The Personalised Reimbursement Models (Prm): Real World Data Collection to Provide Innovative Pricing Solutions in France

VA L U E I N H E A LT H

population representativeness, disease coverage (non-small cell lung cancer [NSCLC], small cell lung cancer [SCLC] and mesothelioma), data quality/completeness, source type, estimated size and other attributes. Final selection was based on: pan-European coverage, research goal alignment and timely data availability.  Results: In addition to SCAN-LEAF (pre-identified Scandinavian RWDS), 490 RWDS were identified with 124 (25.3%) shortlisted. Of the shortlisted, 15 (12.1%) are under appraisal, 91 (73.4%) have completed appraisal, 4 (3.2%) are under assessment and 14 (11.3%) are fully assessed. Of the assessed RWDS, 7 (50.0%) have progressed to a comprehensive appraisal of research goal alignment and capacity (Germany, Italy, Netherlands, Portugal and UK); All 7 address clinical outcomes and treatment/ practice patterns, 6/7 address pharmacovigilance, 5/7 address healthcare resource utilization and 1/7 addresses patient-reported outcomes. These 7 RWDS plus SCANLEAF will provide ~395,000 NSCLC, SCLC and mesothelioma cases.  Conclusions: A structured assessment of RWDS facilitated development of a flexible collaborative research framework. The challenges of conducting such initiatives were also revealed, as most available RWDS proved unsuitable. Drawing together diverse RWDS enables examination of a wide range of research questions, reflects the diversity of real-world clinical practice and provides standardisation that allows insights to be drawn across data sources. I-O Optimise has the potential to elucidate real-world management of lung cancer in Europe, complementing ongoing clinical trial-based research. PRM70 Misclassification of Diabetes Type I in German Claims Data Schmid K, Hapfelmeier J Arvato Health Analytics GmbH, Munich, Germany

Objectives: The two major subtypes of diabetes, type I and II, are based on different pathogenic processes. Their correct diagnosis is important for a proper treatment of patients, assignment to appropriate disease management programs (DMP) and correct billing of medical costs. However, health claims data shows many patients diagnosed with both type I and II. Based on the prevalence of having both types, this number should be much lower, suggesting erroneous coding. To identify miscoding we propose an algorithm that distinguishes type I and type II diabetics.  Methods: We used the health claims database of Arvato Health Analytics containing diagnosis codes and prescriptions of 3 million German insurants for 2008-2015. We extracted all patients showing type I or type II diagnoses. We classified patients without insulin treatment as type II. Patients with a clear majority of diagnoses for either type I or II were classified accordingly. Furthermore, we identified a group of less certain type II diabetics without continuous insulin treatment or with continuous treatment of oral antidiabetics. For evaluation we applied the algorithm to two German health claim data sets from 2011 to 2015, one containing all insurants, one only participants of diabetes type I DMPs.  Results: 88.1% of the insurants with a type I diagnosis (45.632 of 51.812) also had a type II diagnosis. Reducing the population to double diabetics with at least 2 outpatient or 1 inpatient diagnoses in one year, 40.2% had no insulin prescription. We classified 12.4% as type I, 70.6% as type II. Among the DMP participants, 62.5% had additional type II diagnoses. According to our method, 4.8% were assigned incorrectly to the DMP.  Conclusions: The algorithm indicates that many type I diabetes diagnoses are incorrect, as the patients show clear signs for only type II. Subsequently, reasons for the wrong diagnoses coding should be identified and reduced. PRM71 Using Observational Data From Registry In Cost-Effectiveness Evaluation of Metastatic Castration Resistant Prostate Cancer In France Leleu H1, Wapenaar R2, Klumper E3, Capone C4, de Beaucoudrey L4, Thevenon J4, Chevalier J4 1Public Health Expertise, Paris, France, 2Janssen-Cilag B.V., Breda, The Netherlands, 3SMS-Oncology BV, Amsterdam, The Netherlands, 4Janssen-Cilag, Issy-les-Moulineaux, France

