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 \;\nDate: Wednesday 10th June 2020 \; &nbs p\;  \;  \;\n\nTime: \;10:00 - 11:30\nSpeakers: \;Elizabet h Williamson (LSHTM)\, Christen Gray (IQVIA) \;&\; Kirsty Hicks&nbs p\;(GSK)\n \nRegistration:\nThis webinar is part of our 2020 Conference We binar Series. Further information including details of other webinars that are included in the Conference package can be found \;here.\nMembers& nbsp\;receive all webinars in the conference series for free.\nNon-members  \;receive all webinars in the conference series for £\;100+VAT\, which includes complimentary membership* of PSI until the 31st December 2 020. \;\n\nTo register your place for this event\, and others in the C onference webinar series\, please click here.\n\n\nSpeaker Details:\n\n \n \n \n Speaker\n \n \n Biography\n \n \n Session A bstract\n \n \n \n \n \n Elizabeth Williamson\, \;\n LSHTM\n \n \n Elizabeth Williamson is a biostatistician working in the Department of Medical Statistics at LSHTM (London School of Hygien e and Tropical Medicine). Her work focuses on statistical methods for addr essing causal questions using electronic health records. Following an unde rgraduate degree in mathematics at King's College Cambridge and an MSc fro m the University of Leicester she undertook a PhD at the London School of Hygiene &\; Tropical Medicine (LSHTM) investigating the use of propensi ty scores to address confounding in observational data. From 2007 to 2014\ , Elizabeth lived in Australia\, working in a range of biostatistical role s at the University of Sydney\, Murdoch Childrens Research Institute\, Mon ash University and the University of Melbourne. In 2014\, she returned to LSHTM to take a position jointly between LSHTM and the Farr Institute of H ealth Informatics\, London.\n \n \n Using RWD to emulate trials.\n Trials remain the gold standard for e stablishing harms and benefits of drugs. However\, in certain circumstance s it is useful to use observational data to attempt to emulate a particula r randomised trial. This might be prior to running the trial in question\, for the purposes of hypothesis generation or to obtain better estimates o f the parameters required for power calculations. Alternatively\, once the trial has been completed\, trial emulation approaches might be used to ex tend results of the trials to relevant patient populations who are not inc luded or less represented in the trial.\n This talk will explor e different approaches to trial emulation using RWD using two examples bot h using data from the UK Clinical Practice Research Datalink (CPRD)\, a la rge database of UK primary care records. In the first example\, data from the CPRD was used to emulate a trial of macrolide antibiotics on all-cause mortality prior to the relevant trial being conducted. In the second exam ple\, data from CPRD was used to generalise the results of the TORCH COPD trial to a patient group less represented among the original trial partici pants &ndash\;those with mild COPD.\n \n \n \n \n \n Christen Gray\, \;\n I QVIA\n \n \n Christen is a Sr Consultant in Biostatistics for the RWS team at IQVIA. Prior to joining IQVIA\, Chris ten spent four years at the Foundation for Innovation New Diagnostics (FIN D) in Geneva\, Switzerland as the Biostatistics and Data Manager in the cl inical trials team. This team was responsible for the design\, coordinatio n\, and analysis of multi-national clinical trials of diagnostics for tube rculosis\, malaria\, and other neglected diseases. Her work included the a nalysis and reporting for a WHO expert review submission for TB diagnostic s. Prior work experience also includes field studies and analysis of inter ventions for the reduction of indoor air pollution from wood and charcoal cookstoves in developing countries at Berkeley Air in Berkeley\, CA\, USA. Christen also completed a PhD in Medical Statistics at the London School of Hygiene and Tropical Medicine (LSHTM). Her thesis focused on correction for exposure measurement error using Bayesian methods. Christen holds a B sc in Molecular Biology from MIT as well as an MPH in Epidemiology &\; Biostatistics from UC Berkeley. She also has experience in the laboratory side of drug development after working in the Infectious Diseases Departme nt atNovartis Institutes of Biomedical Research in Cambridge\, MA.\n \n \n Comparing the impact of unmeasured confo unding due to selection bias in external comparator studies using RWD.