The document describes a novel platform trial design called REMAP (Randomized, Embedded, Multifactorial, Adaptive Platform) that aims to efficiently test multiple interventions for critical illness. It utilizes a point-of-care embedded design within electronic health records to rapidly enroll patients and assign multifactorial intervention regimens based on a Bayesian statistical model. The model continuously updates probabilities of intervention effectiveness based on accumulating trial data and can trigger results when an intervention is found to be superior, equivalent, or inferior for a given patient subgroup. This allows the trial to efficiently evaluate and adapt multiple treatment options in a real-world intensive care setting.
About the webinar
Flexible Clinical Trial Design | Survival, Stepped-Wedge & MAMS Designs
As clinical trials increase in complexity, the requirement is for trial designs to adapt to these complications.
From dealing with non-proportional hazards in survival analysis to creating seamless Phase II/III clinical trials, it is an exciting time to be involved in clinical trial design and analysis.
In this free webinar, we will explore a select few topics that highlight the additional flexibility available when designing modern clinical trials.
In this free webinar you will learn about:
Flexible Survival Analysis Designs
Non-proportional hazards and other complex survival curves have become of increasing interest, due to being commonly seen in immunotherapy development. This has led to interest in assessing the robustness of standard methods and alternative methods that better adapt to deviations.
In this webinar, we will look at power analysis assuming complex survival curves and the weighted log-rank test as one candidate model to deal with a delayed survival effect.
Stepped-Wedge designs
Cluster-randomized designs are often adopted when there is a high risk of contamination if cluster members were randomized individually. Stepped-wedge designs are useful in cases where it is difficult to apply a particular treatment to half of the clusters at the same time.
In this webinar, we will introduce stepped-wedge designs and provide an insight into the more complex, flexible randomization schedules available.
Multi-Arm Multi-Stage (MAMS)
MAMs designs provide the ability to assess more treatments in less time than could be done with a series of two-arm trials and can offer smaller sample size requirements when compared to that required for the equivalent number of two-arm trials.
In this webinar, we will look at the design of a Group Sequential MAMS design and explore its design requirements.
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
For more webinars check out https://www.statsols.com/webinars
Extending A Trial’s Design Case Studies Of Dealing With Study Design IssuesnQuery
About the webinar
As trials increase in complexity and scope, there is a requirement for trial designs to reflect this.
From dealing with non-proportional hazards in survival analysis to dealing with cluster randomization, we examine how to deal with study design issues of complex trials.
In this free webinar, you will learn about:
Dealing with study design issues
Practical worked examples of
Non-proportional Hazards
Cluster Randomization
Three Armed Trials
Non-proportional Hazards
Non-proportional hazards and complex survival curves have become of increasing interest, due to being commonly seen in immunotherapy development. This has led to interest in assessing the robustness of standard methods and alternative methods that better adapt to deviations.
In this webinar, we look at methods proposed for complex survival curves and the weighted log-rank test as a candidate model to deal with a delayed survival effect.
Cluster Randomization
Cluster-randomized designs are often adopted when there is a high risk of contamination if cluster members were randomized individually. Stepped-wedge designs are useful in cases where it is difficult to apply a particular treatment to half of the clusters at the same time.
In this webinar, we introduce cluster randomization and stepped-wedge designs to provide an insight into the requirements of more complex randomization schedules.
Three Armed Trials
Non-inferiority testing is a common hypothesis test in the development of generic medicine and medical devices. The most common design compares the proposed non-inferior treatment to the standard treatment alone but this leaves uncertain if the treatment effect is the same as from previous studies. This “assay sensitivity” problem can be resolved by using a three arm trial which includes placebo alongside the new and reference treatments for direct comparison.
In this webinar we show a complete testing approach to this gold standard design and how to find the appropriate allocation and sample size for this study.
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Innovative sample size methods for adaptive clinical trials webinar web ver...nQuery
View the video here:
https://www.statsols.com/webinar/innovative-sample-size-methods-for-adaptive-clinical-trials
Given the high failure rates and the increased costs of clinical trials, researchers need innovative design strategies to best optimize financial resources and reduce the risk to patients.
Adaptive designs are emerging as a way to reduce risk and cost associated with clinical trials. The FDA recently published guidance (Innovative Cures Act) and are actively encouraging sponsors to use Adaptive trials.
Adaptive design is a clinical trial design that allows adaptations or modifications to aspects of the trial after its initiation without undermining the validity and integrity of the trial.
In this webinar, Ronan will demonstrate nQuery's new Adaptive module focusing on Sample Size Re-Estimation & Group-Sequential Design.
In this webinar you will learn about:
The pros and cons of adaptive designs
Sample Size Re-Estimation
Group-Sequential Design
Conditional Power
Predictive Power
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.
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
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)
Clinical Research Statistics for Non-StatisticiansBrook White, PMP
Through real-world examples, this presentation teaches strategies for choosing appropriate outcome measures, methods for analysis and randomization, and sample sizes as well as tips for collecting the right data to answer your scientific questions.
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
About the webinar
Flexible Clinical Trial Design | Survival, Stepped-Wedge & MAMS Designs
As clinical trials increase in complexity, the requirement is for trial designs to adapt to these complications.
