Some statistical considerations in the clinical development of cancer immunotherapies

Strategies for successful dose optimization in oncology drug development: a practical guide

Dose optimization is a critical challenge in drug development. Historically, dose determination in oncology has followed a divergent path from other non-oncology therapeutic areas due to the unique characteristics and requirements in Oncology. However, with the emergence of new drug modalities and mechanisms of drugs in oncology, such as immune therapies, radiopharmaceuticals, targeted therapies, cytostatic agents, and others, the dose-response relationship for efficacy and toxicity could be vastly varied compared to the cytotoxic chemotherapies. The doses below the MTD may demonstrate similar efficacy to the MTD with an improved tolerability profile, resembling what is commonly observed in non-oncology treatments. Hence, alternate strategies for dose optimization are required for new modalities in oncology drug development. This paper delves into the historical evolution of dose finding methods from non-oncology to oncology, highlighting examples and summarizing the underlying drivers of change. Subsequently, a practical framework and guidance are provided to illustrate how dose optimization can be incorporated into various stages of the development program. We provide the following general recommendations: 1) The objective for phase I is to identify a dose range rather than a single MTD dose for subsequent development to better characterize the safety and tolerability profile within the dose range. 2) At least two doses separable by PK are recommended for dose optimization in phase II. 3) Ideally, dose optimization should be performed before launching the confirmatory study. Nevertheless, innovative designs such as seamless II/III design can be implemented for dose selection and may accelerate the drug development program.

Adverse Effect of the Duration of Antibiotic Use Prior to Immune Checkpoint Inhibitors on the Overall Survival of Patients with Recurrent Gynecologic Malignancies

PURPOSE

Antibiotic use preceding immune checkpoint inhibitor (ICI) treatment has been associated with a decreased efficacy of ICI in solid tumors. In this study, we evaluated the effect of antibiotic use before ICI therapy on oncological outcomes.

METHODS

We examined patients with recurrent gynecologic malignancies at two academic institutions. The clinical data, including antibiotic use within 60 days of ICI initiation, type of antibiotics, reasons for antibiotic use, body mass index, tumor site, chemotherapy-free interval, prior history of radiotherapy, disease control rate (DCR), and overall survival (OS), were assessed.

RESULTS

Of 215 patients, 22.9% (n = 47) received antibiotics before ICI treatment. The most common cancer was ovarian (52.1%, n = 112), followed by cervical (24.7%, n = 53) and endometrial (16.7%, n = 36). When we divided the cohort based on antibiotic use before ICIs, there were no significant differences in the DCR and baseline characteristics between the two groups. On multivariate analyses, the variables associated with poor OS were previous use of antibiotics for a cumulative duration of >14 days (HR 2.286, 95% CI 1.210-4.318; p = 0.011); Eastern Cooperative Oncology Group 2 or 3 (HR 4.677, 95% CI 2.497-8.762; p < 0.001); and chemotherapy-free interval of <6 months (HR 2.007, 95% CI 1.055-3.819; p = 0.034).

CONCLUSION

Prior use of antibiotics for a cumulative duration of >14 days was associated with reduced survival in recurrent gynecologic malignancies.

Trial Design for Cancer Immunotherapy: A Methodological Toolkit

Immunotherapy with checkpoint inhibitors (CPIs) and cell-based products has revolutionized the treatment of various solid tumors and hematologic malignancies. These agents have shown unprecedented response rates and long-term benefits in various settings. These clinical advances have also pointed to the need for new or adapted approaches to trial design and assessment of efficacy and safety, both in the early and late phases of drug development. Some of the conventional statistical methods and endpoints used in other areas of oncology appear to be less appropriate in immuno-oncology. Conversely, other methods and endpoints have emerged as alternatives. In this article, we discuss issues related to trial design in the early and late phases of drug development in immuno-oncology, with a focus on CPIs. For early trials, we review the most salient issues related to dose escalation, use and limitations of tumor response and progression criteria for immunotherapy, the role of duration of response as an endpoint in and of itself, and the need to conduct randomized trials as early as possible in the development of new therapies. For late phases, we discuss the choice of primary endpoints for randomized trials, review the current status of surrogate endpoints, and discuss specific statistical issues related to immunotherapy, including non-proportional hazards in the assessment of time-to-event endpoints, alternatives to the Cox model in these settings, and the method of generalized pairwise comparisons, which can provide a patient-centric assessment of clinical benefit and be used to design randomized trials.

