Prof Jim Warren
National Institute for Health Innovation, The University of Auckland
With Rekha Gaikwad, Thusitha Mabotuwana, John Kennelly, Timothy Kenealy
Cluster analysis poster by Gracey and MalleyAndrew Bateman
I am pleased to be able to share more work that was presented this year at WFNR Neuropsychological Rehabilitation Special Interest Group. This is an example of the more technical research work done in our team: this poster is a good summary of a paper recently published, illustrating how we are continuing to try to grasp how best to assess and describe the needs of our service users.
This is a book chapter, recently published in Italian as
Bateman, A, (2014) .L’esperienza del NeuroPage: il supporto della tecnologia nella riabilitazione neuropsicologica. In Teleriabilitazione e ausili. La tecnologia in aiuto alla persona con disturbi neuropsicologici (Strum. lavoro psico-sociale e educativo) Editor Anna Cantagallo (Italian Edition Publisher FrancoAngeli) Chapter 7
http://www.amazon.co.uk/Teleriabilitazione-tecnologia-neuropsicologici-psico-sociale-educativo-ebook/dp/B00L8894S2/ref=sr_1_3?s=books&ie=UTF8&qid=1414058893&sr=1-3&keywords=cantagallo
The chapter started life as a lecture to the Italian Group of Neuropsychological Rehabilitation (GIRN) - the V Refresher Course in Neuropsychological Rehabilitation “EXTERNAL AIDS IN NEUROPSYCHOLOGICAL REHABILITATION”.
that took place in Padua in October 2011
The GIRN Group was established in May, 2006 with the aim to promote the improvement of the quality in the Rehabilitation of People with Neuropsychological Disorders resulting from any kind of cerebral dysfunction.
The Course was structured in 4 sessions: The 1st session concerned the pathway prescription to usage by the patient; the 2nd, aids for communication and environmental control; the 3rd aids for memory and the 4th aids for developmental and sensorial disabilities
Using the Bigtown Simulation Model to Predict the Impact of Enhanced Seven Day Services on Hospital Performance and Patient Outcomes
Poster from the 'Delivering NHS services, seven days a week' event held in Birmingham on 16 November 2013
More information about this event can be found at
http://www.nhsiq.nhs.uk/news-events/events/nhs-services-seven-days-a-week.aspx
Prof Jim Warren
National Institute for Health Innovation, The University of Auckland
With Rekha Gaikwad, Thusitha Mabotuwana, John Kennelly, Timothy Kenealy
Cluster analysis poster by Gracey and MalleyAndrew Bateman
I am pleased to be able to share more work that was presented this year at WFNR Neuropsychological Rehabilitation Special Interest Group. This is an example of the more technical research work done in our team: this poster is a good summary of a paper recently published, illustrating how we are continuing to try to grasp how best to assess and describe the needs of our service users.
This is a book chapter, recently published in Italian as
Bateman, A, (2014) .L’esperienza del NeuroPage: il supporto della tecnologia nella riabilitazione neuropsicologica. In Teleriabilitazione e ausili. La tecnologia in aiuto alla persona con disturbi neuropsicologici (Strum. lavoro psico-sociale e educativo) Editor Anna Cantagallo (Italian Edition Publisher FrancoAngeli) Chapter 7
http://www.amazon.co.uk/Teleriabilitazione-tecnologia-neuropsicologici-psico-sociale-educativo-ebook/dp/B00L8894S2/ref=sr_1_3?s=books&ie=UTF8&qid=1414058893&sr=1-3&keywords=cantagallo
The chapter started life as a lecture to the Italian Group of Neuropsychological Rehabilitation (GIRN) - the V Refresher Course in Neuropsychological Rehabilitation “EXTERNAL AIDS IN NEUROPSYCHOLOGICAL REHABILITATION”.
that took place in Padua in October 2011
The GIRN Group was established in May, 2006 with the aim to promote the improvement of the quality in the Rehabilitation of People with Neuropsychological Disorders resulting from any kind of cerebral dysfunction.
