Statistical communications in infectious diseases最新文献

Study design approaches for future active-controlled HIV prevention trials. 未来主动控制艾滋病预防试验的研究设计方法。

Statistical communications in infectious diseases Pub Date : 2024-01-22 eCollection Date: 2024-01-01 DOI: 10.1515/scid-2023-0002

Deborah Donnell, Sheila Kansiime, David V Glidden, Alex Luedtke, Peter B Gilbert, Fei Gao, Holly Janes

{"title":"Study design approaches for future active-controlled HIV prevention trials.","authors":"Deborah Donnell, Sheila Kansiime, David V Glidden, Alex Luedtke, Peter B Gilbert, Fei Gao, Holly Janes","doi":"10.1515/scid-2023-0002","DOIUrl":"10.1515/scid-2023-0002","url":null,"abstract":"Objectives: Vigorous discussions are ongoing about future efficacy trial designs of candidate human immunodeficiency virus (HIV) prevention interventions. The study design challenges of HIV prevention interventions are considerable given rapid evolution of the prevention landscape and evidence of multiple modalities of highly effective products; future trials will likely be 'active-controlled', i.e., not include a placebo arm. Thus, novel design approaches are needed to accurately assess new interventions against these highly effective active controls.Methods: To discuss active control design challenges and identify solutions, an initial virtual workshop series was hosted and supported by the International AIDS Enterprise (October 2020-March 2021). Subsequent symposia discussions continue to advance these efforts. As the non-inferiority design is an important conceptual reference design for guiding active control trials, we adopt several of its principles in our proposed design approaches.Results: We discuss six potential study design approaches for formally evaluating absolute prevention efficacy given data from an active-controlled HIV prevention trial including using data from: 1) a registrational cohort, 2) recency assays, 3) an external trial placebo arm, 4) a biomarker of HIV incidence/exposure, 5) an anti-retroviral drug concentration as a mediator of prevention efficacy, and 6) immune biomarkers as a mediator of prevention efficacy.Conclusions: Our understanding of these proposed novel approaches to future trial designs remains incomplete and there are many future statistical research needs. Yet, each of these approaches, within the context of an active-controlled trial, have the potential to yield reliable evidence of efficacy for future biomedical interventions.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"15 1","pages":"20230002"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10798828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139514088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The role of randomization inference in unraveling individual treatment effects in early phase vaccine trials. 随机化推断在早期疫苗试验中揭示个体治疗效果的作用。

Statistical communications in infectious diseases Pub Date : 2024-01-01 Epub Date: 2024-08-12 DOI: 10.1515/scid-2024-0001

Zhe Chen, Xinran Li, Bo Zhang

{"title":"The role of randomization inference in unraveling individual treatment effects in early phase vaccine trials.","authors":"Zhe Chen, Xinran Li, Bo Zhang","doi":"10.1515/scid-2024-0001","DOIUrl":"https://doi.org/10.1515/scid-2024-0001","url":null,"abstract":"Randomization inference is a powerful tool in early phase vaccine trials when estimating the causal effect of a regimen against a placebo or another regimen. Randomization-based inference often focuses on testing either Fisher's sharp null hypothesis of no treatment effect for any participant or Neyman's weak null hypothesis of no sample average treatment effect. Many recent efforts have explored conducting exact randomization-based inference for other summaries of the treatment effect profile, for instance, quantiles of the treatment effect distribution function. In this article, we systematically review methods that conduct exact, randomization-based inference for quantiles of individual treatment effects (ITEs) and extend some results to a special case where naïve participants are expected not to exhibit responses to highly specific endpoints. These methods are suitable for completely randomized trials, stratified completely randomized trials, and a matched study comparing two non-randomized arms from possibly different trials. We evaluate the usefulness of these methods using synthetic data in simulation studies. Finally, we apply these methods to HIV Vaccine Trials Network Study 086 (HVTN 086) and HVTN 205 and showcase a wide range of application scenarios of the methods. R code that replicates all analyses in this article can be found in first author's GitHub page at https://github.com/Zhe-Chen-1999/ITE-Inference.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11466280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonlinear mixed-effects models for HIV viral load trajectories before and after antiretroviral therapy interruption, incorporating left censoring. 抗逆转录病毒治疗中断前后HIV病毒载量轨迹的非线性混合效应模型，包括左删减。

