Entity

Time filter

Source Type


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. Source


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. Source


Chan A.,Curtin University Australia | Delaloge S.,Institute Gustave Roussy | Holmes F.A.,Texas Oncology | Moy B.,Massachusetts General Hospital | And 26 more authors.
The Lancet Oncology | Year: 2016

Background: Neratinib, an irreversible tyrosine-kinase inhibitor of HER1, HER2, and HER4, has clinical activity in patients with HER2-positive metastatic breast cancer. We aimed to investigate the efficacy and safety of 12 months of neratinib after trastuzumab-based adjuvant therapy in patients with early-stage HER2-positive breast cancer. Methods: We did this multicentre, randomised, double-blind, placebo-controlled, phase 3 trial at 495 centres in Europe, Asia, Australia, New Zealand, and North and South America. Eligible women (aged ≥18 years, or ≥20 years in Japan) had stage 1-3 HER2-positive breast cancer and had completed neoadjuvant and adjuvant trastuzumab therapy up to 2 years before randomisation. Inclusion criteria were amended on Feb 25, 2010, to include patients with stage 2-3 HER2-positive breast cancer who had completed trastuzumab therapy up to 1 year previously. Patients were randomly assigned (1:1) to receive oral neratinib 240 mg per day or matching placebo. The randomisation sequence was generated with permuted blocks stratified by hormone receptor status (hormone receptor-positive [oestrogen or progesterone receptor-positive or both] vs hormone receptor-negative [oestrogen and progesterone receptor-negative]), nodal status (0, 1-3, or ≥4), and trastuzumab adjuvant regimen (sequentially vs concurrently with chemotherapy), then implemented centrally via an interactive voice and web-response system. Patients, investigators, and trial sponsors were masked to treatment allocation. The primary outcome was invasive disease-free survival, as defined in the original protocol, at 2 years after randomisation. Analysis was by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT00878709. Findings: Between July 9, 2009, and Oct 24, 2011, we randomly assigned 2840 women to receive neratinib (n=1420) or placebo (n=1420). Median follow-up time was 24 months (IQR 20-25) in the neratinib group and 24 months (22-25) in the placebo group. At 2 year follow-up, 70 invasive disease-free survival events had occurred in patients in the neratinib group versus 109 events in those in the placebo group (stratified hazard ratio 0·67, 95% CI 0·50-0·91; p=0·0091). The 2-year invasive disease-free survival rate was 93·9% (95% CI 92·4-95·2) in the neratinib group and 91·6% (90·0-93·0) in the placebo group. The most common grade 3-4 adverse events in patients in the neratinib group were diarrhoea (grade 3, n=561 [40%] and grade 4, n=1 [<1%] vs grade 3, n=23 [2%] in the placebo group), vomiting (grade 3, n=47 [3%] vs n=5 [<1%]), and nausea (grade 3, n=26 [2%] vs n=2 [<1%]). QT prolongation occurred in 49 (3%) patients given neratinib and 93 (7%) patients given placebo, and decreases in left ventricular ejection fraction (≥grade 2) in 19 (1%) and 15 (1%) patients, respectively. We recorded serious adverse events in 103 (7%) patients in the neratinib group and 85 (6%) patients in the placebo group. Seven (<1%) deaths (four patients in the neratinib group and three patients in the placebo group) unrelated to disease progression occurred after study drug discontinuation. The causes of death in the neratinib group were unknown (n=2), a second primary brain tumour (n=1), and acute myeloid leukaemia (n=1), and in the placebo group were a brain haemorrhage (n=1), myocardial infarction (n=1), and gastric cancer (n=1). None of the deaths were attributed to study treatment in either group. Interpretation: Neratinib for 12 months significantly improved 2-year invasive disease-free survival when given after chemotherapy and trastuzumab-based adjuvant therapy to women with HER2-positive breast cancer. Longer follow-up is needed to ensure that the improvement in breast cancer outcome is maintained. Funding: Wyeth, Pfizer, Puma Biotechnology. © 2016 Elsevier Ltd. Source


Garcia Barrado L.,Hasselt University | Coart E.,International Drug Development Institute IDDI | Vanderstichele H.M.J.,ADx Neurosciences | Burzykowski T.,Hasselt University | Burzykowski T.,International Drug Development Institute IDDI
Journal of Alzheimer's Disease | Year: 2015

Current technologies quantifying cerebrospinal fluid biomarkers to identify subjects with Alzheimer's disease pathology report different concentrations in function of technology and suffer from between-laboratory variability. Hence, lab-and technology-specific cut-off values are required. It is common practice to establish cut-off values on small datasets and, in the absence of well-characterized samples, to transfer the cut-offs to another assay format using 'side-by-side' testing of samples with both assays. We evaluated the uncertainty in cut-off estimation and the performance of two methods of cut-off transfer by using two clinical datasets and simulated data. The cut-off for the new assay was transferred by applying the commonly-used linear regression approach and a new Bayesian method, which consists of using prior information about the current assay for estimation of the biomarker's distributions for the new assay. Simulations show that cut-offs established with current sample sizes are insufficiently precise and also show the effect of increasing sample sizes on the cut-offs' precision. The Bayesian method results in unbiased and less variable cut-offs with substantially narrower 95 confidence intervals compared to the linear-regression transfer. For the BIODEM datasets, the transferred cut-offs for INNO-BIA Aβ1-42 are 167.5 pg/mL (95 credible interval [156.1, 178.0] and 172.8 pg/mL (95 CI [147.6, 179.6]) with Bayesian and linear regression methods, respectively. For the EUROIMMUN assay, the estimated cut-offs are 402.8 pg/mL (95 credible interval [348.0, 473.9]) and 364.4 pg/mL (95 CI [269.7, 426.8]). Sample sizes and statistical methods used to establish and transfer cut-off values have to be carefully considered to guarantee optimal diagnostic performance of biomarkers. © 2016-IOS Press and the authors. Source


Venet D.,International Drug Development Institute IDDI | Venet D.,Free University of Brussels | Doffagne E.,International Drug Development Institute IDDI | Burzykowski T.,International Drug Development Institute IDDI | And 10 more authors.
Clinical Trials | Year: 2012

Background Classical monitoring approaches rely on extensive on-site visits and source data verification. These activities are associated with high cost and a limited contribution to data quality. Central statistical monitoring is of particular interest to address these shortcomings. Purpose This article outlines the principles of central statistical monitoring and the challenges of implementing it in actual trials. Methods A statistical approach to central monitoring is based on a large number of statistical tests performed on all variables collected in the database, in order to identify centers that differ from the others. The tests generate a high-dimensional matrix of p -values, which can be analyzed by statistical methods and bioinformatics tools to identify extreme centers. Results Results from actual trials are provided to illustrate typical findings that can be expected from a central statistical monitoring approach, which detects abnormal patterns that were not (or could not have been) detected by on-site monitoring. Limitations Central statistical monitoring can only address problems present in the data. Important aspects of trial conduct such as a lack of informed consent documentation, for instance, require other approaches. The results provided here are empirical examples from a limited number of studies. Conclusion Central statistical monitoring can both optimize on-site monitoring and improve data quality and as such provides a cost-effective way of meeting regulatory requirements for clinical trials. © The Author(s), 2012. Source

Discover hidden collaborations