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. We aimed to find a compromise for high-dimensional data between the need for sparse model selection and the need for the hierarchical constraint. RESULTS: To favor the property of the hierarchical interaction constraint, we proposed to create groups composed of the biomarker main effect and its interaction with treatment and to perform the bi-level selection on these groups. We proposed two weighting approaches (Single Wald (SW) and likelihood ratio test (LRT)) for the adaptive lasso method. The selection performance of these two approaches is compared to alternative lasso extensions (adaptive lasso with ridge-based weights, composite Minimax Concave Penalty, group exponential lasso and Sparse Group Lasso) through a simulation study. A RCT (NSABP B-31) randomizing 1574 patients (431 events) with early breast cancer aiming to evaluate the effect of adjuvant trastuzumab on distant-recurrence free survival with expression data from 462 genes measured in the tumour will serve for illustration. The simulation study illustrates that the adaptive lasso LRT and SW, and the group exponential lasso favored the hierarchical interaction constraint. Overall, in the alternative scenarios, they had the best balance of false discovery and false negative rates for the main effects of the selected interactions. For NSABP B-31, 12 gene-treatment interactions were identified more than 20% by the different methods. Among them, the adaptive lasso (SW) approach offered the best trade-off between a high number of selected gene-treatment interactions and a high proportion of selection of both the gene-treatment interaction and its main effect. CONCLUSIONS: Adaptive lasso with Single Wald and likelihood ratio test weighting and the group exponential lasso approaches outperformed their competitors in favoring the hierarchical constraint of the biomarker-treatment interaction. However, the performance of the methods tends to decrease in the presence of prognostic biomarkers.

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. SETTING: Twelve clinical sites in France (ICU and general hospitals). PATIENTS: Patients receiving greater than or equal to 5 L oxygen/min to maintain Sp o2 greater than 93% (World Health Organization scale ≥ 5). Patients received conventional oxygen therapy or high-flow oxygen (HFO)/noninvasive ventilation (NIV) in cohort 1; HFO, NIV, or invasive mechanical ventilation (IMV) in cohort 2; and IMV in cohort 3. INTERVENTIONS: Patients were randomly assigned, in a 1:1 ratio, to receive avdoralimab or placebo. The primary outcome was clinical status on the World Health Organization ordinal scale at days 14 and 28 for cohorts 1 and 3, and the number of ventilator-free days at day 28 (VFD28) for cohort 2. MEASUREMENTS AND MAIN RESULTS: We randomized 207 patients: 99 in cohort 1, 49 in cohort 2, and 59 in cohort 3. During hospitalization, 95% of patients received glucocorticoids. Avdoralimab did not improve World Health Organization clinical scale score on days 14 and 28 (between-group difference on day 28 of -0.26 (95% CI, -1.2 to 0.7; p = 0.7) in cohort 1 and -0.28 (95% CI, -1.8 to 1.2; p = 0.6) in cohort 3). Avdoralimab did not improve VFD28 in cohort 2 (between-group difference of -6.3 (95% CI, -13.2 to 0.7; p = 0.96) or secondary outcomes in any cohort. No subgroup of interest was identified. CONCLUSIONS: In this randomized trial in hospitalized patients with severe COVID-19 pneumonia, avdoralimab did not significantly improve clinical status at days 14 and 28 (funded by Innate Pharma, ClinicalTrials.gov number, NCT04371367).

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. RESULTS: We evaluated different penalizations in a Cox model to select grouped variables in order to further penalize variables that, in addition to having a low effect, belong to a group with a low overall effect; and to favor the selection of variables that, in addition to having a large effect, belong to a group with a large overall effect. We considered the case of prespecified and disjoint groups and proposed diverse weights for the adaptive lasso method. In particular we proposed the product Max Single Wald by Single Wald weighting (MSW*SW) which takes into account the information of the group to which it belongs and of this biomarker. Through simulations, we compared the selection and prediction ability of our approach with the standard lasso, the composite Minimax Concave Penalty (cMCP), the group exponential lasso (gel), the Integrative L1-Penalized Regression with Penalty Factors (IPF-Lasso), and the Sparse Group Lasso (SGL) methods. In addition, we illustrated the methods using gene expression data of 614 breast cancer patients. CONCLUSIONS: The adaptive lasso with the MSW*SW weighting method incorporates both the information in the grouping structure and the individual variable. It outperformed the competitors by reducing the false discovery rate without severely increasing the false negative rate.