On assessing survival benefit of immunotherapy using long-term restricted mean survival time

The pattern of the difference between two survival curves we often observe in randomized clinical trials for evaluating immunotherapy is not proportional hazards; the treatment effect typically appears several months after the initiation of the treatment (ie, delayed difference pattern). The commonly used logrank test and hazard ratio estimation approach will be suboptimal concerning testing and estimation for those trials. The long-term restricted mean survival time (LT-RMST) approach is a promising alternative for detecting the treatment effect that potentially appears later in the study. A challenge in employing the LT-RMST approach is that it must specify a lower end of the time window in addition to a truncation time point that the RMST requires. There are several investigations and suggestions regarding the choice of the truncation time point for the RMST. However, little has been investigated to address the choice of the lower end of the time window. In this paper, we propose a flexible LT-RMST-based test/estimation approach that does not require users to specify a lower end of the time window. Numerical studies demonstrated that the potential power loss by adopting this flexibility was minimal, compared to the standard LT-RMST approach using a prespecified lower end of the time window. The proposed method is flexible and can offer higher power than the RMST-based approach when the delayed treatment effect is expected. Also, it provides a robust estimate of the magnitude of the treatment effect and its confidence interval that corresponds to the test result.

Approaches to Selecting "Time Zero" in External Control Arms with Multiple Potential Entry Points: A Simulation Study of 8 Approaches

BACKGROUND

When including data from an external control arm to estimate comparative effectiveness, there is a methodological choice of when to set "time zero," the point at which a patient would be eligible/enrolled in a contemporary study. Where patients receive multiple lines of eligible therapy and thus alternative points could be selected, this issue is complex.

METHODS

A simulation study was conducted in which patients received multiple prior lines of therapy before entering either cohort. The results from the control and intervention data sets are compared using 8 methods for selecting time zero. The base-case comparison was set up to be biased against the intervention (which is generally received later), with methods compared in their ability to estimate the true intervention effectiveness. We further investigate the impact of key study attributes (such as sample size) and degree of overlap in time-varying covariates (such as prior lines of therapy) on study results.

RESULTS

Of the 8 methods, 5 (all lines, random line, systematically selecting groups based on mean absolute error, root mean square error, or propensity scores) showed good performance in accounting for differences between the line at which patients were included. The first eligible line can be statistically inefficient in some situations. All lines (with censoring) cannot be used for survival outcomes. The last eligible line cannot be recommended.

CONCLUSIONS

Multiple methods are available for selecting the most appropriate time zero from an external control arm. Based on the simulation, we demonstrate that some methods frequently perform poorly, with several viable methods remaining. In selecting between the viable methods, analysts should consider the context of their analysis and justify the approach selected.

HIGHLIGHTS

There are multiple methods available from which an analyst may select "time zero" in an external control cohort.This simulation study demonstrates that some methods perform poorly but most are viable options, depending on context and the degree of overlap in time zero across cohorts.Careful thought and clear justification should be used when selecting the strategy for a study.

Comparison of Duration of Response vs Conventional Response Rates and Progression-Free Survival as Efficacy End Points in Simulated Immuno-oncology Clinical Trials

IMPORTANCE

Phase 2 trials and early efficacy end points play a crucial role in informing decisions about whether to continue to phase 3 trials. Conventional end points, such as objective response rate (ORR) and progression-free survival (PFS), have demonstrated inconsistent associations with overall survival (OS) benefits in immune checkpoint inhibitor (ICI) trials. Restricted mean duration of response (DOR) is a rigorous metric that combines both response status and duration information. However, its utility in clinical development has not been comprehensively explored.

OBJECTIVE

To determine whether using restricted mean DOR in phase 2 trials can advance promising regimens to phase 3 trials sooner and eliminate unfavorable regimens earlier and with a higher degree of confidence compared with PFS and ORR.

DESIGN, SETTING, AND PARTICIPANTS

This simulated modeling study randomized phase 2 screening trials by resampling 1376 patients from 2 completed randomized phase 3 trials of ICIs. Data were analyzed from August 2019 to July 2020.

EXPOSURES

Use of ICIs.

MAIN OUTCOMES AND MEASURES

Restricted mean DOR, PFS, ORR, and OS were estimated and compared between groups. Three scenarios were considered: (1) significant differences in OS, PFS, and ORR; (2) significant differences in OS and noticeable differences in ORR but not PFS; and (3) no differences in OS, PFS, or ORR. For each setting, 5000 randomized phase 2 trials with different sample sizes were simulated, with additional censoring applied to mimic staggered accruals and ensure fair comparisons between different analysis methods. Probabilities of concluding positive phase 2 trials using PFS, ORR, and DOR were summarized and compared.

