PSI Webinar: Risk Based Monitoring & QTL's

PSI Mentoring 2025

Date: Ongoing 6 month cycle beginning late April/early May 2024 Are you a member of PSI looking to further your career or help develop others - why not sign up to the PSI Mentoring scheme? You can expand your network, improve your leadership skills and learn from more senior colleagues in the industry.

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Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars

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.

PSI Career Young Virtual Event (Q1 2025)

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Maths Meets Medicine: Exploring Careers in the Pharmaceutical Industry

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Dissolution Testing: Time for Statistical (r)Evolution

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PSI Career Young Virtual Event (Q2 2025)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Career Young Virtual Event (Q3 2025)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Career Young Virtual Event (Q4 2025)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

Speaker	Biography	Abstract
Tim Rolfe	Tim Rolfe is Director of Central Monitoring & Data Analytics at GlaxoSmithKline & has over 20 years of experience working as a statistician at in the pharmaceutical industry. He has been part of GSKs RBM team since its inception in 2012, providing statistical leadership in the development and implementation of GSK’s RBM strategy within clinical trials. Before joining GSK, Tim studied Applied Statistics at Sheffield Hallam University and holds a MSc in Medical Statistics from the University of Leicester in the UK.	Since the introduction of ICH-E6 R2 Addendum sponsors must introduce formal Quality Risk Management processes and decide which risks to reduce and/or which to accept. Many tools are available to aid with centralised monitoring of Key Risk Indicators. But there has been much less done to address quality tolerance limits (QTLs) i.e. taking into consideration the medical/statistical characteristics of the variables and the statistical design of the trial, to identify systematic issues that can impact subject safety or integrity of trial. The presentation will cover history behind QTLs, difference between QTLs and KRIs and mechanisms to establish, track and report deviations of QTLs in the CSR.
Marcin Macowski	Marcin Makowski is the Head of Centralized Monitoring and Data Analytics at GSK. Previously Marcin held similar positions at AstraZeneca and UCB. Last 10 years of Marcin’s career was revolving around establishing and improving Risk Based Monitoring models including centralized monitoring and quality tolerance limits. Marcin co-led the group that produced the first TransCelerate recommendations on QTLs in 2017 and is member of the TransCelerate topic team that recently published the expanded QTL framework. Marcin holds MD and PhD degrees from the Warsaw Medical University.	This year TransCelerate QTL topic team published new set of deliverables on Quality Tolerance Limits. The documents build on the proposals published in 2017 by proposing a broader list of parameters and process for defining, monitoring, and reporting of QTLs. The presentation will explain the content of the new deliverables. The plans for future publication pertaining historical benchmarking data for QTLs will also be shared.