Objectives: Cost-effectiveness evaluations in oncology based on data available at the time of initial approval can be challenging. Available clinical data are often limited to interim immature survival data; inclusion criteria may limit the external validity of results; evidence limited to one head-to-head comparison limits network meta-analysis (NMA) feasibility. Re-evaluation using real world evidence can solve these issues. However, several shortfalls need to be considered when estimating comparative effectiveness. This study explores potential solutions in the context of real-world evidence on metastatic Castration Resistant Prostate Cancer (mCRPC) in patients pre-treated by docetaxel.  Methods: A three-state survival model was constructed to estimate the cost-effectiveness of post-docetaxel mCRPC treatments in France and populated with data from the Janssen European Prostate Cancer Registry (NCT02236637). Baseline characteristics, progression-free survival and overall survival were obtained for abiraterone acetate plus prednisone (AAP) (n= 199), enzalutamide (n= 98) and cabazitaxel (n= 145). Survival was extrapolated based on the NICE guidelines. Alternative methodologies were tested to take into consideration the differences in patients’characteristics between each treatment arm, including a cox model, adjusted HR, evidence from published NMA based on arm equivalence assumptions, adjusted HR based on propensity score matching and restricting analysis to a subgroup of patients presenting the same characteristics.  Results: Variation in the results was observed with the different methodologies tested. However, AAP and enzalutamide were always the two optimal treatments; cabazitaxel was dominated (the most expensive and less efficient treatment) on the efficiency frontier. In some cases, AAP was more expensive and efficient than enzalutamide with Incremental Cost-Effectiveness Ratios (ICERs) between 2,400 and 46,000 € /QALY. In other cases, enzalutamide was either dominated or more expensive and efficient than AAP with ICERs between 60,000 and 130,000 € /QALY.  Conclusions: Despite the associated shortfalls, using registry data to estimate comparative effectiveness in CE evaluation is feasible and yielded coherent results despite the different methodologies used.

20 (2017) A399–A811

A743

PRM72 The Personalised Reimbursement Models (Prm): Real World Data Collection to Provide Innovative Pricing Solutions in France Doly A1, Coudert B2, Cottu P3, Manson J4, Barletta H5, Perol D6, Aujoulat O7, Azria D8, Pivot X9, Montastier R10, Grandfils N11, Pinguet J12, Machuron V10, Samelson L10, Tehard B12 1CLINIDOM, Clermont-Ferrand, France, 2Centre Georges François Leclerc, Dijon, France, 3Institut Curie, Paris, France, 4Centre hospitalier René Dubos, Cergy Pontoise, France, 5Ramsay Générale de Santé, Hôpital Privé Drome Ardèche, Valence, France, 6Centre Léon Bérard, Lyon, France, 7GHR Mulhouse et Sud Alsace, Mulhouse, France, 8Institut du Cancer de Montpellier, Montpellier, France, 9CHRU Jean Minjoz, Besançon, France, 10Roche France, Boulogne Billancourt, France, 11Levallois, France, 12Roche, Boulogne-Billancourt, France

