Futility monitoring for randomized clinical trials with non-proportional hazards: An optimal conditional power approach.

BACKGROUND: Standard futility analyses designed for a proportional hazards setting may have serious drawbacks when non-proportional hazards are present. One important type of non-proportional hazards occurs when the treatment effect is delayed. That is, there is little or no early treatment effect but a substantial later effect. METHODS: We define optimality criteria for futility analyses in this setting and propose simple search procedures for deriving such rules in practice. RESULTS: We demonstrate the advantages of the optimal rules over commonly used rules in reducing the average number of events, the average sample size, or the average study duration under the null hypothesis with minimal power loss under the alternative hypothesis. CONCLUSION: Optimal futility rules can be derived for a non-proportional hazards setting that control the loss of power under the alternative hypothesis while maximizing the gain in early stopping under the null hypothesis.

A Signature Enrichment Design with Bayesian Adaptive Randomization.

Clinical trials in the era of precision cancer medicine aim to identify and validate biomarker signatures which can guide the assignment of individually optimal treatments to patients. In this article, we propose a group sequential randomized phase II design, which updates the biomarker signature as the trial goes on, utilizes enrichment strategies for patient selection, and uses Bayesian response-adaptive randomization for treatment assignment. To evaluate the performance of the new design, in addition to the commonly considered criteria of type I error and power, we propose four new criteria measuring the benefits and losses for individuals both inside and outside of the clinical trial. Compared with designs with equal randomization, the proposed design gives trial participants a better chance to receive their personalized optimal treatments and thus results in a higher response rate on the trial. This design increases the chance to discover a successful new drug by an adaptive enrichment strategy, i.e., identification and selective enrollment of a subset of patients who are sensitive to the experimental therapies. Simulation studies demonstrate these advantages of the proposed design. It is illustrated by an example based on an actual clinical trial in non-small-cell lung cancer.

Design and analysis of biomarker-integrated clinical trials with adaptive threshold detection and flexible patient enrichment.

We propose a new adaptive threshold detection and enrichment design in which the biomarker threshold is adaptively estimated and updated by optimizing a trade-off between the size of the biomarker positive population and the magnitude of the treatment effect in that population. Enrichment is based on an enrollment criterion that accounts for the uncertainty in estimation of the threshold. Early termination for futility is allowed based on predictive success probability. Valid testing and estimation techniques for the treatment effect overall and inpatient subgroups are studied. Simulations and an example demonstrate advantages of the proposed design over existing designs.

Bias-adjusted Kaplan-Meier survival curves for marginal treatment effect in observational studies.

For time-to-event outcomes, the Kaplan-Meier estimator is commonly used to estimate survival functions of treatment groups and to compute marginal treatment effects, such as the difference in survival rates between treatments at a landmark time. The derived estimates of the marginal treatment effect are uniformly consistent under general conditions when data are from randomized clinical trials. For data from observational studies, however, these statistical quantities are often biased due to treatment-selection bias. Propensity score-based methods estimate the survival function by adjusting for the disparity of propensity scores between treatment groups. Unfortunately, misspecification of the regression model can lead to biased estimates. Using an empirical likelihood (EL) method in which the moments of the covariate distribution of treatment groups are constrained to equality, we obtain consistent estimates of the survival functions and the marginal treatment effect. Equating moments of the covariate distribution between treatment groups simulate the covariate distribution that would have been obtained if the patients had been randomized to these treatment groups. We establish the consistency and the asymptotic limiting distribution of the proposed EL estimators. We demonstrate that the proposed estimator is robust to model misspecification. Simulation is used to study the finite sample properties of the proposed estimator. The proposed estimator is applied to a lung cancer observational study to compare two surgical procedures in treating early-stage lung cancer patients.

Auxiliary variable-enriched biomarker-stratified design.

