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A Discrete Event Simulation Model For Renal Cell Carcinoma For Use In A Cost-Effectiveness Analysis
A748
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
PRM96 Survival Outcomes Predicted By A Discrete Event Simulation Model For Renal Cell Carcinoma For Use In A Cost Effectiveness Analysis: A Comparison With A Traditional Partitioned Survival Model Treharne C1, Dansk V2, Santi I1, Malcolm B3, Johal S1 International, London, UK, 2PAREXEL, Stockholm, Sweden, 3Bristol-Myers Squibb, Uxbridge, UK 1Parexel
Objectives: To assess long-term survival outcomes predicted by a discrete event simulation model (DES) for nivolumab in renal cell carcinoma (RCC) and to compare the results with a more traditional partitioned survival model (PSM) approach. Methods: Two model structures were developed in Microsoft Excel and populated using data from an analysis of 24-month patient-level data from the CheckMate 025 trial. Both models comprised three key health states: progression-free (PF), progressed disease, and death. The DES models patients individually, and their journey through time is characterized as a series of events. Patient history and patient heterogeneity are incorporated by deriving a set of predictive equations to estimate the time to an event (progression, death) based on patient characteristics at baseline and progression. The PSM uses a cohort-based approach to estimate state occupancy based on an ‘area under the curve’ approach using overall survival (OS) and PF survival curves derived from the overall trial population in CheckMate 025. Results: Using Akaike information criterion (AIC) to select curves best fitting the observed trial data, over a 25-year time horizon and using a 3.5% discount rate, the DES estimated 2.82 quality-adjusted life years (QALYs) and 3.64 Life Years (LYs) per person compared to 2.57 QALYs and 3.30 LYs from the PSM. The difference in survival between these models was driven by post-progression survival time (2.83 vs 2.34 LYs, respectively). Conclusions: Both model structures provided a good fit to the trial data, but differences were seen in predictions of long-term outcomes. The PLS model predicted longer post-progression survival, which is consistent with longer term trials in RCC, suggesting that a PLS model may present a more appropriate approach to modelling immunotherapies that, due to their unique mechanism of action, require consideration of patient characteristics and changing hazards over time. PRM97 New Modelling Approaches In Ophthalmology: Partitioned Vision Distrbution Model In Symptomatic Vitreomacular Adhesion (VMA)/ Vitreomacular Traction (VMT) With Or Without Full-Thickness Macular Hole (FTMH) Bennison C1, Schmidt R2, Dugel PU3, Haller J4, Khanani AM5, Wagner A6, Lescrauwaet B7 1Pharmerit International, York, UK, 2Pharmerit International, Berlin, Germany, 3Retina Consultants of Arizona; USC Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA, 4Wills Eye Hospital, Philadelphia, PA, USA, 5Sierra Eye Associates, Reno, NV, USA, 6Wagner Macula and Retina Center, Eastern Virginia Medical School, Virginia Beach, VA, USA, 7Xintera Ltd, Cambridge, UK
Objectives: Previous economic evaluation of ocriplasmin for treatment of VMT used a de novo model consisting of a decision tree and nested Markov components, simulating the MIVI-TRUST trial and extrapolating long-term disease progression. Dependent on patient characteristics and disease history, patients could transition between six different vision health states (HS). Patient-level data requirements for estimating a 6x6 transition matrix poses a challenge with smaller sample sizes of new trial evidence. Learnings from survival partition models were adopted to investigate a more flexible approach to estimate patient distribution in vision HS (partitioned distribution model). Methods: Eight vision HS and eight disease HS were used. Patient vision, for each disease HS, at each time point, is described using mean and standard deviation (SD) following (scaled) beta distribution to capture both lower and upper bounds of the 0-100 Early Treatment Diabetic Retinopathy Study (ETDRS) vision scale. Mean and SD were estimated from OASIS data. Beta distribution was partitioned according