BEGIN:VCALENDAR
VERSION:2.0
CALSCALE:GREGORIAN
PRODID:iCalendar-Ruby
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250123T193000Z
DTSTAMP:20260421T043810Z
DTSTART:20250123T180000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030195141
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250130T193000Z
DTSTAMP:20260421T043810Z
DTSTART:20250130T180000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030196166
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250206T193000Z
DTSTAMP:20260421T043810Z
DTSTART:20250206T180000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030198215
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250213T193000Z
DTSTAMP:20260421T043810Z
DTSTART:20250213T180000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030200264
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250220T193000Z
DTSTAMP:20260421T043810Z
DTSTART:20250220T180000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030201289
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250227T193000Z
DTSTAMP:20260421T043810Z
DTSTART:20250227T180000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030203338
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250306T193000Z
DTSTAMP:20260421T043810Z
DTSTART:20250306T180000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030205387
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250313T183000Z
DTSTAMP:20260421T043810Z
DTSTART:20250313T170000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030206412
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250320T183000Z
DTSTAMP:20260421T043810Z
DTSTART:20250320T170000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030208461
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250327T183000Z
DTSTAMP:20260421T043810Z
DTSTART:20250327T170000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030209486
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250403T183000Z
DTSTAMP:20260421T043810Z
DTSTART:20250403T170000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030211535
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
BEGIN:VEVENT
CATEGORIES:Professional Development
DESCRIPTION:The UCSF-Stanford Center of Excellence in Regulatory Science an
 d Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in C
 linical Research Course.\n\nWhy this course? There are a variety of 4-hour 
 or one-day short courses that cover some Bayesian concepts or examples. The
 re are also many in-depth statistical courses that are steeped in mathemati
 cs\, computation\, and inference. This course is designed to be in the swee
 t spot: A more in-depth course on Bayesian thinking with real-life examples
  and applications that do not involve mathematics. The UCSF-Stanford CERSI 
 Bayesian Thinking in Clinical Research Course is meant to focus on concepts
  that will allow students to have engaging conversations with statisticians
  and review the clinical trial literature with a more educated perspective 
 on inferring what is likely to be true.\n\nBayesian Statistics has been a m
 ajor branch of statistical science for centuries but has had limited utilit
 y in practical applications for a wide variety of reasons. Bayesian methods
  are now emerging as a useful and powerful alternative to hypothesis testin
 g and frequentist statistical approaches based on p-values. Bayesian method
 s offer more information and easier interpretation due to direct estimation
  of the probability that a conclusion is true given the data observed in a 
 trial. Bayesian Statistical methods are based on incorporating prior knowle
 dge into the analysis of newly generated experimental data to update our kn
 owledge of a scientific hypothesis in a quantitative way. In this sense\, t
 he Bayesian approach is more aligned with scientific endeavors that continu
 ally build on previous knowledge by performing experiments and analyzing da
 ta to come to a better understanding of natural phenomena.\n\nParticipants 
 will have the opportunity to learn Bayesian concepts and statistical princi
 ples for how to assess the likelihood of a hypothesis being true or false. 
 The initial set of lectures will focus on broad principles of Bayesian thin
 king with subsequent lectures focused on more detailed implementation in cl
 inical trials. Participants will be exposed to a broad range of case studie
 s covering a variety of therapeutic areas and phases of drug development\, 
 including phase 3 trials for regulatory approval. The lectures will cover k
 ey Bayesian concepts and terminology to enable the audience to read and und
 erstand the publication on Bayesian trials in medical literature. All lectu
 res will focus on principles and concepts without the underlying mathematic
 s. Thus\, the material should be accessible to a broad scientific and clini
 cal audience and may also help statisticians who have not been exposed to B
 ayesian methods.\n\nThis is a virtual course comprised of twelve 90-minute 
 sessions delivered live by experts in the field of Bayesian statistics and 
 its applications to clinical trials. Sessions will be held on Thursdays\, w
 ith some exceptions\, from January 23\, 2025\, through April 10\, 2025\, fr
 om 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may 
 include pre-reading assignments\, lectures\, and discussion of case studies
 . Participants who successfully complete the course will be issued a Statem
 ent of Completion from the UCSF-Stanford Center of Excellence in Regulatory
  Science and Innovation (CERSI). Sessions will be recorded and available to
  all participants for the duration of the course.\n\nNote: This course is i
 ntended for professional development and is not accredited for CME or PMP c
 redit.\n\nLearning objectives\n\nDiscuss how Bayesian methods are used in t
 he design\, analysis\, and interpretation of clinical studies.Explain facto
 rs that are important when considering the use of a Bayesian approach.Expla
 in the fundamental differences between Frequentist hypothesis testing and B
 ayesian inference (particularly the contrast between p-values and Bayesian 
 posterior probabilities).Interpret clinical literature that uses Bayesian m
 ethods for inference and interpretation.Describe the basics of decision-mak
 ing when using Bayesian inference (e.g.\, interim analysis\, study success 
 criteria\, probability of study success\, go/no-go decisions in drug develo
 pment).Explain the flexibility available for adaptive study designs includi
 ng the inclusion of interim analyses.Discuss the use of Bayesian methods to
  extrapolate efficacy or safety findings to another population (e.g.\, adul
 ts to pediatrics) and to borrow information across subgroups to estimate mo
 re precise treatment effects in each subgroup.Target audience\n\nEarly- to 
 mid-career professionals involved in clinical trials (industry\, academia\,
  and government) who would like a broad overview of the latest developments
  in the application of Bayesian methods in clinical research.Faculty member
 s who are interested in using clinical trials to advance medical practice.T
 rainees (students/residents/postdocs) who would like to complement their tr
 aining and research in basic and applied statistics through the review of c
 ase studies and examples.A basic understanding of statistical hypothesis te
 sting and clinical trial design and execution is necessary. Familiarity wit
 h regulated clinical drug development would also be helpful but not necessa
 ry.
DTEND:20250410T183000Z
DTSTAMP:20260421T043810Z
DTSTART:20250410T170000Z
LOCATION:
SEQUENCE:0
SUMMARY:UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course
UID:tag:localist.com\,2008:EventInstance_47747030212560
URL:https://calendar.ucsf.edu/event/ucsf-stanford-cersi-bayesian-thinking-i
 n-clinical-research-course
END:VEVENT
END:VCALENDAR