This document summarizes some key statistical considerations for clinical trials comparing a similar biotherapeutic product (SBP) to a reference biotherapeutic product (RBP). It discusses that equivalence or non-inferiority trials are generally acceptable designs, with equivalence preferred. It notes that determining the appropriate design, equivalence/non-inferiority margins, sample size calculations, and ensuring assay sensitivity and the constancy assumption are all important statistical principles that must be carefully considered in order to draw valid conclusions about the clinical similarity of an SBP to an RBP.
Meta Analysis of Medical Device Data Applications for Designing Studies and R...NAMSA
Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence discusses what meta analysis is as well as the potential benefits.
2.0.statistical methods and determination of sample sizesalummkata1
statistical methods and determination of sample size
These guidelines focus on the validation of the bioanalytical methods generating quantitative concentration data used for pharmacokinetic and toxicokinetic parameter determinations.
Meta Analysis of Medical Device Data Applications for Designing Studies and R...NAMSA
Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence discusses what meta analysis is as well as the potential benefits.
2.0.statistical methods and determination of sample sizesalummkata1
statistical methods and determination of sample size
These guidelines focus on the validation of the bioanalytical methods generating quantitative concentration data used for pharmacokinetic and toxicokinetic parameter determinations.
Combination of informative biomarkers in small pilot studies and estimation ...LEGATO project
Background:
Biomarker candidates are defined as measurable molecules found in biological media. According to Biomarkers Definitions Working Group, 2001, biomarkers cover a rather wide range of parameters. Recently, biomarkers are used widely in medical researches, where single biomarkers may not possess the desired cause-effect association for disease classification and outcome prediction. Therefore the efforts of the researchers currently is to combine biomarkers. By new technologies like microarrays, next generation sequencing and mass spectrometry, researchers can obtain many biomarker candidates that can exceed tens of thousands. To avoid wasting money and time, it is suggested to control the number of patients strictly. However, pilot studies usually have low statistical power which reduces the chance of detecting a true effect .
This presentation is aimed at presenting the issues associated with subgroup analyses in clinical trials: the different types of subgroup analyses and the statistical issues associated with the conduct of subgroup analyses.
Sample size and how to calculate it
- Why sample size is important
- Alpha and beta errors
- Main outcome and Effect size
- Practical examples using Means-Proportions-Correlation- Confidence Interval
Practical Methods To Overcome Sample Size ChallengesnQuery
Watch the video at: https://www.statsols.com/webinars/practical-methods-to-overcome-sample-size-challenges
In this webinar hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - we will examine some of the most common practical challenges you will experience while calculating sample size for your study. These challenges will be split into two categories:
1. Overcoming Sample Size Calculation Challenges
(Survival Analysis Example)
We will examine practical methods to overcome common sample size calculation issues by focusing in on one of the more complex areas for sample size determination; Survival analysis. We will cover difficulties and potential issues surrounding challenges such as:
Drop Out: How to deal with expected dropouts or censoring. We compare the simple loss-to-follow-up method and integrating a dropout process into the sample size model?
Planning Uncertainty: How best to deal with the inevitable uncertainty at the planning stage? We examine how best to apply a sensitivity analysis and Bayesian approaches to explore the uncertainty in your sample size calculations.
Choosing the Effect Size: Various approaches and interpretations exist for how to find the effect size value. We examine those contrasting interpretations and determine the best method and also how to deal with parameterization options.
2. Overcoming Study Design Challenges
(Vaccine Efficacy Example)
The Randomised Controlled Trial (RCT) is considered the gold standard in trial design in drug development. However, there are often practical impediments which mean that adjustments or pragmatic approaches are needed for some trials and studies.
We will examine practical methods how to overcome common study design challenges and how these affect your sample size calculations. In this webinar, we will use common issues in vaccine study design to examine difficulties surrounding issues such as:
Case-Control Analysis: We will examine how to deal with study constraints and how to deal with analyses done during an observational study.
Alternative Randomization Methods: How best to address randomization in your vaccine trial design when full randomization is difficult, expensive or impractical. We examine how sample size calculations are affected with cluster or Mendelian randomization.
