Document Filetype: PDF | KB. 29 November, - Statistics In Clinical Vaccine Trials. VNHIPP PDF > CWPGZ4E2VM. 0. Statistics in Clinical Vaccine Trials. Bearbeitet von. Jozef Nauta. 1. Auflage Buch. xviii, S. Hardcover. ISBN 3 9. Format (B x L): 15,5 x. Guidance on various aspects of clinical trials of vaccines is also avail- .. tion, including statistical considerations, and the conditions under.
|Language:||English, Spanish, Arabic|
|ePub File Size:||27.69 MB|
|PDF File Size:||16.11 MB|
|Distribution:||Free* [*Regsitration Required]|
and expertise on the statistical analysis of data from clinical vaccine trials, i.e., ; Digitally watermarked, DRM-free; Included format: PDF. Request PDF on ResearchGate | Statistics in Clinical Vaccine Trials | This monograph offers well-founded training and expertise on the statistical analysis of. The focus of this book is the standard statistical methods employed in clinical trials developing vaccines. The descriptions of the methods are concise and clear .
Incidence , Cumulative incidence , Prevalence , Point prevalence , Period prevalence. Jonas Salk developed an attenuated vaccine to combat the rising prevalence of the disease around the world. Phase I clinical trials are the first step in drug development to test a new drug or drug combination on humans. The prentice criteria The following set of notations will be used throughout the manuscript: Research, Methods, Statistics Explore the latest in research, methods, and statistics, including topics in clinical research infrastructure, design, conduct, and analysis.
Please check your email for instructions on resetting your password. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username.
Statistics in Medicine Volume 38, Issue 7. Chenguang Wang Corresponding Author E-mail address: Gary L. Richard B. First published: Read the full text.
Tools Request permission Export citation Add to favorites Track citation. Share Give access Share full text access. Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article.
Abstract Phase I clinical trials are the first step in drug development to test a new drug or drug combination on humans. Within level 1, Qin et al.
A statistical SoP is an endpoint that satisfies the Prentice criteria [ 2 ], while a principal SoP is defined using a causal inference framework [ 3 — 5 , 10 , 11 ]. The latter aims to address post-randomisation selection bias by estimating what the vaccine responses would have been if the non-vaccinated group of a trial had been immunised. Such endpoints can be used to predict VE once they are validated and approved by a regulatory body.
In this manuscript, SoP endpoints are referred to as correlates of protection CoPs. Specifically, we address CoP levels 1 and 2, based on Qin et al. Moreover, we address the concept of CoPs in the context of a continuous, rather than a threshold approach [ 1 ]. These include the salmonella typhi vi coniugate [ 12 ], or the combined measles-mumps-rubella-varicella immunisation [ 17 ]. These trials raised the problematic of assessing CoPs in the context of high VE using classical statistical methods.
There is therefore a need to adapt statistical methods for CoP assessment to the context of high efficacy vaccines.
To the best of our knowledge, such tailored approaches are lacking in the literature. The aim of this manuscript is to present statistical solutions and to generate adapted methods to assess CoPs based on Prentice criteria and meta-analytic frameworks by randomized subgroups such as centers and regions in single trial setting STS with high VE.
The Prentice criteria and meta-analytic approach are two classical statistical methods used for assessing vaccine CoPs.
The following sections describe both methods, and our specific adaptations as statistical solutions for high VE settings.
The results section shows the performance of our proposed adapted models using simulations.
The following set of notations will be used throughout the manuscript: Key concepts, including the hypothesis-testing approach to the validation of substitute endpoints using randomised clinical trial data, were introduced by Prentice [ 2 ].
His four criteria for the validation of a surrogate endpoint can be adapted for vaccine trials as follows:. Therefore, criterion 4 is met if the null hypothesis H These include the proportion of treatment explained [ 19 ], the proportion of information gain [ 20 ], and the individual-level surrogacy measured by the information theoretic approach [ 21 ].
For simplicity, we assume no random intercepts here reduced model. This flexible model is popular for several reasons including: The meta-analytic approach can be applied when multiple randomized subgroups are available for analysis. However, when applying this method in a high VE setting, maximum likelihood ML subgroup-specific VE estimates may be infinite, causing classical meta-analytic methods that combine subgroup-specific VE to potentially fail.
To overcome this issue, we estimated subgroup-specific VE using the penalised likelihood method. Penalisation, which is equivalent to using proper priors on coefficients, solves the problem of infinite coefficient estimates.
To achieve this we applied two approaches: Gelman et al. As part of a two-step approach, we first independently executed the Firth method and Gelman approach using the logistf and bayesglm R packages respectively [ 30 , 31 ]. In a second step, we evaluated the performance of both methods as part of a meta-analysis in the context of high VE, by running simulations.
This is due to the lack of fit of the linear effect which is absorbed by the treatment effect, thereby considerably reducing the power to meet Prentice criterion 4. We can see that the scaled logistic model is slightly conservative. Standard errors of this model should be computed by bootstrap [ 27 ]. We considered the meta-analytic approach in a single trial setting.