Statistical communications in infectious diseases Pub Date : 2022-04-04 eCollection Date: 2022-01-01 DOI: 10.1515/scid-2021-0001

Sihaoyu Gao, Lang Wu, Tingting Yu, Roger Kouyos, Huldrych F Günthard, Rui Wang

{"title":"Nonlinear mixed-effects models for HIV viral load trajectories before and after antiretroviral therapy interruption, incorporating left censoring.","authors":"Sihaoyu Gao, Lang Wu, Tingting Yu, Roger Kouyos, Huldrych F Günthard, Rui Wang","doi":"10.1515/scid-2021-0001","DOIUrl":"https://doi.org/10.1515/scid-2021-0001","url":null,"abstract":"Objectives: Characterizing features of the viral rebound trajectories and identifying host, virological, and immunological factors that are predictive of the viral rebound trajectories are central to HIV cure research. We investigate if key features of HIV viral decay and CD4 trajectories during antiretroviral therapy (ART) are associated with characteristics of HIV viral rebound following ART interruption.Methods: Nonlinear mixed effect (NLME) models are used to model viral load trajectories before and following ART interruption, incorporating left censoring due to lower detection limits of viral load assays. A stochastic approximation EM (SAEM) algorithm is used for parameter estimation and inference. To circumvent the computational intensity associated with maximizing the joint likelihood, we propose an easy-to-implement three-step method.Results: We evaluate the performance of the proposed method through simulation studies and apply it to data from the Zurich Primary HIV Infection Study. We find that some key features of viral load during ART (e.g., viral decay rate) are significantly associated with important characteristics of viral rebound following ART interruption (e.g., viral set point).Conclusions: The proposed three-step method works well. We have shown that key features of viral decay during ART may be associated with important features of viral rebound following ART interruption.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"14 1","pages":"20210001"},"PeriodicalIF":0.0,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204768/pdf/scid-14-1-scid-2021-0001.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40635525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Estimation and interpretation of vaccine efficacy in COVID-19 randomized clinical trials COVID-19随机临床试验中疫苗疗效的估计和解释

Statistical communications in infectious diseases Pub Date : 2022-01-01 DOI: 10.1101/2022.02.02.22270317

Hege Michiels, A. Vandebosch, S. Vansteelandt

{"title":"Estimation and interpretation of vaccine efficacy in COVID-19 randomized clinical trials","authors":"Hege Michiels, A. Vandebosch, S. Vansteelandt","doi":"10.1101/2022.02.02.22270317","DOIUrl":"https://doi.org/10.1101/2022.02.02.22270317","url":null,"abstract":"Abstract Objectives An exceptional effort by the scientific community has led to the development of multiple vaccines against COVID-19. Efficacy estimates for these vaccines have been widely communicated to the general public, but are nonetheless challenging to compare because they are based on phase 3 trials that differ in study design, definition of vaccine efficacy and the handling of cases arising shortly after vaccination. We investigate the impact of these choices on vaccine efficacy estimates, both theoretically and by re-analyzing the Janssen and Pfizer COVID-19 trial data under a uniform protocol. We moreover study the causal interpretation that can be assigned to per-protocol analyses typically performed in vaccine trials. Finally, we propose alternative estimands to measure the intrinsic vaccine efficacy in settings with delayed immune response. Methods The data of the Janssen COVID-19 trials were recreated, based on the published Kaplan-Meier curves. An estimator for the alternative causal estimand was developed using a Structural Distribution Model. Results In the data analyses, we observed rather large differences between intention-to-treat and per-protocol effect estimates. In contrast, the causal estimand and the different estimators used for per-protocol effects lead approximately to the same estimates. Conclusions In these COVID-10 vaccine trials, per-protocol effects can be interpreted as the number of cases that can be avoided by vaccination, if the vaccine would immediately induce an immune response. However, it is unclear whether this interpretation also holds in other settings.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"537 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77909421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sample size calculation for active-arm trial with counterfactual incidence based on recency assay. 基于重现性测定的反事实发生率的主动臂试验样本量计算。