to vision HS cut-offs determining the vision HS proportion of patients for each disease HS. Results: Preliminary results (ETDRS letters read) indicate mean (SD) baseline best-corrected visual acuity (BCVA) for HS1 (VMT with FTMH) and HS2 (VMT without FTMH) are 57.82 (9.81) and 66.00 (8.29), respectively. For 24-month disease HS distributions using linear regression models and OASIS observed data, mean BCVA (SD) were: HS3 (unresolved FTMH) 57.69 (9.60), HS4 (surgically resolved FTMH) 68.65 (11.26), HS5 (Non-surgically resolved FTMH) 70.00 (8.23), HS6 (unresolved VMT) 67.96 (10.39), HS7 (surgically resolved VMT) 68.95 (13.24), and HS8 (non-surgically resolved VMT) 75.82 (9.00). Conclusions: Compared to previous modelling techniques, this approach offers a simpler, more clinically intuitive methodology to simulate patient vision. Patient vision over time requires only three parameters (mean, SD and change in mean over time) as opposed to a large and granular transition probability matrix. PRM98 Decision Analytic Modelling Methods For The Assessing The Effectiveness Of Treatment Sequences For Clinical And Economic Decision Making: A Methodological Review Lewis RA, Hughes DA, Wilkinson C Bangor University, Bangor, UK
Objectives: Appropriate recognition of treatment sequencing is crucial to many policy decisions and related economic evaluations. The decision problem can become complex when accounting for extensive treatment sequences and pertinent factors relating to the disease. A methodological review was conducted to identify the breadth of decision analytic modelling approaches used for evaluating treatment sequences. Methods: A comprehensive literature review of MEDLINE, Embase and Cochrane library. Studies were categorised according to modelling approach and decision problem. Treatment sequencing assumptions were analysed. Results: 70 studies and 48 discrete models were included. A wide range of modelling techniques were identified: cohort-based models (deterministic and stochastic decision trees, Markov, semi-Markov, partitioned survival); individual sampling models (state transition, and discrete event simulation (DES)); and open population-based models (DES and Markov cohort). No study systematically tested
different modelling approaches for treatment sequences. Cohort models can be simple and easy to implement. Examples of cohort models adapted successfully to accommodate additional complexity were identified, but these were no longer simple. Individual sampling models are more sophisticated, better able to accommodate greater decision problem complexity, and provide more flexibility. DES appeared the optimum approach in terms of intuitively modelling sequencing algorithms, computational efficacy, and ease of updating, but requires more extensive modelling skills, specialist software, and is data and time intensive. In the absence of sequencing trials, modellers often applied simplifying assumptions to treatment effects obtained from trials of single treatments. These assumptions were frequently not validated, nor their impact assessed: an important limitation of these models. Conclusions: Modelling treatment sequences may require a complex model, which can be time consuming to develop and implement. Using simplistic assumptions, regarding sequencing effects, results in significant uncertainty around the effectiveness and cost-effectiveness estimates, the extent of which is generally unknown. This needs to be recognised in decision making, and further evaluated. PRM99 Implementation Of A Data Mining Model Within A Monitoring Web-Based Tool To Assess HER2 Breast Cancer Status Using The Her-France Real World Database Vauléon T1, Pau D2, Dupin J3, Magrez D2, Martin J2, Bellocq J4 1Lincoln for Roche, Boulogne-Billancourt, France, 2Roche, Boulogne-Billancourt, France, 3ITM stat for Roche, Neuilly-sur-Seine, France, 4AFAQAP and Hospital of Strasbourg, Strasbourg, France
