Timely, consistent, transparent assessment of market access evidence: implementing tools based on the HTA Core Model® in a pharmaceutical company.

OBJECTIVES: Evidence requirements and assessment methods access differ between health technology assessment (HTA) agencies. The HTA Core Model® provides a standardized approach to HTA, targeting evidence sharing and collaboration between participating HTA bodies. It is fit for purpose from an industry perspective and was used by pharmaceutical company Roche to develop a framework for internal assessment of evidence required for market access and coverage/reimbursement ("access evidence"). METHODS: Tools were developed to systematically scope, assess, plan, and summarize access evidence generation. The tools were based mainly on the first four HTA Core Model® domains and rolled-out in selected development teams in 2017. Five months after full implementation, the impact of tools was assessed in an internal survey. RESULTS: Systematic access evidence generation started with the Access Evidence Questionnaire, to scope evidence requirements and identify evidence gaps. Findings were summarized in the Access Evidence Metric, which assessed the alignment of available/planned evidence against HTA bodies' requirements and developed scope mitigation strategies. The Access Evidence Plan was then used to plan and document (additional) evidence generation. Once generated, evidence was summarized in the Access Evidence Dossier. A survey of twenty-seven Roche employees involved in evidence generation showed that the tools made discussions around access strategies and evidence more efficient and transparent. CONCLUSIONS: The HTA Core Model® provided a useful framework around which to optimize internal evidence generation and assessment. The benefits of using a standardized HTA approach in industry mirror those expected from implementing the HTA Core Model® in HTA agencies.

Innovative methods for the identification of predictive biomarker signatures in oncology: Application to bevacizumab.

Current methods for subgroup analyses of data collected from randomized clinical trials (RCTs) may lead to false-positives from multiple testing, lack power to detect moderate but clinically meaningful differences, or be too simplistic in characterizing patients who may benefit from treatment. Herein, we present a general procedure based on a set of newly developed statistical methods for the identification and evaluation of complex multivariate predictors of treatment effect. Furthermore, we implemented this procedure to identify a subgroup of patients who may receive the largest benefit from bevacizumab treatment using a panel of 10 biomarkers measured at baseline in patients enrolled on two RCTs investigating bevacizumab in metastatic breast cancer. Data were collected from patients with human epidermal growth factor receptor 2 (HER2)-negative (AVADO) and HER2-positive (AVEREL) metastatic breast cancer. We first developed a classification rule based on an estimated individual scoring system, using data from the AVADO study only. The classification rule takes into consideration a panel of biomarkers, including vascular endothelial growth factor (VEGF)-A. We then classified the patients in the independent AVEREL study into patient groups according to "promising" or "not-promising" treatment benefit based on this rule and conducted a statistical analysis within these subgroups to compute point estimates, confidence intervals, and p-values for treatment effect and its interaction. In the group with promising treatment benefit in the AVEREL study, the estimated hazard ratio of bevacizumab versus placebo for progression-free survival was 0.687 (95% confidence interval [CI]: 0.462-1.024, p = 0.065), while in the not-promising group the hazard ratio (HR) was 1.152 (95% CI: 0.526-2.524, p = 0.723). Using the median level of VEGF-A from the AVEREL study to divide the study population, then the HR becomes 0.711 (95% CI: 0.435-1.163, p = 0.174) in the promising group and 0.828 (95% CI: 0.496-1.380, p = 0.468) in the not-promising group. Similar results were obtained with the median VEGF-A levels from the AVADO study ("promising" group: HR = 0.709, 95%CI: 0.444-1.133, p = 0.151; "not-promising" group: HR = 0.851, 95% CI: 0.497-1.458, p = 0.556). Our analysis shows it is feasible to employ statistical methods for empirically constructing and validating a scoring system based on a panel of biomarkers. This scoring system can be used to estimate the treatment effect for individual patients and identify a subgroup of patients who may benefit from treatment. The proposed procedure can provide a general framework to organize many statistical methods (existing or to be developed) into a coherent set of analyses for the development of personalized medicines and has the potential of broad applications.

Bevacizumab improves the overall and progression-free survival of patients with metastatic colorectal cancer treated with 5-fluorouracil-based regimens irrespective of baseline risk.

BACKGROUND: Kohne et al. [Ann Oncol 2002;13:308-317] showed that four prognostic variables can be used to classify patients with metastatic colorectal cancer (CRC) treated with 5-fluorouracil (5-FU)/leucovorin (LV) into three risk groups with different overall survival (OS). This model was applied to data from phase II/III trials of first-line bevacizumab plus 5-FU/LV with/without irinotecan (IFL). METHODS: Data on tumor sites, Eastern Cooperative Oncology Group performance status, alkaline phosphatase levels and white blood cell counts were used to classify patients into Kohne prognostic high-, intermediate- and low-risk groups. Median OS and progression-free survival (PFS) were calculated for patients receiving 5-FU/LV plus bevacizumab or placebo (n = 489) and IFL plus bevacizumab or placebo (n = 812). RESULTS: Median OS was longer in 5-FU/LV/bevacizumab (11.2-22.6 months) than in the 5-FU/LV/placebo (5.7-17.5 months), and in the IFL/bevacizumab arm (14.3-22.5 months) than in the IFL/placebo arm (8.4-17.9 months) across the Kohne high-, intermediate- and low-risk groups. The addition of bevacizumab also extended median PFS across the Kohne risk groups compared with placebo. CONCLUSIONS: Bevacizumab improves OS and PFS across the Kohne risk classification in patients with metastatic CRC. The Kohne model can be extended to patients treated with 5-FU/LV/bevacizumab, IFL and IFL/bevacizumab and to PFS data.