Rare Events: How does an outcome being rare affect the types of study design and statistical methods chosen in your study.
In clinical trials and other scientific studies, an interim analysis is an analysis of data that is conducted before data collection has been completed. If a treatment is particularly beneficial or harmful compared to the concurrent placebo group while the study is on-going, the investigators are ethically obliged to assess that difference using the data at hand and to make a deliberate consideration of terminating the study earlier than planned.
In interim analysis, whenever a new drug shows adverse effect on human being while testing the effectiveness of several drugs, we immediately stop the trial by taking into account the fact that maximum number of patients receive most effective treatment at the earliest stage. Interim analysis is also used to possibly reduce the expected number of patients and to shorten the follow-up time needed to make a conclusion. One wouldn't have to spend extra money if he/she already have enough evidence about the outcome. In this presentation, the total sample size is divided into four equal parts to perform the analysis and decision is made based on each individual step.
When designing a clinical study, a fundamental aspect is the sample size. In this article, we describe the rationale for sample size calculations, when it should be calculated and describe the components necessary to calculate it. For simple studies, standard formulae can be
used; however, for more advanced studies, it is generally necessary to use specialized statistical software programs and consult a biostatistician. Sample size calculations for non-randomized studies are also discussed and two clinical examples are used for illustration
5 essential steps for sample size determination in clinical trials slidesharenQuery
In this free webinar hosted by nQuery Researcher & Statistician Eimear Keyes, we map out the 5 essential steps for sample size determination in clinical trials. At each step, Eimear will highlight the important function it plays and how to avoid the errors that will negatively impact your sample size determination and therefore your study.
Watch the Video: https://www.statsols.com/webinar/the-5-essential-steps-for-sample-size-determination
Non-inferiority and Equivalence Study design considerations and sample sizenQuery
About the webinar
This webinar examines the role of non-inferiority and equivalence in study design
In this free webinar, you will learn about:
-Regulatory information on this type of study design
-Considerations for study design and your sample size
-Practical worked examples of
--Non-inferiority Testing
--Equivalence Testing
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Watch the video at: https://www.statsols.com/webinars
Combination of informative biomarkers in small pilot studies and estimation ...LEGATO project
Background:
Biomarker candidates are defined as measurable molecules found in biological media. According to Biomarkers Definitions Working Group, 2001, biomarkers cover a rather wide range of parameters. Recently, biomarkers are used widely in medical researches, where single biomarkers may not possess the desired cause-effect association for disease classification and outcome prediction. Therefore the efforts of the researchers currently is to combine biomarkers. By new technologies like microarrays, next generation sequencing and mass spectrometry, researchers can obtain many biomarker candidates that can exceed tens of thousands. To avoid wasting money and time, it is suggested to control the number of patients strictly. However, pilot studies usually have low statistical power which reduces the chance of detecting a true effect .
This presentation is aimed at presenting the issues associated with subgroup analyses in clinical trials: the different types of subgroup analyses and the statistical issues associated with the conduct of subgroup analyses.
Sample size and how to calculate it
- Why sample size is important
- Alpha and beta errors
- Main outcome and Effect size
- Practical examples using Means-Proportions-Correlation- Confidence Interval
Practical Methods To Overcome Sample Size ChallengesnQuery
Watch the video at: https://www.statsols.com/webinars/practical-methods-to-overcome-sample-size-challenges
In this webinar hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - we will examine some of the most common practical challenges you will experience while calculating sample size for your study. These challenges will be split into two categories:
1. Overcoming Sample Size Calculation Challenges
(Survival Analysis Example)
We will examine practical methods to overcome common sample size calculation issues by focusing in on one of the more complex areas for sample size determination; Survival analysis. We will cover difficulties and potential issues surrounding challenges such as:
Drop Out: How to deal with expected dropouts or censoring. We compare the simple loss-to-follow-up method and integrating a dropout process into the sample size model?
Planning Uncertainty: How best to deal with the inevitable uncertainty at the planning stage? We examine how best to apply a sensitivity analysis and Bayesian approaches to explore the uncertainty in your sample size calculations.
Choosing the Effect Size: Various approaches and interpretations exist for how to find the effect size value. We examine those contrasting interpretations and determine the best method and also how to deal with parameterization options.