The Course was structured in 4 sessions: The 1st session concerned the pathway prescription to usage by the patient; the 2nd, aids for communication and environmental control; the 3rd aids for memory and the 4th aids for developmental and sensorial disabilities
Using the Bigtown Simulation Model to Predict the Impact of Enhanced Seven Day Services on Hospital Performance and Patient Outcomes
Poster from the 'Delivering NHS services, seven days a week' event held in Birmingham on 16 November 2013
More information about this event can be found at
http://www.nhsiq.nhs.uk/news-events/events/nhs-services-seven-days-a-week.aspx
Schmidt and Bateman on implementation of EQ5D in Community settingAndrew Bateman
This poster from 2013 was created by Anja while on an internship at Oliver Zangwill Centre.
It provides a helpful summary of interviews about the issue of being asked to collect PROMS data. Clinicians value being given feedback on the patients they have seen and analysis at a service or organisation level has great value at a personal level too, potentially very rewarding for therapists and assistants.
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
Here is my presentation for an exciting event at King's Fund 26 MARCH 2015
This is the published programme for the day
Session one: Opening plenary
9.45am: Welcome and introduction
Dr Johnny Marshall, Director of Policy, NHS Confederation
9.55am: Transforming community health care services in London
Caroline Alexander, Chief Nurse, NHS England, London Region
10.15am: Panel session: The challenges and opportunities for improving and developing community services
Caroline Alexander, Chief Nurse, NHS England, London Region
Matthew Winn, Chief Executive, Cambridge Community Services NHS Trust and Chair, NHS Confederation Community Health Services Forum
Dr Crystal Oldman, Chief Executive, Queen's Nursing Institute
further panelists to be confirmed
10.55am: Questions and discussion
11.10am: Refreshment break and networking
Session two: What does good look like?
11.40am: Welcome and introduction
Catherine Foot, Assistant Director of Policy, The King’s Fund
11.45am: Regulating community health services
Ellen Armistead, Deputy Chief Inspector, Care Quality Commission
12.05pm: How and what should we measure to ensure quality?
Christina Walters, Programme Director, Community Indicators Programme
Andrew Barber, Technical Consultant, Community Indicators, Outcome Measures and Payment System Development Programme
12.25pm: Questions and discussion
12.40pm: Buffet lunch, networking and exhibition
Session three: Good practice breakout sessions
Sessions will run from 1.40-2.55pm and delegates will have the choice of:
A: Quality assurance: how are you using data locally to measure for quality?
1.40pm: Welcome and introduction
1.45pm: The use of PROMs (Patient Reported Outcome Measures) in a community setting
Iain Cockley-Adams, Service Improvement Manager, Gloucestershire Care Services NHS Trust
2.05pm: Over2You Quality Volunteers
Ruby Smith, Head of Personalisation, South Yorkshire Housing Association
2.25pm: PROMS in Practice: The Collection Analysis and Reporting of quality of life indicator EQ5D in rehabilitation services in Cambridgeshire Community Services
Andrew Bateman PhD, Physiotherapist and Service Manager, Oliver Zangwill Centre for Neuropsychological Rehabilitation, Cambridgeshire Community Services NHS Trust
2.45pm: Questions and discussion
B: Working with patients and communities: what are you doing to involve patients and their families and carers and to make your services more person-centred?
C: Partnerships and relationships with other parts of the system: how are you building effective local partnerships across health and social care?
2.55pm: Refreshment break and networking
Session four: Good practice breakout sessions
Sessions will run from 3.15-4.30pm and delegates will have the choice of:
D: Supporting and encouraging team working: what are you doing to support team working?
E: Working with patients and communities: what are you doing to involve patie
2020 trends in biostatistics what you should know about study design - slid...nQuery
2020 Trends In Biostatistics - What you should know about study design.
In this free webinar you will learn about:
-Adaptive designs in confirmatory trials
-Using external data in study planning
-Innovative designs in early-stage trials
To watch the full webinar:
https://www.statsols.com/webinar/2020-trends-in-biostatistics-what-you-should-know-about-study-design
Nicholas Jewell MedicReS World Congress 2014MedicReS
Statistical Methods for Observational Drug Studies
Nicholas P. Jewell Departments of Statistics &
School of Public Health (Biostatistics)
University of California, Berkeley
Clinical data analytics is an exciting new area of healthcare data analytics. This presentation presents a brief overview of the topic as an introduction and whetting the curiosity of the reader.
