Assessing the Value of Decision Trees As A Method for Identifying Patient Subgroups

A744

VA L U E I N H E A LT H 2 0 ( 2 0 1 7 ) A 3 9 9 – A 8 1 1

laborative scientific program Epidemium.  Results: Data access was granted to an open community of scientists and analysts based on 12 French Non-Interventional studies, more than 1000 sites and 7761 patients: 255 (3%) with follicular lymphoma, 765 (10%) with colo-rectal cancer, 4040 (52%) with neoplasic disease (2969 (38%) solid tumor and 1066 (14%) malignant hemopathy), 793 (10%) with lung cancer, 1908 (25%) with breast cancer. The multi-aggregation algorithm resulted in delivering 240 actionable aggregates, corresponding to 1560 modalities. Mean age was 62.8 ± 12.2 years and 4658 (60.1%) women were included.  Conclusions: Discussions with local authorities helped building a multi-aggregation algorithm which resulted in both ensuring data privacy with a lower wealth of data exploitation than individual data and actionable open access to scientists. PRM75 Assessing the Value of Decision Trees As A Method for Identifying Patient Subgroups Risson V1, Jayanti H2, Filho E3, Rigg J2, Skelly A1, Bezlyak V1, Regnier SA1 1Novartis Pharma AG, Basel, Switzerland, 2QuintilesIMS, London, UK, 3SAS Switzerland, Zurich, Switzerland

Objectives: Standard methods of identifying patient subgroups typically require pre-specifying attributes of interest, potentially overlooking important attributes. This study assessed the value of decision trees as a method for empirically identifying patient subgroups and to evaluate the decision tree and visualization functionality in SAS Enterprise Miner.  Methods: Electronic Medical Record (EMR) data from a panel of US Medical Retina Clinics were used to create a dataset of patients whose eyes were treated using anti-vascular endothelial growth factor (anti-VEGF) products. The decision tree algorithm used variables such as baseline visual acuity (VA) and number of doses to partition the patients into subgroups which were homogenous in their subsequent VA (outcome measure). To ensure that the decision tree remained interpretable, users specified constraints e.g. maximum tree depth and minimum number of observations in terminal nodes. Users also injected clinical domain knowledge by specifying a variable and split point on which to enforce a split at any point in the tree.  Results: Homogenous subgroups generated by the tool largely conformed to the known strata of patient eye segments, including stratification at the approximate VA threshold for legal blindness and threshold for driving eligibility in the US. It also corroborated existing evidence on the positive association between loading dose and subsequent VA.  Conclusions: Decision trees and exploratory tools, such as SAS Enterprise Miner, provide an intuitive visual representation of data and are capable of identifying insights that may be overlooked through conventional, pre-specified statistical analysis. The method lends itself towards exploring multiple pathways to outcomes without the constraint of a specific hypothesis. PRM76 Adjusting for Selection Bias in Evaluating Two-Dose Human Papillomavirus Vaccine Coverage Among Adolescents in the United States Kurosky S, Trantham L RTI Health Solutions, Research Triangle Park, NC, USA

Objectives: Recent changes to the recommended human papillomavirus (HPV) vaccine schedule include reduction from a 3- to 2-dose schedule (if initiated before age 15) and inclusion of a 9-valent vaccine (HPV9). To assess potential impact on vaccine coverage, we examined 2-dose coverage and timing among adolescents and explored the use of survival analysis methods to estimate coverage and identify factors associated with receipt of the second dose.  Methods: Data from the 2015 National Immunization Survey-Teen were analyzed. Among 13-17 year-olds who initiated the series before age 15, the time to the second dose and factors associated with receipt of the second dose were estimated with and without adjusting for follow-up time.  Results: Among those who initiated before age 15, 88% initiated with the quadrivalent vaccine, 59% initiated at age 11-12 years, and 84% received a second dose. Among adolescents who received 2+ doses, median time between the first two doses was 3.0 months. Kaplan-Meier estimated median time between doses was 3.9 months. Logistic regression results indicated adolescents who initiated with HPV9 were less likely to receive a second dose (OR: 0.4, P < 0.0001); however, this was not observed in the Cox model (HR: 1.1, P =  0.5). Initiation at age 9-10 was associated with a greater likelihood of receiving a second dose (OR: 6.1, P <  0.0001). The effect was substantially smaller in the Cox model (HR: 1.2, P =  0.025).  Conclusions: Standard methods for examining dose timing do not account for follow-up time, resulting in underestimation of dose timing and overestimation of the effect of vaccine type and age on receipt of a second dose. Substantial selection bias could affect individuals who initiated with HPV9 due to the limited duration of follow-up time. This research highlights the importance of using methods that account for variable follow-up time. PRM77 Analysis of Using Irp as A Launch Price Setting Mechanism Ladron de Guevara P, Patel P Global Pricing Innovations, London, UK