2. Overcoming Study Design Challenges
(Vaccine Efficacy Example)
The Randomised Controlled Trial (RCT) is considered the gold standard in trial design in drug development. However, there are often practical impediments which mean that adjustments or pragmatic approaches are needed for some trials and studies.
We will examine practical methods how to overcome common study design challenges and how these affect your sample size calculations. In this webinar, we will use common issues in vaccine study design to examine difficulties surrounding issues such as:
Case-Control Analysis: We will examine how to deal with study constraints and how to deal with analyses done during an observational study.
Alternative Randomization Methods: How best to address randomization in your vaccine trial design when full randomization is difficult, expensive or impractical. We examine how sample size calculations are affected with cluster or Mendelian randomization.
Rare Events: How does an outcome being rare affect the types of study design and statistical methods chosen in your study.
In clinical trials and other scientific studies, an interim analysis is an analysis of data that is conducted before data collection has been completed. If a treatment is particularly beneficial or harmful compared to the concurrent placebo group while the study is on-going, the investigators are ethically obliged to assess that difference using the data at hand and to make a deliberate consideration of terminating the study earlier than planned.
In interim analysis, whenever a new drug shows adverse effect on human being while testing the effectiveness of several drugs, we immediately stop the trial by taking into account the fact that maximum number of patients receive most effective treatment at the earliest stage. Interim analysis is also used to possibly reduce the expected number of patients and to shorten the follow-up time needed to make a conclusion. One wouldn't have to spend extra money if he/she already have enough evidence about the outcome. In this presentation, the total sample size is divided into four equal parts to perform the analysis and decision is made based on each individual step.
When designing a clinical study, a fundamental aspect is the sample size. In this article, we describe the rationale for sample size calculations, when it should be calculated and describe the components necessary to calculate it. For simple studies, standard formulae can be
used; however, for more advanced studies, it is generally necessary to use specialized statistical software programs and consult a biostatistician. Sample size calculations for non-randomized studies are also discussed and two clinical examples are used for illustration
5 essential steps for sample size determination in clinical trials slidesharenQuery
In this free webinar hosted by nQuery Researcher & Statistician Eimear Keyes, we map out the 5 essential steps for sample size determination in clinical trials. At each step, Eimear will highlight the important function it plays and how to avoid the errors that will negatively impact your sample size determination and therefore your study.
Watch the Video: https://www.statsols.com/webinar/the-5-essential-steps-for-sample-size-determination
Non-inferiority and Equivalence Study design considerations and sample sizenQuery
About the webinar
This webinar examines the role of non-inferiority and equivalence in study design
In this free webinar, you will learn about:
-Regulatory information on this type of study design
-Considerations for study design and your sample size
-Practical worked examples of
--Non-inferiority Testing
--Equivalence Testing
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Watch the video at: https://www.statsols.com/webinars
· Reflect on the four peer-reviewed articles you critically apprai.docxVannaJoy20
· Reflect on the four peer-reviewed articles you critically appraised in Module 4, related to your clinical topic of interest and PICOT.
· Reflect on your current healthcare organization and think about potential opportunities for evidence-based change, using your topic of interest and PICOT as the basis for your reflection.
· Consider the best method of disseminating the results of your presentation to an audience.
The Assignment: (Evidence-Based Project)
Part 4: Recommending an Evidence-Based Practice Change
Create an 8- to 9-slide
narrated PowerPoint presentation in which you do the following:
· Briefly describe your healthcare organization, including its culture and readiness for change. (You may opt to keep various elements of this anonymous, such as your company name.)
· Describe the current problem or opportunity for change. Include in this description the circumstances surrounding the need for change, the scope of the issue, the stakeholders involved, and the risks associated with change implementation in general.
· Propose an evidence-based idea for a change in practice using an EBP approach to decision making. Note that you may find further research needs to be conducted if sufficient evidence is not discovered.
· Describe your plan for knowledge transfer of this change, including knowledge creation, dissemination, and organizational adoption and implementation.
· Explain how you would disseminate the results of your project to an audience. Provide a rationale for why you selected this dissemination strategy.