Webinar slides- alternatives to the p-value and power nQuery
What are the alternatives to the p-value & power? What is the next step for sample size determination? We will explore these issues in this free webinar presented by nQuery
Sample size for survival analysis - a guide to planning successful clinical t...nQuery
Determining the appropriate number of events needed for survival analysis is a complex task as study planners try to predict what sample size will be needed after accounting for the complications of unequal follow-up, drop-out and treatment crossover.
The statistical, logistical and ethical considerations all complicate life for biostatisticians as issues to balance in planning a survival analysis. However, this complexity has created a need for new analyses and procedures to help the planning process for survival analysis trials.
The wider move from fixed to flexible designs has opened up opportunities for advanced methods such as adaptive design and Bayesian analysis to help deal with the unique complications of planning for survival data but these methods have their own complications that need to be explored too.
Innovative Sample Size Methods For Clinical Trials nQuery
"Innovative Sample Size Methods for Clinical Trials" is hosted to coincide with the Spring 2018 update to nQuery - The leading Sample Size Software.
Hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - you'll learn about the benefits of a range of procedures and how you can implement them into your work:
1) Dose-escalation with the Bayesian Continual Reassessment Method
CRM is a growing alternative to the 3+3 method for Phase I trials finding the Maximum Tolerated Dose (MTD).
See how researchers can overcome 3+3 drawbacks to easily find the required sample size for this beneficial alternative for finding the MTD.
2) Bayesian Assurance with Survival Example
This Bayesian alternative to power has experienced a rapid rise in interest and application from researchers.
See how Assurance is being used by researchers to discover the true “probability of success” of a trial.
3) Mendelian Randomization
Mendelian randomization (MR) is a method that allows testing of a causal effect from observational data in the presence of confounding factors.
However, in order to design efficient Mendelian randomization studies, it is essential to calculate the appropriate sample sizes required. We demonstrate what to do to achieve this.
4) Negative Binomial Distribution
Negative binomial model has been increasingly used to model the count data. One of the challenges of applying negative binomial model in clinical trial design is the sample size estimation.
We demonstrate how best to determine the appropriate sample size in the presence of challenges such as unequal follow-up or dispersion.
Innovative Strategies For Successful Trial Design - Webinar SlidesnQuery
Full webinar available here: https://www.statsols.com/webinar/innovative-strategies-for-successful-trial-design
[Webinar] Innovative Strategies For Successful Trial Design- In this free webinar, you will learn about:
- The challenges facing your trials
- How to calculate the correct sample size
- Worked examples including Mixed/Hierarchical Models
- Posterior Error
- Adaptive Designs For Survival
www.statsols.com
In spite of the efforts to prevent bias, the characteristics of any randomized example are not guaranteed to apply to everybody. That implies the main certainty offered to utilize this strategy is that the information applies to the individuals who take an interest.
Designing studies with recurrent events | Model choices, pitfalls and group s...nQuery
In this free webinar, we will examine the important design considerations for analyzing recurring events and counts.
Watch the webinar at: https://www.statsols.com/en/webinar/designing-studies-with-recurrent-events
Designing studies with recurrent events (Model choices, pitfalls and group sequential design)
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
Bayesian Assurance: Formalizing Sensitivity Analysis For Sample SizenQuery
Title: Bayesian Assurance: Formalizing Sensitivity Analysis For Sample Size
Duration: 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Watch Here: http://bit.ly/2ndRG4B
In this webinar you’ll learn about:
Benefits of Sensitivity Analysis: What does the researcher gain by conducting a sensitivity analysis?
Why isn't Sensitivity Analysis formalized: Why does sensitivity analysis still lack the type of formalized rules and grounding to make it a routine part of sample size determination in every field?