Marta Kozińska	Marta Kozińska is an Associate Director Centralized Monitoring with over 10 years of experience in clinical trials which includes, Data Management, Site Management (CRA), Study Management (Project Manager – Global Study Leader) as well as RBQM implementation and Risk Management. Marta has an MSc Eng in Biotechnology from the Warsaw University of Life Sciences and is a certified PMI Project Manager. For the last 8 years Marta has worked for AstraZeneca, where for 3 years she has been part of Centralized Monitoring Team. During these last 3 years she has been leading multiple projects aiming at e.g. proper implementation of Quality Tolerance Limits, Centralized Statistical Monitoring and improvements in ways of working. Apart from that she represents AstraZeneca in TransCelerate QTL working group.	It has been almost 4 years since Quality Tolerance Limits, as a measure of risks’ control in Clinical Trials, have been introduced into ICH-E6 R2 Addendum. During this time TransCelerate QTL topic team has also published tools supporting QTLs implementation by Sponsors. This presentation will summarize definitions of QTL from ICH GCP R2 and TransCelerate, will cover CtQ – Risk – QTL relationship as well as provide real-life example of an approach to QTL set-up, both in terms of selection of a parameter and identification of a tolerance limit and challenges to QTL implementation at scale and speed.
Chris Wells	Chris Wells is a Senior Statistical Scientist who has a total of 23 years experience in the industry. Chris has an MSc in Medical Statistics from the London School of Hygiene. For the last 11 years Chris has worked for Roche Products Ltd where for 4 years she led the Statistical Monitoring Team which during the past year has also included the application of Quality Tolerance Limits. More recently her work is involving the implementation of Data Surveillance and Advanced Analytics.	Utilizing a Bayesian Hierarchical Model to design quality into a clinical trial and allow compliance with ICH E6 R2 Quality Tolerance Limits and what approach can be used for Early Phase Studies. ICHE6 R2 has mandated the use of Quality Tolerance Limits. Roche have utilized a Bayesian Hierarchical Model methodology, inspired by the Bayesian Meta analysis example in Berry et al (2011). Fixed parameters specify the prior for all unknown parameters, a conservative prior can be used or it can be informed by historical data and profound medical knowledge. The Prior is combined with the observed data (events and exposure) to define a posterior the parameters, computed using Markov Chain Monte Carlo algorithm. We then use percentiles of the posterior distribution for rate to establish limits which can in turn help to establish QTLs and Secondary Limits along with profound medical knowledge. QTLs and Secondary Limits are used to manage parameter risk at the study level and drive quality at the site level by identifying sites with parameters lying beyond the predefined limits (Trent Alert). This presentation will detail the methodology and demonstrate the outputs. We will also take a look at which approach could be useful for early phase studies.

