BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 13.3//EN BEGIN:VTIMEZONE TZID:W. Europe Standard Time BEGIN:STANDARD DTSTART:20231002T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYHOUR=3;BYMINUTE=0;BYMONTH=10 TZNAME:W. Europe Standard Time TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20230301T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYHOUR=2;BYMINUTE=0;BYMONTH=3 TZNAME:W. Europe Daylight Time TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:The PSI One Day Meeting and Workshop "Real World Evidence: Gene ralisability of Treatment Comparisons for Decision Making" took place on T uesday 18th September 2018 in Bad Homburg and was hosted by Lilly Germany GmbH. \; Over 50 participants attended the event. The morning was stru ctured into three sessions that included several talks covering the topics &ldquo\;indirect comparisons with and without adjustment for patient char acteristics&rdquo\;\, &ldquo\;generalisability of clinical trial data into real life settings&rdquo\; and &ldquo\;cross-design approaches combining observational and clinical trial data&rdquo\;. After lunch\, the three ses sions were followed by breakout sessions that offered attendees the opport unity to further discuss relevant topics about needed research and impact for the daily work as a statistician. The main findings of the day and tak e-home messages were summarized in a panel discussion in the end. \;\n \n\nAgenda\n\n \n \n Time \;\n Agenda& nbsp\;\n \n \n 08:30 - 08:50 \;\n Registration\n \n \n 08:50 - 09:00\n W elcome and Introduction\n Alexander Schacht (Lilly)\n \n \n 09:00 - 10:00 \;  \;  \;  \;  \; \n Indirect comparisons with and without adjustment for patient characteristics and related approaches\n Sarah Bö\;hme (Pf izer) \;\n David Phillippo (University Bristol)\n \n \n \n 10:00 - 11:00\n Generalizab ility of clinical trial data into real life settings\n Yann Ruf fieux (University Bern)\n \n Alan Brnabic (Lilly)\n \n \n \n 11:00 - 11:15\n Br eak\n \n \n 11:15 - 12:15\n Cross-desi gn approaches combining observational and clinical trial data \;\n Mark Belger (Lilly)\n \n Keith Abrams (Unive rsity Leicester)\n \n \n \n 12:15 - 13 :15\n Lunch\n \n \n 13:15 - 14:45\n  \;\n Break out #1: Bucher vs matching adjusted in direct comparisons and further refinements of these\n Break out #2: Generalizability of clinical trial data into real life settings\n Break out #3: Cross-design approaches combining observational and clinical trial data\n \n \n \n 14:45 - 15:00\n Break\n \n \n 15:00 - 16:00\n  \;\n Panel discussion\n Alexander S chacht (Lilly\, moderator)\n Carsten Schwenke (SCOSSIS)\n Ralf Bender (IQWiG)\n Nicky Welton (University Bristol)\n Keith Abrams (University Leicester)\n Chrissie Flet cher (Amgen)\n Mark Belger (Lilly)\n \n \n \n 16:00\n Close \;\n \n \n\n\n \n \n \n \n \n \n \n \n \n \n\n\nAbstracts\n\n\n \n \n \n Indirect comparisons with and without adjustment for patient characteri stics and related approaches - \;Sarah Bö\;hme (Pfizer)\, David Ph illippo (University Bristol)\n \n \n \n \n \n Sarah Bö\;hme \n Pfizer\n Indirect comparisons with and without adjustment for patient chara cteristics within the framework of AMNOG\n \n Abstra ct: \;Within the framework of the Act on the Reform of the Market for Medicinal Products (AMNOG) in Germany\, indirect comparisons are allowed t o assess the extent of added benefit in case of a lack of direct evidence. The method proposed by Bucher et al. has been recognized as one of the st andard approaches to perform adjusted IC. Further alternative methods exis t\, e.g. Matching-based approaches\, which aim to overcome different chall enges. However\, all these methods have certain limitations.\n In this talk the statistical properties of the Bucher approach and the Mat ching-adjusted indirect comparison as well as their limitations in practic e will be discussed.\n \n Biography: \;Sarah B&o uml\;hme holds a Master&lsquo\;s degree in Statistics from TU Dortmund Uni versity. In her master&rsquo\;s thesis she worked on the evaluation of met hods for adjusted an unadjusted indirect comparisons within the framework of the German benefit assessment. \; She joined Pfizer in 2015 and wor ks in the Health Technology Assessment &\; Outcomes Research Group at P fizer Germany.