How Bayesian Assurance works: Using Bayesian Assurance provides key contextual information on what is likely to happen over the total range possible values rather than the small number of fixed points used in a sensitivity analysis
Elicitation & SHELF: How expert opinion is elicited and then how to integrate these opinions with each other plus prior data using the Sheffield Elicitation Framework (SHELF)
Why use in both Frequentist or Bayesian analysis: How and why these methods can be used for studies which will use Frequentist or Bayesian methods in their final analysis
Plus more
Bayesian random effects meta-analysis model for normal data - PubricaPubrica
(1) Choosing the Right Priorities
(2) Current Evidence
(3) Posterior
(4) Recapitulating
Continue Reading: https://bit.ly/3i7AMQ4
For our services: https://pubrica.com/services/research-services/meta-analysis/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
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
Schmidt and Bateman on implementation of EQ5D in Community settingAndrew Bateman
This poster from 2013 was created by Anja while on an internship at Oliver Zangwill Centre.
It provides a helpful summary of interviews about the issue of being asked to collect PROMS data. Clinicians value being given feedback on the patients they have seen and analysis at a service or organisation level has great value at a personal level too, potentially very rewarding for therapists and assistants.
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
Here is my presentation for an exciting event at King's Fund 26 MARCH 2015
This is the published programme for the day
Session one: Opening plenary
9.45am: Welcome and introduction
Dr Johnny Marshall, Director of Policy, NHS Confederation
9.55am: Transforming community health care services in London
Caroline Alexander, Chief Nurse, NHS England, London Region
10.15am: Panel session: The challenges and opportunities for improving and developing community services
Caroline Alexander, Chief Nurse, NHS England, London Region
Matthew Winn, Chief Executive, Cambridge Community Services NHS Trust and Chair, NHS Confederation Community Health Services Forum
Dr Crystal Oldman, Chief Executive, Queen's Nursing Institute
further panelists to be confirmed
10.55am: Questions and discussion
11.10am: Refreshment break and networking
Session two: What does good look like?
11.40am: Welcome and introduction
Catherine Foot, Assistant Director of Policy, The King’s Fund
11.45am: Regulating community health services
Ellen Armistead, Deputy Chief Inspector, Care Quality Commission
12.05pm: How and what should we measure to ensure quality?
Christina Walters, Programme Director, Community Indicators Programme
Andrew Barber, Technical Consultant, Community Indicators, Outcome Measures and Payment System Development Programme
12.25pm: Questions and discussion
12.40pm: Buffet lunch, networking and exhibition
Session three: Good practice breakout sessions
Sessions will run from 1.40-2.55pm and delegates will have the choice of:
A: Quality assurance: how are you using data locally to measure for quality?
1.40pm: Welcome and introduction
1.45pm: The use of PROMs (Patient Reported Outcome Measures) in a community setting
Iain Cockley-Adams, Service Improvement Manager, Gloucestershire Care Services NHS Trust
2.05pm: Over2You Quality Volunteers
Ruby Smith, Head of Personalisation, South Yorkshire Housing Association
2.25pm: PROMS in Practice: The Collection Analysis and Reporting of quality of life indicator EQ5D in rehabilitation services in Cambridgeshire Community Services
Andrew Bateman PhD, Physiotherapist and Service Manager, Oliver Zangwill Centre for Neuropsychological Rehabilitation, Cambridgeshire Community Services NHS Trust
2.45pm: Questions and discussion
B: Working with patients and communities: what are you doing to involve patients and their families and carers and to make your services more person-centred?
C: Partnerships and relationships with other parts of the system: how are you building effective local partnerships across health and social care?
2.55pm: Refreshment break and networking
Session four: Good practice breakout sessions
Sessions will run from 3.15-4.30pm and delegates will have the choice of:
D: Supporting and encouraging team working: what are you doing to support team working?
E: Working with patients and communities: what are you doing to involve patie
2020 trends in biostatistics what you should know about study design - slid...nQuery
2020 Trends In Biostatistics - What you should know about study design.
In this free webinar you will learn about:
-Adaptive designs in confirmatory trials
-Using external data in study planning
-Innovative designs in early-stage trials
To watch the full webinar:
https://www.statsols.com/webinar/2020-trends-in-biostatistics-what-you-should-know-about-study-design
Nicholas Jewell MedicReS World Congress 2014MedicReS
Statistical Methods for Observational Drug Studies
Nicholas P. Jewell Departments of Statistics &
School of Public Health (Biostatistics)
University of California, Berkeley
Clinical data analytics is an exciting new area of healthcare data analytics. This presentation presents a brief overview of the topic as an introduction and whetting the curiosity of the reader.