· Describe the measurable outcomes you hope to achieve with the implementation of this evidence-based change.
· Be sure to provide APA citations of the supporting evidence-based peer reviewed articles you selected to support your thinking.
· Add a lessons learned section that includes the following:
· A summary of the critical appraisal of the peer-reviewed articles you previously submitted
· An explanation about what you learned from completing the Evaluation Table within the Critical Appraisal Tool Worksheet Template (1-3 slides)
Zeinab Hazime
Nurs 6052
10/16/2022
Evaluation Table
Use this document to complete the
evaluation table requirement of the Module 4 Assessment,
Evidence-Based Project, Part 3A: Critical Appraisal of Research
Full
APA formatted citation of selected article.
Article #1
Article #2
Article #3
Article #4
Abraham, J., Kitsiou, S., Meng, A., Burton, S., Vatani, H., & Kannampallil, T.
(2020). Effects of CPOE-based medication ordering on outcomes: an overview of systematic reviews.
BMJ Quality & Safety, 29(10), 1-2.
Alanazi, A. (2020). The effect of computerized physician order entry on mortality rates in pediatric and neonatal care setting: Meta-analysis.
Informatics in Medicine
Unlocked, 19, 100308. https.
Regulatory consideration for biosimilars by fdaGopal Agrawal
Need for biosimilars
First to approve guidelines for biosimilars
Clinical Pharmacology Data to Support a Demonstration of Biosimilarity to a Reference Product (as per FDA)
Freshers in clinical research and regulatory affairs must go through this presentation. It will help you to understand the basis of clinical trial design as per European guidelines, which is the most preferred reference guideline. Initially, I also faced many problems to understand this concept. A student who is studying a clinical research diploma can also use this presentation for their basic understanding.
This presentation mainly deals with clinical development of biosimilar products. It also gives enough on non-clinical development so that the audience is well oriented.
Review on Bioanalytical Method Development in Human Plasmaijtsrd
Bioanalytical methods are widely used to quantitate drugs and their metabolites in plasma matrices and this method are applied to study in the areas of human clinical and nonhuman study. Bioanalytical methods employed for the quantitative estimation of drugs and their metabolites plays an important role in estimation and interpretation of bioequivalence, pharmacokinetic, and toxicokinetic studies. The major bioanalytical role is method development, method validation, and sample analysis. Techniques such as high pressure liquid chromatography HPLC and liquid chromatography coupled with double mass spectrometry LCMS MS can be used for bioanalytical studies. Mayuri Gavhane | Dr. Ravindra Patil | Tejaswini Kande "Review on Bioanalytical Method Development in Human Plasma" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-7 , December 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52578.pdf Paper URL: https://www.ijtsrd.com/chemistry/analytical-chemistry/52578/review-on-bioanalytical-method-development-in-human-plasma/mayuri-gavhane
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
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Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
How to Give Better Lectures: Some Tips for Doctors
Statistical considerations for confirmatory clinical trials for similar biotherapeutic products
1. Biologicals 39 (2011) 266e269
Contents lists available at ScienceDirect
Biologicals
journal homepage: www.elsevier.com/locate/biologicals
Statistical considerations for confirmatory clinical trials for similar biotherapeutic
products
Catherine Njue*
Biostatistics Unit, Centre for Vaccine Evaluation, Biologics and Genetic Therapies Directorate, Health Products and Food Branch, Health Canada, 251 Sir Frederick Banting Driveway,
AL 2201E, Tunney’s Pasture, Ottawa, ON K1A 0K9, Canada
a b s t r a c t
Keywords:
Statistical principles
Clinical trials
Equivalence design
Non-inferiority design
For the purpose of comparing the efficacy and safety of a Similar Biotherapeutic Product (SBP) to
a Reference Biotherapeutic Product (RBP), the “Guidelines on Evaluation of Similar Biotherapeutic
Products (SBPs)” issued by the World Health Organisation (WHO), states that equivalence or noninferiority studies may be acceptable. While in principle, equivalence trials are preferred, noninferiority trials may be considered if appropriately justified, such as for a medicinal product with
a wide safety margin. However, the statistical issues involved in the design, conduct, analysis and
interpretation of equivalence and non-inferiority trials are complex and subtle, and require that all
aspects of these trials be given careful consideration. These issues are important in order to ensure that
equivalence and non-inferiority trials provide valid data that are necessary to draw reliable conclusions
regarding the clinical similarity of an SBP to an RBP.
