BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 13.3//EN BEGIN:VTIMEZONE TZID:GMT Standard Time BEGIN:STANDARD DTSTART:20231002T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYHOUR=2;BYMINUTE=0;BYMONTH=10 TZNAME:GMT Standard Time TZOFFSETFROM:+0100 TZOFFSETTO:+0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20230301T010000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYHOUR=1;BYMINUTE=0;BYMONTH=3 TZNAME:GMT Daylight Time TZOFFSETFROM:+0000 TZOFFSETTO:+0100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:Sponsored \;by \;\n \;\nDate: \;Tuesday 9th Jun e 2020 \;  \;  \; \;\n\nTime: \;10:00 - 11:30\nSpeaker s: \;Jack Keeler (IQVIA)\, Ruth Owen (LSHTM)\, Ines Reis (MHRA) &\; Georgios Nikoladis.\nRegistration:\nThis webinar is part of our 2020 Conf erence Webinar Series. Further information including details of other webi nars that are included in the Conference package can be found \;here.\ nMembers \;receive all webinars in the conference series for free.\nNo n-members \;receive all webinars in the conference series for £\; 100+VAT\, which includes complimentary membership* of PSI until the 31st D ecember 2020. \;\n\nTo register your place for this event\, and others in the Conference webinar series\, please click here.\n\nSpeaker Details: \n\n\n\n \n \n \n Speaker\n \n \n Biography\n \n \n Session Abstract\n \n \n \n \n \n Jack Keeler\, \;\n IQVIA\n \ n \n Is currently a new starter to the statistical i ndustry having completed an MSc in Statistics at Lancaster University at g rade Distinction in 2019. The focus of my dissertation was examining how e xactly what this abstract concerns\, Adaptive Enrichment Trials when conce rning survival trials\, achieving a grade of 76%. It was supervised by Dr Fang Wan of Lancaster University. Now working for IQVIA\, I have begun to get to grips with how expansive the pharmaceutical industry is\, and how t he theories of university apply to trials being conducted in reality.\n \n \n Enrichment Designs with Survival Data .\n \n In modern medicine\, it is becoming more appa rent of patient and disease heterogeneity\, and this can have consequences in clinical trials that do not take this into consideration. For example\ , Ellis and Taylor (2002) mention that ACE inhibitors are less effective i n African American patients than White patients when treating heart failur e. Many trials do not have sufficient information from early phase trials to definitively predict treatment effects for differing subgroups and some trials explore subgroups as exploratory endpoints\, but exploratory concl usions are not always considered concrete. A solution for dealing with the differing biomarkers is to use adaptive trial designs\, namely\, enrichme nt designs. Survival trials may benefit greatly from such adaptations as s urvival trials are some of the longest trials conducted. Using Magnusson a nd Turnbull&rsquo\;s (2013) Group Sequential Design incorporating Subgroup Selection (GSDS)\, a trial can use the first interim analysis as a means of discontinuing treatments for subgroups who are not experiencing the des ired effect from the treatment. This is making the trial ethically sound\, as patients in certain subgroups are saved from ineffective treatments. G SDS does not follow the same boundary calculation rules as normal group-se quential designs\, due to the selection criteria\, but the conduct of the trial is very similar\, making it feel familiar to statisticians.Enrichmen t trials are currently rare\, so an example trial\, using simulated surviv al data\, will demonstrate how these trials could perform in reality\, and examine the advantages and disadvantages of such designs.\n \n \n \n \n \n Ruth Owen\,&nb sp\;\n LSHTM\n \n \n Ruth Owen is a research fellow in the Medical Statistics Department at the London S chool of Hygiene and Tropical Medicine. She has been in this role since co mpleting her MSc in Medical Statistics at the school in September 2018 and is currently working in a team of statisticians working under Professor S tuart Pocock. Her projects are mainly in the cardiovascular disease area a nd have included an international clinical trial (SECURE) which investigat es the effects of the polypill in elderly patients with cardiovascular dis ease\, several projects using data from a multi-national cardiovascular re gistry (TIGRIS) and consultancy work with a team of cardiologists at the R oyal Brompton and Harefield NHS Trust. She has also previously worked for one year as a medical statistician in the Unit of Medical Statistics at Ki ng&rsquo\;s College London.\n \n \n Metho ds to Evaluate the Benefit-Risk Trade-Off in Individual Patients.