Webinar slides- alternatives to the p-value and power nQuery
What are the alternatives to the p-value & power? What is the next step for sample size determination? We will explore these issues in this free webinar presented by nQuery
Sample size for survival analysis - a guide to planning successful clinical t...nQuery
Determining the appropriate number of events needed for survival analysis is a complex task as study planners try to predict what sample size will be needed after accounting for the complications of unequal follow-up, drop-out and treatment crossover.
The statistical, logistical and ethical considerations all complicate life for biostatisticians as issues to balance in planning a survival analysis. However, this complexity has created a need for new analyses and procedures to help the planning process for survival analysis trials.
The wider move from fixed to flexible designs has opened up opportunities for advanced methods such as adaptive design and Bayesian analysis to help deal with the unique complications of planning for survival data but these methods have their own complications that need to be explored too.
Innovative Sample Size Methods For Clinical Trials nQuery
"Innovative Sample Size Methods for Clinical Trials" is hosted to coincide with the Spring 2018 update to nQuery - The leading Sample Size Software.
Hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - you'll learn about the benefits of a range of procedures and how you can implement them into your work:
1) Dose-escalation with the Bayesian Continual Reassessment Method
CRM is a growing alternative to the 3+3 method for Phase I trials finding the Maximum Tolerated Dose (MTD).
See how researchers can overcome 3+3 drawbacks to easily find the required sample size for this beneficial alternative for finding the MTD.
2) Bayesian Assurance with Survival Example
This Bayesian alternative to power has experienced a rapid rise in interest and application from researchers.
See how Assurance is being used by researchers to discover the true “probability of success” of a trial.
3) Mendelian Randomization
Mendelian randomization (MR) is a method that allows testing of a causal effect from observational data in the presence of confounding factors.
However, in order to design efficient Mendelian randomization studies, it is essential to calculate the appropriate sample sizes required. We demonstrate what to do to achieve this.
4) Negative Binomial Distribution
Negative binomial model has been increasingly used to model the count data. One of the challenges of applying negative binomial model in clinical trial design is the sample size estimation.
We demonstrate how best to determine the appropriate sample size in the presence of challenges such as unequal follow-up or dispersion.
Innovative Strategies For Successful Trial Design - Webinar SlidesnQuery
Full webinar available here: https://www.statsols.com/webinar/innovative-strategies-for-successful-trial-design
[Webinar] Innovative Strategies For Successful Trial Design- In this free webinar, you will learn about:
- The challenges facing your trials
- How to calculate the correct sample size
- Worked examples including Mixed/Hierarchical Models
- Posterior Error
- Adaptive Designs For Survival
www.statsols.com
In spite of the efforts to prevent bias, the characteristics of any randomized example are not guaranteed to apply to everybody. That implies the main certainty offered to utilize this strategy is that the information applies to the individuals who take an interest.
Designing studies with recurrent events | Model choices, pitfalls and group s...nQuery
In this free webinar, we will examine the important design considerations for analyzing recurring events and counts.
Watch the webinar at: https://www.statsols.com/en/webinar/designing-studies-with-recurrent-events
Designing studies with recurrent events (Model choices, pitfalls and group sequential design)
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
Bayesian Assurance: Formalizing Sensitivity Analysis For Sample SizenQuery
Title: Bayesian Assurance: Formalizing Sensitivity Analysis For Sample Size
Duration: 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Watch Here: http://bit.ly/2ndRG4B
In this webinar you’ll learn about:
Benefits of Sensitivity Analysis: What does the researcher gain by conducting a sensitivity analysis?
Why isn't Sensitivity Analysis formalized: Why does sensitivity analysis still lack the type of formalized rules and grounding to make it a routine part of sample size determination in every field?
