scholarly journals Confidence Intervals of COVID-19 Vaccine Efficacy Rates

Numeracy ◽  
2021 ◽  
Vol 14 (2) ◽  
Author(s):  
Frank Wang
Keyword(s):  
Confidence Intervals ◽  
Vaccine Efficacy ◽  
Unknown Parameter ◽  
Posterior Probability ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Efficacy Rate ◽  
Binomial Model ◽  
Posterior Probability Distribution ◽  
Elementary Statistics

This tutorial uses publicly available data from drug makers and the Food and Drug Administration to guide learners to estimate the confidence intervals of COVID-19 vaccine efficacy rates with a Bayesian framework. Under the classical approach, there is no probability associated with a parameter, and the meaning of confidence intervals can be misconstrued by inexperienced students. With Bayesian statistics, one can find the posterior probability distribution of an unknown parameter, and state the probability of vaccine efficacy rate, which makes the communication of uncertainty more flexible. We use a hypothetical example and a real baseball example to guide readers to learn the beta-binomial model before analyzing the clinical trial data. This note is designed to be accessible for lower-level college students with elementary statistics and elementary algebra skills.

scholarly journals Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

2020 ◽  
Vol 40 (4) ◽  
pp. 460-473 ◽  
Author(s):  
Helen A. Dakin ◽  
José Leal ◽  
Andrew Briggs ◽  
Philip Clarke ◽  
Rury R. Holman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Trial ◽  
Confidence Intervals ◽  
Parameter Uncertainty ◽  
Clinical Trial Data ◽  
Simulation Models ◽  
Trial Data ◽  
Sampling Uncertainty ◽  
Patient Level ◽  
True Value

Introduction. Patient-level simulation models facilitate extrapolation of clinical trial data while allowing for heterogeneity, prior history, and nonlinearity. However, combining different types of uncertainty around within-trial and extrapolated results remains challenging. Methods. We tested 4 methods to combine parameter uncertainty (around the regression coefficients used to predict future events) with sampling uncertainty (uncertainty around mean risk factors within the finite sample whose outcomes are being predicted and the effect of treatment on these risk factors). We compared these 4 methods using a simulation study based on an economic evaluation extrapolating the AFORRD randomized controlled trial using the UK Prospective Diabetes Study Outcomes Model version 2. This established type 2 diabetes model predicts patient-level health outcomes and costs. Results. The 95% confidence intervals around life years gained gave 25% coverage when sampling uncertainty was excluded (i.e., 25% of 95% confidence intervals contained the “true” value). Allowing for sampling uncertainty as well as parameter uncertainty widened confidence intervals by 6.3-fold and gave 96.3% coverage. Methods adjusting for baseline risk factors that combine sampling and parameter uncertainty overcame the bias that can result from between-group baseline imbalance and gave confidence intervals around 50% wider than those just considering parameter uncertainty, with 99.8% coverage. Conclusions. Analyses extrapolating data for individual trial participants should include both sampling uncertainty and parameter uncertainty and should adjust for any imbalance in baseline covariates.

Moving beyond the “LDL hypothesis”

VASA ◽  
2015 ◽  
Vol 44 (5) ◽  
pp. 333-340 ◽  
Author(s):  
Christian Werner ◽  
Ulrich Laufs
Keyword(s):  
Ldl Cholesterol ◽  
Causal Relation ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Clinical Trial Data ◽  
Randomised Clinical Trial ◽  
Trial Data ◽  
Low Density Lipoprotein Cholesterol ◽  
Genetic Studies ◽  
Statin Drug

Abstract. Summary: The term “LDL hypothesis” is frequently used to describe the association of low-density lipoprotein cholesterol (LDL-cholesterol, LDL-C) and cardiovascular (CV) events. Recent data from genetic studies prove a causal relation between serum LDL-C and CV events. These data are in agreement with mechanistic molecular studies and epidemiology. New randomised clinical trial data show that LDL-C lowering with statins and a non-statin drug, ezetimibe, reduces CV events. We therefore believe that the “LDL-hypothesis” has been proven; the term appears to be outdated and should be replaced by “LDL causality”.