Crown Copyright Ó 2011 Published by Elsevier Ltd. All rights reserved.
1. Introduction
According to the guidelines developed by the WHO for the
evaluation of SBPs, clinical trials utilising equivalence or noninferiority designs will be acceptable for the comparison of efficacy and safety of the SBP to the RBP [1]. Equivalence trials are
preferred to ensure that the SBP is not clinically less or more
effective than the RBP when used at the same dosage(s), but noninferiority trials may be considered, if appropriately justified [1,2].
The choice of the clinical trial design is dependent on many factors,
and the specific design selected for a particular study must be
explicitly stated in the trial protocol. Specifying the design selected
is critical, given that equivalence and non-inferiority trials are
designed to test different objectives. In addition, it should also be
recognised that the statistical principles governing equivalence
and non-inferiority trials are complex and subtle, and must be
given careful consideration [3]. This article will address the
statistical principles underlying equivalence and non-inferiority
trials that must be considered in the design, conduct, analysis
and interpretation of trials for the purpose of demonstrating that
the SBP is similar to the RBP.
* Tel.: þ1 613 9141 6160; fax: þ1 613 941 8933.
E-mail address: catherine.njue@hc-sc.gc.ca.
2. Equivalence vs. non-inferiority, and the choice of the
comparability margin
An equivalence trial is designed to show that a test treatment
differs from the standard active treatment by an amount that is
clinically unimportant (the equivalence margin), while a noninferiority trial is designed to show that a test treatment is not
less effective than a standard active treatment by a small predefined margin (the non-inferiority margin) [4]. These are
different objectives, and it is vital that the specific design selected
for a particular study be explicitly stated in the trial protocol [5].
The factors that should be considered in selecting the clinical trial
design include: the product in question, the intended use of the
product, the target population and disease prevalence [1].
Irrespective of the trial design selected, the next critical step is
the determination of the comparability margin. For the equivalence
trial, both the upper and lower equivalence limits are needed to
establish that the SBP response differs from the RBP response by
a clinically unimportant amount. For a non-inferiority trial, only
one limit is required to establish that the SBP response is not
clinically inferior to the RBP response by a pre-specified margin [6].
Determination of this margin must be given careful consideration
as both sample size calculations and interpretation of study results
depend on it. In practical terms, the comparability margin is
defined as the largest difference between the SBP and the RBP that
can be judged as clinically acceptable, and should be smaller than
1045-1056/$36.00 Crown Copyright Ó 2011 Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.biologicals.2011.06.006
2. C. Njue / Biologicals 39 (2011) 266e269
differences observed in superiority trials of the RBP. The choice of
the margin is based upon a combination of clinical judgement and
statistical reasoning to deal with uncertainties and imperfect
information. Adequate evidence of the effect size of the RBP should
be provided to support the proposed margin. The magnitude and
variability of the effect size of the RBP derived from historical trials
should also be taken into consideration.
3. Sample size and power
It is important to note that equivalence and non-inferiority
designs test different hypotheses requiring different formulae for
power and sample size calculations [4]. For an equivalence trial, the
null hypothesis represents lack of equivalence between the test and
reference while for an non-inferiority trial, it represents inferiority
of the test to the reference. Therefore, sample size calculations
should be based on methods specifically designed for either
equivalence or non-inferiority trials. Details of the sample size
calculations should be provided in the study protocol. At
a minimum, the basis of estimates of any quantities used in the
sample size calculation should also be clearly explained, and are
usually derived from results of earlier trials with the RBP or published literature [6].