\n Background: Augmentation of the control arm of a randomized cont rolled trial (RCT) with external data has been proposed in recent years wh ere standard RCTs face enrolment restrictions. Using real-world data (RWD) for external controls is a natural next step. However\, in order to do so \, there need to exist accessible methods for researchers which can minimi ze the risk from unmeasured confounding in this setting. Bayesian borrowin g methods\, which discount the external data dependent upon the similarity of the outcomes to the internal controls\, have been applied when the ext ernal data is prior control arms of clinical trials. The simplest of these approaches is the power prior. In using RWD\, greater variation in the un derlying population and measured variables is expected.\n Objec tive: To assess the ability of simple analytical methods to reduce bias st emming from unmeasured confounding due to selection bias in an augmented R CT.\n Methods: A simulation study of an augmented RCT was perfo rmed with unmeasured confounding to assess the use of propensity score (PS ) methods alone\, the use of a normalized power prior method alone\, and t he use of a combined PS-adjusted power prior. Results: When no unmeasured confounding is present\, the use of traditional PS minimizes bias as expec ted. In the presence of even a weak uncontrolled confounder\, the power pr ior method is necessary to appropriately discount the external data to min imize bias.\n Conclusions: Using a combination of simple analyt ical methods\, rather than a single complex method alone\, may provide a w ay for researchers to implement augmented RCTs.\n \n \n \n \n \n Kirsty Hicks\, \;\n GSK\n \n \n Kirsty is a Senior Statistics Director at GSK\, having joined in 2001 from Roche where she wo rked as a late phase statistician for a number of years. Throughout her ti me at GSK Kirsty has supported all gastrointestinal assets in the early ph ase portfolio covering diseases such as ulcerative colitis and irritable b owel syndrome\; and supported numerous immunological-inflammatory assets a cross a wide range of diseases. Throughout this time Kirsty has managed an ever-growingteam of statisticians in addition to being heavily involved i n numerous non-projectinitiatives. More recently these have included explo ring experimental medicine analysis methods\; facilitating prior elicitati ons and leading the biostatistics team investigating the incorporation and analysis of complex digital data into clinical trials. Kirsty is currentl y leading the UK Oncology Biostatistics team\, and heads up the Cancer Epi genetics Biostatistics group.\n \n \n Adv anced Analytics of Digital Data: A focus on sensor data.\n What is the evolving area of Digital data you may ask? Nowadays most of us wil l be wearing a watch monitoring some aspect of our movement or use phone a pps that collect health information. The data collected can be referred to as digital biomarkers\, and this data\, in particular sensor technology i s becoming a common feature in clinical trials. Digital data can be used t o collect information on sleep patterns\, respiration rate\, step count an d continuous monitoring of heart rate and energy expenditure. Collecting d ata through digital devices also increases patient engagement and can prov ide real time compliance monitoring. The regulations for use of digital te chnologies in clinical trials are still evolving\, and current recommendat ions for the analysis of such data are limited. Utilising sensors\, and co llecting actigraphy data\, is a non-invasive method of monitoring activity . An actigraph sensor is worn for a time period (eg a week or more) to mea sure activity. The movements the actigraph undergoes are continually recor ded\; where 300 data points per second can be collected. As a statistician initial questions that spring to mind include &lsquo\;How can this volume of data be analysed or represented visually?&rsquo\; Obvious summary meas ures would include average step count over a day\; or the percentage of ti me a patient is active or sedentary. But a lot of information is lost\, wh at about the raw data? For example\, the energy used at the minute level o r even second level? Statistical methods that can be applied to this data are under investigation and this presentation will introduce some of these .\n \n \n \n\n \; DTEND:20200610T103000Z DTSTAMP:20240328T113410Z DTSTART:20200610T090000Z LOCATION: SEQUENCE:0 SUMMARY:PSI Conference Webinar: Intersection of Clinical Trials and Real Wo rld Data UID:RFCALITEM638472224508669752 X-ALT-DESC;FMTTYPE=text/html:
\nTime: \;10:00 - 11:30
\nSpeakers: \;Elizabeth
Williamson (LSHTM)\, Christen Gray (IQVIA) \;&\;
Kirsty Hicks \;(GSK)
\n
This webinar is part of our 2020 Conference Webinar Ser
ies. Further information including details of other webinars that are incl
uded in the Conference package can be found \;here.
