Statistical Communications in Infectious Diseases

Published by De Gruyter

Volume 13 Issue 1

January 2021

Issue of Statistical Communications in Infectious Diseases

Contents
Journal Overview

Research Articles

Open Access November 24, 2021

Confidence limits for the averted infections ratio estimated via the counterfactual placebo incidence rate

David T. Dunn, Oliver T. Stirrup, David V. Glidden

Article number: 20210002

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Abstract

Objectives The averted infections ratio (AIR) is a novel measure for quantifying the preservation-of-effect in active-control non-inferiority clinical trials with a time-to-event outcome. In the main formulation, the AIR requires an estimate of the counterfactual placebo incidence rate. We describe two approaches for calculating confidence limits for the AIR given a point estimate of this parameter, a closed-form solution based on a Taylor series expansion (delta method) and an iterative method based on the profile-likelihood. Methods For each approach, exact coverage probabilities for the lower and upper confidence limits were computed over a grid of values of (1) the true value of the AIR (2) the expected number of counterfactual events (3) the effectiveness of the active-control treatment. Results Focussing on the lower confidence limit, which determines whether non-inferiority can be declared, the coverage achieved by the delta method is either less than or greater than the nominal coverage, depending on the true value of the AIR. In contrast, the coverage achieved by the profile-likelihood method is consistently accurate. Conclusions The profile-likelihood method is preferred because of better coverage properties, but the simpler delta method is valid when the experimental treatment is no less effective than the control treatment. A complementary Bayesian approach, which can be applied when the counterfactual incidence rate can be represented as a prior distribution, is also outlined.

November 10, 2021

Sample size calculation for active-arm trial with counterfactual incidence based on recency assay

Fei Gao, David V. Glidden, James P. Hughes, Deborah J. Donnell

Article number: 20200009

Abstract

Objectives The past decade has seen tremendous progress in the development of biomedical agents that are effective as pre-exposure prophylaxis (PrEP) for HIV prevention. To expand the choice of products and delivery methods, new medications and delivery methods are under development. Future trials of non-inferiority, given the high efficacy of ARV-based PrEP products as they become current or future standard of care, would require a large number of participants and long follow-up time that may not be feasible. This motivates the construction of a counterfactual estimate that approximates incidence for a randomized concurrent control group receiving no PrEP. Methods We propose an approach that is to enroll a cohort of prospective PrEP users and aug-ment screening for HIV with laboratory markers of duration of HIV infection to indicate recent infections. We discuss the assumptions under which these data would yield an estimate of the counterfactual HIV incidence and develop sample size and power calculations for comparisons to incidence observed on an investigational PrEP agent. Results We consider two hypothetical trials for men who have sex with men (MSM) and transgender women (TGW) from different regions and young women in sub-Saharan Africa. The calculated sample sizes are reasonable and yield desirable power in simulation studies. Conclusions Future one-arm trials with counterfactual placebo incidence based on a recency assay can be conducted with reasonable total screening sample sizes and adequate power to determine treatment efficacy.

Open Access November 4, 2021

Principal surrogates in context of high vaccine efficacy

Andrea Callegaro, Fabian Tibaldi, Dean Follmann

Article number: 20200003

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Abstract

Objectives The use of correlates of protection (CoPs) in vaccination trials offers significant advantages as useful clinical endpoint substitutes. Vaccines with very high vaccine efficacy (VE) are documented in the literature (95% or above). Callegaro, A., and F. Tibaldi. 2019. “Assessing Correlates of Protection in Vaccine Trials: Statistical Solutions in the Context of High Vaccine Efficacy.” BMC Medical Research Methodology 19: 47 showed that the rare infections observed in the vaccinated groups of these trials poses challenges when applying conventionally-used statistical methods for CoP assessment such as the Prentice criteria and meta-analysis. The objective of this work is to investigate the impact of this problem on another statistical method for the assessment of CoPs called Principal stratification. Methods We perform simulation experiments to investigate the effect of high vaccine efficacy on the performance of the Principal Stratification approach. Results Similarly to the Prentice framework, simulation results show that the power of the Principal Stratification approach decreases when the VE grows. Conclusions It can be challenging to validate principal surrogates (and statistical surrogates) for vaccines with very high vaccine efficacy.