Determination of the appropriate sample size is dependent on
various factors including: the type of primary endpoint (e.g. binary,
quantitative, time-to event etc.), the pre-defined comparability
margin, the probability of a type I error (falsely rejecting the null
hypothesis) and the probability of a type II error (erroneously
failing to reject the null hypothesis) [4,6]. The expected rates of
patient dropouts and withdrawals should also be taken into
consideration. Keeping all factors the same, including the comparability margin, the type I error rate and the type II error rate, an
equivalence trial tends to need a larger sample size than a noninferiority trial. When estimating the sample size for equivalence
or non-inferiority trials, it is usually under the assumption that
there is no difference between the SBP and RBP. An equivalence trial
could be underpowered if the true difference is not zero. Similarly,
a non-inferiority trial could be underpowered if the SBP is actually
less effective than the RBP. To minimise the risk of an underpowered study, the equivalence or non-inferiority trial should only be
considered and designed once similarity of the SBP to the RBP has
been ascertained using all available comparative data that has been
generated between the SBP and the RBP [1]. In addition, keeping
the probability of a type II error low by increasing the study power
will increase the ability of the study to show equivalence or noninferiority of the SBP to the RBP.
4. Important principles: assay sensitivity and the constancy
assumption
Potential differences between the SBP and the RBP should be
investigated in a sensitive and well established clinical model [1,2].
The sensitivity of the clinical model is important, and there should
be some confidence in the trial’s ability to detect a difference
between the SBP and the RBP if a real difference exists. This is the
concept of assay sensitivity, and refers to the ability of a specific
trial to demonstrate a difference between treatments if such
a difference truly exists [7].
Assay sensitivity is crucial to any trial, but has different implications depending on the trial design. A superiority trial that lacks
assay sensitivity will fail to show superiority of the test treatment (a
failed study), and a superiority trial that demonstrates superiority
has simultaneously demonstrated assay sensitivity. However,
a non-inferiority or equivalence trial that lacks assay sensitivity
may find an ineffective treatment to be non-inferior or equivalent
267
and could lead to an erroneous conclusion of efficacy [4]. The effect
size of the RBP in the current non-inferiority or equivalence trial is
not measured directly relative to placebo, and assay sensitivity
must be deduced. In fact, the current non-inferiority or equivalence
trial must rely on the assumption of assay sensitivity based on
information external to the trial. Factors which can reduce assay
sensitivity include poor compliance with study medication,
concomitant medications, missing data, patient withdrawals, variability in measurements, poor trial quality etc. The design and
conduct of the non-inferiority or equivalence trial must attempt to
avoid or at least minimise these factors to ensure the credibility of
trial results.
Another important principle in equivalence and non-inferiority
trials is the constancy assumption, which means that the historical
active control effect size compared to placebo is unchanged in the
setting of the current trial [7]. To ensure the validity of the
constancy assumption, the equivalence or non-inferiority trial must
be designed and conducted in the same manner as the trial that
established the efficacy of the RBP. The study population,
concomitant therapy, endpoints, trial duration, assessments and
other important aspects of the trial should be similar to those in the
trial used to demonstrate the effectiveness of the RBP.
Trial conduct should not only adhere closely to that of the trial
used to demonstrate the effectiveness of the RBP, but it should also
be conducted with high quality to ensure assay sensitivity.
5. Data analysis and analysis populations
Data analysis from equivalence and non-inferiority studies is
generally based on the use of two-sided confidence intervals
(typically at the 95% level) for the metric of treatment effect.
Examples of metrics of treatment effect include absolute difference
in proportions, relative risk, odds ratio and hazard ratio. Analysis of
non-inferiority trials can also be based on a one sided confidence
interval (typically at the 97.5% level) [6]. For equivalence trials,
equivalence is demonstrated when the entire confidence interval
falls within the lower and upper equivalence limits. Non-inferiority
trials are one sided, and statistical inference is based only on the
upper or lower confidence limit, whichever is appropriate for
a given study [8]. For example, if a lower non-inferiority margin is
defined, non-inferiority is demonstrated when the lower limit of
the confidence interval is above this margin. Likewise, if an upper
non-inferiority margin is defined, non-inferiority is demonstrated
when the upper limit of the confidence interval is below this
margin.