\n \n Click here to view the slides.\n \n \n \n  \;\n \n David M Phillippo\, \n University of Bristol\n \n  \;\n Multilevel network meta-regression fo r population adjustment based on individual and aggregate level data\n Abstract: Standard network meta-analysis (NMA) and indirect compar isons combine aggregate data (AgD) from multiple studies on treatments of interest\, assuming that any effect modifiers are balanced across populati ons. We can relax this assumption if individual patient data (IPD) are ava ilable from all studies by fitting an IPD meta-regression. However\, in ma ny cases IPD are only available from a subset of studies.\n In the simplest scenario\, IPD are available for an AB study but only AgD for an AC study. Methods such as Matching Adjusted Indirect Comparison (MAIC) create a population-adjusted indirect comparison between treatments B and C. However\, the resulting comparison is only valid in the AC population without additional assumptions\, and the methods cannot be extended to lar ger treatment networks. Meta-regression-based approaches can be used in la rger networks. However\, these typically fit the same model at both the in dividual and aggregate level which incurs aggregation bias.\n W e propose a general method for synthesising evidence from individual and a ggregate data in networks of all sizes\, Multilevel Network Meta-Regressio n\, extending the standard NMA framework. An individual-level regression m odel is defined\, and aggregate study data are fitted by integrating this model over the covariate distributions of the respective studies. Since in tegration is often complex or even intractable\, we take a flexible numeri cal approach using Quasi-Monte Carlo integration\, allowing for easy imple mentation regardless of model form or complexity. Correlation structures b etween covariates are accounted for using copulae.\n We illustr ate the method using an example and compare the results to those obtained using current methods. Where heterogeneity may be explained by imbalance i n effect modifiers between studies we achieve similar fit to a random effe cts NMA\, but uncertainty is substantially reduced\, and the model is more interpretable. Crucially for decision making\, comparisons may be provide d in any target population with a given covariate distribution.\n \n Biography: David Phillippo is a statistician at the Unive rsity of Bristol. His research focuses on methodology for evidence synthes is\, Bayesian Network Meta-Analysis\, and indirect comparisons. He is the lead author of a recent Technical Support Document published by the NICE D ecision Support Unit on population-adjusted indirect comparisons\, on whic h he is also undertaking his PhD.\n \n Click here to view the slides.\n \n \n  \;\n Ge neralizability of clinical trial data into real life settings - Yann Ruffi eux (University Bern)\, Alan Brnabic (Lilly)\n \n \n \n  \;\n \n Yann Ruffieux\, MSc\, University Bern\n \n  \;\n Combining RCT efficacy data and real-world evidence to predict drug effectiveness & ndash\; a case study in Rheumatoid Arthritis.\n Abstract: \ ;Decision-makers often need to assess the real-world effectiveness of a ne w drug before it is on the market. We propose a method to predict drug eff ectiveness pre-launch\, and apply it in a case study in rheumatoid arthrit is. Our approach comprises several steps: 1) identify an existing treatmen t similar to the new drug\, 2) quantify the impact of treatment\, prognost ic factors\, and effect modifiers on clinical outcome\, 3) determine the c haracteristics of patients likely to receive the new drug in routine care\ , 4) predict treatment outcome for patients with these characteristics.\n \n Biography: \;Yann Ruffieux is a Statistician at the Institute of Social and Preventive Medecine (ISPM) in Bern\, Switze rland. He has an MSc in Mathematical Engineering from the Swiss Federal In stitute of Technology in Lausanne (EPFL). After briefly working in pharma as Biostatistician\, he joined ISPM in 2015\, where he has contributed to the GetReal project and to HIV-related epidemiological research.\n \n Click here to view the slides.