Clinical trials in the era of precision medicine require assessment of biomarkers to identify appropriate subgroups of patients for targeted therapy. In a biomarker-stratified design (BSD), biomarkers are measured on all patients and used as stratification variables. However, such a trial can be both inefficient and costly, especially when the prevalence of the subgroup of primary interest is low and the cost of assessing the biomarkers is high. Efficiency can be improved and costs reduced by using enriched biomarker-stratified designs, in which patients of primary interest, typically the biomarker-positive patients, are oversampled. We consider a special type of enrichment design, an auxiliary variable-enriched design (AEBSD), in which enrichment is based on some inexpensive auxiliary variable that is positively correlated with the true biomarker. The proposed AEBSD reduces the total cost of the trial compared with a standard BSD when the prevalence rate of true biomarker positivity is small and the positive predictive value (PPV) of the auxiliary biomarker is larger than the prevalence rate. In addition, for an AEBSD, we can immediately randomize the patients selected in the screening process without waiting for the result of the true biomarker test, reducing the treatment waiting time. We propose an adaptive Bayesian method to adjust the assumed PPV while the trial is ongoing. Numerical studies and an example illustrate the approach. An R package is available.

On Enrichment Strategies for Biomarker Stratified Clinical Trials.

In the era of precision medicine, drugs are increasingly developed to target subgroups of patients with certain biomarkers. In large all-comer trials using a biomarker stratified design, the cost of treating and following patients for clinical outcomes may be prohibitive. With a fixed number of randomized patients, the efficiency of testing certain treatments parameters, including the treatment effect among biomarker-positive patients and the interaction between treatment and biomarker, can be improved by increasing the proportion of biomarker positives on study, especially when the prevalence rate of biomarker positives is low in the underlying patient population. When the cost of assessing the true biomarker is prohibitive, one can further improve the study efficiency by oversampling biomarker positives with a cheaper auxiliary variable or a surrogate biomarker that correlates with the true biomarker. To improve efficiency and reduce cost, we can adopt an enrichment strategy for both scenarios by concentrating on testing and treating patient subgroups that contain more information about specific treatment parameters of primary interest to the investigators. In the first scenario, an enriched biomarker stratified design enriches the cohort of randomized patients by directly oversampling the relevant patients with the true biomarker, while in the second scenario, an auxiliary-variable-enriched biomarker stratified design enriches the randomized cohort based on an inexpensive auxiliary variable, thereby avoiding testing the true biomarker on all screened patients and reducing treatment waiting time. For both designs, we discuss how to choose the optimal enrichment proportion when testing a single hypothesis or two hypotheses simultaneously. At a requisite power, we compare the two new designs with the BSD design in terms of the number of randomized patients and the cost of trial under scenarios mimicking real biomarker stratified trials. The new designs are illustrated with hypothetical examples for designing biomarker-driven cancer trials.

Validation of Progression-Free Survival as a Surrogate Endpoint for Overall Survival in Malignant Mesothelioma: Analysis of Cancer and Leukemia Group B and North Central Cancer Treatment Group (Alliance) Trials.

PURPOSE: The aim of this study was to investigate whether progression-free survival (PFS) can be considered a surrogate endpoint for overall survival (OS) in malignant mesothelioma. MATERIALS AND METHODS: Individual data were collected from 15 Cancer and Leukemia Group B (615 patients) and 2 North Central Cancer Treatment Group (101 patients) phase II trials. The effects of 5 risk factors for OS and PFS, including age, histology, performance status (PS), white blood cell count, and European Organisation for Research and Treatment of Cancer (EORTC) risk score, were used in the analysis. Individual-level surrogacy was assessed by Kendall's tau through a Clayton bivariate Copula survival (CBCS) model. Summary-level surrogacy was evaluated via the association between logarithms of the hazard ratio (log HR)-log HROS and log HRPFS-measured in R2 from a weighted least-square (WLS) regression model and the CBCS model. RESULTS: The median PFS for all patients was 3.0 months (95% confidence interval [CI], 2.8-3.5 months) and the median OS was 7.2 months (95% CI, 6.5-8.0 months). Moderate correlations between PFS and OS were observed across all risk factors at the individual level, with Kendall's tau ranging from 0.46 to 0.47. The summary-level surrogacy varied among risk factors. The Copula R2 ranged from 0.51 for PS to 0.78 for histology. The WLS R2 ranged from 0.26 for EORTC and PS to 0.67 for age. CONCLUSIONS: The analyses demonstrated low to moderate individual-level surrogacy between PFS and OS. At the summary level, the surrogacy between PFS and OS varied significantly across different risk factors. With a short postprogression survival and a moderate correlation between PFS and OS, there is no evidence that PFS is a valid surrogate endpoint for OS in malignant mesothelioma. The Oncologist 2017;22:189-198Implications for Practice: For better disease management and for more efficient clinical trial designs, it is important to know if progression-free survival (PFS) is a good surrogate endpoint for overall survival in malignant mesothelioma. With a relatively large database of 17 phase II trials and 716 patients from Cancer and Leukemia Group B and North Central Cancer Treatment Group, we conducted statistical analyses and found that there is no evidence to suggest that PFS is a valid surrogate endpoint for OS for malignant mesothelioma. Future research work is needed to find alternative surrogate endpoints for OS.