From dealing with non-proportional hazards in survival analysis to creating seamless Phase II/III clinical trials, it is an exciting time to be involved in clinical trial design and analysis.
In this free webinar, we will explore a select few topics that highlight the additional flexibility available when designing modern clinical trials.
In this free webinar you will learn about:
Flexible Survival Analysis Designs
Non-proportional hazards and other complex survival curves have become of increasing interest, due to being commonly seen in immunotherapy development. This has led to interest in assessing the robustness of standard methods and alternative methods that better adapt to deviations.
In this webinar, we will look at power analysis assuming complex survival curves and the weighted log-rank test as one candidate model to deal with a delayed survival effect.
Stepped-Wedge designs
Cluster-randomized designs are often adopted when there is a high risk of contamination if cluster members were randomized individually. Stepped-wedge designs are useful in cases where it is difficult to apply a particular treatment to half of the clusters at the same time.
In this webinar, we will introduce stepped-wedge designs and provide an insight into the more complex, flexible randomization schedules available.
Multi-Arm Multi-Stage (MAMS)
MAMs designs provide the ability to assess more treatments in less time than could be done with a series of two-arm trials and can offer smaller sample size requirements when compared to that required for the equivalent number of two-arm trials.
In this webinar, we will look at the design of a Group Sequential MAMS design and explore its design requirements.
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
For more webinars check out https://www.statsols.com/webinars
Extending A Trial’s Design Case Studies Of Dealing With Study Design IssuesnQuery
About the webinar
As trials increase in complexity and scope, there is a requirement for trial designs to reflect this.
From dealing with non-proportional hazards in survival analysis to dealing with cluster randomization, we examine how to deal with study design issues of complex trials.
In this free webinar, you will learn about:
Dealing with study design issues
Practical worked examples of
Non-proportional Hazards
Cluster Randomization
Three Armed Trials
Non-proportional Hazards
Non-proportional hazards and complex survival curves have become of increasing interest, due to being commonly seen in immunotherapy development. This has led to interest in assessing the robustness of standard methods and alternative methods that better adapt to deviations.
In this webinar, we look at methods proposed for complex survival curves and the weighted log-rank test as a candidate model to deal with a delayed survival effect.
Cluster Randomization
Cluster-randomized designs are often adopted when there is a high risk of contamination if cluster members were randomized individually. Stepped-wedge designs are useful in cases where it is difficult to apply a particular treatment to half of the clusters at the same time.
In this webinar, we introduce cluster randomization and stepped-wedge designs to provide an insight into the requirements of more complex randomization schedules.
Three Armed Trials
Non-inferiority testing is a common hypothesis test in the development of generic medicine and medical devices. The most common design compares the proposed non-inferior treatment to the standard treatment alone but this leaves uncertain if the treatment effect is the same as from previous studies. This “assay sensitivity” problem can be resolved by using a three arm trial which includes placebo alongside the new and reference treatments for direct comparison.
In this webinar we show a complete testing approach to this gold standard design and how to find the appropriate allocation and sample size for this study.
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Innovative sample size methods for adaptive clinical trials webinar web ver...nQuery
View the video here:
https://www.statsols.com/webinar/innovative-sample-size-methods-for-adaptive-clinical-trials
Given the high failure rates and the increased costs of clinical trials, researchers need innovative design strategies to best optimize financial resources and reduce the risk to patients.
Adaptive designs are emerging as a way to reduce risk and cost associated with clinical trials. The FDA recently published guidance (Innovative Cures Act) and are actively encouraging sponsors to use Adaptive trials.
Adaptive design is a clinical trial design that allows adaptations or modifications to aspects of the trial after its initiation without undermining the validity and integrity of the trial.
In this webinar, Ronan will demonstrate nQuery's new Adaptive module focusing on Sample Size Re-Estimation & Group-Sequential Design.
In this webinar you will learn about:
The pros and cons of adaptive designs
Sample Size Re-Estimation
Group-Sequential Design
Conditional Power
Predictive Power
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.
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
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)
Clinical Research Statistics for Non-StatisticiansBrook White, PMP
Through real-world examples, this presentation teaches strategies for choosing appropriate outcome measures, methods for analysis and randomization, and sample sizes as well as tips for collecting the right data to answer your scientific questions.
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
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
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.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
Sample size calculation in medical researchKannan Iyanar
A short description on estimation of sample size in health care research. It describes the basic concepts in sample size estimation and various important formulae used for it.
This content is suitable for medical technologists/technicians/lab assistants/scientists writing the SMLTSA board exam. The content is also suitable for biomedical technology students and people also interested in learning about test methodologies used in medical technology. This chapter describes test quality assurance (QA) and quality control (QC). Please note that these notes are a collection I used to study for my board exam and train others who got distinctions using these.
Disclaimer: Credit goes to those who wrote the notes and the examiners of each exam question. Please use only as a reference guide and use your prescribed textbook for the latest and most accurate notes and ranges. The material here is not referenced as it is a collection of pieces of study notes from multiple people, and thus will not be held viable for any misinterpretations. Please use at your own discretion.
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
Clinical trials: quo vadis in the age of covid?Stephen Senn
A discussion of the role of clinical trials in the age of COVID. My contribution to the phastar 2020 life sciences summit https://phastar.com/phastar-life-science-summit
Does clinical research help me take care of my patient?scanFOAM
A presentation by Derek Angus at the 2017 meeting of the Scandinavian Society of Anaestesiology and Intensive Care Medicine.