\n \n \n \n  \;\n \n Alan J. M. Brnabi c\, \n BA Dip Ed\, MA Statistics\, \n Eli Lilly\n \n  \;\n Reweighting randomized control led trial (RCT) evidence to better reflect real life &ndash\; a case study of the Innovation in Medicine initiative using patients with non-small ce ll lung cancer (NSCLC)\n Abstract: The objective of the present ation will be to present a case study that assesses the generalizability o f efficacy (overall survival [OS]) from the pivotal RCT (JMDB) comparing p emetrexed with gemicitabine to treat non-squamous non-small cell lung canc er using real-world data from a prospective observational study (FRAME) us ing a reweighting approach. Both inverse propensity scoring and entropy ba lancing were used to reweight the RCT data based on the real-world FRAME d ata in an attempt to mirror routine clinical practice in the trial setting .\n \n Biography: \;Mr. Brnabic is currently Pri ncipal Research Scientist at Eli Lilly working in Real World evidence (RWE ) with a focus on specialized analysis that supports this area. Prior to t his he was the Asia Pacific Director of the Health Outcomes and Health Eco nomics\, Life Sciences for OPTUM. Whilst at Eli Lilly he has been the Heal th Outcomes and Statistics Asia Pacific statistical sciences group leader and manager. His work has included: designing/reviewing and analyzing conc epts and studies (Phase IIIb &\; IV observational studies)\, as well as leading and reviewing external methodologies/ guidelines for use within t he company as well as consulting/coordinating strategy for analysis on Rei mbursement dossiers &\; other related Health Outcome activities for cou ntries like Australia\, Canada &\; Korea. He worked as a Consultant Bio statistician for 5 years in Public Health NSW Health Department. Following that he was a Senior Biostatistician at the George Institute which is aff iliated with UNSW where he worked on epidemiological studies and RCTs. Bef ore joining Eli Lilly he also took a position at the NSW Department of Cor rective Services as Deputy Director of the Research &\; Statistics\, Sy dney.\n \n Mr. Brnabic&rsquo\;s interests are in the design and analysis of observational studies with a focus on methodologie s related to subgroup identification as well as selection bias adjustment tools including matching\, propensity score analysis and local control. He is also interested in Health Outcomes and statistical approaches used to help support the reimbursement of medicines like matched adjusted indirect comparisons as well as mixed treatment comparisons.\n He has A -STAT Professional Accreditation with the Statistical Society of Australia (SSAI). He is co-chair and previous Chair for the Australian Pharmaceutic al Biostatistics Group (APBG).\n \n Click here to vi ew the slides.\n \n \n \n  \;\n Cross-design approaches combining observational and clinical tria l data - Mark Belger (Lilly)\, Keith Abrams (University Leicester)\n \n \n \n  \;\n \n Mark Belger\, BSc\, \n Eli Lilly\n \n &n bsp\;\n Cross-design approaches combining observational and cli nical trial data for HTA\n \n Abstract: The Innovati ve Medicines Initiative (IMI) &ldquo\;GetReal&rdquo\; project explored met hods for combing Randomised Clinical Trials (RCT) data with non-RCT data w ithin the same Network Meta-Analysis (NMA). Methods such as\, the design-a djusted analysis\, using informative priors and three-level hierarchical m odels have been summarised in the manuscript. &ldquo\;Combining randomized and nonrandomized evidence in network meta-analysis &ldquo\;[Orestis Efth imiou et al.]. We will discuss how to incorporate these methods within an HTA setting. Outlining the limitations in combining this type of evidence\ , and exploring how these methods are used to improve our understanding of how a new intervention will perform outside of the clinical trial environ ment.