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. The classifier was externally validated on the MACH-NC data set. The study tested for interactions between the ω score and treatment effects on overall survival (OS). RESULTS: Factors associated with a higher ω score were a younger age, a better performance status, an oral cavity site, higher T and N categories, and a p16-negative/unknown status. The effect of AFX on OS was greater in patients with high ω scores (hazard ratio [HR], 0.92; 95% confidence interval [CI], 0.85-0.99) and medium ω scores (HR, 0.91; 95% CI, 0.84-0.98) versus low ω scores (HR, 0.97; 95% CI, 0.90-1.05; P for interaction = .086). The effect of chemotherapy on OS was significantly greater in patients with high ω scores (HR, 0.81; 95% CI, 0.75-0.88) and medium ω scores (HR, 0.86; 95% CI, 0.78-0.93) versus low ω scores (HR, 0.96; 95% CI, 0.86-1.08; P for interaction = .011). CONCLUSIONS: LAHNC patients with a higher risk of cancer progression relative to competing mortality, as reflected by a higher ω score, selectively benefit from more intensive treatment.

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. Eligible patients had histologically confirmed relapsed or refractory primary cutaneous T-cell lymphoma, an Eastern Cooperative Oncology group performance score of 2 or less, were aged 18 years or older, and had received at least two previous systemic therapies. Ten dose levels of IPH4102, administered as an intravenous infusion, ranging from 0·0001 mg/kg to 10 mg/kg, were assessed using an accelerated 3 + 3 design. The primary endpoint was the occurrence of dose-limiting toxicities during the first 2 weeks of treatment, defined as toxicity grade 3 or worse lasting for 8 or more days, except for lymphopenia. Global overall response by cutaneous T-cell lymphoma subtype was a secondary endpoint. Safety and activity analyses were done in the per-protocol population. The study is ongoing and recruitment is complete. This trial is registered with ClinicalTrials.gov, number NCT02593045. FINDINGS: Between Nov 4, 2015, and Nov 20, 2017, 44 patients were enrolled. 35 (80%) patients had Sézary syndrome, eight (18%) had mycosis fungoides, and one (2%) had primary cutaneous T-cell lymphoma, not otherwise specified. In the dose-escalation part, no dose limiting toxicity was reported and the trial's safety committee recommended a flat dose of 750 mg for the cohort-expansion, corresponding to the maximum administered dose. The most common adverse events were peripheral oedema (12 [27%] of 44 patients) and fatigue (nine [20%]), all of which were grade 1-2. Lymphopenia was the most common grade 3 or worse adverse event (three [7%]). One patient developed possibly treatment-related fulminant hepatitis 6 weeks after IPH4102 discontinuation and subsequently died. However, the patient had evidence of human herpes virus-6B infection. Median follow-up was 14·1 months (IQR 11·3-20·5). A confirmed global overall response was achieved in 16 (36·4% [95% CI 23·8-51·1]) of 44 patients, and of those, 15 responses were observed in 35 patients with Sézary syndrome (43% [28·0-59·1]). INTERPRETATION: IPH4102 is safe and shows encouraging clinical activity in patients with relapsed or refractory cutaneous T-cell lymphoma, particularly those with Sézary syndrome. If confirmed in future trials, IPH4102 could become a novel treatment option for these patients. A multi-cohort, phase 2 trial (TELLOMAK) is underway to confirm the activity in patients with Sézary syndrome and explore the role of IPH4102 in other subtypes of T-cell lymphomas that express KIR3DL2. FUNDING: Innate Pharma.

