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PurposeThe R2Pulm trial was conducted to evaluate the effect of busulfan-melphalan high-dose chemotherapy with autologous stem-cell rescue (BuMel) without whole-lung irradiation (WLI) on event-free survival (main end point) and overall survival, compared with standard chemotherapy with WLI in Ewing sarcoma (ES) presenting with pulmonary and/or pleural metastases.MethodsFrom 2000 to 2015, we enrolled patients younger than 50 years of age with newly diagnosed ES and with only pulmonary or pleural metastases. Patients received chemotherapy with six courses of vincristine, ifosfamide, doxorubicin, and etoposide (VIDE) and one course of vincristine, dactinomycin, and ifosfamide (VAI) before either BuMel or seven courses of VAI and WLI (VAI plus WLI) by randomized assignment. The analysis was conducted as intention to treat. The estimates of the hazard ratio (HR), 95% CI, and P value were corrected for the three previous interim analyses by the inverse normal method.ResultsOf 543 potentially eligible patients, 287 were randomly assigned to VAI plus WLI (n = 143) or BuMel (n = 144). Selected patients requiring radiotherapy to an axial primary site were excluded from randomization to avoid excess organ toxicity from interaction between radiotherapy and busulfan. Median follow-up was 8.1 years. We did not observe any significant difference in survival outcomes between treatment groups. Event-free survival was 50.6% versus 56.6% at 3 years and 43.1% versus 52.9% at 8 years, for VAI plus WLI and BuMel patients, respectively, resulting in an HR of 0.79 (95% CI, 0.56 to 1.10; P = .16). For overall survival, the HR was 1.00 (95% CI, 0.70 to 1.44; P = .99). Four patients died as a result of BuMel-related toxicity, and none died after VAI plus WLI. Significantly more patients in the BuMel arm experienced severe acute toxicities than in the VAI plus WLI arm.ConclusionIn ES with pulmonary or pleural metastases, there is no clear benefit from BuMel compared with conventional VAI plus WLI.

The number of meta‐analyses of aggregate data has dramatically increased due to the facility of obtaining data from publications and the development of free, easy‐to‐use, and specialised statistical software. Even when meta‐analyses include the same studies, their results may vary owing to different methodological choices. Assessment of the replication of meta‐analysis provides an example of the variation of effect 'naturally' observed between multiple research projects. Reproducibility of results has mostly been reported using graphical descriptive representations. A quantitative analysis of such results would enable (i) breakdown of the total observed variability with quantification of the variability generated by the replication process and (ii) identification of which variables account for this variability, such as methodological quality or the statistical analysis procedures used. These variables might explain systematic mean differences between results and dispersion of the results. To quantitatively characterise the reproducibility of meta‐analysis results, a bivariate linear mixed‐effects model was developed to simulate both mean results and their corresponding uncertainty. Results were assigned to several replication groups, those assessing the same studies, outcomes, treatment indication and comparisons classified in the same replication group. A nested random effect structure was used to break down the total variability within each replication group and between these groups to enable calculation of an intragroup correlation coefficient and quantification of reproducibility. Determinants of variability were investigated by modelling both mean and variance parameters using covariates. The proposed model was applied to the example of meta‐analyses evaluating direct oral anticoagulants in the acute treatment of venous thromboembolism. [ABSTRACT FROM AUTHOR]

Network meta‐analysis (NMA) allows the combination of direct and indirect evidence from a set of randomized clinical trials. Performing NMA using individual patient data (IPD) is considered as a "gold standard" approach as it provides several advantages over NMA based on aggregate data. For example, it allows to perform advanced modeling of covariates or covariate‐treatment interactions. An important issue in IPD NMA is the selection of influential parameters among terms that account for inconsistency, covariates, covariate‐by‐treatment interactions or nonproportionality of treatments effect for time to event data. This issue has not been deeply studied in the literature yet and in particular not for time‐to‐event data. A major difficulty is to jointly account for between‐trial heterogeneity which could have a major influence on the selection process. The use of penalized generalized mixed effect model is a solution, but existing implementations have several shortcomings and an important computational cost that precludes their use for complex IPD NMA. In this article, we propose a penalized Poisson regression model to perform IPD NMA of time‐to‐event data. It is based only on fixed effect parameters which improve its computational cost over the use of random effects. It could be easily implemented using existing penalized regression package. Computer code is shared for implementation. The methods were applied on simulated data to illustrate the importance to take into account between trial heterogeneity during the selection procedure. Finally, it was applied to an IPD NMA of overall survival of chemotherapy and radiotherapy in nasopharyngeal carcinoma. [ABSTRACT FROM AUTHOR]

In rare diseases, fully powered large trials may not be doable in a reasonable time frame even with international collaborations. In a previous work, we proposed an approach based on a series of smaller parallel group two-arm randomised controlled trials (RCT) performed over a long research horizon. Within the series of trials, the treatment selected after each trial becomes the control treatment of the next one. We concluded that running more trials with smaller sample sizes and relaxed α-levels leads in the long term and under reasonable assumptions to larger survival benefits with a moderate increase of risk as compared to traditional designs based on larger but fewer trials designed to meet stringent evidence criteria. We now extend this quantitative framework with more 'flexible' designs including interim analyses for futility and/or efficacy, and three-arm adaptive designs with treatment selection at interim. In the simulation study, we considered different disease severities, accrual rates, and hypotheses of how treatments improve over time. For each design, we estimated the long-term survival benefit as the relative difference in hazard rates between the end and the start of the research horizon, and the risk defined as the probability of selecting at the end of the research horizon a treatment inferior to the initial control. We assessed the impact of the α-level and the choice of the stopping rule on the operating characteristics. We also compared the performance of series based on two- vs. three-arm trials. We show that relaxing α-levels within the limit of 0.1 is associated with larger survival gains and moderate increase of risk which remains within acceptable ranges. Including an interim analysis with a futility rule is associated with an additional survival gain and a better risk control as compared to series with no interim analysis, when the α-level is below or equal to 0.1, whereas the benefit of including an interim analysis is rather small for higher α-levels. Including an interim analysis for efficacy yields almost no additional gain. Series based on three-arm trials are associated with a systematic improvement in terms of survival gain and risk control as compared to series of two-arm trials. [ABSTRACT FROM AUTHOR]