\n \n Efthimiou O\, Mavridis D1\, Debray TP\, S amara M\, Belger M\, Siontis GC\, Leucht S\, Salanti G\; GetReal Work Pack age 4. Combining randomized and non-randomized evidence in network meta-an alysis. Stat Med. 2017 Apr 15\;36(8):1210-1226. doi: 10.1002/sim.7223. Epu b 2017 Jan 12\n Biography: Mark Belger been a statistician for the last 34 years mainly working in the area of non-RCT studies. I joined the pharmaceutical industry 14 years ago prior to that I worked in the NHS . I draw from extensive experience of conducting studies in Non RCT popula tions from both an industry and non-industry perspective. My current respo nsibilities with Eli Lilly are to support the companies submissions to HTA &rsquo\;s with a focus on our Neurodegeneration and pain indications. In a ddition\, I also lead on a number of Real World analytical methodological projects within the company. I was an active member of IMI &ldquo\;GetReal &rdquo\;\, and I am currently involved in two Alzheimer&rsquo\;s disease I MI projects &ldquo\;ROADMAP&rdquo\; and &ldquo\;MOPEAD&rdquo\;. I have co- authored publications that focus on methods for analysing non-RCT data\, a nd clinical papers reporting results from non-RCT studies conducted by Eli Lilly.\n \n Click here to view the slides.\n \n \n \n \n \n \n Keith Abrams\, \n PhD CStat\, University of Leicester\, UK\n \n  \;\n Incorporating Real Worl d Evidence (RWE) in Network Meta-Analysis (NMA) &ndash\; Experiences from the Innovative Medicines Initiative (IMI) GetReal Project\n Abs tract: In this talk the possible situations in which Real World Evidence ( RWE)\, both comparative and single arm studies\, could be included in a Ne twork Meta-Analysis (NMA) will be described and discussed. These include\; sparse networks\, disconnected networks\, multiple outcome networks\, and the use of such NMAs in terms of decision making and designing future Ran domised Controlled Trials (RCTs). In particular\, methods for the allowanc e of potential biases and selection effects associated with RWE and how th ese may also be incorporated into NMAs will be discussed. The methods will be illustrated using examples from the IMI GetReal Project on patients wi th Multiple Sclerosis or Rheumatoid Arthritis. \;\n \n Biography: \;Keith Abrams is Professor of Medical Statistics\, within the Department of Health Sciences at the University of Leicester\, where he heads the Biostatistics Research Group. His research interests\, for which he has an international reputation\, are primarily concerned wit h the development and application of Bayesian statistical methods in Healt h Technology Assessment (HTA)\, in particular regarding clinical trials\, evidence synthesis\, and economic decision modelling\, and Non-Communicabl e Disease (NCD) epidemiology. This work is primarily supported with fundin g from EU\, Medical Research Council (MRC)\, National Institute for Health Research (NIHR) and industry (with a total value in excess of £\;20M over the last 5 years). Prof Abrams has been extensively involved with th e UK NIHR HTA Programme and UK National Institute for Health &\; Care E xcellence (NICE) appraisal process since their inception. He was a member of the NICE Technology Appraisals Committee for over 8 years until 2015\, is a member of the NICE Decision Support Unit and NICE Technical Support U nit\, acts as a consultant to the NICE Scientific Advice Programme\, and i s a NIHR Senior Investigator Emeritus. \; He is also a Fellow of the R oyal Statistical Society\, and a Chartered Statistician. He has published widely in both substantive and methodological areas [h-index 69] including co-authoring books on Methods for Meta-Analysis in Medical Research\, Bay esian Approaches to Clinical Trials and Healthcare Evaluation\, and Eviden ce Synthesis for Decision Making in Healthcare\, in addition to co-editing one of the first texts on Methods for Evidence-based Healthcare. Prof Abr ams has extensive experience over the last 25 years as a consultant to bot h the pharmaceutical and healthcare consultancy sectors\, providing both m ethodological and strategic HTA advice across a wide range of therapeutic areas. \;  \;\n \n Click here to view the sl ides.\n \n \n \n\n\n \; DTEND:20180918T140000Z DTSTAMP:20240329T121305Z DTSTART:20180918T063000Z LOCATION:Germany\,61352 Bad Homburg\,Lilly Germany\, Werner-Reimers-Straße 2-4 SEQUENCE:0 SUMMARY:PSI One Day Meeting and Workshop: Real World Evidence: Generalisabi lity of Treatment Comparisons for Decision Making UID:RFCALITEM638473111857371658 X-ALT-DESC;FMTTYPE=text/html:
The PSI One Day Meeting and Workshop "Real
World Evidence: Generalisability of Treatment Comparisons for Decision Mak
ing" took place on Tuesday 18th September 2018 in Bad Homburg and was host
ed by Lilly Germany GmbH. \; Over 50 participants attended the event.