Speaker	Biography	Abstract
Tim Rolfe	Tim Rolfe is Director of Central Monitoring & Data Analytics at GlaxoSmithKline & has over 20 years of experience working as a statistician at in the pharmaceutical industry. He has been part of GSKs RBM team since its inception in 2012, providing statistical leadership in the development and implementation of GSK’s RBM strategy within clinical trials. Before joining GSK, Tim studied Applied Statistics at Sheffield Hallam University and holds a MSc in Medical Statistics from the University of Leicester in the UK.	Since the introduction of ICH-E6 R2 Addendum sponsors must introduce formal Quality Risk Management processes and decide which risks to reduce and/or which to accept. Many tools are available to aid with centralised monitoring of Key Risk Indicators. But there has been much less done to address quality tolerance limits (QTLs) i.e. taking into consideration the medical/statistical characteristics of the variables and the statistical design of the trial, to identify systematic issues that can impact subject safety or integrity of trial. The presentation will cover history behind QTLs, difference between QTLs and KRIs and mechanisms to establish, track and report deviations of QTLs in the CSR.
Marcin Macowski	Marcin Makowski is the Head of Centralized Monitoring and Data Analytics at GSK. Previously Marcin held similar positions at AstraZeneca and UCB. Last 10 years of Marcin’s career was revolving around establishing and improving Risk Based Monitoring models including centralized monitoring and quality tolerance limits. Marcin co-led the group that produced the first TransCelerate recommendations on QTLs in 2017 and is member of the TransCelerate topic team that recently published the expanded QTL framework. Marcin holds MD and PhD degrees from the Warsaw Medical University.	This year TransCelerate QTL topic team published new set of deliverables on Quality Tolerance Limits. The documents build on the proposals published in 2017 by proposing a broader list of parameters and process for defining, monitoring, and reporting of QTLs. The presentation will explain the content of the new deliverables. The plans for future publication pertaining historical benchmarking data for QTLs will also be shared.
Marta Kozińska	Marta Kozińska is an Associate Director Centralized Monitoring with over 10 years of experience in clinical trials which includes, Data Management, Site Management (CRA), Study Management (Project Manager – Global Study Leader) as well as RBQM implementation and Risk Management. Marta has an MSc Eng in Biotechnology from the Warsaw University of Life Sciences and is a certified PMI Project Manager. For the last 8 years Marta has worked for AstraZeneca, where for 3 years she has been part of Centralized Monitoring Team. During these last 3 years she has been leading multiple projects aiming at e.g. proper implementation of Quality Tolerance Limits, Centralized Statistical Monitoring and improvements in ways of working. Apart from that she represents AstraZeneca in TransCelerate QTL working group.	It has been almost 4 years since Quality Tolerance Limits, as a measure of risks’ control in Clinical Trials, have been introduced into ICH-E6 R2 Addendum. During this time TransCelerate QTL topic team has also published tools supporting QTLs implementation by Sponsors. This presentation will summarize definitions of QTL from ICH GCP R2 and TransCelerate, will cover CtQ – Risk – QTL relationship as well as provide real-life example of an approach to QTL set-up, both in terms of selection of a parameter and identification of a tolerance limit and challenges to QTL implementation at scale and speed.
Chris Wells	Chris Wells is a Senior Statistical Scientist who has a total of 23 years experience in the industry. Chris has an MSc in Medical Statistics from the London School of Hygiene. For the last 11 years Chris has worked for Roche Products Ltd where for 4 years she led the Statistical Monitoring Team which during the past year has also included the application of Quality Tolerance Limits. More recently her work is involving the implementation of Data Surveillance and Advanced Analytics.	Utilizing a Bayesian Hierarchical Model to design quality into a clinical trial and allow compliance with ICH E6 R2 Quality Tolerance Limits and what approach can be used for Early Phase Studies. ICHE6 R2 has mandated the use of Quality Tolerance Limits. Roche have utilized a Bayesian Hierarchical Model methodology, inspired by the Bayesian Meta analysis example in Berry et al (2011). Fixed parameters specify the prior for all unknown parameters, a conservative prior can be used or it can be informed by historical data and profound medical knowledge. The Prior is combined with the observed data (events and exposure) to define a posterior the parameters, computed using Markov Chain Monte Carlo algorithm. We then use percentiles of the posterior distribution for rate to establish limits which can in turn help to establish QTLs and Secondary Limits along with profound medical knowledge. QTLs and Secondary Limits are used to manage parameter risk at the study level and drive quality at the site level by identifying sites with parameters lying beyond the predefined limits (Trent Alert). This presentation will detail the methodology and demonstrate the outputs. We will also take a look at which approach could be useful for early phase studies.