Open Access October 11, 2021

Evaluating the power of the causal impact method in observational studies of HCV treatment as prevention

Pantelis Samartsidis, Natasha N. Martin, Victor De Gruttola, Frank De Vocht, Sharon Hutchinson, Judith J. Lok, Amy Puenpatom, Rui Wang, Matthew Hickman, Daniela De Angelis

Article number: 20200005

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Abstract

Objectives The causal impact method (CIM) was recently introduced for evaluation of binary interventions using observational time-series data. The CIM is appealing for practical use as it can adjust for temporal trends and account for the potential of unobserved confounding. However, the method was initially developed for applications involving large datasets and hence its potential in small epidemiological studies is still unclear. Further, the effects that measurement error can have on the performance of the CIM have not been studied yet. The objective of this work is to investigate both of these open problems. Methods Motivated by an existing dataset of HCV surveillance in the UK, we perform simulation experiments to investigate the effect of several characteristics of the data on the performance of the CIM. Further, we quantify the effects of measurement error on the performance of the CIM and extend the method to deal with this problem. Results We identify multiple characteristics of the data that affect the ability of the CIM to detect an intervention effect including the length of time-series, the variability of the outcome and the degree of correlation between the outcome of the treated unit and the outcomes of controls. We show that measurement error can introduce biases in the estimated intervention effects and heavily reduce the power of the CIM. Using an extended CIM, some of these adverse effects can be mitigated. Conclusions The CIM can provide satisfactory power in public health interventions. The method may provide misleading results in the presence of measurement error.

June 4, 2021

GLM based auto-regressive process to model Covid-19 pandemic in Turkey

Aylin Alin

Article number: 20200006

Abstract

Objectives: Our objective is to propose a robust approach to model daily new cases and daily new deaths due to covid-19 infection in Turkey. Methods: We consider the generalized linear model (GLM) approach for the autoregressive process (AR) with log link for modelling. We study the data between March 11, 2020 that is the date first confirmed case occurred and October 20, 2020. After a month of the first outbreak in Turkey, the first official curfew has been imposed during the weekend. Since then there have been curfews each weekend till June 1st. Hence, we include intervention effects as well as some outlying data points in the model where necessary. We use the data between March 11 and September 15 to build the models, and test the performance on the data from September 16 till October 20. We also study the consistency of the model statistics. Results: Estimated models fit data quite well. Results reveal that after the first curfew daily new Covid-19 cases decrease 18.5%. As expected, effect of the curfew gets more significant once a month is past, and daily new cases cut down 24.9%. Our approach also gives a robust estimate for the effective reproduction number that is approximately 2 meaning as of October 20, 2020 there is still a risk for an infected person to cause 2 secondary infections despite all the interventions, preventions, and rules. Conclusion: The GLM approach for AR process with log link produces consistent and robust estimates for the daily new cases and daily new deaths for the data covering almost the first year of the pandemic in Turkey. The proposed approach can also be used to model the cases in other countries.

January 8, 2021

Contact network uncertainty in individual level models of infectious disease transmission

Waleed Almutiry, Rob Deardon

Article number: 20190012

Abstract

Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. This contact network can be spatial in nature, with connections between individuals closer in space being more likely. However, contact network data are often unobserved. Here, we consider the fit of an individual level model containing a spatially-based contact network that is either entirely, or partially, unobserved within a Bayesian framework, using data augmented Markov chain Monte Carlo (MCMC). We also incorporate the uncertainty about event history in the disease data. We also examine the performance of the data augmented MCMC analysis in the presence or absence of contact network observational models based upon either knowledge about the degree distribution or the total number of connections in the network. We find that the latter tend to provide better estimates of the model parameters and the underlying contact network.

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