The morning was structured into three sessions that included several talks
covering the topics &ldquo\;indirect comparisons with and without adjustm
ent for patient characteristics&rdquo\;\, &ldquo\;generalisability of clin
ical trial data into real life settings&rdquo\; and &ldquo\;cross-design a
pproaches combining observational and clinical trial data&rdquo\;. After l
unch\, the three sessions were followed by breakout sessions that offered
attendees the opportunity to further discuss relevant topics about needed
research and impact for the daily work as a statistician. The main finding
s of the day and take-home messages were summarized in a panel discussion
in the end. \;
\n
\n
Time \; | \nAgenda \; | \n
08:30 - 08:50 \; | \nRegistration | \n
08:50 - 09:00 | \nWelcome and Introduction\n Alexander Schacht (Lilly) | \n
09:00 - 10:00 \;  \;  \;   \;  \; | \n\n Sar ah Bö\;hme (Pfizer) \; \n David Phillippo (University Bristol) \n | \n
10:00 - 11:00 | \nGeneralizability of clinical trial data
into real life settings \n Yann Ruff ieux (University Bern) \n \ n Alan Brnabic (Lilly) \n | \n
11:00 - 11:15 | \nBreak | \n
11:15 - 12:15 | \nCross-design approache
s combining observational and clinical trial data \; \n Mark Belger (Lilly) \n \n Keith Abrams (University Leicester) \n < /td>\n |
12:15 - 13:15 | \nLunch | \n
13:15 - 14:45 | \ n \;\n
Break out #1: Bucher vs matching adjusted indirect co mparisons and further refinements of these \nBre ak out #2: Generalizability of clinical trial data into real life settings \nBreak out #3: Cross-design approaches combining observational and clinical trial data \n | \n
14:45 - 15:00 | \nBreak | \n
15:00 - 16:00\n |  \;\n
Panel discussion \nAlexander
Schacht (Lilly\, moderator) | \n
16:00 | \nClose \; | \n
\n Indirect comparisons with and without adjustment for patient characteristics and related approaches - \;Sarah Bö\; hme (Pfizer)\, David Phillippo (University Bristol) \n \n | \n |
\n Sarah Bö\;hme \n Pfizer | \n Indirect compari
sons with and without adjustment for patient characteristics within the fr
amework of AMNOG \n \n Abstract: \; strong>Within the framework of the Act on the Reform of the Market for Med icinal Products (AMNOG) in Germany\, indirect comparisons are allowed to a ssess the extent of added benefit in case of a lack of direct evidence. Th e method proposed by Bucher et al. has been recognized as one of the stand ard approaches to perform adjusted IC. Further alternative methods exist\, e.g. Matching-based approaches\, which aim to overcome different challeng es. However\, all these methods have certain limitations. \n In this talk the statistical properties of the Bucher approach and t
he Matching-adjusted indirect comparison as well as their limitations in p
ractice will be discussed. | \n
&nbs
p\;\n
| \n  \;\n
Multilevel network meta-regression for population adju stment based on individual and aggregate level data \n Abstract: Standard network meta-analysis (NMA) and in direct comparisons combine aggregate data (AgD) from multiple studies on t reatments of interest\, assuming that any effect modifiers are balanced ac ross populations. We can relax this assumption if individual patient data (IPD) are available from all studies by fitting an IPD meta-regression. Ho wever\, in many cases IPD are only available from a subset of studies.\nIn the simplest scenario\, IPD are available for an AB study but only AgD for an AC study. Methods such as Matching Adjusted Indirect C omparison (MAIC) create a population-adjusted indirect comparison between treatments B and C. However\, the resulting comparison is only valid in th e AC population without additional assumptions\, and the methods cannot be extended to larger treatment networks. Meta-regression-based approaches c an be used in larger networks. However\, these typically fit the same mode l at both the individual and aggregate level which incurs aggregation bias . \nWe propose a general method for synthesising evidenc e from individual and aggregate data in networks of all sizes\, Multilevel Network Meta-Regression\, extending the standard NMA framework. An indivi dual-level regression model is defined\, and aggregate study data are fitt ed by integrating this model over the covariate distributions of the respe ctive studies. Since integration is often complex or even intractable\, we take a flexible numerical approach using Quasi-Monte Carlo integration\, allowing for easy implementation regardless of model form or complexity. C orrelation structures between covariates are accounted for using copulae.< /p>\n We illustrate the method using an example and compare the results to those obtained using current methods. Where heterogeneity m ay be explained by imbalance in effect modifiers between studies we achiev e similar fit to a random effects NMA\, but uncertainty is substantially r educed\, and the model is more interpretable. Crucially for decision makin g\, comparisons may be provided in any target population with a given cova riate distribution. \n\n Biography : David Phillippo is a statistician at the University of Bristol. His research focuses on methodology for evidence synthesis\, Bayesian Net work Meta-Analysis\, and indirect comparisons. He is the lead author of a recent Technical Support Document published by the NICE Decision Support U nit on population-adjusted indirect comparisons\, on which he is also unde rtaking his PhD. \n \n Click here to view the slid es. | \n