In a superiority trial, analysis based on the Intent to Treat (ITT)
population is considered the primary analysis, and is considered
conservative in this setting. However, for equivalence and noninferiority trials, the ITT population is not considered conservative as it tends to bias the results towards equivalence or noninferiority. The per-protocol (PP) population excludes data from
patients with major protocol violations, and excluding data from
these patients can also substantially bias the results, especially if
a large proportion of subjects are excluded. Hence, a conservative
analysis cannot always be defined for equivalence or noninferiority trials, and this will present challenges during the analysis of the trial results [9]. Equivalence or non-inferiority trials
should be analysed based on both the ITT and PP approaches, and
both sets of results should support the conclusion of equivalence or
non-inferiority.
6. Trial results: possible outcomes and interpretation
Irrespective of the trial design, the totality of the actual observed
results obtained from the clinical trial will determine whether the
3. 268
C. Njue / Biologicals 39 (2011) 266e269
SBP and the RBP can be considered clinically similar. Fig. 1 shows
the possible results (confidence intervals) from a non-inferiority or
equivalence trial designed to compare a test to a reference for
which a higher value in the endpoint corresponds to better efficacy,
and the difference between treatments is defined as test minus
reference.
As illustrated in Fig. 1 where the lower limit of the equivalence
region also represents the non-inferiority margin, several outcomes
can be obtained from the analysis based on the confidence interval
approach. The following is an interpretation of each outcome:
1. Trial failed, regardless of objective (Test is worse than
Reference);
2. Trial failed, regardless of objective (Test is not better, but could
be worse than Reference);
3. Trial failed, regardless of objective (Trial is completely noninformative: Test could be worse than or better than
Reference);
4. Test equivalent and therefore, also non-inferior to Reference
(entire confidence interval within equivalence region);
5. Test not equivalent but non-inferior to Reference (lower limit of
confidence interval above non-inferiority margin);
6. Test not equivalent but statistically and clinically superior to
Reference (lower limit of confidence interval above equivalence
region).
For comparing the SBP to the RBP, outcome 4 is the most
desirable, but as illustrated by the simulation study described
below, such an outcome can only be obtained from an adequately
powered equivalence trial, and would be unlikely to be obtained
from a trial that was designed as a non-inferiority study.
Outcome 5 has different implications for a non-inferiority trial
compared to an equivalence trial, resulting in a successful noninferiority trial, but a failed equivalence trial. Outcome 6 would
result in a failed equivalence trial, but would be acceptable in
a non-inferiority trial if it does not matter whether the test treatment can yield a much better response than the reference [4].
However, in the context of comparing the SBP to the RBP for the
purpose of demonstrating that the SBP is not clinically less or more
effective than the RBP when used at the same dosage(s), outcome 6
is not desirable as it suggests superiority of the SBP relative to the
RBP. As stated in the WHO guidance document, such a finding, if
clinically relevant, would contradict the principle of similarity and
a post-hoc justification that a finding of statistical superiority is not
clinically relevant would be difficult to defend [1]. If similarity of
the SBP to the RBP has been ascertained prior to initiation of the
confirmatory trial, outcome 6 is expected to be a rare event. This
was revealed in the simulation study described below where the
proportion of simulated confidence intervals showing statistical
superiority was about 2%.
If the statistical superiority observed is considered clinically
relevant, then the SBP would not be considered similar to the RBP
and should be developed as a stand alone product [1,2]. This is
clearly an undesirable finding at the end of a confirmatory trial that
marks the end of the comparability exercise [1]. Demonstration
that superior efficacy of the SBP is not associated with adverse
events, if the SBP is prescribed at the same dosage as the RBP would
also be required in all cases [1,2]. To minimise the risk of a superiority finding, all comparative data that have been generated
between the SBP and the RBP should be carefully reviewed and
analysed to ensure that similarity between the SBP and the RBP has
been established with a high degree of confidence prior to initiating
the confirmatory trial [1].
7. A simulation study
A direct simulation of 95% confidence intervals for differences in
proportions was carried out. Each simulation was set up to generate
data from a non-inferiority study with a binary primary endpoint
under the assumption that the proportions in the test and reference
groups are equal. The goal of the simulation study was to demonstrate that equivalence trials require more subjects than noninferiority trials, and that an adequately powered equivalence
trial is needed to obtain outcome 4 (test equivalent to reference).