Speaker	Biography	Abstract
Tim Rolfe	Tim Rolfe is Director of Central Monitoring & Data Analytics at GlaxoSmithKline & has over 20 years of experience working as a statistician at in the pharmaceutical industry. He has been part of GSKs RBM team since its inception in 2012, providing statistical leadership in the development and implementation of GSK’s RBM strategy within clinical trials. Before joining GSK, Tim studied Applied Statistics at Sheffield Hallam University and holds a MSc in Medical Statistics from the University of Leicester in the UK.	Since the introduction of ICH-E6 R2 Addendum sponsors must introduce formal Quality Risk Management processes and decide which risks to reduce and/or which to accept. Many tools are available to aid with centralised monitoring of Key Risk Indicators. But there has been much less done to address quality tolerance limits (QTLs) i.e. taking into consideration the medical/statistical characteristics of the variables and the statistical design of the trial, to identify systematic issues that can impact subject safety or integrity of trial. The presentation will cover history behind QTLs, difference between QTLs and KRIs and mechanisms to establish, track and report deviations of QTLs in the CSR.
Marcin Macowski	Marcin Makowski is the Head of Centralized Monitoring and Data Analytics at GSK. Previously Marcin held similar positions at AstraZeneca and UCB. Last 10 years of Marcin’s career was revolving around establishing and improving Risk Based Monitoring models including centralized monitoring and quality tolerance limits. Marcin co-led the group that produced the first TransCelerate recommendations on QTLs in 2017 and is member of the TransCelerate topic team that recently published the expanded QTL framework. Marcin holds MD and PhD degrees from the Warsaw Medical University.	This year TransCelerate QTL topic team published new set of deliverables on Quality Tolerance Limits. The documents build on the proposals published in 2017 by proposing a broader list of parameters and process for defining, monitoring, and reporting of QTLs. The presentation will explain the content of the new deliverables. The plans for future publication pertaining historical benchmarking data for QTLs will also be shared.
Marta Kozińska	Marta Kozińska is an Associate Director Centralized Monitoring with over 10 years of experience in clinical trials which includes, Data Management, Site Management (CRA), Study Management (Project Manager – Global Study Leader) as well as RBQM implementation and Risk Management. Marta has an MSc Eng in Biotechnology from the Warsaw University of Life Sciences and is a certified PMI Project Manager. For the last 8 years Marta has worked for AstraZeneca, where for 3 years she has been part of Centralized Monitoring Team. During these last 3 years she has been leading multiple projects aiming at e.g. proper implementation of Quality Tolerance Limits, Centralized Statistical Monitoring and improvements in ways of working. Apart from that she represents AstraZeneca in TransCelerate QTL working group.	It has been almost 4 years since Quality Tolerance Limits, as a measure of risks’ control in Clinical Trials, have been introduced into ICH-E6 R2 Addendum. During this time TransCelerate QTL topic team has also published tools supporting QTLs implementation by Sponsors. This presentation will summarize definitions of QTL from ICH GCP R2 and TransCelerate, will cover CtQ – Risk – QTL relationship as well as provide real-life example of an approach to QTL set-up, both in terms of selection of a parameter and identification of a tolerance limit and challenges to QTL implementation at scale and speed.
Chris Wells	Chris Wells is a Senior Statistical Scientist who has a total of 23 years experience in the industry. Chris has an MSc in Medical Statistics from the London School of Hygiene. For the last 11 years Chris has worked for Roche Products Ltd where for 4 years she led the Statistical Monitoring Team which during the past year has also included the application of Quality Tolerance Limits. More recently her work is involving the implementation of Data Surveillance and Advanced Analytics.	Utilizing a Bayesian Hierarchical Model to design quality into a clinical trial and allow compliance with ICH E6 R2 Quality Tolerance Limits and what approach can be used for Early Phase Studies. ICHE6 R2 has mandated the use of Quality Tolerance Limits. Roche have utilized a Bayesian Hierarchical Model methodology, inspired by the Bayesian Meta analysis example in Berry et al (2011). Fixed parameters specify the prior for all unknown parameters, a conservative prior can be used or it can be informed by historical data and profound medical knowledge. The Prior is combined with the observed data (events and exposure) to define a posterior the parameters, computed using Markov Chain Monte Carlo algorithm. We then use percentiles of the posterior distribution for rate to establish limits which can in turn help to establish QTLs and Secondary Limits along with profound medical knowledge. QTLs and Secondary Limits are used to manage parameter risk at the study level and drive quality at the site level by identifying sites with parameters lying beyond the predefined limits (Trent Alert). This presentation will detail the methodology and demonstrate the outputs. We will also take a look at which approach could be useful for early phase studies.