All available content from SSAI2017: https://scanfoam.org/ssai2017/
Delivered in collaboration between scanFOAM, SSAI & SFAI.
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
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.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
Sample size calculation in medical researchKannan Iyanar
A short description on estimation of sample size in health care research. It describes the basic concepts in sample size estimation and various important formulae used for it.
This content is suitable for medical technologists/technicians/lab assistants/scientists writing the SMLTSA board exam. The content is also suitable for biomedical technology students and people also interested in learning about test methodologies used in medical technology. This chapter describes test quality assurance (QA) and quality control (QC). Please note that these notes are a collection I used to study for my board exam and train others who got distinctions using these.
Disclaimer: Credit goes to those who wrote the notes and the examiners of each exam question. Please use only as a reference guide and use your prescribed textbook for the latest and most accurate notes and ranges. The material here is not referenced as it is a collection of pieces of study notes from multiple people, and thus will not be held viable for any misinterpretations. Please use at your own discretion.
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
Clinical trials: quo vadis in the age of covid?Stephen Senn
A discussion of the role of clinical trials in the age of COVID. My contribution to the phastar 2020 life sciences summit https://phastar.com/phastar-life-science-summit
Does clinical research help me take care of my patient?scanFOAM
A presentation by Derek Angus at the 2017 meeting of the Scandinavian Society of Anaestesiology and Intensive Care Medicine.
All available content from SSAI2017: https://scanfoam.org/ssai2017/
Delivered in collaboration between scanFOAM, SSAI & SFAI.
Introduction: what is the SW-CRT?: Defining features, common variations, and some salient examples
Dr Karla Hemming
Society for Clinical Trials Conference; Arlington, Virginia
May 17th 2015
This presentation was part of the workshop organised by Karla Hemming: Research and reporting methods for the stepped wedge cluster randomised controlled trial
An early and overlooked causal revolution in statistics was the development of the theory of experimental design, initially associated with the "Rothamstead School". An important stage in the evolution of this theory was the experimental calculus developed by John Nelder in the 1960s with its clear distinction between block and treatment factors in designed experiments. This experimental calculus produced appropriate models automatically from more basic formal considerations but was, unfortunately, only ever implemented in Genstat®, a package widely used in agriculture but rarely so in medical research. In consequence its importance has not been appreciated and the approach of many statistical packages to designed experiments is poor. A key feature of the Rothamsted School approach is that identification of the appropriate components of variation for judging treatment effects is simple and automatic.
The impressive more recent causal revolution in epidemiology, associated with Judea Pearl, seems to have no place for components of variation, however. By considering the application of Nelder’s experimental calculus to Lord’s Paradox, I shall show that this reveals that solutions that have been proposed using the more modern causal calculus are problematic. I shall also show that lessons from designed clinical trials have important implications for the use of historical data and big data more generally.
Data Con LA 2019 - Best Practices for Prototyping Machine Learning Models for...Data Con LA
Medical institutions, universities and software giants like Google and Microsoft are dedicating increasing resources to machine learning for healthcare. This is a very exciting but relatively young field. However, best practices for methods and reporting of results are not yet fully established. I have 2.5 years of experience as data scientist at a national cancer center working on clinical data, evaluating external vendors and peer reviewing machine learning in healthcare papers. The talk gives an overview of best practices in prototyping machine learning models on data from the patient electronic health record (EHR). The topics addressed are:1. Introduction to the EHR2. Overview of machine learning applications to the EHR3. Cohort definition for survival problems4. Data cleaning5. Performance metricsExcerpts of papers from renowned institutions will be critically reviewed. The material is intended to be useful not only to machine learning for healthcare professionals, but to practitioners dealing with very unbalanced dataset in the temporal domain. For example, customer churn prediction can be modeled as survival problem.
CNV Annotations: a crucial step in your variant analysisGolden Helix
Since the development of our NGS-based CNV solutions for VarSeq and SVS, we've generated a long list of content demonstrating simple workflows to help isolate clinically relevant events for a given sample. However, it's just as important to talk about the exclusionary filters that help remove any extraneous CNVs from the analysis.
Golden Helix stands alone in our delivery of multiple methods for filtering down to top-quality, rare, and clinically relevant variants. This webcast will focus on the application of the various CNV annotations, discussing their purpose and usability in quickly removing CNVs with high-population frequency, duplicated regions inherent to the human genome, benign events, and events known in healthy individuals.
Please join us in an exploration of VarSeq's unique CNV annotation capabilities to see how users can overcome the challenges of NGS-based CNV detection.
What will you learn in this webcast?
Initial assessment of sample and CNV event quality
General review and understanding of various CNV annotations in VarSeq
The application of CNV annotations to eliminate common and benign CNVs
Clinical Validation of Copy Number Variants Using the AMP GuidelinesGolden Helix
The common approaches to detecting copy number variants (CNVs) are chromosomal microarray and MLPA. However, both options increase analysis time, per sample costs, and are limited to the size of CNV events that can be detected. VarSeq’s CNV caller, on the other hand, allows users to detect CNVs from the coverage profile stored in the BAM file, which allows you to utilize your existing NGS data and perform the analysis all in one suite. Coupled with this innovative feature is the ability to annotate CNV events against a variety of databases, and by incorporating our VSClinical AMP workflow, we can now assess CNVs as potential biomarkers. Most importantly, Golden Helix CancerKB is an AMP workflow feature that provides expert-curated biomarker interpretations, including those for common somatic CNVs, that will streamline the analysis time and report generation.