Objectives: International Reference Pricing (IRP) is a common mechanism used by payers worldwide to set and manage medicine prices. The study objective is to determine whether countries apply formal IRP rules, investigate any differences across regions and verify if the medicine price has any correlation to the application of an IRP rule.  Methods: Leveraging real world data from GPI pulse®, we selected Eribulin, Dapagliflozin and Dornase alfa which represent low to high price bands across France, Germany, Netherlands, Switzerland, Jordan, Lebanon, Canada, Brazil and Japan. Launch prices were analysed against projected prices simulated by the application of formal IRP rule. Post AMNOG prices in Germany and historical exchange rate fluctuations were taken into consideration. The analysis investigated two key areas: 1. % differential between the simulated price at launch using IRP and the real price at launch 2. Correlation between the application of IRP rules against

the country, region, and medicine price.  Results: The analysis shows that Middle Eastern countries are less likely to exercise IRP rules when launching a medicine while Brazil, Canada and Japan are likely to apply IRP depending on the price of the medicine. European countries tested in the study are most likely to apply IRP rules consistently with no dependency on the medicine price. It is worthwhile noting that for value driven countries such as France and Germany, the application of IRP was directly linked to HTA assessment outcome.  Conclusions: Although countries may define IRP rules as formal, there is significant variability in the application of the rule leading to inconsistency across countries. Factors correlating to the application of a rule include to the region, price of medicine and value assessment. A careful review of country nuances is important and should be taken into consideration when considering the commercial impact during launch and revenue forecasting. PRM78 Comparison of Various Meta-Analysis Techniques Wang L1, Lewis-Beck C2, Fritschel E3, Baser E1, Shrestha S1, Baser O4 Research, Plano, TX, USA, 2Iowa State University, Ames, IA, USA, 3UCLA Health, Los Angeles, CA, USA, 4Columbia University and STATinMED Research, New York, NY, USA

1STATinMED

Objectives: Meta-analysis is a statistical approach to systematically integrate and interpret several analyses from similar studies to draw a single conclusion. The objective of this study was to review meta-analysis methods and their assumptions, apply various meta-techniques to empirical data, and compare the results from each method.  Methods: Proper meta-analysis includes 5 basic steps: identify relevant studies, extract summary data from each paper, compute study effect sizes, perform statistical analysis, and interpret/report the results. A retrospective analysis on the efficacy of the Bacille Calmette-Guerin vaccine for tuberculosis (TB) was performed using data from 13 trials and 3 different meta-analysis techniques. First, a fixedeffects model was applied, followed by a random-effects model; lastly, results of meta-regression with study-level covariates were added. In each trial, a vaccinated group was compared to a non-vaccinated control group. Odds ratio was calculated based on the number of subjects contracting TB in the treated and control populations. Overall and stratified results by geographic latitude were reported.  Results: All techniques showed statistically-significant protective effects from the vaccination. However, once covariates were added, efficacy diminished. Independent variables, such as the latitude of the location in which the study was performed, appeared to be partially driving the results.  Conclusions: Meta-analysis is useful in combining results from various studies and drawing general conclusions. However, with numerous assumptions, methods, and reported statistics available, understanding and identifying the appropriate study and model selection are important in ensuring the correct interpretation of results. PRM79 Guidance for Developing A Study Protocol of A Causal Comparative Effectiveness Analysis in “Big Data”: The Case of When To Start Statin Treatment Kuehne F1, Jahn B1, Conrads-Frank A1, Urach C2, Popper N3, Siebert U4 of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria, 2DEXHELPP, Vienna, Austria, 3dwh GmbH, simulation services / Technical University, Institute for Analysis and Scientific Computing, Vienna, Austria, 4UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria. Department of Health Policy and Management, Harvard T.H. Chan School of Public Health. Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, USA 1Department

Objectives: Within DEXHELPP, a large project on decision support tools for healthpolicy planning, causal relations are estimated from routine data as source to gain information outside the artificial setting of clinical trials. Common challenges occurring with real world evidence are confounding, missing/misclassified data, no clear treatment assignment, dynamic treatment regimens, and switching. The aim of this project is to describe a causal (counterfactual) approach for analyzing such datasets gaining insight when to start statin treatment to prevent cardiovascular disease (CVD).  Methods: We determined three comparative strategies, starting statin treatment when the ESC-SCORE exceeds 1%, 5%, and 10%. We assess potential time-independent and time-dependent confounding and selection bias using directed acyclic graphs (DAGs). We generate a study protocol following the “target trial” approach, describe data structure needed for the causal assessment, and provide solutions where necessity and availability of data deviate.  Results: Individuals between 40 and 75 years of age and no history of diagnosis of stroke or myocardial infarction (MI) within the last month enter the study at the time they first exceed the risk-threshold of 1% and are followed up for 5 years. Replicates of all patients are assigned to each treatment arm. A per protocol analysis is applied. Individuals who do not follow the assigned treatment protocol are censored at the time of protocol violation. As censoring is informative and time dependent confounding is present, inverse probability of censoring weighting is performed. The Austrian GAP-DRG database contains ICD9/ICD10 codes from 2006-2013. As the ESC-SCORE requires continuously measured values which do not exist for all variables, rules are designed to estimate the risk score.  Conclusions: DAGs and a protocol following the “target trial” approach are important tools to guide the database structure, data assessment, and the choice of the analytic strategy in deriving causal effects from big data. PRM80 Real World Evidence in Glaucomatous Diseases: Emr Data is Indispensible For Understanding Patient Journeys and Optimizing Care Spera C1, Kim Y1, Bezlyak V1, Sagkriotis A1, Durus A1, Milnes F1, Gondos A2, Boxall NS3, Wintermantel T4, Ahmed F5 1Novartis Pharma AG, Basel, Switzerland, 2QuintilesIMS, Frankfurt, Germany, 3QuintilesIMS, London, UK, 4QuintilesIMS, Basel, Switzerland, 5Imperial College Ophthalmic Research Group, Western Eye Hospital, London, UK