Clinical Trial Data Management Software: A Review of the Technical Features

2019 ◽  
Vol 14 (3) ◽  
pp. 160-172 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technical Features

Background:Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems.Methods:Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study.Results:Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development.Conclusion:It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

scholarly journals AB0882-HPR ANCA-ASSOCIATED VASCULITIS: A CLINICAL PRACTICE ASSESSMENT

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1464.2-1465
Author(s):  
S. Mendly ◽  
G. Boutsalis
Keyword(s):  
Clinical Practice ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Remission Induction ◽  
Refractory Disease ◽  
Maintenance Strategy ◽  
Combination Therapies ◽  
Research Support ◽  
Educational Grant ◽  
Anca Associated Vasculitis

Background:AAV (ANCA-associated vasculitis) is a group of progressive, rare, severe autoimmune diseases1,2. AAV can affect blood vessels in different parts of the body resulting in damage to vital organs such as the lungs, kidneys, nervous system, gastrointestinal system, skin, eyes, and heart.2 There are currently no approved therapies for remission-induction in patients with AAV. The current treatment armamentarium for AAV is comprised of various immunosuppressive therapies in combination with steroid treatment. Understanding clinical practice gaps in the management of AAV, can inform development of tools to improve physician practices.Objectives:This medical education activity aims to assess physicians’ knowledge on the various manifestations of antineutrophil cytoplasmic antibody-associated vasculitis (AAV), current guideline-recommended treatment strategies for remission induction in patients with AAV, as well as recent clinical trial data for combination therapies used for remission induction.Methods:A 24-question survey consisting of multiple-choice knowledge and case-based questions was made available to nephrologists and rheumatologists without monetary compensation or charge. The questions were designed to evaluate knowledge regarding the various manifestations of AAV and the results from clinical trials that have compared the efficacy of combination therapies used for remission induction in patients with AAV. As well as application of guideline-recommended therapies and clinical trial data for remission induction in patients with AAV within clinical practice. The survey launched online on a website dedicated to continuous professional development. (www.medscape.org/viewarticle/920320) on July 15, 2020. Data were collected until October 1, 2020.Results:363 nephrologists and 190 rheumatologists completed the survey. Physicians demonstrated gaps in the following areas:TopicIncorrect Responses to Knowledge and Clinical Decision-Making Questions (%)NephrologistsRheumatologistsSystemic diseases associated with AAV59%45%How to confirm diagnosis of AAV42%25%Therapy selection to induce remission that would be consistent with guidelines recommendations71%51%Guideline-recommended therapy for patients that do not respond to the induction regimen32%34%Definition of refractory disease95%94%Most effective maintenance strategy for a patient once remission is achieved80%64%Where would an emerging therapy such as a C5a receptor inhibitor fit into the therapeutic armamentarium of AAV?62%47%What are the guideline-recommended therapies to reduce remission in patients without organ-threatening disease?71%51%Most effective maintenance strategy for a patient once remission is achieved80%64%Guideline-recommendations on length of time to continue maintenance therapy31%35%Conclusion:This educational research on assessment of physicians’ (nephrologists and rheumatologists) clinical practices yielded important insights into clinical gaps related to understanding of the disease pathophysiology and progression of AAV, guideline recommendations on diagnosing and managing AAV with guideline-directed medical therapies (GDMTs), strategies for the management of relapsing and refractory disease in AAV and positioning of emerging therapies in the treatment paradigm.References:[1]www.medscape.org/viewarticle/920320.[2]Hutton HL, et al. Semin Nephrol 2017;37(5):418–35.[3]Al-Hussain T, et al. Adv Anat Pathol 2017;24(4):226–34.Disclosure of Interests:Sarah Mendly Grant/research support from: Supported by an independent educational grant from Vifor Pharma, George Boutsalis Grant/research support from: Supported by an independent educational grant from Vifor Pharma

Sign in / Sign up

Export Citation Format

Share Document