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. (https://CRAN.R-project.org/package=biospear). Contact: stefan.michiels@gustaveroussy.fr. Supplementary information: Supplementary data are available at Bioinformatics online.

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). We compared these strategies through a simulation study covering scenarios with or without biomarker effects. We applied the strategies to a large randomized phase III clinical trial that evaluated the effect of adding trastuzumab to chemotherapy in 1574 early breast cancer patients, for which the expression of 462 genes was measured. RESULTS: In our simulations, penalized regression models using the adaptive lasso estimated the survival probability of new patients with low bias and standard error; bootstrapped confidence intervals had empirical coverage probability close to the nominal level across very different scenarios. The double cross-validation performed on the training data set closely mimicked the predictive accuracy of the selected models in external validation data. We also propose a useful visual representation of the expected survival probabilities using splines. In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4 clinical covariates, the main effects of 98 biomarkers and 24 biomarker-by-treatment interactions, but there was high variability of the expected survival probabilities, with very large confidence intervals. CONCLUSION: Based on our simulations, we propose a unified framework for: developing a prediction model with biomarker-by-treatment interactions in a high-dimensional setting and validating it in absence of external data; accurately estimating the expected survival probability of future patients with associated confidence intervals; and graphically visualizing the developed prediction model. All the methods are implemented in the R package biospear, publicly available on the CRAN.

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. We varied the number of biomarkers, of nonnull main effects and true biomarker-by-treatment interactions. We also proposed a novel measure evaluating the interaction strength of the developed gene signatures. In the null scenarios, the group-lasso, two-I model, and gradient boosting performed poorly in the presence of nonnull main effects, and performed well in alternative scenarios with also high interaction strength. The adaptive lasso with grouped weights was too conservative. The modified covariates, PCA+lasso, PLS+lasso, and ridge+lasso performed moderately. The full-lasso and adaptive lassos performed well, with the exception of the full-lasso in the presence of only nonnull main effects. The univariate approach performed poorly in alternative scenarios. We also illustrate the methods using gene expression data from 614 breast cancer patients treated with adjuvant chemotherapy.

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. Usually, λ is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of λ. We propose an empirical extension of the cvl by adding a penalization term, which trades off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach with the standard lasso and 10 other competitors: Akaike information criterion (AIC), corrected AIC, Bayesian information criterion (BIC), extended BIC, Hannan and Quinn information criterion (HQIC), risk information criterion (RIC), one-standard-error rule, adaptive lasso, stability selection, and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC, and the adaptive lasso, which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted. Copyright © 2016 John Wiley & Sons, Ltd.

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. We propose the incorporation of correlated frailties in the transition-specific hazard function, thanks to a nested hierarchy. We studied a semiparametric estimation approach based on maximum integrated partial likelihood. We show in a simulation study that the nested frailty multi-state model improves the estimation of the effect of covariates, as well as the coverage probability of their confidence intervals. We present a case study concerning a prostate cancer multicenter clinical trial. The multi-state nature of the model allows us to evidence the effect of treatment on death taking into account intermediate events.

