Aim: The aim of the study is to assess the accuracy of overall survival (OS) extrapolations in cost-effectiveness analysis made by the Dental and Pharmaceutical Benefits Agency (TLV) to decide on the reimbursement and use of oncology drugs in Sweden.

Material and methods: TLV appraisals for oncology drugs were identified during a 5-year period (2013-2017). To be included each appraisal and health economic model must include a TLV base case extrapolation of OS. Further, Kaplan-Meier (KM) estimates on OS from the original and follow-up clinical trials must be available. Potential follow-up trials on OS were identified in ClinicalTrials.gov and the Lund University Libraries databases, and in the Swedish Medical Products Agency (MPA) and the European Medicines Agency (EMA) databases. In cases where the KM estimates were not available, data points from figures published in TLV's appraisals were extracted using the semi-automated tools Digitizelt and WebPlotDigitizer. The accuracy of survival extrapolations was assessed by comparing extrapolated and observed life-years (LYs), using three different measures: 1) difference in LYs between the treatment and control group; 2) LYs in the treatment group, 3) LYs in the control group.

Results: We study TLV's preferred OS extrapolations and show that on average they overestimate the observed mean gain in LYs by 17%, and underestimate observed LYs by 5% and 1% in the treatment and control group, respectively.

Conclusions: We conclude that it is feasible to validate OS extrapolations by comparing extrapolated and observed life-years. Even if survival extrapolations are reasonably accurate for the treatment and control group, respectively, this may still imply that extrapolations of LYs gained deviates to a larger extent. Follow-up studies on OS should be carried out to an increased extent to be able to validate, update and improve OS extrapolations in cost-effectiveness analysis of oncology drugs.

We analyze a large merger in the Swedish market for analgesics (painkillers). The merging firms raised prices by 40 percent, and some outsiders raised prices by more than 10 percent. We confront these changes with predictions from a merger simulation model. With basic supply side assumptions, the models correctly or moderately underpredict the merging firms' price increase. However, they predict a larger price increase for the smaller firm, which was not the case in practice, and they underpredict the outsiders' responses. We consider several supply side explanations: a plausible cost increase after the merger and the possibility of partial collusion.