\nMembers \;receive all webinars in the conference series for free.
\
nNon-members \;receive all webinars in the conference
series for £\;100+VAT\, which includes complimentary membership* of
PSI until the 31st December 2020. \;
\n
\nTo register your p
lace for this event\, and others in the Conference webinar series\, please
click here.
\n
\n
\n Speake r \n | \n \n Biography \n | \n \n < p>Session Abstract\n | \n
\n
| \n \n Elizabeth Williamson i s a biostatistician working in the Department of Medical Statistics at LSH TM (London School of Hygiene and Tropical Medicine). Her work focuses on s tatistical methods for addressing causal questions using electronic health records. Following an undergraduate degree in mathematics at King's Colle ge Cambridge and an MSc from the University of Leicester she undertook a P hD at the London School of Hygiene &\; Tropical Medicine (LSHTM) invest igating the use of propensity scores to address confounding in observation al data. From 2007 to 2014\, Elizabeth lived in Australia\, working in a r ange of biostatistical roles at the University of Sydney\, Murdoch Childre ns Research Institute\, Monash University and the University of Melbourne. In 2014\, she returned to LSHTM to take a position jointly between LSHTM and the Farr Institute of Health Informatics\, London. \n td>\n | \n Using RWD to emulate trials. \nTrials r emain the gold standard for establishing harms and benefits of drugs. Howe ver\, in certain circumstances it is useful to use observational data to a ttempt to emulate a particular randomised trial. This might be prior to ru nning the trial in question\, for the purposes of hypothesis generation or to obtain better estimates of the parameters required for power calculati ons. Alternatively\, once the trial has been completed\, trial emulation a pproaches might be used to extend results of the trials to relevant patien t populations who are not included or less represented in the trial. \nThis talk will explore different approaches to trial emulat ion using RWD using two examples both using data from the UK Clinical Prac tice Research Datalink (CPRD)\, a large database of UK primary care record s. In the first example\, data from the CPRD was used to emulate a trial o f macrolide antibiotics on all-cause mortality prior to the relevant trial being conducted. In the second example\, data from CPRD was used to gener alise the results of the TORCH COPD trial to a patient group less represen ted among the original trial participants &ndash\;those with mild COPD. \n | \n
\n
| \n \n
Christen is a Sr Consultant in Biostatistics for the RWS team at IQ VIA. Prior to joining IQVIA\, Christen spent four years at the Foundation for Innovation New Diagnostics (FIND) in Geneva\, Switzerland as the Biost atistics and Data Manager in the clinical trials team. This team was respo nsible for the design\, coordination\, and analysis of multi-national clin ical trials of diagnostics for tuberculosis\, malaria\, and other neglecte d diseases. Her work included the analysis and reporting for a WHO expert review submission for TB diagnostics. Prior work experience also includes field studies and analysis of interventions for the reduction of indoor ai r pollution from wood and charcoal cookstoves in developing countries at B erkeley Air in Berkeley\, CA\, USA. Christen also completed a PhD in Medic al Statistics at the London School of Hygiene and Tropical Medicine (LSHTM ). Her thesis focused on correction for exposure measurement error using B ayesian methods. Christen holds a Bsc in Molecular Biology from MIT as wel l as an MPH in Epidemiology &\; Biostatistics from UC Berkeley. She als o has experience in the laboratory side of drug development after working in the Infectious Diseases Department atNovartis Institutes of Biomedical Research in Cambridge\, MA. \n | \n \n Comparing the impac t of unmeasured confounding due to selection bias in external comparator s tudies using RWD. \nBackground: Augmentation of the control arm of a randomized controlled trial (RCT) with external data has been proposed in recent years where standard RCTs face enrolment rest rictions. Using real-world data (RWD) for external