The single trial was split into several relatively small randomized subgroups such as geographical regions or centers , and these small subgroups were used as units for the meta-analysis.
For illustration purposes, we analysed a publicly available simulated dataset containing both continuous outcome and surrogate endpoints [ 21 ]. This dataset consists of 50 subgroups characterised by a 1: Meta-analytic approach results on Alonso et al.
Alonso et al. Std R 2. MSE R 2. In fact, when the VE is 0. Both penalised approaches show very similar results.
Despite recent advances in immunology, we are only beginning to understand how vaccines work best, and how we can improve vaccine design for higher protective efficacy [ 32 ]. Although not common, vaccines with a high efficacy, are documented in the literature [ 12 — 17 , 33 ].
These include the salmonella typhi vi conjugate [ 12 ], or the combined measles-mumps-rubella-varicella immunisation [ 17 ]. Rare events data obtained in high VE trials make it challenging for statisticians to apply classical methods used for CoP assessment due to the lack of available information.
These include ML estimators, where bias, infinite estimates, multicollinearity and convergence issues can arise and negatively impact Prentice criteria and meta-analytic frameworks commonly used to assess vaccine CoPs, as shown in this paper [ 24 , 26 , 27 ]. To overcome this problem, we evaluated the impact of high VE using two classical statistical approaches: We chose these methods for their common usage in CoP assessments, and their user-friendly characteristics. We performed data simulations with high VE to illustrate the problems and to evaluate the proposed solutions.
By working on the Prentice framework, we show that it is critical to both design and evaluate flexible and adaptable models that are tailored to high VE cases, as the lack of fit of a model leads to substantial loss in power. Accordingly, we propose to analyse data using a logistic model with non-linear surrogate effect.
This popular model is flexible, with known properties, easy to fit and implemented in many standard softwares. The number of additional parameters should be small to avoid overfitting. Other models with flexible link functions have also been proposed that can be used within the Prentice framework [ 26 , 27 ]. Furthermore, adjustments for baseline covariates can play an important role in improving model fit. This problem can occur when VE is high where there is a high probability of observing zero cases in certain subgroups of the vaccinated group, as we have also shown.
For simplicity, we used a two-stage approach where treatment effects were estimated for each subgroup using a penalised likelihood approach, followed by a fixed effect meta-analysis to combine results from different subgroups. Another possibility is to use a mixed model with WIP or Jeffrey priors. For example, it is straightforward to implement the bivariate model, depicted in Eq.
Additional simulation studies, comparing one and two-stage penalised approaches, would therefore be worth pursuing to help overcome these problematics in the context of high VE. It is noteworthy that the concept of a vaccine CoP often refers to the establishment of a protective immunogenicity threshold as alluded to earlier, above which disease acquisition is unlikely to happen.
However, relating immunological biomarkers to disease risk and therefore VE can also be made possible as part of a continuous approach, without the assumption of a threshold titre. This manuscript addressed this type of continuous approach that employs fitted regression models on antibody titres in vaccinated and non-vaccinated individuals to show the statistical association between antibody titres and disease incidence [ 1 , 26 , 34 , 35 ].
Although this study was limited by its use of simulated data only, our results suggest that the solutions we propose substantially increase the power of classical statistical approaches for CoP assessment, when dealing with high VE.
Furthermore, they are straight-forward and compatible with standard statistical software. Following our observation that CoP assessments for high VE vaccines comes with statistical issues using standard methods, we devised flexible non-linear models to counteract the lack of fit in the Prentice framework, and propose penalized likelihood approaches for meta-analysis.
These statistical solutions are easy-to-implement adaptations to both conventional methods for application in high VE cases.
Such statistical challenges associated with high VE may have so far been overlooked due to their low occurrence, yet high VE cases exist. This study evaluates the inclusion of studies identified by the FDA as having falsified data in the results of meta-analyses.
This Viewpoint discusses the utility and limitations of big data analysis and machine learning in mental health care. This cross-sectional review evaluates the registration practices of randomized clinical trials in rhinosinusitis.
This randomized clinical trial examines whether an exercise intervention improves the Framingham Risk Score for cardiovascular disease among women with early-stage breast cancer with overweight condition or obesity. This note from the editors commends the honesty of authors who exemplify scientific integrity by having reported data errors in their article, which was retracted.
This Viewpoint highlights the drawbacks of US Food and Drug Administration approval of lenvatinib for unresectable hepatocellular cancer based on the results of a noninferiority trial. This Guide to Statistics and Methods discusses analytical approaches to accounting for differences in treatment effect by study center when randomized trials enroll patients and administer interventions at multiple sites.
All Rights Reserved. Research, Methods, Statistics Explore the latest in research, methods, and statistics, including topics in clinical research infrastructure, design, conduct, and analysis. Add to Your Interests. All Journals. Check All. Uncheck All. JAMA JAMA Surgery JAMA Pediatrics JAMA Psychiatry JAMA Oncology JAMA Neurology JAMA Ophthalmology JAMA Dermatology JAMA Cardiology All Article Types. Research