A337treatment line. Association between the annual treatment cost per patient and the attributes was studied using Pearson correlation coefficient (for quantitative variables), ANOVA (for qualitative variables), and generalized linear model using gamma distribution. Results: Annual treatment costs varied from 1,500€ to almost 1,000,000€ . Bivariate analysis showed a significant association between the annual treatment cost and disease prevalence, age group, treatment line, alternative treatments, therapeutic area, and ATC class (p< 0.05 for all). Significantly higher cost was observed in pediatric population, first treatment line, metabolic diseases, or in case of absence of alternative treatment. Multivariate analysis including these variables did not showed significant results. Given the complex correlation structure between the co-variates, the model including only the prevalence and alternative treatments was tested. Both co-variates were significant. ConClusions: Disease prevalence and unmet needs seems to be the main drivers of ODs prices while the level of clinical evidence and disease severity had no impact. However, this is difficult to justify statistically because of high variance and small number of observations. Interestingly, ASMR score presented by the HAS as main price driver was not shown having an impact on drug prices.
7.15 and 7.36 years, in the 18-and 30-month DBLs, respectively. Extrapolated 18-month DBL landmark survival at 5, 10, and 20 years was 39.9%, 23.0%, and 11.1%, which remained stable at the 30-month DBL as 40.7%, 23.9%, and 11.8%, all respectively. Conclusions: Nivolumab+ipilimumab is the first immunooncology combination that has shown significant and stable long-term OS benefit in 1L RCC compared with standard of care (sunitinib). The new DBLs confirm the robustness of the 18-month DBL results, including the choice of a log-normal distribution for extrapolation.
A head-to-head comparison, or "pseudo-trial", was constructed between fingolimod (pre-launch) and other disease-modifying therapies (DMTs) given CT data and pre-launch observational data. We assessed the projected relapse probability (RP) of fingolimod, as if it were available to treat the entire pre-2010 RW MS population. This was achieved in a two-step modeling process: (1) we selected a population of MS patients as a reference population and characterized this population in the pre-2010 claims data by estimating the joint distribution of the covariates, and (2) we then modeled projections for the RP using CT data standardized by RW population characteristics. Using a machine learning platform, Reverse Engineering and Forward Simulation (REFSTM), we built an ensemble of predictive models to create weights that were used to standardize the CT population to the pre-2010 RW MS population. The method was further validated using post-2010 fingolimod claims data. Results: The RW MS population was older (7,471 patients; mean age= 42 years, SD= 8.6) than the CT population (243 fingolimod patients; mean age= 37 years, SD= 7.9). The projected RP in the RW setting was 0.12-0.24 and was similar to that in the CT setting (0.11-0.22). ConClusions: We developed a causal methodology that estimated the RP among MS patients treated with fingolimod from CT data by standardizing to a reference RW pre-launch population. Our E2E methodology is generalizable and thus proposes a framework for translation of intervention efficacy data into estimates of intervention effectiveness. PND9Glatiramer acetate (Ga) results iN siGNificaNt reDuctioNs iN aNNualizeD relaPse rate (arr) aND Protective effect oN Disibiltiy
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