overview

Editorial

November 24, 2020

Forward

Misrak Gezmu, C. Jason Liang

Article number: 20200010

Research Articles

December 11, 2020

Pre-selected class-level testing of longitudinal biomarkers reduces required multiple testing corrections to yield novel insights in longitudinal small sample human studies

Andrea S. Foulkes, Livio Azzoni, Luis J. Montaner

Article number: 20190018

Abstract

Objectives Exploratory studies that aim to evaluate novel therapeutic strategies in human cohorts often involve the collection of hundreds of variables measured over time on a small sample of individuals. Stringent error control for testing hypotheses in this setting renders it difficult to identify statistically signification associations. The objective of this study is to demonstrate how leveraging prior information about the biological relationships among variables can increase power for novel discovery. Methods We apply the class level association score statistic for longitudinal data (CLASS-LD) as an analysis strategy that complements single variable tests. An example is presented that aims to evaluate the relationships among 14 T-cell and monocyte activation variables measured with CD4 T-cell count over three time points after antiretroviral therapy (n=62). Results CLASS-LD using three classes with emphasis on T-cell activation with either classical vs. intermediate/inflammatory monocyte subsets detected associations in two of three classes, while single variable testing detected only one out of the 14 variables considered. Conclusions Application of a class-level testing strategy provides an alternative to single immune variables by defining hypotheses based on a collection of variables that share a known underlying biological relationship. Broader use of class-level analysis is expected to increase the available information that can be derived from limited sample clinical studies.

September 24, 2020

Joint modeling of time-varying HIV exposure and infection for estimation of per-act efficacy in HIV prevention trials

Elizabeth R. Brown, Clara P. Dominguez Islas, Jingyang Zhang

Article number: 20190016

Abstract

Objectives: Using the MTN-020/ASPIRE HIV prevention trial as a motivating example, our objective is to construct a joint model for the HIV exposure process through vaginal intercourse and the time to HIV infection in a population of sexually active women. By modeling participants’ HIV infection in terms of exposures, rather than time exposed, our aim is to obtain a valid estimate of the per-act efficacy of a preventive intervention. Methods: Within the context of HIV prevention trials, in which the frequency of sex acts is self-reported periodically by the participants, we model the exposure process of the trial participants with a non-homogeneous Poisson process. This approach allows for variability in the rate of sexual contacts between participants as well as variability in the rate of sexual contacts over time. The time to HIV infection for each participant is modeled as the time to the exposure that results in HIV infection, based on the modeled sexual contact rate. We propose an empirical Bayes approach for estimation. Results: We report the results of a simulation study where we evaluate the performance of our proposed approach and compare it to the traditional approach of estimating the overall reduction in HIV incidence using a Proportional Hazards Cox model. The proposed approach is also illustrated with data from the MTN-020/ASPIRE trial. Conclusions: The proposed joint modeling, along with the proposed empirical Bayes estimation approach, can provide valid estimation of the per-exposure efficacy of a preventive intervention.

August 21, 2020

Comparison of empirical and dynamic models for HIV viral load rebound after treatment interruption

Ante Bing, Yuchen Hu, Melanie Prague, Alison L. Hill, Jonathan Z. Li, Ronald J. Bosch, Victor DeGruttola, Rui Wang

Article number: 20190021

Abstract

Objective To compare empirical and mechanistic modeling approaches for describing HIV-1 RNA viral load trajectories after antiretroviral treatment interruption and for identifying factors that predict features of viral rebound process. Methods We apply and compare two modeling approaches in analysis of data from 346 participants in six AIDS Clinical Trial Group studies. From each separate analysis, we identify predictors for viral set points and delay in rebound. Our empirical model postulates a parametric functional form whose parameters represent different features of the viral rebound process, such as rate of rise and viral load set point. The viral dynamics model augments standard HIV dynamics models–a class of mathematical models based on differential equations describing biological mechanisms–by including reactivation of latently infected cells and adaptive immune response. We use Monolix, which makes use of a Stochastic Approximation of the Expectation–Maximization algorithm, to fit non-linear mixed effects models incorporating observations that were below the assay limit of quantification. Results Among the 346 participants, the median age at treatment interruption was 42. Ninety-three percent of participants were male and sixty-five percent, white non-Hispanic. Both models provided a reasonable fit to the data and can accommodate atypical viral load trajectories. The median set points obtained from two approaches were similar: 4.44 log 10 copies/mL from the empirical model and 4.59 log 10 copies/mL from the viral dynamics model. Both models revealed that higher nadir CD4 cell counts and ART initiation during acute/recent phase were associated with lower viral set points and identified receiving a non-nucleoside reverse transcriptase inhibitor (NNRTI)-based pre-ATI regimen as a predictor for a delay in rebound. Conclusion Although based on different sets of assumptions, both models lead to similar conclusions regarding features of viral rebound process.