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. METHODS: We compared four methods: the area between Kaplan-Meier curves (experimental vs. control arm) ignoring the trial effect (Naïve Kaplan-Meier); the area between Peto curves computed at quintiles of event times (Peto-quintile); the weighted average of the areas between either trial-specific Kaplan-Meier curves (Pooled Kaplan-Meier) or trial-specific exponential curves (Pooled Exponential). In a simulation study, we varied the between-trial heterogeneity for the baseline hazard and for the treatment effect (possibly correlated), the overall treatment effect, the time horizon [Formula: see text], the number of trials and of patients, the use of fixed or DerSimonian-Laird random effects model, and the proportionality of hazards. We compared the methods in terms of bias, empirical and average standard errors. We used IPD from the Meta-Analysis of Chemotherapy in Nasopharynx Carcinoma (MAC-NPC) and its updated version MAC-NPC2 for illustration that included respectively 1,975 and 5,028 patients in 11 and 23 comparisons. RESULTS: The Naïve Kaplan-Meier method was unbiased, whereas the Pooled Exponential and, to a much lesser extent, the Pooled Kaplan-Meier methods showed a bias with non-proportional hazards. The Peto-quintile method underestimated the [Formula: see text], except with non-proportional hazards at [Formula: see text]= 5 years. In the presence of treatment effect heterogeneity, all methods except the Pooled Kaplan-Meier and the Pooled Exponential with DerSimonian-Laird random effects underestimated the standard error of the [Formula: see text]. Overall, the Pooled Kaplan-Meier method with DerSimonian-Laird random effects formed the best compromise in terms of bias and variance. The [Formula: see text] estimated with the Pooled Kaplan-Meier method was 0.49 years (95% CI: [-0.06;1.03], p = 0.08) when comparing radiotherapy plus chemotherapy vs. radiotherapy alone in the MAC-NPC and 0.59 years (95% CI: [0.34;0.84], p < 0.0001) in the MAC-NPC2. CONCLUSIONS: We recommend the Pooled Kaplan-Meier method with DerSimonian-Laird random effects to estimate the difference in restricted mean survival time from an individual-patient data meta-analysis.

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. We investigated for the first time the impact of reclassifications of causes of death (CoD) after recurrences, and of misclassification of CoD. METHODS: We present a simulation study in which we varied the censoring rate and the correlation between CD and NCD times, we generated recurrence times to study the role of the reclassification of CoD, and we added 20% misclassified CoD. We considered four scenarios for the treatment effect: none; none for CD and negative for NCD; positive for CD and none for NCD; positive for CD and negative for NCD. We applied the three tests to a randomized clinical trial evaluating adjuvant chemotherapy in 1,867 patients with non-small-cell lung cancer. RESULTS: Most often the three tests well preserved their nominal size, Gray's test did not when the treatment had an effect on the competing CoD. With a high rate of misclassified CoD, Gray's and the cause-specific tests lost much of their power, whereas the Peto's test had the highest power. The cause-specific test had inflated size for NCD when the treatment was beneficial for CD with many misclassified CoD, but had the highest power for NCD when the treatment had no effect on CD, and had similar power to Peto's test for CD when the treatment had no effect on NCD. Gray's test performed best when the effect on the two CoD was opposite. The higher the censoring, the lower the rejection probabilities of all the tests and the smaller their differences. CONCLUSIONS: In this first head-to-head comparison of the three tests, the cause-specific test often proved to be the most reliable. Comparing results with and without misclassification of the CoD, Peto's test was the least influenced by the presence of such misclassification.

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. Patients received 40 µg/kg moxetumomab pasudotox intravenously on Days 1, 3, and 5 of each 28-day cycle for up to six cycles. Disease response and minimal residual disease (MRD) status were determined by blinded independent central review. The primary endpoint was durable complete response (CR), defined as achieving CR with hematologic remission (HR, blood counts for CR) lasting > 180 days. RESULTS: Eighty adult patients were treated with moxetumomab pasudotox and 63% completed six cycles. Patients had received a median of three lines of prior systemic therapy; 49% were PNA-refractory, and 38% were unfit for PNA retreatment. At a median follow-up of 24.6 months, the durable CR rate (CR with HR > 180 days) was 36% (29 patients; 95% confidence interval: 26-48%); CR with HR ≥ 360 days was 33%, and overall CR was 41%. Twenty-seven complete responders (82%) were MRD-negative (34% of all patients). CR lasting ≥ 60 months was 61%, and the median progression-free survival without the loss of HR was 71.7 months. Hemolytic uremic and capillary leak syndromes were each reported in ≤ 10% of patients, and ≤ 5% had grade 3-4 events; these events were generally reversible. No treatment-related deaths were reported. CONCLUSIONS: Moxetumomab pasudotox resulted in a high rate of durable responses and MRD negativity in heavily pre-treated patients with HCL, with a manageable safety profile. Thus, it represents a new and viable treatment option for patients with R/R HCL, who currently lack adequate therapy. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01829711; first submitted: April 9, 2013. https://clinicaltrials.gov/ct2/show/NCT01829711.

