Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicine.

Background: The research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an interaction should only be in a model if its main effects are also in the model. However, this constraint is not guaranteed in the standard penalized statistical approaches.

Avdoralimab (Anti-C5aR1 mAb) Versus Placebo in Patients With Severe COVID-19: Results From a Randomized Controlled Trial (FOR COVID Elimination [FORCE]).

Objectives: Severe COVID-19 is associated with exaggerated complement activation. We assessed the efficacy and safety of avdoralimab (an anti-C5aR1 mAb) in severe COVID-19.

Design: FOR COVID Elimination (FORCE) was a double-blind, placebo-controlled study.

Moxetumomab pasudotox in heavily pre-treated patients with relapsed/refractory hairy cell leukemia (HCL): long-term follow-up from the pivotal trial.

Background: Moxetumomab pasudotox is a recombinant CD22-targeting immunotoxin. Here, we present the long-term follow-up analysis of the pivotal, multicenter, open-label trial (NCT01829711) of moxetumomab pasudotox in patients with relapsed/refractory (R/R) hairy cell leukemia (HCL).

Methods: Eligible patients had received ≥ 2 prior systemic therapies, including ≥ 2 purine nucleoside analogs (PNAs), or ≥ 1 PNA followed by rituximab or a BRAF inhibitor.

Predictive classifier for intensive treatment of head and neck cancer.

Background: This study was designed to test the hypothesis that the effectiveness of intensive treatment for locoregionally advanced head and neck cancer (LAHNC) depends on the proportion of patients' overall event risk attributable to cancer.

Methods: This study analyzed 22,339 patients with LAHNC treated in 81 randomized trials testing altered fractionation (AFX; Meta-Analysis of Radiotherapy in Squamous Cell Carcinomas of Head and Neck [MARCH] data set) or chemotherapy (Meta-Analysis of Chemotherapy in Head and Neck Cancer [MACH-NC] data set). Generalized competing event regression was applied to the control arms in MARCH, and patients were stratified by tertile according to the ω score, which quantified the relative hazard for cancer versus competing events.

Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models.

Background: The standard lasso penalty and its extensions are commonly used to develop a regularized regression model while selecting candidate predictor variables on a time-to-event outcome in high-dimensional data. However, these selection methods focus on a homogeneous set of variables and do not take into account the case of predictors belonging to functional groups; typically, genomic data can be grouped according to biological pathways or to different types of collected data. Another challenge is that the standard lasso penalisation is known to have a high false discovery rate.

An alternative trial-level measure for evaluating failure-time surrogate endpoints based on prediction error.

To validate a failure-time surrogate for an established failure-time clinical endpoint such as overall survival, the meta-analytic approach is commonly used. The standard correlation approach considers two levels: the individual level, with Kendall's measuring the rank correlation between the endpoints, and the trial level, with the coefficient of determination measuring the correlation between the treatment effects on the surrogate and on the final endpoint. However, the estimation of is not robust with respect to the estimation error of the trial-specific treatment effects.

IPH4102, a first-in-class anti-KIR3DL2 monoclonal antibody, in patients with relapsed or refractory cutaneous T-cell lymphoma: an international, first-in-human, open-label, phase 1 trial.

Background: IPH4102 is a first-in-class monoclonal antibody targeting KIR3DL2, a cell surface protein that is expressed in cutaneous T-cell lymphoma, and predominantly in its leukaemic form, Sézary syndrome. We aimed to assess the safety and activity of IPH4102 in cutaneous T-cell lymphoma.

Methods: We did an international, first-in-human, open-label, phase 1 clinical trial with dose-escalation and cohort-expansion parts in five academic hospitals in the USA, France, the UK, and the Netherlands.

Tumor Mutation Burden as a Biomarker in Resected Non-Small-Cell Lung Cancer.

Purpose: The survival benefit with adjuvant chemotherapy for patients with resected stage II-III non-small-cell lung cancer (NSCLC) is modest. Efforts to develop prognostic or predictive biomarkers in these patients have not yielded clinically useful tests. We report findings from the Lung Adjuvant Cisplatin Evaluation (LACE)-Bio-II study, in which we analyzed next-generation sequencing and long-term outcomes data from > 900 patients with early-stage NSCLC treated prospectively in adjuvant landmark clinical trials.

Evaluation of Treatment Effect with Paired Failure Times in a Single-Arm Phase II Trial in Oncology.

In early phase clinical trials of cytotoxic drugs in oncology, the efficacy is typically evaluated based on the tumor shrinkage. However, this criterion is not always appropriate for more recent cytostatic agents, and alternative endpoints have been proposed. The growth modulation index (GMI), defined as the ratio between the times to progression in two successive treatment lines, has been proposed for a single-arm phase II trials.

surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials.

Background And Objective: Surrogate endpoints are attractive for use in clinical trials instead of well-established endpoints because of practical convenience. To validate a surrogate endpoint, two important measures can be estimated in a meta-analytic context when individual patient data are available: the R or the Kendall's τ at the individual level, and the R at the trial level. We aimed at providing an R implementation of classical and well-established as well as more recent statistical methods for surrogacy assessment with failure time endpoints.

biospear: an R package for biomarker selection in penalized Cox regression.