Objectives: HER-France is a French national database focused on HER2 status in breast cancer and provided by 125 pathology laboratories (PL) since 2011. PL used to compare their HER2 positivity (HER2+) rate with the calculated national average. To provide a more sensitive monitoring tool evaluating their practices quality through indicators, a new strategy considering a predicted estimate of HER2+ rate by PL instead of national average was investigated. Methods: Model to predict probability of HER2+ on core-needle biopsies (CNBs) was developed using penalized logistic regression on tumor characteristics. PL HER2+ rate estimations were obtained by averaging individual HER2+ probabilities. A PL having included more than 100 CNBs between January 2014 (ASCO-CAP recommendations) and April 2016 is considered as outlier when its averaging HER2+ predicted rate is outside a 99% confidence interval (CI) limits of its observed rate. Other indicators are absolute and relative percentage differences between observed and predicted HER2+ rates. Results: Prediction accuracy (AUC) on 30,777 CNBs was higher than 0.77. Among the 56 PL with at least 100 CNBs (i.e. 95% of all the analysed CNBs), 6 (10.7%) were identified as outliers. Illustration with 4 PL: 2 not outliers: PL1 (n= 676): observed rate: 13.6%; 99%CI: [10.2; 17.0]; predicted rate: 10.9%; absolute (relative) difference: 2.7 (20%) PL2 (n= 2,062): observed rate: 9.1%; 99%CI: [7.4; 10.7]; predicted rate: 10.1%; absolute (relative) difference: 1.0 (11.8%) 2 outliers: PL3 (n= 1,468): observed rate: 13.5%; 99%CI: [11.2; 15.8]; predicted rate: 10.9%; absolute (relative) difference: 2.6 (19%) PL4 (n= 1,854): observed rate: 9.6%; 99%CI: [7.8; 11.4]; predicted rate: 12.0%; absolute (relative) difference: 2.4 (25%) Conclusions: PL tumor characteristics provide better accuracy in quality assessment practices than comparison to national average. Data mining models implemented in the HER-France monitoring web-based tool will help PL to assess their own rate through consistency indicators. PRM100 A Discrete Event Simulation Model For Renal Cell Carcinoma For Use In A Cost-Effectiveness Analysis Dansk V1, Treharne C2, Santi I2, Malcolm B3, Johal S2 International, London, UK, 3Bristol-Myers Squibb, Uxbridge, UK 1PAREXEL, Stockholm, Sweden, 2Parexel
Objectives: To develop a flexible and comprehensive discrete event simulation model (DES) for nivolumab versus everolimus in the treatment of renal cell carcinoma (RCC) that captures differences in treatment outcomes and costs between patients with heterogeneous baseline characteristics. Methods: A DES was developed in Microsoft Excel based on individuals experiencing three key events: disease progression, treatment discontinuation, and death. Risk prediction equations were derived from 24-month patient-level data from the CheckMate 025 trial, using a step-wise backward elimination process to identify relevant predictors of the risk of progression or death. Hypothetical patients were generated from a multivariate normal distribution based on the characteristics of patients enrolled in CheckMate 025. Times to event for individuals were estimated from the predictive equations for overall survival, progression-free survival and post-progression survival. The survival curves chosen were based on comparison of the Akaike information criterion. The model accounted for differences in survival between patients who continued on their allocated treatment beyond progression and those who did not. Results: Four predictive equations were derived from the trial data. The following baseline characteristics were identified as significant predictors of survival; age, Karnofsky performance status, haemoglobin, time from diagnosis to randomisation and tumour size. Mean overall survival of 53.0 months and 34.8 months were estimated for nivolumab and everolimus, respectively, with mean post-progression survival of 43.0 and 26.3 months, respectively. Conclusions: The DES provides a flexible approach to capture patient heterogeneity and history when predicting long-term disease progression and survival outcomes for use in a cost-effectiveness analysis, and may be applied to other oncology settings. This becomes important in the context of immunotherapies with a novel mechanism of action and where extrapolation beyond trial duration requires consideration of such issues. Standard modelling approaches, such as the partitioned survival model, may not completely address these challenges. PRM101 Exploration Of Run-Time Requirements In Probabilistic Sensitivity Analysis Utilizing A Patient Level Based Type 2 Diabetes Simulation Model Foos V1, Lamotte M1, Sathananthan A2, Altrabsheh E2, McEwan P3