A Multi-state Model for Designing Clinical Trials for Testing Overall Survival Allowing for Crossover after Progression.

In designing a clinical trial for comparing two or more treatments with respect to overall survival (OS), a proportional hazards assumption is commonly made. However, in many cancer clinical trials, patients pass through various disease states prior to death and because of this may receive treatments other than originally assigned. For example, patients may crossover from the control treatment to the experimental treatment at progression. Even without crossover, the survival pattern after progression may be very different than the pattern prior to progression. The proportional hazards assumption will not hold in these situations and the design power calculated on this assumption will not be correct. In this paper we describe a simple and intuitive multi-state model allowing for progression, death before progression, post-progression survival and crossover after progression and apply this model to the design of clinical trials for comparing the OS of two treatments. For given values of the parameters of the multi-state model, we simulate the required number of deaths to achieve a specified power and the distribution of time required to achieve the requisite number of deaths. The results may be quite different from those derived using the usual PH assumption.

Time-dependent classification accuracy curve under marker-dependent sampling.

Evaluating the classification accuracy of a candidate biomarker signaling the onset of disease or disease status is essential for medical decision making. A good biomarker would accurately identify the patients who are likely to progress or die at a particular time in the future or who are in urgent need for active treatments. To assess the performance of a candidate biomarker, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are commonly used. In many cases, the standard simple random sampling (SRS) design used for biomarker validation studies is costly and inefficient. In order to improve the efficiency and reduce the cost of biomarker validation, marker-dependent sampling (MDS) may be used. In a MDS design, the selection of patients to assess true survival time is dependent on the result of a biomarker assay. In this article, we introduce a nonparametric estimator for time-dependent AUC under a MDS design. The consistency and the asymptotic normality of the proposed estimator is established. Simulation shows the unbiasedness of the proposed estimator and a significant efficiency gain of the MDS design over the SRS design.

Relationship between obesity and clinical outcome in adults with acute myeloid leukemia: A pooled analysis from four CALGB (alliance) clinical trials.

Obesity has been previously suggested as an adverse prognostic marker in patients with acute leukemia. To evaluate the relationship between obesity and clinical outcome, disease-free survival (DFS) and overall survival (OS), in patients with acute myelogenous leukemia (AML), including acute promyelocytic leukemia (APL), we performed a pooled analysis of four CALGB (Alliance) clinical trials. Our study included 446 patients with APL from CALGB 9710, and 1,648 patients between 18 and 60 years of age with non-APL AML from CALGB 9621, 10503, and 19808. Obesity was defined as BMI ≥30 kg/m(2). Multivariate Cox proportional-hazard regression models were fitted for DFS and OS. Obesity was seen in 50% and 38% of APL and non-APL AML patients, respectively. In APL patients, obesity was associated with worse DFS (HR 1.53, 95% CI 1.03-2.27; P = 0.04) and OS (HR 1.72, 95% CI 1.15-2.58; P = 0.01) after adjusting for age, sex, performance status, race, ethnicity, treatment arm and baseline white blood cell count. Obesity was not significantly associated with DFS or OS in the non-APL AML patients. In conclusion, our study indicates that obesity has significant prognostic value for DFS and OS in APL patients, but not for non-APL AML patients.

Research misconduct and data fraud in clinical trials: prevalence and causal factors.

The disclosure of cases of research misconduct in clinical trials, conventionally defined as fabrication, falsification or plagiarism, has been a disturbingly common phenomenon in recent years. Such cases can potentially harm patients enrolled on the trials in question or patients treated based on the results of those trials and can seriously undermine the scientific and public trust in the validity of clinical trial results. Here, I review what is known about the prevalence of research misconduct in general and the contributing or causal factors leading to the misconduct. The evidence on prevalence is unreliable and fraught with definitional problems and with study design issues. Nevertheless, the evidence taken as a whole seems to suggest that cases of the most serious types of misconduct, fabrication and falsification (i.e., data fraud), are relatively rare but that other types of questionable research practices are quite common. There have been many individual, institutional and scientific factors proposed for misconduct but, as is the case with estimates of prevalence, reliable empirical evidence on the strength and relative importance of these factors is lacking. However, it seems clear that the view of misconduct as being simply the result of aberrant or self-delusional personalities likely underestimates the effect of other important factors and inhibits the development of effective prevention strategies.

