Béranger Lueza

Difference in Restricted Mean Survival Time for Cost-Effectiveness Analysis Using Individual Patient Data Meta-Analysis: Evidence from a Case Study

Objective In economic evaluation, a commonly used outcome measure for the treatment effect is the between-arm difference in restricted mean survival time (rmstD). This study illustrates how different survival analysis methods can be used to estimate the rmstD for economic evaluation using individual patient data (IPD) meta-analysis. Our aim was to study if/how the choice of a method impacts on cost-effectiveness results. Methods We used IPD from the Meta-Analysis of Radiotherapy in Lung Cancer concerning 2,000 patients with locally advanced non-small cell lung cancer, included in ten trials. We considered methods either used in the field of meta-analysis or in economic evaluation but never applied to assess the rmstD for economic evaluation using IPD meta-analysis. Methods were classified into two approaches. With the first approach, the rmstD is estimated directly as the area between the two pooled survival curves. With the second approach, the rmstD is based on the aggregation of the rmstDs estimated in each trial. Results The average incremental cost-effectiveness ratio (ICER) and acceptability curves were sensitive to the method used to estimate the rmstD. The estimated rmstDs ranged from 1.7 month to 2.5 months, and mean ICERs ranged from € 24,299 to € 34,934 per life-year gained depending on the chosen method. At a ceiling ratio of € 25,000 per life year-gained, the probability of the experimental treatment being cost-effective ranged from 31% to 68%. Conclusions This case study suggests that the method chosen to estimate the rmstD from IPD meta-analysis is likely to influence the results of cost-effectiveness analyses.

Extrapolation of Survival Curves from Cancer Trials Using External Information

Background: Estimates of life expectancy are a key input to cost-effectiveness analysis (CEA) models for cancer treatments. Due to the limited follow-up in Randomized Controlled Trials (RCTs), parametric models are frequently used to extrapolate survival outcomes beyond the RCT period. However, different parametric models that fit the RCT data equally well may generate highly divergent predictions of treatment-related gain in life expectancy. Here, we investigate the use of information external to the RCT data to inform model choice and estimation of life expectancy. Methods: We used Bayesian multi-parameter evidence synthesis to combine the RCT data with external information on general population survival, conditional survival from cancer registry databases, and expert opinion. We illustrate with a 5-year follow-up RCT of cetuximab plus radiotherapy v. radiotherapy alone for head and neck cancer. Results: Standard survival time distributions were insufficiently flexible to simultaneously fit both the RCT data and external data on general population survival. Using spline models, we were able to estimate a model that was consistent with the trial data and all external data. A model integrating all sources achieved an adequate fit and predicted a 4.7-month (95% CrL: 0.4; 9.1) gain in life expectancy due to cetuximab. Conclusions: Long-term extrapolation using parametric models based on RCT data alone is highly unreliable and these models are unlikely to be consistent with external data. External data can be integrated with RCT data using spline models to enable long-term extrapolation. Conditional survival data could be used for many cancers and general population survival may have a role in other conditions. The use of external data should be guided by knowledge of natural history and treatment mechanisms.

LACE-Bio: Validation of Predictive and/or Prognostic Immunohistochemistry/Histochemistry Based Biomarkers in Resected Non-Small Cell Lung Cancer

Background: Complete resection of non–small‐cell lung cancer (NSCLC) offers the potential for cure after surgery and adjuvant chemotherapy. Patients may not benefit and may experience severe toxicity. There are no validated molecular tools to allow better patient selection. Materials and Methods: The LACE‐Bio (LACE [Lung Adjuvant Cisplatin Evaluation]) project includes 4 trials (International Adjuvant Lung Cancer Trial [IALT], Adjuvant Navelbine International Trialist Association [ANITA], JBR10, and Cancer and Leukemia Group B (CALGB)‐9633). Immunohistochemistry biomarkers shown in one trial to have a prognostic/predictive effect on overall survival were tested. Results: The majority of the promising biomarkers could not be validated; the prognostic effect of tumor infiltrating lymphocytes and &bgr;‐tubulin was confirmed. Potential causes include tissue fixation, storage, the use of tissue microarrays, and varying reagent/antibody batches. Conclusions: Immunohistochemistry assays from single trials may be misleading and require validation before being used for patient selection. LACE‐Bio‐2 is evaluating potential genomic biomarkers that may allow more precise selection of patients with NSCLC for adjuvant chemotherapy in NSCLC. Micro‐Abstract: There are no validated molecular tools to allow patient selection for adjuvant chemotherapy after complete resection of non–small‐cell lung cancer. Immunohistochemistry biomarkers shown in one trial to have a prognostic/predictive effect on overall survival were tested. The majority of the promising biomarkers could not be validated, and none were predictive of benefit. Immunohistochemistry assays from single trials may be misleading.

Prognostic value of HLA-A2 status in advanced non-small cell lung cancer patients

INTRODUCTION The class I human leucocyte antigen (HLA) molecules play a critical role as an escape mechanism of antitumoral immunity. HLA-A2 status has been evaluated as a prognostic factor in lung cancer, mostly in localized disease and with inconsistent findings. We evaluated the role of HLA-A2 status as a prognostic factor in a large and homogeneus cohort of advanced NSCLC patients. METHODS Advanced NSCLC patients eligible for platinum-based chemotherapy were consecutively included in a single center between October 2009 and July 2015 in the prospective MSN study (NCT02105168). HLA-A2 status was analysed by flow cytometry. Clinical, pathological and molecular data were collected. A Cox model was used for prognostic analyses. RESULTS Of 545 stage IIIB/IV NSCLC patients included, 344 (63%) were male, 466 (85%) were smokers, 447 (83%) had PS 0-1, 508 (93%) had stage IV, 407 (75%) had an adenocarcinoma and median age was 61 years (range, 21-84). Incidence of patients with EGFRmut, ALK-positive and KRASmut was 14% (49/361), 9% (29/333) and 31% (107/350), respectively. The overall rate of HLA-A2 positivity was 48%. No association was observed between HLA-A2 status and any patient or tumor characteristics analyzed. With a median follow-up of 27.1 months, median OS was 12.8 months [95%CI 11.0-14.6] in HLA-A2+ vs. 12.5 months [95%CI 10.4-15.3] in HLA-A2- patients (HR 1.05 [95%CI 0.86-1.29], p=0.61). Median progression-free survival was similar in the two cohorts. CONCLUSION HLA-A2 status was not identified as prognostic for benefit in a large advanced NSCLC population treated with platinum-based chemotherapy.

Les nouvelles applications des méta-analyses d’essais randomisés sur données individuelles

Meta-analyses of randomized trials using individual-participant data, which represent the highest level of evidence for the evaluation of a treatment effect, are now used in different contexts in clinical research. This article aims at reviewing some of these new applications. Meta-analyses are increasingly used in economic evaluation, which implies new measure outcomes of the treatment effect, as well as in biomarkers evaluations thanks to their higher statistical power and the possibility to validate findings on independent data. This article also considers the perspectives opened up by new data sources, such as randomized trials registers, and data sharing policies.