Objectives: Oncology medicines reimbursed in France have one fixed price whereas benefits vary across patient groups. Herceptin®, anti-HER2 targeted therapy, obtained reimbursement successively for metastatic breast cancer (2002), adjuvant setting (2006) and neo-adjuvant setting (2012). Consequently, its price decreased regularly on a volume based agreement ignoring the scaling of patients clinical benefit. The Personalised Reimbursement Models (PRM) project is an infrastructure validated by the French National Data Privacy Committee to collect real life data of HER2+ breast cancer (BC) patients receiving Roche targeted therapies since January 2011. The French PRM database has been active since 2015. It provides fully available datasets extracted from chemotherapy prescriptions, which gives the opportunity of modeling reimbursement agreements for Roche BC drugs next indications.  Methods: BC patients at 105 centers recorded in the Electronic Pharmacy Record system with at least one HER2 targeted Roche therapy administration since January 2011. PRM scenarios have been simulated to reflect the impact of price rebate of trastuzumab regarding its indications.  Results: From > 18,000 HER2+BC patient files extracted, 13,535 patients from 97 centers were analyzed, accounting for around 45% of all 2011-2016 French HER2+BC. 7,658 patients had at least one prescription for 2015 or 2016 and at least one treatment line initiation for 5,347 of them within this period (respectively 3,728 (64%) / 2,128 (36%) early / advanced treatments). 49,372 trastuzumab injections are recorded in the database for 2016; 31,030 (63%) were related to early treatments and 18,342 (37%) to advanced treatments. According to price decrease over time, PRM agreements could have resulted in 40% price difference for Herceptin between early and metastatic indications.  Conclusions: PRM database provides a reliable reference basis for substantial and relevant price agreements in HER2+ breast cancer drugs opening innovative pricing solutions based on in real life care practices that could be extended to new therapies in breast cancer. PRM73 Identifying Patients With Lupus Nephritis in the United Kingdom (Uk) Clinical Practice Research Datalink (Cprd) McDonald L1, Ramagopalan S1, Burns L2, Postema R1 1Bristol-Myers Squibb, Uxbridge, UK, 2Bristol-Myers Squibb, Lawrence Township, NJ, USA

Objectives: Systemic lupus erythematosus (SLE) is a rheumatic disease affecting many organs. Lupus nephritis (LN), which is kidney inflammation linked to SLE, is a potentially severe SLE complication, yet data from the UK regarding the characteristics and outcomes of LN patients is lacking. As such, investigations of LN patients in databases such as the UK CPRD can help to fill this evidence gap. It is thought that the prevalence of LN in SLE patients is between 20-60%; however, identification of LN patients in CPRD using two read codes specific for LN identified only approximately 3% of SLE patients as having LN. The aim of this work was to develop a disease phenotyping algorithm for LN to increase sensitivity of identifying LN patients in CPRD data.  Methods: Using a linked dataset of CPRD and Hospital Episode Statistics (HES), we assessed whether additional LN patients could be identified by 1) including secondary care data and 2) by utilising a wider range of potentially relevant codes for LN encompassing screening, diagnosis, and management with the 4 coding systems used in CPRD: Read (primary care diagnoses and procedures), British National Formulary (primary care prescriptions), ICD-10 (secondary care diagnoses) and OPCS-4 (secondary care procedures).  Results: More than 128 additional Read codes and 12 ICD-10 codes potentially reflecting LN were identified. The frequency of use of all potential LN codes and combinations of codes in SLE patients will be presented, with results highlighting whether or not linking HES data to CPRD increased the proportion of LN cases identified. The development of a phenotype algorithm to identify LN patients from these codes will also be presented.  Conclusions: This work will increase sensitivity of identifying patients with LN in CPRD data to aid future studies involving this patient group. PRM74 Roche Oncology Data in Open Access: An Innovative Step for Scientists Vlamynck G1, Pau D1, Petzold L1, Magrez D1, Petit-Nivard J2 Billancourt, France

1Roche, Boulogne-Billancourt, France, 2Roche, Boulogne

Objectives: The objective was to share oncology data from several studies sponsored by Roche based on several thousands of patients insuring both patient data privacy and actionable data access to the scientists’ community.  Methods: Twelve French local NIS were anonymised in accordance with the G29 article methodology. First, raw data from 12 NIS were pooled. Then, to ensure anonymization, multiple actionable aggregated data were generated. Aggregate generation did not contain less than 10 patients for a modality (if necessary was combined with the closest modality to achieve at least 10 patients per modality). Pooled variables were patient characteristics at study inclusion: demographics, disease and medical history, concomitant treatments, biological data, vital signs. No individual data was generated, therefore no minimum and maximum was displayed, only quartiles with mean, standard deviation and median. Local authorizations for data privacy were performed and acceptance notice was received in March 2016. The data covering period was from 2003 to 2013. Data was made available to the entire scientific community on the Internet through a 6 month data challenge organized by a col-