In this demonstration we will cover:
Setting up the VS-CNV caller using BAM files from whole exome data
Filtering down to high quality, high confidence CNV events
Annotating CNVs using publicly curated catalogs and databases
Adding clinically relevant CNVs to the VSClinical AMP workflow
Utilizing Golden Helix CancerKB to obtain expert-curated interpretations
Showing updated features and polishes to the software
Together, VarSeq incorporates the ability to accurately call and annotate CNVs and evaluate germline and somatic mutations according to the ACMG and AMP guidelines, respectively. This webcast demonstration will provide insight into these best practice workflows and will hopefully show you how you can implement this top-quality software into your pipeline solution.
The statistical revolution of the 20th century was largely concerned with developing methods for analysing small datasets. Student’s paper of 1908 was the first in the English literature to address the problem of second order uncertainty (uncertainty about the measures of uncertainty) seriously and was hailed by Fisher as heralding a new age of statistics. Much of what Fisher did was concerned with problems of what might be called ‘small data’, not only as regards efficient analysis but also as regards efficient design and in addition paying close attention to what was necessary to measure uncertainty validly.
I shall consider the history of some of these developments, in particular those that are associated with what might be called the Rothamsted School, starting with Fisher and having its apotheosis in John Nelder’s theory of General Balance and see what lessons they hold for the supposed ‘big data’ revolution of the 21st century.
A talk by Sara Crager at TBS24
Shock isn’t about hypotension, it’s about hypoperfusion. While we know this in theory, we don’t do a great job of applying it in practice. In order to move beyond our reliance on blood pressure to recognize shock at the bedside, we need to stop thinking about shock as a diagnosis and instead think about it as a continuum.
Fully Automated CPR | Jason van der Velde | TBS24scanFOAM
Embark on a fascinating exploration of Fully Automated Cardiac Arrest Management with Dr. Jason van der Velde, who’s been part of a team refining the FA-CPR algorithm since 2019. Gain unique insights into real-world applications and ongoing research opportunities in optimising the “Low Flow State” through innovative approaches like Chest Compression Synchronised Ventilation (CCSV). Dr. Van der Velde shares an iterative journey, supported by real-life data, underscoring the profound impact of personalised CPR tailored to individual patients in rural Ireland. The talk goes beyond conventional guidelines, delving into the intricate science and human factors essential for achieving substantial improvements in Return of Spontaneous Circulation (ROSC) rates. Attendees will leave with a deep understanding of the potential of Fully Automated CPR with CCSV as a dynamic and continually evolving strategy, acting as a strategic placeholder to buy essential time for comprehensive diagnostics and personalised interventions. The presentation hints at transformative possibilities in resuscitation science, featuring case studies that showcase the concept of bridging patients to definitive interventions such as cardiac angiography and Extracorporeal Membrane Oxygenation (ECMO).
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
263778731218 Abortion Clinic /Pills In Harare ,ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group of receptionists, nurses, and physicians have worked together as a teamof receptionists, nurses, and physicians have worked together as a team wwww.lisywomensclinic.co.za/
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
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.
The Gram stain is a fundamental technique in microbiology used to classify bacteria based on their cell wall structure. It provides a quick and simple method to distinguish between Gram-positive and Gram-negative bacteria, which have different susceptibilities to antibiotics
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
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
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
CDSCO and Phamacovigilance {Regulatory body in India}NEHA GUPTA
The Central Drugs Standard Control Organization (CDSCO) is India's national regulatory body for pharmaceuticals and medical devices. Operating under the Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India, the CDSCO is responsible for approving new drugs, conducting clinical trials, setting standards for drugs, controlling the quality of imported drugs, and coordinating the activities of State Drug Control Organizations by providing expert advice.
Pharmacovigilance, on the other hand, is the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems. The primary aim of pharmacovigilance is to ensure the safety and efficacy of medicines, thereby protecting public health.
In India, pharmacovigilance activities are monitored by the Pharmacovigilance Programme of India (PvPI), which works closely with CDSCO to collect, analyze, and act upon data regarding adverse drug reactions (ADRs). Together, they play a critical role in ensuring that the benefits of drugs outweigh their risks, maintaining high standards of patient safety, and promoting the rational use of medicines.
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.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
1. CRISMA Center, Department of Critical Care Medicine
Department of Health Policy and Management
McGowan Institute for Regenerative Medicine
Clinical and Translational Science Institute
University of Pittsburgh Schools of the Health Sciences
Derek C. Angus, MD, MPH, FRCP
2. CRISMA Center, Department of Critical Care Medicine
Department of Health Policy and Management
McGowan Institute for Regenerative Medicine
Clinical and Translational Science Institute
University of Pittsburgh Schools of the Health Sciences
Derek C. Angus, MD, MPH, FRCP
Big Data vs. the RCT?
3. C R I S M A
Evidence-based medicine
“Doctors must base what they do on
randomized clinical trials (RCTs)”
Archie Cochrane
4. C R I S M A
How we know something works ...