\n \n Introduction: \n For many RCTs the efficacy of a new treatment is accompanied by safety concerns. While overall result s may demonstrate a favourable risk-benefit trade-off there may be individ uals where the harm outweighs the benefit. \n \n Met hods: We describe methods to predict the individual patient&rsquo\;s absol ute benefit and risk based on multivariable models using patient baseline characteristic. Taking account of the relative clinical importance of the respective benefits and harms we develop an algorithm for clinical use whe reby rapid decisions can be made on the preferred treatment strategy for e ach individual patient. \n \n Results: We illustrate this approach with findings from three major cardiovascular studies:\n 1) the SPRINT trial of intensive versus standard blood pressure l owering\, where ischaemic benefits are accompanied by some major adverse e vents\n 2) the TIMI 50 trial of vorapaxar versus placebo post-m yocardial infarction\, where ischaemic benefits are accompanied by increas ed risk of major bleeding\n 3) a meta-analysis of 7 studies in coronary patients receiving a stent\, with the goal of identifying which p atients at high risk of bleeding need a shorter duration of effective dual anti-platelet drugs. \n \n Conclusions: Our finding s illustrate how quantitative methods can help identify those individual p atients for whom the risk of harms outweighs the benefits of a new treatme nt.\n \n \n \n \n \n Inê\;s \;Reis\, \;\n MHRA\n \n \n Inê\;s Reis has been a Statistical Assessor in the Statistics and Pharmacokinetics Unit of the Medicines and Healthcare p roducts Regulatory Agency (MHRA) since 2018. She has previous experience w orking in the Biostatistics and Methodology Support Office at the European Medicines Agency. Inê\;s has been collaborating with the ICH E9(R1) expert working group on estimands since 2016 and was an important contribu tor to the training material presentations accessible in the ICH website. She holds an MPharm and a Post-graduate Diploma in Biostatistics\, both fr om the University of Lisbon.\n \n \n The Young Statistician's Guide to regulatory statistics.\n \n Successful and safe devolvement\, licensing and marketing of medicin es cannot happen without intense cooperation and dialogue between regulato rs and pharmaceutical companies. On the regulatory side\, the Medicines an d Healthcare products Regulatory Agency (MHRA) has decades of experience i n medicines and medical devices regulation\, during many of which statisti cians have been deeply involved. Not only at the level of licensing of med icines\, but also in the pharmacovigilance and medical device areas\, stat isticians play an important role in the Agency's activities\, not forgetti ng their involvement in real-world data collection and analysis (CPRD) and characterisation\, standardisation and control of biological medicines (N IBSC). \n \n In this talk you will learn about the M HRA\, how we work\, and the statistician's roles in the system\, as well a s some hints of current hot topics in regulatory statistics such as estima nds and real-world data. You will also discover the types of interactions that can be held with regulators at the UK (MHRA) and European (EMA) level s\, the different types of regulatory procedures and how statisticians fro m both sides of the table can contribute to such dialogue\, ultimately hel ping their companies navigate through the regulatory system more smoothly. \n \n \n \n \n \n Georgios Nikoladis\, \;\n CHE\n \n \n Georgios is a Research Fellow (Health Economics) in the Cent re for Health Economics (CHE) in the University of York working on the dev elopment of evidence synthesis and decision modelling methods for Health T echnology Assessment and on evaluating manufacturers submissions to the Na tional Institute for Health and Care Excellence (NICE) in the UK.Until Oct ober 2019\, he was a PhD student in CHE affiliated with the Team for Econo mic Evaluation and Health Technology Assessment where he undertook a thesi s entitled &ldquo\;Borrowing strength from indirect evidence in HTA: metho ds and policy implications&rdquo\;. His work has been presented in multipl e conferences including the Royal Statistical Society conference (2018)\, Health Economics Study Group (2018)\, and Health Technology Assessment Int ernational (2017).\n Georgios graduated with distinction from t he MSc in Health Economics in the University of York and holds an MPharm f rom the Aristotle University of Thessaloniki.Georgios has a genuine intere st in statistical (network) meta-analytic methodologies and\, specifically \, in Bayesian methods for multi-parameter evidence synthesis. George is a lso interested in methods for decision analytic modelling\, exploring the value of further research\, and evaluating diagnostics.\n \n \n Borrowing strength from indirect evidence in HTA.\n \n Background: \n Sparse relative effect iveness evidence is a common problem in Health Technology Assessment (HTA) . For example\, evidence on a paediatric population may be limited. Where evidence directly pertaining to the decision problem is sparse\, one optio n is to expand the evidence-base and include studies that relate to the de cision problem only indirectly\; for instance\, a decision on children may borrow strength from evidence on adults. Usually\, in HTA\, such indirect evidence is either included by ignoring any differences (`lumping`) or is completely disregarded (`splitting`). However\, more sophisticated method s exist in the literature which\, rather than `lumping` or `splitting`\, i mpose more moderate\, perhaps more appropriate\, degrees of information-sh aring. \n \n Methodology:\n We developed network-meta analytic methods for the combination of\, aggregate-level\, b inary\, direct and indirect evidence. These can be categorized into functi onal-\, exchangeability-based\, prior-based and correlation-based relation ships. The estimates produced with each method were subsequently used in a case-study that evaluated the cost-effectiveness and value of information of intravenous-immunoglobulin (IVIG) for adults with severe sepsis and se ptic shock. \n \n Results: Depending on the informat ion-sharing method used\, Incremental cost-effectiveness ratios (ICERs) va ried between around 20\,000 &ndash\;50\,000 £\; per Quality-Adjusted Life Year (QALY)\, and the optimal sample size of a future trial ranged be tween 3500 patients and 0 patients (i.e. no further trial is needed).