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. We present a new approach which employs a bivariate survival model with an individual random effect shared between the two endpoints and correlated treatment-by-trial interactions. We fit this model using auxiliary mixed Poisson models. We study via simulations the operating characteristics of this mixed Poisson approach as compared to the two-step copula approach. We illustrate the application of the methods on two individual patient data meta-analyses in gastric cancer, in the advanced setting (4069 patients from 20 randomized trials) and in the adjuvant setting (3288 patients from 14 randomized trials).

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 τ measuring the rank correlation between the endpoints, and the trial level, with the coefficient of determination R 2 measuring the correlation between the treatment effects on the surrogate and on the final endpoint. However, the estimation of R 2 is not robust with respect to the estimation error of the trial-specific treatment effects. The alternative proposed in this article uses a prediction error based on a measure of the weighted difference between the observed treatment effect on the final endpoint and a model-based predicted effect. The measures can be estimated by cross-validation within the meta-analytic setting or external validation on a set of trials. Several distances are presented, with varying weights, based on the standard error of the observed treatment effect and of its predicted value. A simulation study was conducted under different scenarios, varying the number and the size of the trials, Kendall's τ and R 2 . These measures have been applied to individual patient data from a meta-analysis of trials in advanced/recurrent gastric cancer (20 randomized trials of chemotherapy, 4069 patients). The distance-based measures appeared to be robust with respect to different values of simulation parameters in several scenarios (such as Kendall's τ, size and number of clinical trials). The absolute prediction error can be an alternative to the trial-level R 2 for evaluation of candidate time-to-event surrogates.

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 Rindiv2 or the Kendall's τ at the individual level, and the Rtrial2 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. We also intended incorporating utilities for model checking and visualization and data generating methods described in the literature to date. METHODS: In the case of failure time endpoints, the classical approach is based on two steps. First, a Kendall's τ is estimated as measure of individual level surrogacy using a copula model. Then, the Rtrial2 is computed via a linear regression of the estimated treatment effects; at this second step, the estimation uncertainty can be accounted for via measurement-error model or via weights. In addition to the classical approach, we recently developed an approach based on bivariate auxiliary Poisson models with individual random effects to measure the Kendall's τ and treatment-by-trial interactions to measure the Rtrial2. The most common data simulation models described in the literature are based on: copula models, mixed proportional hazard models, and mixture of half-normal and exponential random variables. RESULTS: The R package surrosurv implements the classical two-step method with Clayton, Plackett, and Hougaard copulas. It also allows to optionally adjusting the second-step linear regression for measurement-error. The mixed Poisson approach is implemented with different reduced models in addition to the full model. We present the package functions for estimating the surrogacy models, for checking their convergence, for performing leave-one-trial-out cross-validation, and for plotting the results. We illustrate their use in practice on individual patient data from a meta-analysis of 4069 patients with advanced gastric cancer from 20 trials of chemotherapy. CONCLUSIONS: The surrosurv package provides an R implementation of classical and recent statistical methods for surrogacy assessment of failure time endpoints. Flexible simulation functions are available to generate data according to the methods described in the literature.

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. The treatment effect is evaluated by estimating the rate of patients having a GMI superior to a given threshold. To estimate this rate, we investigated a parametric method based on the distribution of the times to progression and a nonparametric one based on a midrank estimator. Through simulations, we studied their operating characteristics and the impact of different design parameters (censoring, dependence, and distribution) on them. In these simulations, the nonparametric estimator slightly underestimated the rate and had slightly overconservative confidence intervals in some cases. Conversely, the parametric estimator overestimated the rate and had anticonservative confidence intervals in some cases. The nonparametric method appeared to be more robust to censoring than the parametric one. In conclusion, we recommend the nonparametric method, but the parametric method can be used as a supplementary tool.