 \;
\n Generalizability of clinical trial data into real life settings - Yann Ruffieux (University Bern)\, Alan Brnabic (Lilly ) \n | \n |
 \; \n
| \n  \;\n
Combining RCT efficacy data and real-world evidence to predic t drug effectiveness &ndash\; a case study in Rheumatoid Arthritis. \n Abstract: \;Decision-makers often need to assess the real-world effectiveness of a new drug before it is on the market. We propose a method to predict drug effectiveness pre-launch\ , and apply it in a case study in rheumatoid arthritis. Our approach compr ises several steps: 1) identify an existing treatment similar to the new d rug\, 2) quantify the impact of treatment\, prognostic factors\, and effec t modifiers on clinical outcome\, 3) determine the characteristics of pati ents likely to receive the new drug in routine care\, 4) predict treatment outcome for patients with these characteristics.\n \n Biography: \;Yann Ruffieux is a Statis tician at the Institute of Social and Preventive Medecine (ISPM) in Bern\, Switzerland. He has an MSc in Mathematical Engineering from the Swiss Fed eral Institute of Technology in Lausanne (EPFL). After briefly working in pharma as Biostatistician\, he joined ISPM in 2015\, where he has contribu ted to the GetReal project and to HIV-related epidemiological research. \n \n Click here to view the slides. \n | \n
 \; \n
| \n
 \;\n Reweighting randomized controlled trial (RCT) evidence to better reflect real life &ndash\; a case study of the Innovation in Medicine initiative using patients with non-small cell l ung cancer (NSCLC) \n Abstract: Th e objective of the presentation will be to present a case study that asses ses the generalizability of efficacy (overall survival [OS]) from the pivo tal RCT (JMDB) comparing pemetrexed with gemicitabine to treat non-squamou s non-small cell lung cancer using real-world data from a prospective obse rvational study (FRAME) using a reweighting approach. Both inverse propens ity scoring and entropy balancing were used to reweight the RCT data based on the real-world FRAME data in an attempt to mirror routine clinical pra ctice in the trial setting.\n \n Biography: \;Mr. Brnabic is currently Principal Research Sci entist at Eli Lilly working in Real World evidence (RWE) with a focus on s pecialized analysis that supports this area. Prior to this he was the Asia Pacific Director of the Health Outcomes and Health Economics\, Life Scien ces for OPTUM. Whilst at Eli Lilly he has been the Health Outcomes and Sta tistics Asia Pacific statistical sciences group leader and manager. His wo rk has included: designing/reviewing and analyzing concepts and studies (P hase IIIb &\; IV observational studies)\, as well as leading and review ing external methodologies/ guidelines for use within the company as well as consulting/coordinating strategy for analysis on Reimbursement dossiers &\; other related Health Outcome activities for countries like Austral ia\, Canada &\; Korea. He worked as a Consultant Biostatistician for 5 years in Public Health NSW Health Department. Following that he was a Seni or Biostatistician at the George Institute which is affiliated with UNSW w here he worked on epidemiological studies and RCTs. Before joining Eli Lil ly he also took a position at the NSW Department of Corrective Services as Deputy Director of the Research &\; Statistics\, Sydney. \n \n Mr. Brnabic&rsquo\;s interests are in the des ign and analysis of observational studies with a focus on methodologies re lated to subgroup identification as well as selection bias adjustment tool s including matching\, propensity score analysis and local control. He is also interested in Health Outcomes and statistical approaches used to help support the reimbursement of medicines like matched adjusted indirect com parisons as well as mixed treatment comparisons. \nHe ha
s A-STAT Professional Accreditation with the Statistical Society of Austra
lia (SSAI). He is co-chair and previous Chair for the Australian Pharmaceu
tical Biostatistics Group (APBG). | \n
 \;\n Cross-design appr oaches combining observational and clinical trial data - Mark Belger ( Lilly)\, Keith Abrams (University Leicester) \n | \n |
 \;<
br />\n