Patients with
disease X
5. C R I S M A
How we know something works ...
Patients with
disease X
6. C R I S M A
How we know something works ...
Patients with
disease X
7. C R I S M A
How we know something works ...
Patients with
disease X
8. C R I S M A
How we know something works ...
Patients with
disease X
10. C R I S M A
The good news …
•We now test many ideas with RCTs
- 37,000 started in 2010 …
-All FDA drug and device approvals
•We now conduct RCTs very well
- Methodologic conduct
- Ethical oversight
- Reporting
11. C R I S M A
But …
• RCTs are too narrow
• Cherry-picked population; not the real world
• RCTs are too broad
• No data on treatment effects across patients
• No comparative effectiveness
• Rx A vs. B is not very helpful
• What about A vs. B, vs. C, vs. D … etc.
• Depending on whether I give E or F …
• Having said all that, I just want the answer …
• I don’t want my patient to be a guinea pig …
12. C R I S M A
Clinical care Clinical research
Parallel universes …
13. C R I S M A
Enter the era of ‘Big Data’
• Integration of ‘deep’ personalized data
• Causal inferences on optimal care
• Broad – ‘real-world’ practice
• Narrow – ‘personal’ estimates
• Comparative – considers all options
• Vanderbilt-IBM ‘BioVU’ initiative
• ‘Live’ presentation of information at time
of clinical decision-making
• ‘Just-in-time’ cohort study in EHR
• No guinea pigs
• Longhurst et al. Health Affairs 2013
16. C R I S M A
Point-of-care (POC) Clinical Trials
• A clinical moment in the EHR ‘alerts’ the clinical trial machinery
• VA EHR
• In-patient diabetics with poor glucose control
• When physician placed insulin order in CPOE system …
• Opportunity to randomize
• Sliding scale
• Weight-based algorithm
• Fiore et al. Clinical Trials 2011
• Targeting the large ‘pragmatic’ trial arena
• 2 thiazide diuretics in >13k high BP Veterans (NCT02185417)
• 2 aspirin doses in 20k CVD patients (ADAPTABLE) (PCORI)
18. C R I S M A
Platform Trials
• Adaptive trials
• Focus on disease, not a particular Rx
• Multiple interventions (arms)
• ‘Perpetual’ enrollment
• Often based on Bayes’ theorem
• Tailor choices over time
Berry et al JAMA 2015
• Focus on pre-approval space
• Emphasis on efficiency with (very) small sample sizes
• Different therapies ‘graduate’ to next phase while trial continues
Woodcock and Lavange NEJM 2017
20. C R I S M A
Response-adaptive randomization
Rugo et al. NEJM 2016
21. C R I S M A
The traditional RCT ...
Patients with
disease X
At the start,
50% chance
that A > B
22. C R I S M A
The traditional RCT ...
Patients with
disease X
At the end, >99% sure that A > B
What about in the middle?
23. C R I S M A
A planned trial of A vs. B in 400 patients
The probability that A > B = 78%
Start randomizing MORE patients to A than B …
Alive
Dead
40
20
No. of
patients
A B
After 40 enrolled ….
24. C R I S M A
After 80 patients …
Now, the probability that A > B = 99.9%
Stop the trial!
Alive
Dead
40
20
No. of
patients
A B
25. C R I S M A
Caveats
1. If the ‘second’ 40 was flat or opposite direction …
• Trial continues and the next ‘bet’ swings back closer to 50:50
2. When only 2 groups, power still driven by the smaller group
• So, NOT very helpful if …
• Single homogenous cohort
• Two arms
• But, becomes VERY interesting when …
• Multiple arms
• Multiple subgroups
33. C R I S M A
A novel blend of ‘POC’ + platform designs
•REMAP
•Randomized
•Embedded
•Multifactorial
•Adaptive
•Platform trial
REMAP
✔
✔
✔
✔
✔
✔
✔
Angus DC. JAMA 2015
34. C R I S M A
• Funding
• EU FP7 PREPARE WP 5 program (25M euro)
• Australian NHMRC ‘OPTIMISE’ program ($6M)
• New Zealand NHMRC ($2M)
• Simultaneously test
• Different anti-microbial strategies
• Different host immunomodulation strategies
• Different ventilation strategies
• Separate RAR and stopping rules for multiple subgroups
Angus DC. JAMA 2015
35. C R I S M A Angus DC. JAMA 2015
• Patients are preferentially assigned to best performing arm
• Allocation is random, but NOT 50:50
• Odds of assignment proportional to odds of success
• Not a guinea pig!
• Embedded
• ICU admission orders
• Approved in Netherlands and New Zealand
with delayed consent
36. C R I S M A
REMAP-CAP elements
• Domain – an area where a question is asked …
• Domain #1 – choice of antibiotic
• Domain #2 – whether to give steroids or not
• Domain #3 – whether to extend macrolide or not
• Domain #4 – choice of ventilator strategy
• Domain #5 – oxygen titration strategy
• Etc. ….