Speaker	Biography	Abstract
Tim Rolfe	Tim Rolfe is Director of Central Monitoring & Data Analytics at GlaxoSmithKline & has over 20 years of experience working as a statistician at in the pharmaceutical industry. He has been part of GSKs RBM team since its inception in 2012, providing statistical leadership in the development and implementation of GSK’s RBM strategy within clinical trials. Before joining GSK, Tim studied Applied Statistics at Sheffield Hallam University and holds a MSc in Medical Statistics from the University of Leicester in the UK.	Since the introduction of ICH-E6 R2 Addendum sponsors must introduce formal Quality Risk Management processes and decide which risks to reduce and/or which to accept. Many tools are available to aid with centralised monitoring of Key Risk Indicators. But there has been much less done to address quality tolerance limits (QTLs) i.e. taking into consideration the medical/statistical characteristics of the variables and the statistical design of the trial, to identify systematic issues that can impact subject safety or integrity of trial. The presentation will cover history behind QTLs, difference between QTLs and KRIs and mechanisms to establish, track and report deviations of QTLs in the CSR.
Marcin Macowski	Marcin Makowski is the Head of Centralized Monitoring and Data Analytics at GSK. Previously Marcin held similar positions at AstraZeneca and UCB. Last 10 years of Marcin’s career was revolving around establishing and improving Risk Based Monitoring models including centralized monitoring and quality tolerance limits. Marcin co-led the group that produced the first TransCelerate recommendations on QTLs in 2017 and is member of the TransCelerate topic team that recently published the expanded QTL framework. Marcin holds MD and PhD degrees from the Warsaw Medical University.	This year TransCelerate QTL topic team published new set of deliverables on Quality Tolerance Limits. The documents build on the proposals published in 2017 by proposing a broader list of parameters and process for defining, monitoring, and reporting of QTLs. The presentation will explain the content of the new deliverables. The plans for future publication pertaining historical benchmarking data for QTLs will also be shared.
Marta Kozińska	Marta Kozińska is an Associate Director Centralized Monitoring with over 10 years of experience in clinical trials which includes, Data Management, Site Management (CRA), Study Management (Project Manager – Global Study Leader) as well as RBQM implementation and Risk Management. Marta has an MSc Eng in Biotechnology from the Warsaw University of Life Sciences and is a certified PMI Project Manager. For the last 8 years Marta has worked for AstraZeneca, where for 3 years she has been part of Centralized Monitoring Team. During these last 3 years she has been leading multiple projects aiming at e.g. proper implementation of Quality Tolerance Limits, Centralized Statistical Monitoring and improvements in ways of working. Apart from that she represents AstraZeneca in TransCelerate QTL working group.	It has been almost 4 years since Quality Tolerance Limits, as a measure of risks’ control in Clinical Trials, have been introduced into ICH-E6 R2 Addendum. During this time TransCelerate QTL topic team has also published tools supporting QTLs implementation by Sponsors. This presentation will summarize definitions of QTL from ICH GCP R2 and TransCelerate, will cover CtQ – Risk – QTL relationship as well as provide real-life example of an approach to QTL set-up, both in terms of selection of a parameter and identification of a tolerance limit and challenges to QTL implementation at scale and speed.
Chris Wells	Chris Wells is a Senior Statistical Scientist who has a total of 23 years experience in the industry. Chris has an MSc in Medical Statistics from the London School of Hygiene. For the last 11 years Chris has worked for Roche Products Ltd where for 4 years she led the Statistical Monitoring Team which during the past year has also included the application of Quality Tolerance Limits. More recently her work is involving the implementation of Data Surveillance and Advanced Analytics.	Utilizing a Bayesian Hierarchical Model to design quality into a clinical trial and allow compliance with ICH E6 R2 Quality Tolerance Limits and what approach can be used for Early Phase Studies. ICHE6 R2 has mandated the use of Quality Tolerance Limits. Roche have utilized a Bayesian Hierarchical Model methodology, inspired by the Bayesian Meta analysis example in Berry et al (2011). Fixed parameters specify the prior for all unknown parameters, a conservative prior can be used or it can be informed by historical data and profound medical knowledge. The Prior is combined with the observed data (events and exposure) to define a posterior the parameters, computed using Markov Chain Monte Carlo algorithm. We then use percentiles of the posterior distribution for rate to establish limits which can in turn help to establish QTLs and Secondary Limits along with profound medical knowledge. QTLs and Secondary Limits are used to manage parameter risk at the study level and drive quality at the site level by identifying sites with parameters lying beyond the predefined limits (Trent Alert). This presentation will detail the methodology and demonstrate the outputs. We will also take a look at which approach could be useful for early phase studies.