How Bayesian Assurance works: Using Bayesian Assurance provides key contextual information on what is likely to happen over the total range possible values rather than the small number of fixed points used in a sensitivity analysis
Elicitation & SHELF: How expert opinion is elicited and then how to integrate these opinions with each other plus prior data using the Sheffield Elicitation Framework (SHELF)
Why use in both Frequentist or Bayesian analysis: How and why these methods can be used for studies which will use Frequentist or Bayesian methods in their final analysis
Plus more
Bayesian random effects meta-analysis model for normal data - PubricaPubrica
(1) Choosing the Right Priorities
(2) Current Evidence
(3) Posterior
(4) Recapitulating
Continue Reading: https://bit.ly/3i7AMQ4
For our services: https://pubrica.com/services/research-services/meta-analysis/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
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
Bayesian Approaches To Improve Sample Size WebinarnQuery
Title: Bayesian Approaches To Improve Sample Size
Duration: 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
In this webinar you'll learn about:
Bayesian Sample Size Determination: See how the growth of Bayesian analysis has helped transform our ideas about statistical inference and methodologies in clinical trials
Bayesian Assurance: Get an informative answer on how likely it is to see a “positive” outcome from the trial and then make better decisions on what trials to back
Posterior Credible Intervals and Mixed Bayesian Likelihood: Enable researchers to use prior information from pilot studies and other sources to make quicker and better decisions
Plus much more
Webinar slides sample size for survival analysis - a guide to planning succ...nQuery
Determining the appropriate number of events needed for survival analysis is a complex task as study planners try to predict what sample size will be needed after accounting for the complications of unequal follow-up, drop-out and treatment crossover.
The statistical, logistical and ethical considerations all complicate life for biostatisticians as issues to balance in planning a survival analysis. However, this complexity has created a need for new analyses and procedures to help the planning process for survival analysis trials.
The wider move from fixed to flexible designs has opened up opportunities for advanced methods such as adaptive design and Bayesian analysis to help deal with the unique complications of planning for survival data but these methods have their own complications that need to be explored too.
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.
An overview of fixed effects assumptions for meta analysis - PubricaPubrica
• The specific goals of meta-analysis include the estimation of an overall effect using different studies.
• The use of multiple studies provides a more robust test of the statistical use of the effect; and identification of variables affecting the estimated impact in different studies.
Continue Reading: https://bit.ly/35CHxm7
Reference: https://pubrica.com/services/research-services/meta-analysis/
Why Pubrica?
When you order our services, we promise you the following – Plagiarism free, always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Sample Size: A couple more hints to handle it right using SAS and RDave Vanz
Andrii Artemchuk from Intego Group, a Ukrainian offshore staffing company, presented this power point to the audience at a phUSE conference in Frankfurt Germany in 2018 on SAS and R
How Randomized Controlled Trials are Used in Meta-Analysis Pubrica
Randomized Controlled Trials (RCTs) are a commonly used research design in medical and scientific studies to assess the effectiveness of interventions or treatments. Meta-analysis, on the other hand, is a statistical technique used to combine and analyze the results of multiple studies on a particular topic to draw more robust conclusions.
Continue reading @ https://pubrica.com/academy/meta-analysis/how-randomized-controlled-trials-are-used-in-meta-analysis/
For all your research assistance visit us @ https://pubrica.com/services/research-services/
Recent position statements on the misuse of p-values and significance testing have led to a reassessment of how study results are reported in journals. Increased use of point
estimates and confidence intervals can help avoid the misinterpretation encountered with significance testing. Greater use of confidence intervals can lead to more criticallyand clinically-relevant discussions of study results
A Systematic Review of ADaM IG Interpretation presented by Angelo Tinazzi, Cytel
East bayesian power calculations
1. PROBLEM
Sample size and power calculations can be highly depen-
dent on the assumed magnitude of the treatment effect.
Sensitivity analysis is typically performed by checking calcu-
lations for a range of potential values. Indeed, East is ideally
suited for performing sensitivity analysis of this kind.
This ad hoc approach to sensitivity analysis can be comple-
mented by a Bayesian approach, which addresses uncer-
tainty about the treatment effect in a more formal fashion.