November 12, 2020

Comparison of machine learning methods for predicting viral failure: a case study using electronic health record data

Allan Kimaina, Jonathan Dick, Allison DeLong, Stavroula A. Chrysanthopoulou, Rami Kantor, Joseph W. Hogan

Article number: 20190017

Abstract

Background Human immunodeficiency virus (HIV) viral failure occurs when antiretroviral therapy fails to suppress and sustain a person’s viral load count below 1,000 copies of viral ribonucleic acid per milliliter. For those newly diagnosed with HIV and living in a setting where healthcare resources are limited, such as a low- and middle-income country, the World Health Organization recommends viral load monitoring six months after initiation of antiretroviral treatment and yearly thereafter. Deviations from this schedule are made in cases where viral failure occurs or at the discretion of the clinician. Failure to detect viral failure in a timely fashion can lead to delayed administration of essential interventions. Clinical prediction models based on information available in the patient medical record are increasingly being developed and deployed for decision support in clinical medicine and public health. This raises the possibility that prediction models can be used to detect potential for viral failure in advance of viral measurements, particularly when those measurements occur infrequently. Objective Our goal is to use electronic health record data from a large HIV care program in Kenya to characterize and compare the predictive accuracy of several statistical machine learning methods for predicting viral failure at the first and second measurements following initiation of antiretroviral therapy. Predictive accuracy is measured in terms of sensitivity, specificity and area under the receiver-operator characteristic curve. Methods We trained and cross-validated 10 statistical machine learning models and algorithms on data from over 10,000 patients in the Academic Model Providing Access to Healthcare care program in western Kenya. These included parametric, non-parametric, ensemble, and Bayesian methods. The input variables included 50 items from the clinical record, hand picked in consultation with clinician experts. Predictive accuracy measures were calculated using 10-fold cross validation. Results Viral load failure rate is about 20% in this patient cohort at both the first and second measurements. Ensemble techniques generally outperformed other methods. For predicting viral failure at the first follow up measure, specificity was over 90% for these methods, but sensitivity was typically in the 50–60% range. Predictive accuracy was greater for the second follow up measure, with sensitivities over 80%. Super Learner, gradient boosting and Bayesian additive regression trees consistently outperformed other methods. For a viral failure rate of 20%, the positive predictive value for the top-performing methods is between 75 and 85%, while the negative predictive value is over 95%. Conclusion Evidence from this study suggests that machine learning techniques have potential to identify patients at risk for viral failure prior to their scheduled measurements. Ultimately, prognostic virologic assessment can help guide the administration of earlier targeted intervention such as enhanced drug resistance monitoring, rigorous adherence counseling, or appropriate next-line therapy switching. External validation studies should be used to confirm the results found here.

November 11, 2020

A semiparametric method for the analysis of outcomes during a gap in HIV care under incomplete outcome ascertainment

Giorgos Bakoyannis, Lameck Diero, Ann Mwangi, Kara K. Wools-Kaloustian, Constantin T. Yiannoutsos

Article number: 20190013

Abstract

Objectives Estimation of the cascade of HIV care is essential for evaluating care and treatment programs, informing policy makers and assessing targets such as 90-90-90. A challenge to estimating the cascade based on electronic health record concerns patients “churning” in and out of care. Correctly estimating this dynamic phenomenon in resource-limited settings, such as those found in sub-Saharan Africa, is challenging because of the significant death under-reporting. An approach to partially recover information on the unobserved deaths is a double-sampling design, where a small subset of individuals with a missed clinic visit is intensively outreached in the community to actively ascertain their vital status. This approach has been adopted in several programs within the East Africa regional IeDEA consortium, the context of our motivating study. The objective of this paper is to propose a semiparametric method for the analysis of competing risks data with incomplete outcome ascertainment. Methods Based on data from double-sampling designs, we propose a semiparametric inverse probability weighted estimator of key outcomes during a gap in care, which are crucial pieces of the care cascade puzzle. Results Simulation studies suggest that the proposed estimators provide valid estimates in settings with incomplete outcome ascertainment under a set of realistic assumptions. These studies also illustrate that a naïve complete-case analysis can provide seriously biased estimates. The methodology is applied to electronic health record data from the East Africa IeDEA Consortium to estimate death and return to care during a gap in care. Conclusions The proposed methodology provides a robust approach for valid inferences about return to care and death during a gap in care, in settings with death under-reporting. Ultimately, the resulting estimates will have significant consequences on program construction, resource allocation, policy and decision making at the highest levels.

October 7, 2020

Errors in multiple variables in human immunodeficiency virus (HIV) cohort and electronic health record data: statistical challenges and opportunities

Bryan E. Shepherd, Pamela A. Shaw

Article number: 20190015

Abstract

Objectives: Observational data derived from patient electronic health records (EHR) data are increasingly used for human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) research. There are challenges to using these data, in particular with regards to data quality; some are recognized, some unrecognized, and some recognized but ignored. There are great opportunities for the statistical community to improve inference by incorporating validation subsampling into analyses of EHR data. Methods: Methods to address measurement error, misclassification, and missing data are relevant, as are sampling designs such as two-phase sampling. However, many of the existing statistical methods for measurement error, for example, only address relatively simple settings, whereas the errors seen in these datasets span multiple variables (both predictors and outcomes), are correlated, and even affect who is included in the study. Results/Conclusion: We will discuss some preliminary methods in this area with a particular focus on time-to-event outcomes and outline areas of future research.