| \n  \;\n Cross-design approaches combining observa tional and clinical trial data for HTA \n\n Abstract: The Innovative Medicines Initiative (IMI) &ldquo\;GetReal&rdquo\; project explored methods for combing Randomi sed Clinical Trials (RCT) data with non-RCT data within the same Network M eta-Analysis (NMA). Methods such as\, the design-adjusted analysis\, using informative priors and three-level hierarchical models have been summaris ed in the manuscript. &ldquo\;Combining randomized and nonrandomized evide nce in network meta-analysis &ldquo\;[Orestis Efthimiou et al.]. We will d iscuss how to incorporate these methods within an HTA setting. Outlining t he limitations in combining this type of evidence\, and exploring how thes e methods are used to improve our understanding of how a new intervention will perform outside of the clinical trial environment. \n \n Efthimiou O\, Mavri dis D1\, Debray TP\, Samara M\, Belger M\, Siontis GC\, Leucht S\, Salanti G\; GetReal Work Package 4. Combining randomized and non-randomized evide nce in network meta-analysis. Stat Med. 2017 Apr 15\;36(8):1210-1226. doi: 10.1002/sim.7223. Epub 2017 Jan 12 \n Biogra phy: Mark Belger been a statistician for the last 34 years mainly working in the area of non-RCT studies. I joined the pharmaceutical indus try 14 years ago prior to that I worked in the NHS. I draw from extensive experience of conducting studies in Non RCT populations from both an indus try and non-industry perspective. My current responsibilities with Eli Lil ly are to support the companies submissions to HTA&rsquo\;s with a focus o n our Neurodegeneration and pain indications. In addition\, I also lead on a number of Real World analytical methodological projects within the comp any. I was an active member of IMI &ldquo\;GetReal&rdquo\;\, and I am curr ently involved in two Alzheimer&rsquo\;s disease IMI projects &ldquo\;ROAD MAP&rdquo\; and &ldquo\;MOPEAD&rdquo\;. I have co-authored publications th at focus on methods for analysing non-RCT data\, and clinical papers repor ting results from non-RCT studies conducted by Eli Lilly.\n \n Click here to view the slides. \n \n | \n
<
br />\n \n Keith Abrams\, | \n  \;\n Incorporating R eal World Evidence (RWE) in Network Meta-Analysis (NMA) &ndash\; Experienc es from the Innovative Medicines Initiative (IMI) GetReal Project \n Abstract: In this talk the possible sit uations in which Real World Evidence (RWE)\, both comparative and single a rm studies\, could be included in a Network Meta-Analysis (NMA) will be de scribed and discussed. These include\; sparse networks\, disconnected netw orks\, multiple outcome networks\, and the use of such NMAs in terms of de cision making and designing future Randomised Controlled Trials (RCTs). In particular\, methods for the allowance of potential biases and selection effects associated with RWE and how these may also be incorporated into NM As will be discussed. The methods will be illustrated using examples from the IMI GetReal Project on patients with Multiple Sclerosis or Rheumatoid Arthritis. \;\n \n Biography : \;Keith Abrams is Professor of Medical Statistics\, within the Department of Health Sciences at the University of Leicester\, where h e heads the Biostatistics Research Group. His research interests\, for whi ch he has an international reputation\, are primarily concerned with the d evelopment and application of Bayesian statistical methods in Health Techn ology Assessment (HTA)\, in particular regarding clinical trials\, evidenc e synthesis\, and economic decision modelling\, and Non-Communicable Disea se (NCD) epidemiology. This work is primarily supported with funding from EU\, Medical Research Council (MRC)\, National Institute for Health Resear ch (NIHR) and industry (with a total value in excess of £\;20M over t he last 5 years). Prof Abrams has been extensively involved with the UK NI HR HTA Programme and UK National Institute for Health &\; Care Excellen ce (NICE) appraisal process since their inception. He was a member of the NICE Technology Appraisals Committee for over 8 years until 2015\, is a me mber of the NICE Decision Support Unit and NICE Technical Support Unit\, a cts as a consultant to the NICE Scientific Advice Programme\, and is a NIH R Senior Investigator Emeritus. \; He is also a Fellow of the Royal St atistical Society\, and a Chartered Statistician. He has published widely in both substantive and methodological areas [h-index 69] including co-aut horing books on Methods for Meta-Analysis in Medical Research\, Bayesian A pproaches to Clinical Trials and Healthcare Evaluation\, and Evidence Synt hesis for Decision Making in Healthcare\, in addition to co-editing one of the first texts on Methods for Evidence-based Healthcare. Prof Abrams has extensive experience over the last 25 years as a consultant to both the p harmaceutical and healthcare consultancy sectors\, providing both methodol ogical and strategic HTA advice across a wide range of therapeutic areas.& nbsp\;  \; \n \n Click here to view the slides. \n | \n