The assurance (O’Hagan et al., 2005), or probability of
success, is a Bayesian version of power, which corresponds
to the unconditional probability that the trial will yield a
significant result. Specifically, it is the expectation of the
power, averaged over a prior distribution for the unknown
treatment effect. This prior distribution expresses the
uncertainty about the treatment effect, before the trial
began, in terms of the relative plausibility of different
parameter values.
Depending on one's goals, a prior distribution can take one
of many forms. It may be non-informative (e.g., uniform); it
may represent the beliefs of an "enthusiastic" or "skeptical"
stakeholder, or it may adopt a complex shape that
represents diverse opinions from a group of experts.
The probability of success is an important consideration for
your clinical trial at the design stage. Another Bayesian
measure, known as predictive power (Lan et al., 2009) aids
decision making at the interim monitoring stage. During the
course of a trial, it is often helpful to calculate the condi-
tional power: the probability of obtaining a significant result
when the trial ends, given the current results. If the condi-
tional power is low, the trial may stop early for futility, or
there may be an opportunity to re-estimate and increase
the sample size.
As with computing power in the design stage, the condition-
al power calculation depends on the assumed treatment
effect, such as an estimate at the interim. However, empiri-
cal estimates may not be reliable. Thus, rather than assum-
ing a single value for the treatment effect, one could calcu-
late conditional power for several different values, and
weigh them by the posterior distribution for the treatment
effect.
PROBLEM - Uncertainty about treatment effects impede clinical trial design
SOLUTION - East® allows Bayesian calculations to formally address this uncertainty
BENEFITS - Ability to incorporate prior information leads to more accurate predictions of trial success
Bayesian Power Calculations:
Probability of Success and Predictive Power
…assurance should be a
major consideration when
designing a confirmatory
trial.
-Chuang-Stein, et al. (2011)
Case Study
2. East offers the flexibility to use a standard parametric prior
distribution, or a user-defined CSV file for more complex
prior distributions. Bayesian measures, such as assurance and
predictive power, are calculated from these priors.
Suppose we are designing a group sequential clinical trial for a
weight loss treatment, with the inputs displayed above. In
particular, note that we target 90% power to detect a treat-
ment difference of 3 kg.
This calculation rests on the assumption that the magnitude of
the treatment effect (3 kg) is known with certainty. A more
realistic scenario is that the true treatment effect lies within
some range of possible values. The uncertainty about the
treatment effect can be represented, for example, as a
Normal distribution with mean 3, and standard deviation of 2.
In this more realistic scenario, East shows that the probability
of success (72%) is lower than the desired 90% power.
Once the trial is underway, the interim monitoring dashboard
in East can compare the conditional power calculated at the
estimated treatment effect, the predictive power based on a
posterior distribution derived from a diffuse prior, and the
Bayes predictive power based on a posterior distribution
derived from the user-specified prior. The difference in these
three estimate are striking and highlight the importance of
incorporating prior beliefs into the decision making, especial-
ly for futility analysis
BENEFITS
The Bayesian approach to statistical decision making is becoming increasingly accepted as a valuable way to manage uncertainty
in clinical trial design and analysis. One key advantage is the ability to incorporate quantitative prior information to support calcula-
tions and decision making. In fact, any prior information about the treatment effect - whether gained from previous trials or from
expert opinions - can be accounted for in a power calculation. As the industry standard software for clinical trial design, East
continues to incorporate Bayesian and related methodologies to improve clinical success rates.
References
Chuang-Stein, C., Kirby, S., Hirsch, I., & Atkinson, G. (2011). The role of
the minimum clinically important difference and its impact on design-
ing a trial. Pharmaceutical Statistics, 10, 250-256.
Lan, K., Hu, P., & Proschan, M. (2009). A conditional power approach
to the evaluation of predictive power. Statistics in Biopharmaceutical
Research, 1, 131-136.
O’Hagan, A., Stevens, J.W., & Campbell, M.J. (2005). Assurance in
clinical trial design. Pharmaceutical Statistics, 4, 187-201.
Bayesian Power Calculations:
Probability of Success and Predictive Power
SOLUTION
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