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
PRM96 Survival Outcomes Predicted By A Discrete Event Simulation Model For Renal Cell Carcinoma For Use In A Cost Effectiveness Analysis: A Comparison With A Traditional Partitioned Survival Model Treharne C1, Dansk V2, Santi I1, Malcolm B3, Johal S1 International, London, UK, 2PAREXEL, Stockholm, Sweden, 3Bristol-Myers Squibb, Uxbridge, UK 1Parexel
Objectives: To assess long-term survival outcomes predicted by a discrete event simulation model (DES) for nivolumab in renal cell carcinoma (RCC) and to compare the results with a more traditional partitioned survival model (PSM) approach. Methods: Two model structures were developed in Microsoft Excel and populated using data from an analysis of 24-month patient-level data from the CheckMate 025 trial. Both models comprised three key health states: progression-free (PF), progressed disease, and death. The DES models patients individually, and their journey through time is characterized as a series of events. Patient history and patient heterogeneity are incorporated by deriving a set of predictive equations to estimate the time to an event (progression, death) based on patient characteristics at baseline and progression. The PSM uses a cohort-based approach to estimate state occupancy based on an ‘area under the curve’ approach using overall survival (OS) and PF survival curves derived from the overall trial population in CheckMate 025. Results: Using Akaike information criterion (AIC) to select curves best fitting the observed trial data, over a 25-year time horizon and using a 3.5% discount rate, the DES estimated 2.82 quality-adjusted life years (QALYs) and 3.64 Life Years (LYs) per person compared to 2.57 QALYs and 3.30 LYs from the PSM. The difference in survival between these models was driven by post-progression survival time (2.83 vs 2.34 LYs, respectively). Conclusions: Both model structures provided a good fit to the trial data, but differences were seen in predictions of long-term outcomes. The PLS model predicted longer post-progression survival, which is consistent with longer term trials in RCC, suggesting that a PLS model may present a more appropriate approach to modelling immunotherapies that, due to their unique mechanism of action, require consideration of patient characteristics and changing hazards over time. PRM97 New Modelling Approaches In Ophthalmology: Partitioned Vision Distrbution Model In Symptomatic Vitreomacular Adhesion (VMA)/ Vitreomacular Traction (VMT) With Or Without Full-Thickness Macular Hole (FTMH) Bennison C1, Schmidt R2, Dugel PU3, Haller J4, Khanani AM5, Wagner A6, Lescrauwaet B7 1Pharmerit International, York, UK, 2Pharmerit International, Berlin, Germany, 3Retina Consultants of Arizona; USC Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA, 4Wills Eye Hospital, Philadelphia, PA, USA, 5Sierra Eye Associates, Reno, NV, USA, 6Wagner Macula and Retina Center, Eastern Virginia Medical School, Virginia Beach, VA, USA, 7Xintera Ltd, Cambridge, UK
Objectives: Previous economic evaluation of ocriplasmin for treatment of VMT used a de novo model consisting of a decision tree and nested Markov components, simulating the MIVI-TRUST trial and extrapolating long-term disease progression. Dependent on patient characteristics and disease history, patients could transition between six different vision health states (HS). Patient-level data requirements for estimating a 6x6 transition matrix poses a challenge with smaller sample sizes of new trial evidence. Learnings from survival partition models were adopted to investigate a more flexible approach to estimate patient distribution in vision HS (partitioned distribution model). Methods: Eight vision HS and eight disease HS were used. Patient vision, for each disease HS, at each time point, is described using mean and standard deviation (SD) following (scaled) beta distribution to capture both lower and upper bounds of the 0-100 Early Treatment Diabetic Retinopathy Study (ETDRS) vision scale. Mean and SD were estimated from OASIS data. Beta distribution was partitioned according to vision HS cut-offs determining the vision HS proportion of patients for each