• Intervention
• Any option within a domain …
• Regimen
• Unique combination of interventions within a domain …
• Stratum
• Baseline subgroup
• Ex. shock or not
37. Multifactorial intervention assignments
Regimen = set of domain-specific interventions
Effect of an intervention is conditional upon
• Stratum
• Interventions within other domains
Regimen Domain A Domain B Domain C
#1 A1 B1 C1
#2 A1 B1 C2
#3 A1 B2 C1
#4 A1 B2 C2
#5 A2 B1 C1
…..
#n An Bn Cn
38. Multifactorial intervention assignments
Regimen = set of domain-specific
interventions
Effect of an intervention is conditional upon
• Stratum
• Interventions within other domainsRegimen Domain A Domain B Domain C
#1 A1 B1 C1
#2 A1 B1 C2
#3 A1 B2 C1
#4 A1 B2 C2
#5 A2 B1 C1
…..
#n An Bn Cn
Pre-specified architecture determined by
• Choice of domains, strata, etc.
• Choice of potential interactions
Choices inform a Bayesian inference model
• Pre-trial simulations evaluate performance
Each external adaptation (ex. new domain)
• Modify elements in Bayesian model
• Re-simulate before ‘live’ deployment
Pre-trial design and construction
39. Pre-specified architecture determined by
• Choice of domains, strata, etc.
• Choice of potential interactions
Choices inform a Bayesian inference model
• Pre-trial simulations evaluate performance
Each external adaptation (ex. new domain)
• Modify elements in Bayesian model
• Re-simulate before ‘live’ deployment
Pre-trial design and construction
41. Multifactorial intervention assignments
Regimen = set of domain-specific
interventions
Effect of an intervention is conditional upon
• Stratum
• Interventions within other domainsRegimen Domain A Domain B Domain C
#1 A1 B1 C1
#2 A1 B1 C2
#3 A1 B2 C1
#4 A1 B2 C2
#5 A2 B1 C1
…..
#n An Bn Cn
Pre-specified architecture determined by
• Choice of domains, strata, etc.
• Choice of potential interactions
Choices inform a Bayesian inference model
• Pre-trial simulations evaluate performance
Each external adaptation (ex. new domain)
• Modify elements in Bayesian model
• Re-simulate before ‘live’ deployment
Pre-trial design and construction
42. Statistical trigger
Steering Committee can
• Add strata, domains & interventions
DSMB can
• Request new external data be incorporated in priors
• Overrule statistical triggers
Multifactorial intervention assignments
Regimen = set of domain-specific
interventions
Effect of an intervention is conditional upon
• Stratum
• Interventions within other domainsRegimen Domain A Domain B Domain C
#1 A1 B1 C1
#2 A1 B1 C2
#3 A1 B2 C1
#4 A1 B2 C2
#5 A2 B1 C1
…..
#n An Bn Cn
Result declared when, within stratum, an intervention is
• Superior >99% likely to be best
• Equivalent >90% likely to be D<3%
• Inferior <1% likely to be best
Pre-specified architecture determined by
• Choice of domains, strata, etc.
• Choice of potential interactions
Choices inform a Bayesian inference model
• Pre-trial simulations evaluate performance
Each external adaptation (ex. new domain)
• Modify elements in Bayesian model
• Re-simulate before ‘live’ deployment
Pre-trial design and construction
Patients
Presumed severe CAP
• Different strata
(ex. shock or not)
• Collected at sites
• Managed at regional data centers
• Merged at central statistical center
Data collection
Re-estimate Bayesian inference model
with new data to update probabilities
Update and adapt
External adaptations
43. Statistical trigger
Steering Committee can
• Add strata, domains & interventions
DSMB can
• Request new external data be incorporated in priors
• Overrule statistical triggers
Multifactorial intervention assignments
Regimen = set of domain-specific
interventions
Effect of an intervention is conditional upon
• Stratum
• Interventions within other domains
Embedding
Patient identification and enrollment
• Tied to clinical ‘point-of-care’
Randomized interventions
• Issued as ‘order set’ regimen
EHR embedding
• Screen and flag patient
• Consent documentation
• Generate regimen order set
• Flag downstream states
• Data collection
Regimen Domain A Domain B Domain C
#1 A1 B1 C1
#2 A1 B1 C2
#3 A1 B2 C1
#4 A1 B2 C2
#5 A2 B1 C1
…..
#n An Bn Cn
Result declared when, within stratum, an intervention is
• Superior >99% likely to be best
• Equivalent >90% likely to be D<3%
• Inferior <1% likely to be best
Pre-specified architecture determined by
• Choice of domains, strata, etc.
• Choice of potential interactions
Choices inform a Bayesian inference model
• Pre-trial simulations evaluate performance
Each external adaptation (ex. new domain)
• Modify elements in Bayesian model
• Re-simulate before ‘live’ deployment
Pre-trial design and construction
Patients
Presumed severe CAP
• Different strata
(ex. shock or not)
• Collected at sites
• Managed at regional data centers
• Merged at central statistical center
Data collection
Re-estimate Bayesian inference model
with new data to update probabilities
Update and adapt
External adaptations
44. Statistical trigger
• Launch with initial weights
• Update based on new probabilities
Steering Committee can
• Add strata, domains & interventions
DSMB can
• Request new external data be incorporated in priors
• Overrule statistical triggers
Multifactorial intervention assignments
Regimen = set of domain-specific
interventions
Effect of an intervention is conditional upon
• Stratum
• Interventions within other domains
Embedding
Patient identification and enrollment
• Tied to clinical ‘point-of-care’
Randomized interventions
• Issued as ‘order set’ regimen
EHR embedding
• Screen and flag patient
• Consent documentation
• Generate regimen order set
• Flag downstream states
• Data collection
Regimen Domain A Domain B Domain C
#1 A1 B1 C1
#2 A1 B1 C2
#3 A1 B2 C1
#4 A1 B2 C2
#5 A2 B1 C1
…..
