Statistical Communications in Infectious Diseases

Published by De Gruyter

Volume 10 Issue 1 -

December 2018

Issue of Statistical Communications in Infectious Diseases

Contents
Journal Overview

Articles

July 31, 2018

Cross-Sectional HIV Incidence Estimation with Missing Biomarkers

Doug Morrison, Oliver Laeyendecker, Jacob Konikoff, Ron Brookmeyer

Article number: 20170003

Abstract

Considerable progress has been made in the development of approaches for HIV incidence estimation based on a cross-sectional survey for biomarkers of recent infection. Multiple biomarkers when used in combination can increase the precision of cross-sectional HIV incidence estimates. Multi-assay algorithms (MAAs) for cross-sectional HIV incidence estimation are hierarchical stepwise algorithms for testing the biological samples with multiple biomarkers. The objective of this paper is to consider some of the statistical challenges for addressing the problem of missing biomarkers in such testing algorithms. We consider several methods for handling missing biomarkers for (1) estimating the mean window period, and (2) estimating HIV incidence from a cross sectional survey once the mean window period has been determined. We develop a conditional estimation approach for addressing the missing data challenges and compare that method with two naïve approaches. Using MAAs developed for HIV subtype B, we evaluate the methods by simulation. We show that the two naïve estimation methods lead to biased results in most of the missing data scenarios considered. The proposed conditional approach protects against bias in all of the scenarios.

August 1, 2018

Spatially Informed Back-Calculation for Spatio-Temporal Infectious Disease Models

Gyanendra Pokharel, Rob Deardon

Article number: 20170001

Abstract

In epidemiological studies, the complete history of the disease system is seldom available; for example, we rarely observe the infection times of individuals but rather dates of diagnosis/disease reporting. The method of back-calculation together with prior knowledge about the distribution of the time from the infection to the disease reporting, called the incubation period, can be used to estimate unobserved infection times. Here, we consider the use of back-calculation in the context of spatial infectious disease models, extending the method to incorporate spatial information in the back-calculation method itself. Such a method should improve the quality of the fitted model, allowing us to better identify characteristics of the disease system of interest. We show that it is possible to better infer the underlying disease dynamics via the method of spatial back-calculation.

article-commentary

Publicly Available August 29, 2018

Analysis Populations in Anti-Infective Clinical Trials: Whom to Analyze?

Scott Evans, Daniel B. Rubin, John H. Powers, Dean Follmann

Article number: 20170002

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Abstract

Investigators can choose to analyze different patient populations in clinical trials. The different analysis populations answer different types of research questions, estimate different quantities, and evaluate the robustness of the trial results. Various analysis populations have different strengths and weaknesses depending on the type of question being addressed and the potential for bias from the selection of various groups of trial participants. We discuss analysis populations in the context of anti-infective clinical trials.

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