Data fraud in clinical trials.

Highly publicized cases of fabrication or falsification of data in clinical trials have occurred in recent years and it is likely that there are additional undetected or unreported cases. We review the available evidence on the incidence of data fraud in clinical trials, describe several prominent cases, present information on motivation and contributing factors and discuss cost-effective ways of early detection of data fraud as part of routine central statistical monitoring of data quality. Adoption of these clinical trial monitoring procedures can identify potential data fraud not detected by conventional on-site monitoring and can improve overall data quality.

Recombinant interleukin-2 in patients aged younger than 60 years with acute myeloid leukemia in first complete remission: results from Cancer and Leukemia Group B 19808.

BACKGROUND: Recombinant interleukin-2 (rIL-2) induces cellular cytotoxicity against leukemia blasts. Patients with acute myeloid leukemia (AML) in first complete remission (CR) may harbor minimal residual disease that is susceptible to rIL-2-activated effector cells. METHODS: In the Cancer and Leukemia Group B (CALGB) 19808 study, patients with AML in first CR were randomly assigned after all planned chemotherapy to receive a 90-day course of subcutaneously administered rIL-2 or no further therapy. The primary objective was to compare disease-free survival (DFS) between the 2 treatment arms. A total of 534 patients achieved a CR, 214 of whom were randomized. Six courses of low-dose daily rIL-2 were given for the expansion of cytotoxic effector cells, each followed by 3-day high-dose boluses given to trigger cytotoxicity against minimal residual disease. RESULTS: On the protocol-specified intention-to-treat analysis, the hazards ratio for DFS was 0.75 (95% confidence interval, 0.52-1.09; P = .13); the 5-year DFS rate was 42% in the observation arm and 53% in the rIL-2 treatment arm. The hazards ratio for overall survival (OS) was 0.88 (95% confidence interval, 0.54-1.23; P = .34); the 5-year OS rate was 58% for the observation arm and 63% for the rIL-2 treatment arm. Twenty-five of the 107 patients randomized to treatment with rIL-2 either refused or were unable to initiate therapy and 30 patients did not complete their assigned therapy. However, significant toxicities were not commonly observed. The trial design did not anticipate the difficulties patients would encounter with protocol compliance. CONCLUSIONS: The efficacy of immunotherapy with rIL-2 administered after intensive postremission treatment was not assessed as planned because of unexpected refusals by patients and/or their physicians to comply with protocol-directed therapy. Neither DFS nor OS was found to be significantly improved.

ROC curve estimation under test-result-dependent sampling.

The receiver operating characteristic (ROC) curve is often used to evaluate the performance of a biomarker measured on continuous scale to predict the disease status or a clinical condition. Motivated by the need for novel study designs with better estimation efficiency and reduced study cost, we consider a biased sampling scheme that consists of a SRC and a supplemental TDC. Using this approach, investigators can oversample or undersample subjects falling into certain regions of the biomarker measure, yielding improved precision for the estimation of the ROC curve with a fixed sample size. Test-result-dependent sampling will introduce bias in estimating the predictive accuracy of the biomarker if standard ROC estimation methods are used. In this article, we discuss three approaches for analyzing data of a test-result-dependent structure with a special focus on the empirical likelihood method. We establish asymptotic properties of the empirical likelihood estimators for covariate-specific ROC curves and covariate-independent ROC curves and give their corresponding variance estimators. Simulation studies show that the empirical likelihood method yields good properties and is more efficient than alternative methods. Recommendations on number of regions, cutoff points, and subject allocation is made based on the simulation results. The proposed methods are illustrated with a data example based on an ongoing lung cancer clinical trial.

Gene selection using iterative feature elimination random forests for survival outcomes.