Summary: The R package biospear allows selecting the biomarkers with the strongest impact on survival and on the treatment effect in high-dimensional Cox models, and estimating expected survival probabilities. Most of the implemented approaches are based on penalized regression techniques.

Availability And Implementation: The package is available on the CRAN.

A Poisson approach to the validation of failure time surrogate endpoints in individual patient data meta-analyses.

Surrogate endpoints are often used in clinical trials instead of well-established hard endpoints for practical convenience. The meta-analytic approach relies on two measures of surrogacy: one at the individual level and one at the trial level. In the survival data setting, a two-step model based on copulas is commonly used.

Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical trials.

Background: Thanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized clinical trial. Despite the methodological framework for the development and validation of prediction models in a high-dimensional setting is getting more and more established, no clear guidance exists yet on how to estimate expected survival probabilities in a penalized model with biomarker-by-treatment interactions.

Methods: Based on a parsimonious biomarker selection in a penalized high-dimensional Cox model (lasso or adaptive lasso), we propose a unified framework to: estimate internally the predictive accuracy metrics of the developed model (using double cross-validation); estimate the individual survival probabilities at a given timepoint; construct confidence intervals thereof (analytical or bootstrap); and visualize them graphically (pointwise or smoothed with spline).

Surrogate End Points for Overall Survival in Loco-Regionally Advanced Nasopharyngeal Carcinoma: An Individual Patient Data Meta-analysis.

Background: Our objective was to evaluate progression-free survival (PFS) and distant metastasis-free survival (DMFS) as surrogate end points for overall survival (OS) in randomized trials of chemotherapy in loco-regionally advanced nasopharyngeal carcinomas (NPCs).

Methods: Individual patient data were obtained from 19 trials of the updated Meta-Analysis of Chemotherapy in Nasopharyngeal Carcinoma (MAC-NPC) plus one additional trial (total = 5144 patients). Surrogacy was evaluated at the individual level using a rank correlation coefficient ρ and at the trial level using a correlation coefficient R between treatment effects on the surrogate end point and OS.

Identification of biomarker-by-treatment interactions in randomized clinical trials with survival outcomes and high-dimensional spaces.

Stratified medicine seeks to identify biomarkers or parsimonious gene signatures distinguishing patients that will benefit most from a targeted treatment. We evaluated 12 approaches in high-dimensional Cox models in randomized clinical trials: penalization of the biomarker main effects and biomarker-by-treatment interactions (full-lasso, three kinds of adaptive lasso, ridge+lasso and group-lasso); dimensionality reduction of the main effect matrix via linear combinations (PCA+lasso (where PCA is principal components analysis) or PLS+lasso (where PLS is partial least squares)); penalization of modified covariates or of the arm-specific biomarker effects (two-I model); gradient boosting; and univariate approach with control of multiple testing. We compared these methods via simulations, evaluating their selection abilities in null and alternative scenarios.

[Not Available].

HOW A BIOMARKER CAN BECOME AN ACCEPTABLE SUBSTITUTION CRITERIA ?: Numerous biomarkers of the treatment activity are now available as a result of the fascinating progresses in biology and biotechnology. Together with the rapidly growing understanding of the mechanisms of action of new agents, these biomarkers provide promising tools to evaluate early the effect of treatments against cancer. It is tempting to use these new markers of activity as primary endpoints to evaluate new treatments in the context of randomized clinical trials.

Bias and precision of methods for estimating the difference in restricted mean survival time from an individual patient data meta-analysis.

Background: The difference in restricted mean survival time ([Formula: see text]), the area between two survival curves up to time horizon [Formula: see text], is often used in cost-effectiveness analyses to estimate the treatment effect in randomized controlled trials. A challenge in individual patient data (IPD) meta-analyses is to account for the trial effect. We aimed at comparing different methods to estimate the [Formula: see text] from an IPD meta-analysis.

Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models.

Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter λ.

Incorporation of nested frailties into semiparametric multi-state models.

Proportional hazards models are among the most popular regression models in survival analysis. Multi-state models generalize them by jointly considering different types of events and their interrelations, whereas frailty models incorporate random effects to account for unobserved risk factors, possibly shared by clusters of subjects. The integration of multi-state and frailty methodology is an interesting way to control for unobserved heterogeneity in the presence of complex event history structures and is particularly appealing for multicenter clinical trials.

Testing the treatment effect on competing causes of death in oncology clinical trials.

Background: Chemotherapy is expected to reduce cancer deaths (CD), while possibly being harmful in terms of non-cancer deaths (NCD) because of toxicity. Peto's log-rank test is popular in the medical literature, but its operating characteristics are barely known. We compared this test to the most common ones in the statistical literature: the cause-specific hazard test and Gray's test on the hazard of the subdistribution.

A simulation procedure based on copulas to generate clustered multi-state survival data.

Generating survival data with a clustered and multi-state structure is useful to study finite sample properties of multi-state models, competing risks models and frailty models. We propose a simulation procedure based on a copula model for each competing events block, allowing to introduce dependence between times of different transitions and between those of grouped subjects. The effect of simulated frailties and covariates can be added in a proportional hazards way.