\n \n Conclusion:\n Information-sharing method choice can lead to different adoption and further research recommendation decisions. It is\, hence\, important to scrutinize methods&rsquo\; underl ying assumptions and create a transparent\, systematic\, process that anal ysts can use when facing evidence sparsity problems.\n \n \n \n\n \;\n \;\n \; DTEND:20200609T103000Z DTSTAMP:20240329T095139Z DTSTART:20200609T090000Z LOCATION: SEQUENCE:0 SUMMARY:PSI Conference Webinar: Career Young Statistician Session UID:RFCALITEM638473026990679376 X-ALT-DESC;FMTTYPE=text/html:
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\n Speaker \n | \n \n Biography \n | \n
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p> \n Jack Keeler\, \; \n | \n \n Is currently a new starter to the statisti cal industry having completed an MSc in Statistics at Lancaster University at grade Distinction in 2019. The focus of my dissertation was examining how exactly what this abstract concerns\, Adaptive Enrichment Trials when concerning survival trials\, achieving a grade of 76%. It was supervised b y Dr Fang Wan of Lancaster University. Now working for IQVIA\, I have begu n to get to grips with how expansive the pharmaceutical industry is\, and how the theories of university apply to trials being conducted in reality. \n | \n \n Enrichment Designs with Survival Data. | \n
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| \n \n < p>Ruth Owen is a research fellow in the Medical Statistics Department at t he London School of Hygiene and Tropical Medicine. She has been in this ro le since completing her MSc in Medical Statistics at the school in Septemb er 2018 and is currently working in a team of statisticians working under Professor Stuart Pocock. Her projects are mainly in the cardiovascular dis ease area and have included an international clinical trial (SECURE) which investigates the effects of the polypill in elderly patients with cardiov ascular disease\, several projects using data from a multi-national cardio vascular registry (TIGRIS) and consultancy work with a team of cardiologis ts at the Royal Brompton and Harefield NHS Trust. She has also previously worked for one year as a medical statistician in the Unit of Medical Stati stics at King&rsquo\;s College London.\n | \n\n Methods
to Evaluate the Benefit-Risk Trade-Off in Individual Patients. | \n
\n Inê\;s Reis has been a Statistical Assessor in the Statistics and Pharmacokinetics Unit of the Medicines and Healthcar e products Regulatory Agency (MHRA) since 2018. She has previous experienc e working in the Biostatistics and Methodology Support Office at the Europ ean Medicines Agency. Inê\;s has been collaborating with the ICH E9(R 1) expert working group on estimands since 2016 and was an important contr ibutor to the training material presentations accessible in the ICH websit e. She holds an MPharm and a Post-graduate Diploma in Biostatistics\, both from the University of Lisbon. \n | \n \n The Young Stati
stician's Guide to regulatory statistics. | \n |
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| \n \n Georgios is a Research Fellow (Health Economi cs) in the Centre for Health Economics (CHE) in the University of York wor king on the development of evidence synthesis and decision modelling metho ds for Health Technology Assessment and on evaluating manufacturers submis sions to the National Institute for Health and Care Excellence (NICE) in t he UK.Until October 2019\, he was a PhD student in CHE affiliated with the Team for Economic Evaluation and Health Technology Assessment where he un dertook a thesis entitled &ldquo\;Borrowing strength from indirect evidenc e in HTA: methods and policy implications&rdquo\;. His work has been prese nted in multiple conferences including the Royal Statistical Society confe rence (2018)\, Health Economics Study Group (2018)\, and Health Technology Assessment International (2017). \nGeorgios graduated w ith distinction from the MSc in Health Economics in the University of York and holds an MPharm from the Aristotle University of Thessaloniki.Georgio s has a genuine interest in statistical (network) meta-analytic methodolog ies and\, specifically\, in Bayesian methods for multi-parameter evidence synthesis. George is also interested in methods for decision analytic mode lling\, exploring the value of further research\, and evaluating diagnosti cs. \n | \n \n Borrowing strength from indirect evidence i
n HTA. | \n
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