November 11, 2020

Challenges in evaluating the use of viral sequence data to identify HIV transmission networks for public health

Rami Kantor, John P. Fulton, Jon Steingrimsson, Vladimir Novitsky, Mark Howison, Fizza Gillani, Yuanning Li, Akarsh Manne, Zoanne Parillo, Matthew Spence, Theodore Marak, Philip Chan, Casey W. Dunn, Thomas Bertrand, Utpala Bandy, Nicole Alexander-Scott, Joseph W. Hogan

Article number: 20190019

Abstract

Great efforts are devoted to end the HIV epidemic as it continues to have profound public health consequences in the United States and throughout the world, and new interventions and strategies are continuously needed. The use of HIV sequence data to infer transmission networks holds much promise to direct public heath interventions where they are most needed. As these new methods are being implemented, evaluating their benefits is essential. In this paper, we recognize challenges associated with such evaluation, and make the case that overcoming these challenges is key to the use of HIV sequence data in routine public health actions to disrupt HIV transmission networks.

December 16, 2020

Evaluating the relative contribution of data sources in a Bayesian analysis with the application of estimating the size of hard to reach populations

Jacob Parsons, Xiaoyue Niu, Le Bao

Article number: 20190020

Abstract

Objectives When using multiple data sources in an analysis, it is important to understand the influence of each data source on the analysis and the consistency of the data sources with each other and the model. We suggest the use of a retrospective value of information framework in order to address such concerns. Methods Value of information methods can be computationally difficult. We illustrate the use of computational methods that allow these methods to be applied even in relatively complicated settings. In illustrating the proposed methods, we focus on an application in estimating the size of hard to reach populations. Specifically, we consider estimating the number of injection drug users in Ukraine by combining all available data sources spanning over half a decade and numerous sub-national areas in the Ukraine. This application is of interest to public health researchers as this hard to reach population that plays a large role in the spread of HIV. Results and conclusions We apply a Bayesian hierarchical model and evaluate the contribution of each data source in terms of absolute influence, expected influence, and level of surprise. Finally we apply value of information methods to inform suggestions on future data collection.

About this journal

Objective
The mission of Statistical Communications in Infectious Diseases is to serve as the primary vehicle for the communication and education of statistical thinking in infectious disease research and policy.

The infectious diseases community faces many difficult challenges. These include: (1) coping with continuing high-impact diseases such as HIV, malaria, TB, and flu; (2) dealing with infectious disease outbreaks such as Ebola, pandemic avian influenza, or SARS; and (3) preparing for the inevitable emergence of diseases that are unknown or are recognized but will reemerge in a more threatening form (e.g., antibiotic resistant [nightmare] bacteria or superbugs). Research in infectious diseases is also challenged by funding limitations, politics, and ethical dilemmas.

Increasingly complex data is also creating new challenges to the design and analysis of research studies. Enhanced statistical expertise is essential to address these challenges and to develop and evaluate medical and public health responses to potential outbreaks and epidemics.

Statistical Communications in Infectious Diseases (SCID) publishes significant research on the application of statistical ideas to problems arising from studies of infectious diseases. SCID takes a broad perspective on the role of statistics in infectious disease research including application, policy, education and theory. SCID fosters much-needed communication among statisticians on the best approaches to evolving complex infectious disease data, and is a venue for statisticians to enter a dialogue with other scientists and policy makers on the strengths and limitations of policies and methods for design, monitoring, analysis, and reporting of infectious disease research studies.

SCID goes beyond the application of statistical methods to data arising from infectious disease studies or new statistical strategies. SCID serves as a sounding board to discuss policy issues, as well as to accomplish the following goals: (1) engage and raise the quality of the discussions of important issues in the public and scientific press; (2) ensure that optimal scientific tools are utilized in infectious disease research; (3) improve the training and practice of the next generation of professionals involved in infectious diseases research through educational articles; (4) raise the profile of quantitative science in infectious disease research by demonstrating its value in decision making and public health.

Topics

How heavily should the public invest in research or implementation of different prevention modalities?
What is necessary to achieve control of epidemics (for example, do we require a vaccine to control the spread of HIV or what procedures are required to halt the spread of Ebola)?
How can statisticians help with grass roots epidemic control efforts?
How can we optimally evaluate the benefits vs. harms vs. costs of competing interventions?
How can we best characterize the trade-offs between civil liberties and public safety?
How should we adjust the traditionally required level of evidence to address areas of unmet medical need?
How do we revise our traditional approaches to the design, monitoring, analyses, and reporting of infectious disease studies to improve medical practice and public health?

Article formats
Original research articles, book reviews