Michiels S.,Service de Biostatistique et d'Epidemiologie |
Michiels S.,University Paris - Sud |
Saad E.D.,International Drug Development Institute IDDI |
Buyse M.,Hasselt University |
Buyse M.,International Drug Development Institute IDDI
Drugs | Year: 2017
Over the past 15 years, targeted therapy has revolutionized the systemic treatment of cancer. In parallel, there has been a growing debate on the choice of end points in clinical trials in oncology. This debate basically hinges on the choice between overall survival (OS) and progression-free survival (PFS). PFS is advantageous because it is measured earlier than OS, requires a smaller sample size than OS to achieve the desired power, and is not influenced by cross-over. On the other hand, PFS is prone to measurement error and bias, and may not capture the entire treatment effect on the outcomes of most interest to patients with an incurable disease: a prolonged survival and improved quality of life. Therefore, how can we choose between two imperfect end points? The answer to this question would certainly be made easier if PFS could be demonstrated to be a valid surrogate for OS. The validation of a surrogate end point is best made using individual-patient data (IPD) from randomized trials, which allows for standardized assessments of the patient-level and the trial-level correlations between surrogate and final end points. Proper IPD meta-analytical evaluations for targeted agents have still been rare, and to our knowledge only three studies on this topic are currently available in the metastatic setting: one in breast cancer, one in colorectal cancer and one in lung cancer. Although these three studies suffer from limitations inherent to the availability of IPD and the design of the original clinical trials, they have not been able to validate PFS as surrogate for OS, because only modest correlations were found between these two end points, both at the patient and at the trial level. Even if properly conducted surrogate-endpoint evaluations have thus far been unsuccessful, these evaluations are a step in the right direction and can be expected to be applied on a much larger scale in the era of data sharing of clinical trials. © 2017 Springer International Publishing Switzerland
Peron J.,Center Hospitalier Lyon Sud |
Peron J.,University Claude Bernard Lyon 1 |
Roy P.,Center Hospitalier Lyon Sud |
Roy P.,University Claude Bernard Lyon 1 |
And 5 more authors.
British Journal of Cancer | Year: 2015
Background: Efficacy and safety are the two considerations when characterising the effects of a new therapy. We sought to apply an innovative method of assessing the benefit-risk balance using data from a completed randomised controlled trial that compared erlotinib vs placebo added to gemcitabine in patients with advanced pancreatic cancer (NCIC CTG PA.3). Methods: We applied generalised pairwise comparisons with several prioritised outcome measures (e.g., one or more benefit outcomes and one or more risk outcomes). Here, the first priority outcome was overall survival (OS) time. Differences in OS that exceeded 2 months were considered clinically meaningful. The second priority outcome was toxicity. The overall treatment effect was quantified using the proportion in favour of erlotinib, which can be interpreted as the net proportion of patients who have a better overall outcome with erlotinib as compared with placebo. Sensitivity analyses were performed. Results: In this trial 569 patients were randomly assigned in a 1: 1 ratio to receive gemcitabine plus either erlotinib or a matched placebo. Overall, the method indicated no statistically significant overall treatment effect in favour of erlotinib; if anything, the point estimate of the net proportion leaned in favour of the placebo group (overall proportion in favour of erlotinib=-3.6%, 95% CI, -14.2- 7.1%; P=0.51). The net proportion was never in favour of the erlotinib group throughout all sensitivity analyses. Conclusions: Generalised pairwise comparisons make it possible to assess the benefit-risk balance of new treatments using a single statistical test for any number of prioritised outcomes. The benefit-risk assessment was not in favour of adding erlotinib to gemcitabine for the treatment of patients with advanced pancreatic cancer. © 2015 Cancer Research UK. All rights reserved.
Laporte S.,Jean Monnet University |
Laporte S.,French Institute of Health and Medical Research |
Squifflet P.,International Drug Development Institute IDDI |
Baroux N.,Institute Cancerologie Of La Loire |
And 9 more authors.
BMJ Open | Year: 2013
Objectives: To investigate whether progression-free survival (PFS) can be considered a surrogate endpoint for overall survival (OS) in advanced non-small-cell lung cancer (NSCLC). Design: Meta-analysis of individual patient data from randomised trials. Setting: Five randomised controlled trials comparing docetaxel-based chemotherapy with vinorelbine-based chemotherapy for the first-line treatment of NSCLC. Participants: 2331 patients with advanced NSCLC. Primary and secondary outcome measures: Surrogacy of PFS for OS was assessed through the association between these endpoints and between the treatment effects on these endpoints. The surrogate threshold effect was the minimum treatment effect on PFS required to predict a non-zero treatment effect on OS. Results: The median follow-up of patients still alive was 23.4 months. Median OS was 10 months and median PFS was 5.5 months. The treatment effects on PFS and OS were correlated, whether using centres (R2=0.62, 95% CI 0.52 to 0.72) or prognostic strata (R2=0.72, 95% CI 0.60 to 0.84) as units of analysis. The surrogate threshold effect was a PFS hazard ratio (HR) of 0.49 using centres or 0.53 using prognostic strata. Conclusions: These analyses provide only modest support for considering PFS as an acceptable surrogate for OS in patients with advanced NSCLC. Only treatments that have a major impact on PFS (risk reduction of at least 50%) would be expected to also have a significant effect on OS. Whether these results also apply to targeted therapies is an open question that requires independent evaluation.
