Once-daily oral AZD2171 at doses of 45 mg or less was generally well tolerated and was associated with encouraging antitumor activity in patients with a broad range of advanced solid tumors.

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Relapsed/refractory multiple myeloma (RRMM) patients have poor overall survival (OS). Pomalidomide plus low-dose dexamethasone (POM + LoDEX) significantly extends OS in RRMM vs. high-dose dexamethasone. Survival of patients with stable disease (SD) was compared to patients with progressive disease (PD) or ≥ partial response (≥PR) at cycles (C) 3, 5, and 7. Among 302 patients randomized to POM + LoDEX, at C3 19.2% achieved ≥ PR, 38.4% SD, and 14.6% PD. Patients with SD at C3 (17.4%) and C5 (13.6%) showed improved responses at C7. Median OS from randomization by response at C3 was 22.4 months for ≥ PR (n = 58, HR 0.66; 95% CI 0.40-1.08, p = 0.0976 vs. SD), 16.2 months for SD (n = 116), and 6.3 months for PD (n = 44, HR 3.43; 95% CI 2.23-5.27, p < 0.0001 vs. SD). Similar patterns were observed for C5 and C7. Results show that POM + LoDEX should be a standard treatment after lenalidomide and bortezomib, including in SD patients.

Introduction: Health economics models are typically built in Microsoft Excel ® owing to its wide familiarity, accessibility and perceived transparency. However, given the increasingly rapid and analytically complex decision-making needs of both the pharmaceutical industry and the field of health economics and outcomes research (HEOR), the demands of cost-effectiveness analyses may be better met by the programming language R.Objective: This case study provides an explicit comparison between Excel and R for contemporary cost-effectiveness analysis. Methods:We constructed duplicate cost-effectiveness models using Excel and R (with a user interface built using the Shiny package) to address a hypothetical case study typical of contemporary health technology assessment. Results:We compared R and Excel versions of the same model design to determine the advantages and limitations of the modelling platforms in terms of (i) analytical capability, (ii) data safety, (iii) building considerations, (iv) usability for technical and non-technical users and (v) model adaptability. Conclusions:The findings of this explicit comparison are used to produce recommendations for when R might be more suitable than Excel in contemporary cost-effectiveness analyses. We conclude that selection of appropriate modelling software needs to consider case-by-case modelling requirements, particularly (i) intended audience, (ii) complexity of analysis, (iii) nature and frequency of updates and (iv) anticipated model run-time.