controls is a natural n ext step. However\, in order to do so\, there need to exist accessible met hods for researchers which can minimize the risk from unmeasured confoundi ng in this setting. Bayesian borrowing methods\, which discount the extern al data dependent upon the similarity of the outcomes to the internal cont rols\, have been applied when the external data is prior control arms of c linical trials. The simplest of these approaches is the power prior. In us ing RWD\, greater variation in the underlying population and measured vari ables is expected. \nObjective: To assess the ability of simple analytical methods to reduce bias stemming from unmeasured confoun ding due to selection bias in an augmented RCT. \nMethod s: A simulation study of an augmented RCT was performed with unmeasured co nfounding to assess the use of propensity score (PS) methods alone\, the u se of a normalized power prior method alone\, and the use of a combined PS -adjusted power prior. Results: When no unmeasured confounding is present\ , the use of traditional PS minimizes bias as expected. In the presence of even a weak uncontrolled confounder\, the power prior method is necessary to appropriately discount the external data to minimize bias. \nConclusions: Using a combination of simple analytical methods\, r ather than a single complex method alone\, may provide a way for researche rs to implement augmented RCTs. \n | \n
\n
\n Kirsty Hicks\, \; | \n \n Kirsty is a Senior Statistics Director a t GSK\, having joined in 2001 from Roche where she worked as a late phase statistician for a number of years. Throughout her time at GSK Kirsty has supported all gastrointestinal assets in the early phase portfolio coverin g diseases such as ulcerative colitis and irritable bowel syndrome\; and s upported numerous immunological-inflammatory assets across a wide range of diseases. Throughout this time Kirsty has managed an ever-growingteam of statisticians in addition to being heavily involved in numerous non-projec tinitiatives. More recently these have included exploring experimental med icine analysis methods\; facilitating prior elicitations and leading the b iostatistics team investigating the incorporation and analysis of complex digital data into clinical trials. Kirsty is currently leading the UK Onco logy Biostatistics team\, and heads up the Cancer Epigenetics Biostatistic s group. \n | \n \n Advanced Analytics of Digital Data: A focus on sensor data. \nWhat is the evolving ar ea of Digital data you may ask? Nowadays most of us will be wearing a watc h monitoring some aspect of our movement or use phone apps that collect he alth information. The data collected can be referred to as digital biomark ers\, and this data\, in particular sensor technology is becoming a common feature in clinical trials. Digital data can be used to collect informati on on sleep patterns\, respiration rate\, step count and continuous monito ring of heart rate and energy expenditure. Collecting data through digital devices also increases patient engagement and can provide real time compl iance monitoring. The regulations for use of digital technologies in clini cal trials are still evolving\, and current recommendations for the analys is of such data are limited. Utilising sensors\, and collecting actigraphy data\, is a non-invasive method of monitoring activity. An actigraph sens or is worn for a time period (eg a week or more) to measure activity. The movements the actigraph undergoes are continually recorded\; where 300 dat a points per second can be collected. As a statistician initial questions that spring to mind include &lsquo\;How can this volume of data be analyse d or represented visually?&rsquo\; Obvious summary measures would include average step count over a day\; or the percentage of time a patient is act ive or sedentary. But a lot of information is lost\, what about the raw da ta? For example\, the energy used at the minute level or even second level ? Statistical methods that can be applied to this data are under investiga tion and this presentation will introduce some of these. \n | \n
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END:VEVENT END:VCALENDAR