Two independent binomial samples of given sample sizes and
underlying proportions of success were generated, and the
approximate 95% two-sided confidence interval for the difference
in proportions (test proportion minus reference proportion) was
calculated for each simulation. A range of proportions was
considered, and both proportions were set equal. With the noninferiority margin set to 10%, the simulations were carried out
10,000 times, with the number of subjects in each treatment group
selected in order for each simulated study to have a specified
power. From all the simulations, the observed frequency of confidence intervals supporting claims of non-inferiority (test proportion at most 10% lower) and the observed frequency of confidence
intervals supporting claims of equivalence (entire confidence
interval within the equivalence region) were calculated.
The simulation results are summarised in Table 1. The results in
Table 1 illustrate that outcome 4 (Test equivalent to Reference:
entire confidence interval within equivalence region) can be difficult to obtain from a trial designed as a non-inferiority study, and
show that the proportion of simulated confidence intervals supporting claims of equivalence is about 20% lower (for 80% power)
and about 10% lower (for 90% power) compared to the proportion of
simulated confidence intervals supporting claims of non-
1
2
Table 1
Proportion of simulated confidence intervals supporting claims of non-inferiority or
equivalence of test to reference.
3
4
Power
Proportion
of success
(p1 ¼ p2)
Required
number of
subjects
(N1 ¼ N2)
Proportion of
confidence intervals
supporting noninferiority
Proportion of
confidence intervals
supporting
equivalence
80%
0.60
0.70
0.80
0.90
0.60
0.70
0.80
0.90
400
347
273
163
520
455
360
210
80.1%
79.9%
80.5%
80.5%
89.9%
89.7%
90.4%
90.0%
60.5%
59.5%
60.8%
60.9%
79.8%
79.3%
80.7%
80.0%
5
6
Reference
Superior
Equivalence
Region
Test
Superior
Fig. 1. Possible results (confidence intervals) from a non-inferiority or equivalence
trial.
90%
4. C. Njue / Biologicals 39 (2011) 266e269
inferiority. In other words, the absolute loss in power ranges from
about 10% to 20% depending on study power.
269
Conflict of interest
Author has no potential conflicts of interest.
8. Conclusion
Acknowledgements
Equivalence trials are preferred, and non-inferiority trials may
be considered if appropriately justified [1,2]. Prior to initiating an
equivalence or non-inferiority trial for the purpose of demonstrating that the SBP has similar efficacy and safety to the RBP,
a careful evaluation of all comparative data that have been generated between the SBP and the RBP should be performed to ensure
that similarity between the SBP and the RBP has been clearly
established. This is important, as the equivalence or non-inferiority
study marks the last step of the comparability exercise, and the
study should be initiated when there is unequivocal evidence that
the SBP can be considered similar to the RBP. It is also vital that the
protocol of the trial designed to demonstrate equivalence or noninferiority of the SBP to the RBP contains a clear statement that
this is the intention.
The choice of the RBP is critical. It should be a widely used
therapy whose efficacy in the relevant indication has been clearly
established and quantified in well-designed and well-documented
placebo controlled trials. The choice of the equivalence or noninferiority margin must be clearly justified. Equivalence and noninferiority trials require different formulae for power and sample
size calculations, and the appropriate set of formulae should be
used in each setting. Factors which reduce the assay sensitivity of
a trial should be avoided or minimised, and the equivalence or noninferiority trial must be designed and conducted in the same
manner as the trial that established the efficacy of the RBP to ensure
the constancy assumption. The totality of the data obtained from
the equivalence or non-inferiority trial will determine whether the
SBP can be considered clinically similar to the RBP.
The author thanks Mike Walsh, Centre for Vaccine Evaluation,
Biologics and Genetic Therapies Directorate, Health Canada and
Bob Li, Office of Science, Therapeutic Products Directorate, Health
Canada for their input and suggestions on the original draft; Keith
O’Rourke, Centre for Vaccine Evaluation, Biologics and Genetic
Therapies Directorate, Health Canada for the running the simulations; and Bill Casley, Centre for Vaccine Evaluation, Biologics and
Genetic Therapies Directorate, Health Canada for proof reading the
article.
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