Although many feature selection methods for classification have been developed, there is a need to identify genes in high-dimensional data with censored survival outcomes. Traditional methods for gene selection in classification problems have several drawbacks. First, the majority of the gene selection approaches for classification are single-gene based. Second, many of the gene selection procedures are not embedded within the algorithm itself. The technique of random forests has been found to perform well in high-dimensional data settings with survival outcomes. It also has an embedded feature to identify variables of importance. Therefore, it is an ideal candidate for gene selection in high-dimensional data with survival outcomes. In this paper, we develop a novel method based on the random forests to identify a set of prognostic genes. We compare our method with several machine learning methods and various node split criteria using several real data sets. Our method performed well in both simulations and real data analysis.Additionally, we have shown the advantages of our approach over single-gene-based approaches. Our method incorporates multivariate correlations in microarray data for survival outcomes. The described method allows us to better utilize the information available from microarray data with survival outcomes.

Comparison of reduced-intensity hematopoietic cell transplantation with chemotherapy in patients age 60-70 years with acute myelogenous leukemia in first remission.

We compared the outcomes of patients age 60-70 years with acute myelogenous leukemia receiving reduced-intensity allogeneic hematopoietic cell transplantation (HCT) in first remission (CR1) reported to the Center for International Blood and Marrow Research (n = 94) with the outcomes in patients treated with induction and postremission chemotherapy on Cancer and Leukemia Group B protocols (n = 96). All patients included had been in CR1 for at least 4 months. The HCT recipients were slightly younger than the chemotherapy patients (median age, 63 years vs 65 years; P < .001), but there were no significant between-group differences in the proportion with therapy-related leukemia or in different cytogenetic risk groups. Time from diagnosis to CR1 was longer for the HCT recipients (median, 44 days vs 38 days; P = .031). Allogeneic HCT was associated with significantly lower risk of relapse (32% vs 81% at 3 years; P < .001), higher nonrelapse mortality (36% vs 4% at 3 years; P < .001), and longer leukemia-free survival (32% vs 15% at 3 years; P = .001). Although overall survival was longer for HCT recipients, the difference was not statistically significant (37% vs 25% at 3 years; P = .08). Our findings suggest that reduced-intensity conditioning allogeneic HCT in patients age 60-70 with acute myelogenous leukemia in CR1 reduces relapse and improves leukemia-free survival. Strategies that reduce nonrelapse mortality may yield significant improvements in overall survival.

Multiple testing for gene sets from microarray experiments.

BACKGROUND: A key objective in many microarray association studies is the identification of individual genes associated with clinical outcome. It is often of additional interest to identify sets of genes, known a priori to have similar biologic function, associated with the outcome. RESULTS: In this paper, we propose a general permutation-based framework for gene set testing that controls the false discovery rate (FDR) while accounting for the dependency among the genes within and across each gene set. The application of the proposed method is demonstrated using three public microarray data sets. The performance of our proposed method is contrasted to two other existing Gene Set Enrichment Analysis (GSEA) and Gene Set Analysis (GSA) methods. CONCLUSIONS: Our simulations show that the proposed method controls the FDR at the desired level. Through simulations and case studies, we observe that our method performs better than GSEA and GSA, especially when the number of prognostic gene sets is large.

Individual patient data meta-analysis of randomized trials evaluating IL-2 monotherapy as remission maintenance therapy in acute myeloid leukemia.

IL-2 is a natural, T cell-derived cytokine that stimulates the cytotoxic functions of T and natural killer cells. IL-2 monotherapy has been evaluated in several randomized clinical trials (RCTs) for remission maintenance in patients with acute myeloid leukemia (AML) in first complete remission (CR1), and none demonstrated a significant benefit of IL-2 monotherapy. The objective of this meta-analysis was to reliably determine IL-2 efficacy by combining all available individual patient data (IPD) from 5 RCTs (N = 905) and summary data from a sixth RCT (N = 550). Hazard ratios (HRs) were estimated using Cox regression models stratified by trial, with HR < 1 indicating treatment benefit. Combined IPD showed no benefit of IL-2 over no treatment in terms of leukemia-free survival (HR = 0.97; P = .74) or overall survival (HR = 1.08; P = .39). Analyses including the sixth RCT yielded qualitatively identical results (leukemia-free survival HR = 0.96, P = .52; overall survival HR = 1.06; P = .46). No significant heterogeneity was found between the trials. Prespecified subset analyses showed no interaction between the lack of IL-2 effect and any factor, including age, sex, baseline performance status, karyotype, AML subtype, and time from achievement of CR1 to initiation of maintenance therapy. We conclude that IL-2 alone is not an effective remission maintenance therapy for AML patients in CR1.