#n An Bn Cn
Result declared when, within stratum, an intervention is
• Superior >99% likely to be best
• Equivalent >90% likely to be D<3%
• Inferior <1% likely to be best
Pre-specified architecture determined by
• Choice of domains, strata, etc.
• Choice of potential interactions
Choices inform a Bayesian inference model
• Pre-trial simulations evaluate performance
Each external adaptation (ex. new domain)
• Modify elements in Bayesian model
• Re-simulate before ‘live’ deployment
Pre-trial design and construction
Patients
Presumed severe CAP
• Different strata
(ex. shock or not)
• Collected at sites
• Managed at regional data centers
• Merged at central statistical center
Data collection
Re-estimate Bayesian inference model
with new data to update probabilities
Update and adapt
Response-adaptive randomization
External adaptations
45. Statistical trigger
• Launch with initial weights
• Update based on new probabilities
Steering Committee can
• Add strata, domains & interventions
DSMB can
• Request new external data be incorporated in priors
• Overrule statistical triggers
Multifactorial intervention assignments
Regimen = set of domain-specific
interventions
Effect of an intervention is conditional upon
• Stratum
• Interventions within other domains
Embedding
Patient identification and enrollment
• Tied to clinical ‘point-of-care’
Randomized interventions
• Issued as ‘order set’ regimen
EHR embedding
• Screen and flag patient
• Consent documentation
• Generate regimen order set
• Flag downstream states
• Data collection
Regimen Domain A Domain B Domain C
#1 A1 B1 C1
#2 A1 B1 C2
#3 A1 B2 C1
#4 A1 B2 C2
#5 A2 B1 C1
…..
#n An Bn Cn
Result declared when, within stratum, an intervention is
• Superior >99% likely to be best
• Equivalent >90% likely to be D<3%
• Inferior <1% likely to be best
Pre-specified architecture determined by
• Choice of domains, strata, etc.
• Choice of potential interactions
Choices inform a Bayesian inference model
• Pre-trial simulations evaluate performance
Each external adaptation (ex. new domain)
• Modify elements in Bayesian model
• Re-simulate before ‘live’ deployment
Pre-trial design and construction
Patients
Presumed severe CAP
• Different strata
(ex. shock or not)
• Collected at sites
• Managed at regional data centers
• Merged at central statistical center
Data collection
Re-estimate Bayesian inference model
with new data to update probabilities
Update and adapt
Response-adaptive randomization
External adaptations
46. Statistical trigger
• Launch with initial weights
• Update based on new probabilities
Steering Committee can
• Add strata, domains & interventions
DSMB can
• Request new external data be incorporated in priors
• Overrule statistical triggers
Multifactorial intervention assignments
Regimen = set of domain-specific
interventions
Effect of an intervention is conditional upon
• Stratum
• Interventions within other domains
Embedding
Patient identification and enrollment
• Tied to clinical ‘point-of-care’
Randomized interventions
• Issued as ‘order set’ regimen
EHR embedding
• Screen and flag patient
• Consent documentation
• Generate regimen order set
• Flag downstream states
• Data collection
Regimen Domain A Domain B Domain C
#1 A1 B1 C1
#2 A1 B1 C2
#3 A1 B2 C1
#4 A1 B2 C2
#5 A2 B1 C1
…..
#n An Bn Cn
Result declared when, within stratum, an intervention is
• Superior >99% likely to be best
• Equivalent >90% likely to be D<3%
• Inferior <1% likely to be best
Pre-specified architecture determined by
• Choice of domains, strata, etc.
• Choice of potential interactions
Choices inform a Bayesian inference model
• Pre-trial simulations evaluate performance
Each external adaptation (ex. new domain)
• Modify elements in Bayesian model
• Re-simulate before ‘live’ deployment
Pre-trial design and construction
Patients
Presumed severe CAP
• Different strata
(ex. shock or not)
• Collected at sites
• Managed at regional data centers
• Merged at central statistical center
Data collection
Re-estimate Bayesian inference model
with new data to update probabilities
Update and adapt
Response-adaptive randomization
External adaptations
47. Statistical trigger
• Launch with initial weights
• Update based on new probabilities
Steering Committee can
• Add strata, domains & interventions
DSMB can
• Request new external data be incorporated in priors
• Overrule statistical triggers
Multifactorial intervention assignments
Regimen = set of domain-specific
interventions
Effect of an intervention is conditional upon
• Stratum
• Interventions within other domains
Embedding
Patient identification and enrollment
• Tied to clinical ‘point-of-care’
Randomized interventions
• Issued as ‘order set’ regimen
EHR embedding
• Screen and flag patient
• Consent documentation
• Generate regimen order set
• Flag downstream states
• Data collection
Regimen Domain A Domain B Domain C
#1 A1 B1 C1
#2 A1 B1 C2
#3 A1 B2 C1
#4 A1 B2 C2
#5 A2 B1 C1
…..