disease HS. Results: Preliminary results (ETDRS letters read) indicate mean (SD) baseline best-corrected visual acuity (BCVA) for HS1 (VMT with FTMH) and HS2 (VMT without FTMH) are 57.82 (9.81) and 66.00 (8.29), respectively. For 24-month disease HS distributions using linear regression models and OASIS observed data, mean BCVA (SD) were: HS3 (unresolved FTMH) 57.69 (9.60), HS4 (surgically resolved FTMH) 68.65 (11.26), HS5 (Non-surgically resolved FTMH) 70.00 (8.23), HS6 (unresolved VMT) 67.96 (10.39), HS7 (surgically resolved VMT) 68.95 (13.24), and HS8 (non-surgically resolved VMT) 75.82 (9.00). Conclusions: Compared to previous modelling techniques, this approach offers a simpler, more clinically intuitive methodology to simulate patient vision. Patient vision over time requires only three parameters (mean, SD and change in mean over time) as opposed to a large and granular transition probability matrix. PRM98 Decision Analytic Modelling Methods For The Assessing The Effectiveness Of Treatment Sequences For Clinical And Economic Decision Making: A Methodological Review Lewis RA, Hughes DA, Wilkinson C Bangor University, Bangor, UK
Objectives: Appropriate recognition of treatment sequencing is crucial to many policy decisions and related economic evaluations. The decision problem can become complex when accounting for extensive treatment sequences and pertinent factors relating to the disease. A methodological review was conducted to identify the breadth of decision analytic modelling approaches used for evaluating treatment sequences. Methods: A comprehensive literature review of MEDLINE, Embase and Cochrane library. Studies were categorised according to modelling approach and decision problem. Treatment sequencing assumptions were analysed. Results: 70 studies and 48 discrete models were included. A wide range of modelling techniques were identified: cohort-based models (deterministic and stochastic decision trees, Markov, semi-Markov, partitioned survival); individual sampling models (state transition, and discrete event simulation (DES)); and open population-based models (DES and Markov cohort). No study systematically tested
different modelling approaches for treatment sequences. Cohort models can be simple and easy to implement. Examples of cohort models adapted successfully to accommodate additional complexity were identified, but these were no longer simple. Individual sampling models are more sophisticated, better able to accommodate greater decision problem complexity, and provide more flexibility. DES appeared the optimum approach in terms of intuitively modelling sequencing algorithms, computational efficacy, and ease of updating, but requires more extensive modelling skills, specialist software, and is data and time intensive. In the absence of sequencing trials, modellers often applied simplifying assumptions to treatment effects obtained from trials of single treatments. These assumptions were frequently not validated, nor their impact assessed: an important limitation of these models. Conclusions: Modelling treatment sequences may require a complex model, which can be time consuming to develop and implement. Using simplistic assumptions, regarding sequencing effects, results in significant uncertainty around the effectiveness and cost-effectiveness estimates, the extent of which is generally unknown. This needs to be recognised in decision making, and further evaluated. PRM99 Implementation Of A Data Mining Model Within A Monitoring Web-Based Tool To Assess HER2 Breast Cancer Status Using The Her-France Real World Database Vauléon T1, Pau D2, Dupin J3, Magrez D2, Martin J2, Bellocq J4 1Lincoln for Roche, Boulogne-Billancourt, France, 2Roche, Boulogne-Billancourt, France, 3ITM stat for Roche, Neuilly-sur-Seine, France, 4AFAQAP and Hospital of Strasbourg, Strasbourg, France
