This intensive course will cover all basic and advanced aspects of synthesis of evidence from studies comparing competing treatments for the same health condition. By the end of this course participants will have an understanding of the role and potential of network meta-analysis, the principles, steps and statistical methods involved; the biases that can distort indirect comparisons and network meta-analysis.
This course is aimed at statisticians, epidemiologists and other quantitatively-minded researchers who are involved or may be involved in the future in the preparation of HTA submissions. Knowledge of systematic reviews and the fundamentals of meta-analysis is expected of all participants.
The course will consist of lectures, practical examples and discussions. Participants will gain practical experience in performing analyses in R software and the freely available web application CINeMA.
Key Topics:
Assumptions underlying indirect comparisons
Statistical methods in network meta-analysis
CINeMA: a framework and software to evaluate Confidence in Network Meta-Analysis
This intensive course will cover all basic and advanced aspects of synthesis of evidence from studies comparing competing treatments for the same health condition. By the end of this course participants will have an understanding of the role and potential of network meta-analysis, the principles, steps and statistical methods involved; the biases that can distort indirect comparisons and network meta-analysis.
This course is aimed at statisticians, epidemiologists and other quantitatively-minded researchers who are involved or may be involved in the future in the preparation of HTA submissions. Knowledge of systematic reviews and the fundamentals of meta-analysis is expected of all participants.
The course will consist of lectures, practical examples and discussions. Participants will gain practical experience in performing analyses in R software and the freely available web application CINeMA.
Key Topics:
Assumptions underlying indirect comparisons
Statistical methods in network meta-analysis
CINeMA: a framework and software to evaluate Confidence in Network Meta-Analysis
This intensive course will cover all basic and advanced aspects of synthesis of evidence from studies comparing competing treatments for the same health condition. By the end of this course participants will have an understanding of the role and potential of network meta-analysis, the principles, steps and statistical methods involved; the biases that can distort indirect comparisons and network meta-analysis.
This course is aimed at statisticians, epidemiologists and other quantitatively-minded researchers who are involved or may be involved in the future in the preparation of HTA submissions. Knowledge of systematic reviews and the fundamentals of meta-analysis is expected of all participants.
The course will consist of lectures, practical examples and discussions. Participants will gain practical experience in performing analyses in R software and the freely available web application CINeMA.
Key Topics:
Assumptions underlying indirect comparisons
Statistical methods in network meta-analysis
CINeMA: a framework and software to evaluate Confidence in Network Meta-Analysis
This intensive course will cover all basic and advanced aspects of synthesis of evidence from studies comparing competing treatments for the same health condition. By the end of this course participants will have an understanding of the role and potential of network meta-analysis, the principles, steps and statistical methods involved; the biases that can distort indirect comparisons and network meta-analysis.
This course is aimed at statisticians, epidemiologists and other quantitatively-minded researchers who are involved or may be involved in the future in the preparation of HTA submissions. Knowledge of systematic reviews and the fundamentals of meta-analysis is expected of all participants.
The course will consist of lectures, practical examples and discussions. Participants will gain practical experience in performing analyses in R software and the freely available web application CINeMA.
Key Topics:
Assumptions underlying indirect comparisons
Statistical methods in network meta-analysis
CINeMA: a framework and software to evaluate Confidence in Network Meta-Analysis
This intensive course will cover all basic and advanced aspects of synthesis of evidence from studies comparing competing treatments for the same health condition. By the end of this course participants will have an understanding of the role and potential of network meta-analysis, the principles, steps and statistical methods involved; the biases that can distort indirect comparisons and network meta-analysis.
This course is aimed at statisticians, epidemiologists and other quantitatively-minded researchers who are involved or may be involved in the future in the preparation of HTA submissions. Knowledge of systematic reviews and the fundamentals of meta-analysis is expected of all participants.
The course will consist of lectures, practical examples and discussions. Participants will gain practical experience in performing analyses in R software and the freely available web application CINeMA.
Key Topics:
Assumptions underlying indirect comparisons
Statistical methods in network meta-analysis
CINeMA: a framework and software to evaluate Confidence in Network Meta-Analysis
This intensive course will cover all basic and advanced aspects of synthesis of evidence from studies comparing competing treatments for the same health condition. By the end of this course participants will have an understanding of the role and potential of network meta-analysis, the principles, steps and statistical methods involved; the biases that can distort indirect comparisons and network meta-analysis.
This course is aimed at statisticians, epidemiologists and other quantitatively-minded researchers who are involved or may be involved in the future in the preparation of HTA submissions. Knowledge of systematic reviews and the fundamentals of meta-analysis is expected of all participants.
The course will consist of lectures, practical examples and discussions. Participants will gain practical experience in performing analyses in R software and the freely available web application CINeMA.
Key Topics:
Assumptions underlying indirect comparisons
Statistical methods in network meta-analysis
CINeMA: a framework and software to evaluate Confidence in Network Meta-Analysis
Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars
Date: The Second Wednesday of every Month
Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.
Date: Tuesday 10th December 2024
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
Date: Thursday 12th December 2024
Chaired by Jenny Devenport, join us to hear Andy Grieve and Zhiwei Zhang discuss their recent work on group sequential designs.
PSI Introduction to Industry Training (ITIT) Course - 2024/2025
Date: October 2024 - July 2025
An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.
PSI Training Course: Mixed Models and Repeated Measures
Dates:
Monday 3rd February 2025: 08:30-13:00 BST
Tuesday 4th February 2025: 08:30 - 13:00 BST
Wednesday 5th February 2025: 08:30 - 13:00 BST
Thursday 6th February 2025: 08:30 - 13:00 BST
This course is presented through lectures and practical sessions using SAS code. It is suitable for statisticians working on clinical trials, who already have a good understanding of linear and generalised linear models.