Sorafenib for the treatment of advanced hepatocellular carcinoma

Efficacy of Drug-Eluting Beads Transarterial Chemoembolization Plus Apatinib Compared with Conventional Transarterial Chemoembolization Plus Apatinib in the Treatment of Unresectable Hepatocellular Carcinoma

Objective

The aim of the study was to compare the efficacy and safety of drug-eluting beads TACE plus apatinib (D-TACE-A) with those of conventional TACE plus apatinib (C-TACE-A) for the treatment of unresectable HCC.

Methods

We retrospectively reviewed 187 consecutive patients who received TACE plus apatinib in our institution from January 1, 2017, to July 1, 2019. Among them, 91 patients received C-TACE-A, and 96 patients received D-TACE-A. The primary endpoint was overall survival (OS), and the secondary endpoints were progression-free survival (PFS) and disease control rate (DCR). Propensity score matching (PSM) was used to reduce selection bias.

Results

Before PSM, the median OS was 15 months (95% CI: 12.5–17.5) and 13 months (95% CI: 11.1–14.9; P=0.480) in the C-TACE-A and D-TACE-A groups, respectively. The median PFS was 7 months (95% CI: 5.9–8.1) in the C-TACE-A group and 7 months (95% CI: 5.6–8.4; p=0.677) in the D-TACE-A group. The DCR was 81.3% in the C-TACE-A group and 72.9% in the D-TACE-A group. Cox regression analysis showed that D-TACE-A did not increase mortality risk or tumor recurrence risk. After PSM, there was no statistically significant difference in median OS or PFS between the two groups. In the subgroup analysis, after adjusting for relative factors, D-TACE-A increased the mortality risk more than C-TACE-A in patients with BCLC stage C (HR: 1.678, 95% CI: 1.129–2.495; P=0.011), but D-TACE-A lowered the tumor recurrence risk compared with C-TACE-A in patients with Child–Pugh B (HR: 0.210, 95% CI: 0.082–0.538; P=0.001) and cirrhosis (HR: 0.481, 95% CI: 0.293–0.791; P=0.004). Grade III and IV adverse events in patients with D-TACE-A were similar to those in patients treated with C-TACE-A (P>0.05).

Conclusion

Patients with unresectable HCC treated with D-TACE-A might not show increased survival compared with patients treated with C-TACE-A. Advanced HCC patients without cirrhosis may receive greater survival benefits from C-TACE-A than D-TACE-A.

Cost-Effectiveness of Sorafenib Monotherapy and Selected Combination Therapy with Sorafenib in Patients with Advanced Hepatocellular Carcinoma

OBJECTIVES

To evaluate the cost-effectiveness of sorafenib treatment in combination with other therapies versus sorafenib monotherapy among patients with advanced hepatocellular carcinoma (HCC) who are enrolled in Taiwan's National Health Insurance.

METHODS

A Markov model was constructed to simulate treatment outcomes and direct medical costs of sorafenib combination therapy and monotherapy from the perspective of the healthcare payer in Taiwan. Both life-years (LYs) and quality-adjusted life-years (QALYs) were used to measure treatment outcomes, and all costs were expressed in 2014 New Taiwan dollars (NT$). Model parameters were acquired primarily using data from population-based administrative databases: the Cancer Registry, National Health Insurance Research Database, and the Death Registry. Willingness-to-pay (WTP) threshold was set at three times the per capita gross domestic product at NT$2,133,930. Deterministic and probabilistic sensitivity analyses were conducted.

RESULTS

For advanced HCC patients, sorafenib combined with other treatments might not be a cost-effective option when compared with sorafenib therapy alone. In the base-case analysis, combination treatment with sorafenib was estimated to increase costs by NT$434,788 compared with monotherapy, with a gain of 0.1595 QALYs. The resulting incremental cost-effectiveness ratio (ICER) was NT$2,725,943 per QALY gained. Results were sensitive to health utility values and monthly costs accrued in the progression-free survival state of the combination therapy group.

CONCLUSIONS

Our evidence from Taiwan demonstrated that while sorafenib in combination with other therapeutic approaches might improve treatment outcome when compared with sorafenib monotherapy, its ICER exceeded the WTP threshold and was considered not cost-effective.

Cautions regarding the fitting and interpretation of survival curves: examples from NICE single technology appraisals of drugs for cancer

The UK National Institute for Health and Clinical Excellence (NICE) has used its Single Technology Appraisal (STA) programme to assess several drugs for cancer. Typically, the evidence submitted by the manufacturer comes from one short-term randomized controlled trial (RCT) demonstrating improvement in overall survival and/or in delay of disease progression, and these are the pre-eminent drivers of cost effectiveness. We draw attention to key issues encountered in assessing the quality and rigour of the manufacturers' modelling of overall survival and disease progression. Our examples are two recent STAs: sorafenib (Nexavar®) for advanced hepatocellular carcinoma, and azacitidine (Vidaza®) for higher-risk myelodysplastic syndromes (MDS). The choice of parametric model had a large effect on the predicted treatment-dependent survival gain. Logarithmic models (log-Normal and log-logistic) delivered double the survival advantage that was derived from Weibull models. Both submissions selected the logarithmic fits for their base-case economic analyses and justified selection solely on Akaike Information Criterion (AIC) scores. AIC scores in the azacitidine submission failed to match the choice of the log-logistic over Weibull or exponential models, and the modelled survival in the intervention arm lacked face validity. AIC scores for sorafenib models favoured log-Normal fits; however, since there is no statistical method for comparing AIC scores, and differences may be trivial, it is generally advised that the plausibility of competing models should be tested against external data and explored in diagnostic plots. Function fitting to observed data should not be a mechanical process validated by a single crude indicator (AIC). Projective models should show clear plausibility for the patients concerned and should be consistent with other published information. Multiple rather than single parametric functions should be explored and tested with diagnostic plots. When trials have survival curves with long tails exhibiting few events then the robustness of extrapolations using information in such tails should be tested.