Ciani O.,University of Exeter |
Ciani O.,Bocconi University |
Buyse M.,Hasselt University |
Buyse M.,International Drug Development Institute IDDI |
And 5 more authors.
Journal of Clinical Epidemiology | Year: 2015
Objectives To quantify and compare the treatment effects on three surrogate end points, progression-free survival (PFS), time to progression (TTP), and tumor response rate (TR) vs. overall survival (OS) based on a meta-analysis of randomized controlled trials (RCTs) of drug interventions in advanced colorectal cancer (aCRC). Study Design and Setting We systematically searched for RCTs of pharmacologic therapies in aCRC between 2003 and 2013. Trial characteristics, risk of bias, and outcomes were recorded based on a predefined form. Univariate and multivariate random-effects meta-analyses were used to estimate pooled summary treatment effects. The ratio of hazard ratios (HRs)/odds ratios (ORs) and difference in medians were used to quantify the degree of difference in treatment effects on the surrogate end points and OS. Spearman ρ, surrogate threshold effect (STE), and R2 were also estimated across predefined trial-level covariates. Results We included 101 RCTs. In univariate and multivariate meta-analyses, we found larger treatment effects for the surrogates than for OS. Compared with OS, treatment effects were on average 13% higher when HRs were measured and 3% to 45% higher when ORs were considered; differences in median PFS/TTP were higher than on OS by an average of 0.5 month. Spearman ρ ranged from 0.39 to 0.80, mean R2 from 0.06 to 0.65, and STE was 0.8 for HRPFS, 0.64 for HRTTP, or 0.28 for ORTR. The stratified analyses revealed high variability across all strata. Conclusion None of the end points in this study were found to achieve the level of evidence (ie, mean R2trial > 0.60) that has been set to select high or excellent correlation levels by common surrogate evaluation tools. Previous surrogacy relationships observed between PFS and TTP vs. OS in selected settings may not apply across other classes or lines of therapy. © 2015 Elsevier Inc. All rights reserved.
PubMed | Institute Of Cancerologie Of Lorraine, Institute Regional du Cancer Montpellier, International Drug Development Institute IDDI, Center Leon Berard and 2 more.
Type: Journal Article | Journal: Oncotarget | Year: 2016
We sought to assess the benefit-risk balance of FOLFIRINOX versus gemcitabine in patients with metastatic pancreatic adenocarcinoma.We used generalized pairwise comparisons. This statistical method permits the simultaneous analysis of several prioritized outcome measures. The first priority outcome was survival time (OS). Differences in OS that exceeded two months were considered clinically relevant. The second priority outcome was toxicity. The overall treatment effect was quantified using the net chance of a better outcome, which can be interpreted as the net probability for a random patient treated in the FOLFIRINOX group to have a better overall outcome than a random patient in the gemcitabine group.In this trial 342 patients received either FOLFIRINOX or gemcitabine. The net chance of a better outcome favored strongly and significantly the FOLFIRINOX group (24.7; P<.001), suggesting a favorable benefit-risk balance of FOLFIRINOX versus gemcitabine. The positive benefit-risk balance of FOLFIRINOX was observed throughout all sensitivity analyses.Generalized pairwise comparisons are useful to perform a quantitative assessment of the benefit-risk balance of new treatments. It provides a clinically intuitive way of comparing patients with respect to all important efficacy and toxicity outcomes. Overall the benefit-risk balance of FOLFIRINOX was strongly positive.
Garcia Barrado L.,Hasselt University |
Coart E.,International Drug Development Institute IDDI |
Burzykowski T.,Hasselt University |
Burzykowski T.,International Drug Development Institute IDDI
Statistics in Medicine | Year: 2016
Ignoring the fact that the reference test used to establish the discriminative properties of a combination of diagnostic biomarkers is imperfect can lead to a biased estimate of the diagnostic accuracy of the combination. In this paper, we propose a Bayesian latent-class mixture model to select a combination of biomarkers that maximizes the area under the ROC curve (AUC), while taking into account the imperfect nature of the reference test. In particular, a method for specification of the prior for the mixture component parameters is developed that allows controlling the amount of prior information provided for the AUC. The properties of the model are evaluated by using a simulation study and an application to real data from Alzheimer's disease research. In the simulation study, 100 data sets are simulated for sample sizes ranging from 100 to 600 observations, with a varying correlation between biomarkers. The inclusion of an informative as well as a flat prior for the diagnostic accuracy of the reference test is investigated. In the real-data application, the proposed model was compared with the generally used logistic-regression model that ignores the imperfectness of the reference test. Conditional on the selected sample size and prior distributions, the simulation study results indicate satisfactory performance of the model-based estimates. In particular, the obtained average estimates for all parameters are close to the true values. For the real-data application, AUC estimates for the proposed model are substantially higher than those from the 'traditional' logistic-regression model. © 2016 John Wiley & Sons, Ltd.