Speaker	Biography	Abstract
Tim Rolfe	Tim Rolfe is Director of Central Monitoring & Data Analytics at GlaxoSmithKline & has over 20 years of experience working as a statistician at in the pharmaceutical industry. He has been part of GSKs RBM team since its inception in 2012, providing statistical leadership in the development and implementation of GSK’s RBM strategy within clinical trials. Before joining GSK, Tim studied Applied Statistics at Sheffield Hallam University and holds a MSc in Medical Statistics from the University of Leicester in the UK.	Since the introduction of ICH-E6 R2 Addendum sponsors must introduce formal Quality Risk Management processes and decide which risks to reduce and/or which to accept. Many tools are available to aid with centralised monitoring of Key Risk Indicators. But there has been much less done to address quality tolerance limits (QTLs) i.e. taking into consideration the medical/statistical characteristics of the variables and the statistical design of the trial, to identify systematic issues that can impact subject safety or integrity of trial. The presentation will cover history behind QTLs, difference between QTLs and KRIs and mechanisms to establish, track and report deviations of QTLs in the CSR.
Marcin Macowski	Marcin Makowski is the Head of Centralized Monitoring and Data Analytics at GSK. Previously Marcin held similar positions at AstraZeneca and UCB. Last 10 years of Marcin’s career was revolving around establishing and improving Risk Based Monitoring models including centralized monitoring and quality tolerance limits. Marcin co-led the group that produced the first TransCelerate recommendations on QTLs in 2017 and is member of the TransCelerate topic team that recently published the expanded QTL framework. Marcin holds MD and PhD degrees from the Warsaw Medical University.	This year TransCelerate QTL topic team published new set of deliverables on Quality Tolerance Limits. The documents build on the proposals published in 2017 by proposing a broader list of parameters and process for defining, monitoring, and reporting of QTLs. The presentation will explain the content of the new deliverables. The plans for future publication pertaining historical benchmarking data for QTLs will also be shared.
Marta Kozińska	Marta Kozińska is an Associate Director Centralized Monitoring with over 10 years of experience in clinical trials which includes, Data Management, Site Management (CRA), Study Management (Project Manager – Global Study Leader) as well as RBQM implementation and Risk Management. Marta has an MSc Eng in Biotechnology from the Warsaw University of Life Sciences and is a certified PMI Project Manager. For the last 8 years Marta has worked for AstraZeneca, where for 3 years she has been part of Centralized Monitoring Team. During these last 3 years she has been leading multiple projects aiming at e.g. proper implementation of Quality Tolerance Limits, Centralized Statistical Monitoring and improvements in ways of working. Apart from that she represents AstraZeneca in TransCelerate QTL working group.	It has been almost 4 years since Quality Tolerance Limits, as a measure of risks’ control in Clinical Trials, have been introduced into ICH-E6 R2 Addendum. During this time TransCelerate QTL topic team has also published tools supporting QTLs implementation by Sponsors. This presentation will summarize definitions of QTL from ICH GCP R2 and TransCelerate, will cover CtQ – Risk – QTL relationship as well as provide real-life example of an approach to QTL set-up, both in terms of selection of a parameter and identification of a tolerance limit and challenges to QTL implementation at scale and speed.
Chris Wells	Chris Wells is a Senior Statistical Scientist who has a total of 23 years experience in the industry. Chris has an MSc in Medical Statistics from the London School of Hygiene. For the last 11 years Chris has worked for Roche Products Ltd where for 4 years she led the Statistical Monitoring Team which during the past year has also included the application of Quality Tolerance Limits. More recently her work is involving the implementation of Data Surveillance and Advanced Analytics.	Utilizing a Bayesian Hierarchical Model to design quality into a clinical trial and allow compliance with ICH E6 R2 Quality Tolerance Limits and what approach can be used for Early Phase Studies. ICHE6 R2 has mandated the use of Quality Tolerance Limits. Roche have utilized a Bayesian Hierarchical Model methodology, inspired by the Bayesian Meta analysis example in Berry et al (2011). Fixed parameters specify the prior for all unknown parameters, a conservative prior can be used or it can be informed by historical data and profound medical knowledge. The Prior is combined with the observed data (events and exposure) to define a posterior the parameters, computed using Markov Chain Monte Carlo algorithm. We then use percentiles of the posterior distribution for rate to establish limits which can in turn help to establish QTLs and Secondary Limits along with profound medical knowledge. QTLs and Secondary Limits are used to manage parameter risk at the study level and drive quality at the site level by identifying sites with parameters lying beyond the predefined limits (Trent Alert). This presentation will detail the methodology and demonstrate the outputs. We will also take a look at which approach could be useful for early phase studies.

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