Objectives: HER-France is a French national database focused on HER2 status in breast cancer and provided by 125 pathology laboratories (PL) since 2011. PL used to compare their HER2 positivity (HER2+) rate with the calculated national average. To provide a more sensitive monitoring tool evaluating their practices quality through indicators, a new strategy considering a predicted estimate of HER2+ rate by PL instead of national average was investigated. Methods: Model to predict probability of HER2+ on core-needle biopsies (CNBs) was developed using penalized logistic regression on tumor characteristics. PL HER2+ rate estimations were obtained by averaging individual HER2+ probabilities. A PL having included more than 100 CNBs between January 2014 (ASCO-CAP recommendations) and April 2016 is considered as outlier when its averaging HER2+ predicted rate is outside a 99% confidence interval (CI) limits of its observed rate. Other indicators are absolute and relative percentage differences between observed and predicted HER2+ rates. Results: Prediction accuracy (AUC) on 30,777 CNBs was higher than 0.77. Among the 56 PL with at least 100 CNBs (i.e. 95% of all the analysed CNBs), 6 (10.7%) were identified as outliers. Illustration with 4 PL: 2 not outliers: PL1 (n= 676): observed rate: 13.6%; 99%CI: [10.2; 17.0]; predicted rate: 10.9%; absolute (relative) difference: 2.7 (20%) PL2 (n= 2,062): observed rate: 9.1%; 99%CI: [7.4; 10.7]; predicted rate: 10.1%; absolute (relative) difference: 1.0 (11.8%) 2 outliers: PL3 (n= 1,468): observed rate: 13.5%; 99%CI: [11.2; 15.8]; predicted rate: 10.9%; absolute (relative) difference: 2.6 (19%) PL4 (n= 1,854): observed rate: 9.6%; 99%CI: [7.8; 11.4]; predicted rate: 12.0%; absolute (relative) difference: 2.4 (25%) Conclusions: PL tumor characteristics provide better accuracy in quality assessment practices than comparison to national average. Data mining models implemented in the HER-France monitoring web-based tool will help PL to assess their own rate through consistency indicators. PRM100 A Discrete Event Simulation Model For Renal Cell Carcinoma For Use In A Cost-Effectiveness Analysis Dansk V1, Treharne C2, Santi I2, Malcolm B3, Johal S2 International, London, UK, 3Bristol-Myers Squibb, Uxbridge, UK 1PAREXEL, Stockholm, Sweden, 2Parexel
Objectives: To develop a flexible and comprehensive discrete event simulation model (DES) for nivolumab versus everolimus in the treatment of renal cell carcinoma (RCC) that captures differences in treatment outcomes and costs between patients with heterogeneous baseline characteristics. Methods: A DES was developed in Microsoft Excel based on individuals experiencing three key events: disease progression, treatment discontinuation, and death. Risk prediction equations were derived from 24-month patient-level data from the CheckMate 025 trial, using a step-wise backward elimination process to identify relevant predictors of the risk of progression or death. Hypothetical patients were generated from a multivariate normal distribution based on the characteristics of patients enrolled in CheckMate 025. Times to event for individuals were estimated from the predictive equations for overall survival, progression-free survival and post-progression survival. The survival curves chosen were based on comparison of the Akaike information criterion. The model accounted for differences in survival between patients who continued on their allocated treatment beyond progression and those who did not. Results: Four predictive equations were derived from the trial data. The following baseline characteristics were identified as significant predictors of survival; age, Karnofsky performance status, haemoglobin, time from diagnosis to randomisation and tumour size. Mean overall survival of 53.0 months and 34.8 months were estimated for nivolumab and everolimus, respectively, with mean post-progression survival of 43.0 and 26.3 months, respectively. Conclusions: The DES provides a flexible approach to capture patient heterogeneity and history when predicting long-term disease progression and survival outcomes for use in a cost-effectiveness analysis, and may be applied to other oncology settings. This becomes important in the context of immunotherapies with a novel mechanism of action and where extrapolation beyond trial duration requires consideration of such issues. Standard modelling approaches, such as the partitioned survival model, may not completely address these challenges. PRM101 Exploration Of Run-Time Requirements In Probabilistic Sensitivity Analysis Utilizing A Patient Level Based Type 2 Diabetes Simulation Model Foos V1, Lamotte M1, Sathananthan A2, Altrabsheh E2, McEwan P3