Garcia Barrado L.,Hasselt University |
Coart E.,International Drug Development Institute IDDI |
Burzykowski T.,Hasselt University
Biometrics | Year: 2016
Estimating biomarker-index accuracy when only imperfect reference-test information is available is usually performed under the assumption of conditional independence between the biomarker and imperfect reference-test values. We propose to define a latent normally-distributed tolerance-variable underlying the observed dichotomous imperfect reference-test results. Subsequently, we construct a Bayesian latent-class model based on the joint multivariate normal distribution of the latent tolerance and biomarker values, conditional on latent true disease status, which allows accounting for conditional dependence. The accuracy of the continuous biomarker-index is quantified by the AUC of the optimal linear biomarker-combination. Model performance is evaluated by using a simulation study and two sets of data of Alzheimer's disease patients (one from the memory-clinic-based Amsterdam Dementia Cohort and one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database). Simulation results indicate adequate model performance and bias in estimates of the diagnostic-accuracy measures when the assumption of conditional independence is used when, in fact, it is incorrect. In the considered case studies, conditional dependence between some of the biomarkers and the imperfect reference-test is detected. However, making the conditional independence assumption does not lead to any marked differences in the estimates of diagnostic accuracy. © 2016, The International Biometric Society.
PubMed | Hasselt University and International Drug Development Institute IDDI
Type: Journal Article | Journal: Statistics in medicine | Year: 2016
Ignoring the fact that the reference test used to establish the discriminative properties of a combination of diagnostic biomarkers is imperfect can lead to a biased estimate of the diagnostic accuracy of the combination. In this paper, we propose a Bayesian latent-class mixture model to select a combination of biomarkers that maximizes the area under the ROC curve (AUC), while taking into account the imperfect nature of the reference test. In particular, a method for specification of the prior for the mixture component parameters is developed that allows controlling the amount of prior information provided for the AUC. The properties of the model are evaluated by using a simulation study and an application to real data from Alzheimers disease research. In the simulation study, 100 data sets are simulated for sample sizes ranging from 100 to 600 observations, with a varying correlation between biomarkers. The inclusion of an informative as well as a flat prior for the diagnostic accuracy of the reference test is investigated. In the real-data application, the proposed model was compared with the generally used logistic-regression model that ignores the imperfectness of the reference test. Conditional on the selected sample size and prior distributions, the simulation study results indicate satisfactory performance of the model-based estimates. In particular, the obtained average estimates for all parameters are close to the true values. For the real-data application, AUC estimates for the proposed model are substantially higher than those from the traditional logistic-regression model.
PubMed | International Drug Development Institute IDDI
Type: Journal Article | Journal: Chinese clinical oncology | Year: 2016
Phase III clinical trials are the gold standard to demonstrate the effects of an experimental therapy compared to standard therapy for a disease of interest. The first step when planning a phase III trial is to specify the statistical hypothesis that the trial purports to test, which is usually that the experimental therapy provides some efficacy benefit over standard therapy, without adding significant harm. In a phase III trial, a pre-specified number of patients from the target population are randomized to receive experimental or standard therapy. The patients are treated and followed up according to a protocol that also defines the endpoints of interest, in particular the primary endpoint which is chosen to reflect a clinical benefit of experimental therapy over standard therapy. The trial data are typically monitored by an independent committee who may recommend stopping the trial early, if appropriate. The benefit of experimental therapy over standard therapy, if any, may be observed across all patients, or may be confined to a subset of patients.
PubMed | Catholic University of Leuven, French Institute of Health and Medical Research, University of Tokyo, Hasselt University and International Drug Development Institute IDDI
Type: Journal Article | Journal: Biometrical journal. Biometrische Zeitschrift | Year: 2016
A surrogate endpoint is intended to replace a clinical endpoint for the evaluation of new treatments when it can be measured more cheaply, more conveniently, more frequently, or earlier than that clinical endpoint. A surrogate endpoint is expected to predict clinical benefit, harm, or lack of these. Besides the biological plausibility of a surrogate, a quantitative assessment of the strength of evidence for surrogacy requires the demonstration of the prognostic value of the surrogate for the clinical outcome, and evidence that treatment effects on the surrogate reliably predict treatment effects on the clinical outcome. We focus on these two conditions, and outline the statistical approaches that have been proposed to assess the extent to which these conditions are fulfilled. When data are available from a single trial, one can assess the individual level association between the surrogate and the true endpoint. When data are available from several trials, one can additionally assess the trial level association between the treatment effect on the surrogate and the treatment effect on the true endpoint. In the latter case, the surrogate threshold effect can be estimated as the minimum effect on the surrogate endpoint that predicts a statistically significant effect on the clinical endpoint. All these concepts are discussed in the context of randomized clinical trials in oncology, and illustrated with two meta-analyses in gastric cancer.