Genome-wide copy number analyses of samples from LACE-Bio project identify novel prognostic and predictive markers in early stage non-small cell lung cancer.

BACKGROUND: Adjuvant chemotherapy (ACT) provides modest benefit in resected non-small cell lung cancer (NSCLC) patients. Genome-wide studies have identified gene copy number aberrations (CNA), but their prognostic implication is unknown. METHODS: DNA from 1,013 FFPE tumor samples from three pivotal multicenter randomized trials (ACT vs. control) in the LACE-Bio consortium (median follow-up: 5.2 years) was successfully extracted, profiled using a molecular inversion probe SNP assay, normalized relative to a pool of normal tissues and segmented. Minimally recurrent regions were identified. P values were adjusted to control the false discovery rate (Q values). RESULTS: A total of 976 samples successfully profiled, 414 (42%) adenocarcinoma (ADC), 430 (44%) squamous cell carcinoma (SCC) and 132 (14%) other NSCLC; 710 (73%) males. We identified 431 recurrent regions, with on average 51 gains and 43 losses; 253 regions (59%) were ≤3 Mb. Most frequent gains (up to 48%) were on chr1, 3q, 5p, 6p, 8q, 22q; most frequent losses (up to 40%) on chr3p, 8p, 9p. CNA frequency of 195 regions was significantly different (Q≤0.05) between ADC and SCC. Fourteen regions (7p11-12, 9p21, 18q12, and 19p11-13) were associated with disease-free survival (DFS) (univariate P≤0.005, Q<0.142), with poorer DFS for losses of regions including CDKN2A/B [hazard ratio (HR) for 2-fold lower CN: 1.5 (95% CI: 1.2-1.9), P<0.001, Q=0.020] and STK11 [HR =2.4 (1.3-4.3), P=0.005, Q=0.15]. Chromosomal instability was associated with poorer DFS (HR =1.5, P=0.015), OS (HR =1.2, P=0.189) and lung-cancer specific survival (HR =1.7, P=0.003). CONCLUSIONS: These large-scale genome-wide analyses of gene CNA provide new candidate prognostic markers for stage I-III NSCLC.

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. We used a targeted gene panel to assess the prognostic and predictive effect of mutations in individual genes, DNA repair pathways, and tumor mutation burden (TMB). METHODS: A total of 908 unmatched, formalin-fixed, paraffin-embedded, resected lung cancer tumor specimens were sequenced using a targeted panel of 1,538 genes. Stringent filtering criteria were applied to exclude germline variants and artifacts related to formalin fixation. Disease-free survival, overall survival, and lung cancer-specific survival (LCSS) were assessed in Cox models stratified by trial and adjusted for treatment, age, sex, performance score, histology, type of surgery, and stage. RESULTS: Nonsynonymous mutations were identified in 1,515 genes in 908 tumor samples. High nonsynonymous TMB (> 8 mutations/Mb) was prognostic for favorable outcomes (ie, overall survival, disease-free survival, and LCSS) in patients with resected NSCLC. LCSS benefit with adjuvant chemotherapy was more pronounced in patients with low nonsynonymous TMBs (≤ 4 mutations/Mb). Presence of mutations in DNA repair pathways, tumor-infiltrating lymphocytes, TP53 alteration subtype, and intratumor heterogeneity was neither prognostic nor predictive. Statistically significant effect of mutations in individual genes was difficult to determine due to high false-discovery rates. CONCLUSION: High nonsynonymous TMB was associated with a better prognosis in patients with resected NSCLC. In addition, the benefit of adjuvant chemotherapy on LCSS was more pronounced in patients with low nonsynonymous TMBs. Studies are warranted to confirm these findings.