Statistical communications in infectious diseases Pub Date : 2021-11-10 eCollection Date: 2021-01-01 DOI: 10.1515/scid-2020-0009

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

{"title":"Sample size calculation for active-arm trial with counterfactual incidence based on recency assay.","authors":"Fei Gao, David V Glidden, James P Hughes, Deborah J Donnell","doi":"10.1515/scid-2020-0009","DOIUrl":"10.1515/scid-2020-0009","url":null,"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.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":" ","pages":"20200009"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8865397/pdf/scid-13-1-scid-2020-0009.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40540204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Evaluating the power of the causal impact method in observational studies of HCV treatment as prevention. 评价因果影响法在HCV治疗预防观察性研究中的有效性。

Statistical communications in infectious diseases Pub Date : 2021-10-11 eCollection Date: 2021-01-01 DOI: 10.1515/scid-2020-0005

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

{"title":"Evaluating the power of the causal impact method in observational studies of HCV treatment as prevention.","authors":"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","doi":"10.1515/scid-2020-0005","DOIUrl":"https://doi.org/10.1515/scid-2020-0005","url":null,"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.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":" ","pages":"20200005"},"PeriodicalIF":0.0,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204771/pdf/scid-13-1-scid-2020-0005.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40540203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Contact network uncertainty in individual level models of infectious disease transmission. 传染病传播个体水平模型中的接触网络不确定性。

Statistical communications in infectious diseases Pub Date : 2021-01-08 eCollection Date: 2021-01-01 DOI: 10.1515/scid-2019-0012

Waleed Almutiry, Rob Deardon

引用次数: 2

GLM based auto-regressive process to model Covid-19 pandemic in Turkey 基于GLM的自回归过程模拟土耳其Covid-19大流行

Statistical communications in infectious diseases Pub Date : 2021-01-01 DOI: 10.1515/scid-2020-0006

A. Alin

{"title":"GLM based auto-regressive process to model Covid-19 pandemic in Turkey","authors":"A. Alin","doi":"10.1515/scid-2020-0006","DOIUrl":"https://doi.org/10.1515/scid-2020-0006","url":null,"abstract":"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.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84902662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Confidence limits for the averted infections ratio estimated via the counterfactual placebo incidence rate. 通过反事实安慰剂发生率估计的避免感染率的置信限。

Statistical communications in infectious diseases Pub Date : 2021-01-01 DOI: 10.1515/scid-2021-0002

David T Dunn, Oliver T Stirrup, David V Glidden

{"title":"Confidence limits for the averted infections ratio estimated via the counterfactual placebo incidence rate.","authors":"David T Dunn, Oliver T Stirrup, David V Glidden","doi":"10.1515/scid-2021-0002","DOIUrl":"https://doi.org/10.1515/scid-2021-0002","url":null,"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.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"13 1","pages":"20210002"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10115973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluating the relative contribution of data sources in a Bayesian analysis with the application of estimating the size of hard to reach populations. 评估贝叶斯分析中数据源的相对贡献，并应用于估计难以到达的人口的规模。

Statistical communications in infectious diseases Pub Date : 2020-12-16 DOI: 10.1515/scid-2019-0020

Jacob Parsons, Xiaoyue Niu, Le Bao

{"title":"Evaluating the relative contribution of data sources in a Bayesian analysis with the application of estimating the size of hard to reach populations.","authors":"Jacob Parsons, Xiaoyue Niu, Le Bao","doi":"10.1515/scid-2019-0020","DOIUrl":"https://doi.org/10.1515/scid-2019-0020","url":null,"abstract":"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. 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. 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.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/scid-2019-0020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39379908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2