#n An Bn Cn
Result declared when, within stratum, an intervention is
• Superior >99% likely to be best
• Equivalent >90% likely to be D<3%
• Inferior <1% likely to be best
Pre-specified architecture determined by
• Choice of domains, strata, etc.
• Choice of potential interactions
Choices inform a Bayesian inference model
• Pre-trial simulations evaluate performance
Each external adaptation (ex. new domain)
• Modify elements in Bayesian model
• Re-simulate before ‘live’ deployment
Pre-trial design and construction
Patients
Presumed severe CAP
• Different strata
(ex. shock or not)
• Collected at sites
• Managed at regional data centers
• Merged at central statistical center
Data collection
Re-estimate Bayesian inference model
with new data to update probabilities
Update and adapt
Response-adaptive randomization
External adaptations
48. Statistical trigger
• Launch with initial weights
• Update based on new probabilities
Multifactorial intervention assignments
Regimen = set of domain-specific
interventions
Effect of an intervention is conditional upon
• Stratum
• Interventions within other domains
Embedding
Patient identification and enrollment
• Tied to clinical ‘point-of-care’
Randomized interventions
• Issued as ‘order set’ regimen
EHR embedding
• Screen and flag patient
• Consent documentation
• Generate regimen order set
• Flag downstream states
• Data collection
Regimen Domain A Domain B Domain C
#1 A1 B1 C1
#2 A1 B1 C2
#3 A1 B2 C1
#4 A1 B2 C2
#5 A2 B1 C1
…..
#n An Bn Cn
Result declared when, within stratum, an intervention is
• Superior >99% likely to be best
• Equivalent >90% likely to be D<3%
• Inferior <1% likely to be best
Pre-specified architecture determined by
• Choice of domains, strata, etc.
• Choice of potential interactions
Choices inform a Bayesian inference model
• Pre-trial simulations evaluate performance
Each external adaptation (ex. new domain)
• Modify elements in Bayesian model
• Re-simulate before ‘live’ deployment
Pre-trial design and construction
Patients
Presumed severe CAP
• Different strata
(ex. shock or not)
• Collected at sites
• Managed at regional data centers
• Merged at central statistical center
Data collection
Re-estimate Bayesian inference model
with new data to update probabilities
Update and adapt
Response-adaptive randomization
49. Statistical trigger
• Launch with initial weights
• Update based on new probabilities
Steering Committee can
• Add strata, domains & interventions
DSMB can
• Request new external data be incorporated in priors
• Overrule statistical triggers
Multifactorial intervention assignments
Regimen = set of domain-specific
interventions
Effect of an intervention is conditional upon
• Stratum
• Interventions within other domains
Embedding
Patient identification and enrollment
• Tied to clinical ‘point-of-care’
Randomized interventions
• Issued as ‘order set’ regimen
EHR embedding
• Screen and flag patient
• Consent documentation
• Generate regimen order set
• Flag downstream states
• Data collection
Regimen Domain A Domain B Domain C
#1 A1 B1 C1
#2 A1 B1 C2
#3 A1 B2 C1
#4 A1 B2 C2
#5 A2 B1 C1
…..
#n An Bn Cn
Result declared when, within stratum, an intervention is
• Superior >99% likely to be best
• Equivalent >90% likely to be D<3%
• Inferior <1% likely to be best
Pre-specified architecture determined by
• Choice of domains, strata, etc.
• Choice of potential interactions
Choices inform a Bayesian inference model
• Pre-trial simulations evaluate performance
Each external adaptation (ex. new domain)
• Modify elements in Bayesian model
• Re-simulate before ‘live’ deployment
Pre-trial design and construction
Patients
Presumed severe CAP
• Different strata
(ex. shock or not)
• Collected at sites
• Managed at regional data centers
• Merged at central statistical center
Data collection
Re-estimate Bayesian inference model
with new data to update probabilities
Update and adapt
Response-adaptive randomization
External adaptations
50. C R I S M A
REMAP severe pneumonia …
• Gets closer to individualized treatment decisions …
• For example, should my patient receive IV hydrocortisone?
• Depends on
• Whether shock is present
• How sick (hypoxic) the patient is
• Whether underlying cause is viral or not
• Whether an anti-viral is being administered
• Whether other strategies are being used that may minimize lung injury and
inflammation (protective vs. ultraprotective ventilation)
• Separate probability estimate for each consideration …
• Trial enrolls until a predefined level of certainty
• As soon as one question hits threshold, answer is announced
51. C R I S M A
Run the trial ‘in silico’ ahead of time …
• Monte-Carlo simulations
• Run 1,000s of times under different scenarios
56. C R I S M A
Ok, but …
• EHR data quality
• Institutional commitment
• Ethics
• Statistics and design
• Reporting and dissemination of results
• Funding
• Oversight
• Integration with other clinical research programs
57. C R I S M A
Conclusions
• RCTs remain our strongest ‘truth’ finder
• But, current RCT enterprise LETS US DOWN
• ‘Big Data’ should not be cast as an alternative to the RCT
• This is a false choice
• Instead, the digital age enables novel RCTs designs
• Smarter and safer
58. C R I S M A
Self-learning healthcare is …
fused care and research