RESULTS

The restricted mean DOR difference correctly estimated a positive OS benefit more frequently than did the ORR or PFS tests, across different sample sizes, significance levels, and censoring levels evaluated. When both OS and PFS differed, the ranges of true-positive or power rates were 79.2% to 98.7% for DOR, 56.3% to 93.2% for PFS, and 67.0% to 96.0% for ORR. When OS differed but PFS did not, the ranges of power rates were 24.0% to 76.0% for DOR, 3.0% to 19.0% for PFS, and 10.5% to 38.0% for ORR. When OS was similar, the false-positive rate of restricted mean DOR test was close to the chosen significance level.

CONCLUSIONS AND RELEVANCE

These findings suggest that restricted mean DOR in randomized phase 2 trials is potentially more sensitive and useful than PFS and ORR in estimating the subsequent phase 3 conclusions and, thus, may be considered to complementarily facilitate decision-making in future clinical development.

What is the Impact of the Analysis Method Used for Health State Utility Values on QALYs in Oncology? A Simulation Study Comparing Progression-Based and Time-to-Death Approaches

BACKGROUND

Health state utility values ('utilities') are an integral part of health technology assessment. Though traditionally categorised by disease status in oncology (i.e. progression), several recent assessments have adopted values calculated according to the time that measures were recorded before death. We conducted a simulation study to understand the limitations of each approach, with a focus on mismatches between the way utilities are generated, and analysed.

METHODS

Survival times were simulated based on published literature, with permutations of three utility generation mechanisms (UGMs) and utility analysis methods (UAMs): (1) progression based, (2) time-to-death based, and (3) a 'combination approach'. For each analysis quality-adjusted life-years (QALYs) were estimated. Goodness of fit was assessed via percentage mean error (%ME) and mean absolute error (%MAE). Scenario analyses were performed varying individual parameters, with complex scenarios mimicking published studies. The statistical code is provided for transparency and to aid future work in the area.

RESULTS

%ME and %MAE were lowest when the correct analysis form was specified (i.e. UGM and UAM aligned). Underestimates were produced when a time-to-death element was present in the UGM but not included in the UAM, while the 'combined' UAM produced overestimates irrespective of the UGM. Scenario analysis demonstrated the importance of the volume of available data beyond the initial time period, for example follow-up.

CONCLUSIONS

We show that the use of an incorrectly or over-specified UAM can result in substantial bias in the estimation of utilities. We present a flowchart to highlight the issues that may be faced.

Insights for Quantifying the Long-Term Benefit of Immunotherapy Using Quantile Regression

PURPOSE

Immunotherapy has been approved to treat many tumor types. However, one characteristic of this therapeutic class is that survival benefit is due to late immune response, which leads to a delayed treatment effect. Quantifying the benefit, if any, of such treatment, will thus require other metrics than the usual hazard ratio and different approaches have been proposed to quantify the long-term response of immunotherapy.

METHOD

In this paper, we suggest to use quantile regression for survival data to quantify the long-term benefit of immunotherapy. Our motivation is that this approach is not trial-specific and provides clinically understandable results without specifying arbitrary time points or the necessity to reach median survival, as is the case with other methods. We use reconstructed data from published Kaplan-Meier curves to illustrate our method.

RESULTS

On average, patients from the immunotherapy group have 60% chance to survive 5.46 months (95% CI, 2.57 to 9.02) more than patients in the chemotherapy group.

Analysis of time-to-event data using a flexible mixture model under a constraint of proportional hazards

Cox proportional hazards (PH) model evaluates the effects of interested covariates under PH assumption without specified the baseline hazard. In clinical trial applications, however, the explicitly estimated hazard or cumulative survival function for each treatment group helps to assess and interpret the meaning of treatment difference. In this paper, we propose to use a flexible mixture model under the PH constraint to fit the underline survival functions. Simulations are conducted to evaluate its performance and show that the proposed mixture PH model is very similar to the Cox PH model in terms of estimating the hazard ratio, bias, confidence interval coverage, type-I error and testing power. Application to several real clinical trial examples demonstrates that the results from this approach are almost identical to the results from Cox PH model. The explicitly estimated hazard function for each treatment group provides additional useful information and helps the interpretation of hazard comparisons.

Nonproportional Hazards in Network Meta-Analysis: Efficient Strategies for Model Building and Analysis

OBJECTIVES

To develop efficient approaches for fitting network meta-analysis (NMA) models with time-varying hazard ratios (such as fractional polynomials and piecewise constant models) to allow practitioners to investigate a broad range of models rapidly and to achieve a more robust and comprehensive model selection strategy.

METHODS

We reformulated the fractional polynomial and piecewise constant NMA models using analysis of variance-like parameterization. With this approach, both models are expressed as generalized linear models (GLMs) with time-varying covariates. Such models can be fitted efficiently with standard frequentist techniques. We applied our approach to the example data from the study by Jansen et al, in which fractional polynomial NMA models were introduced.

RESULTS

Fitting frequentist fixed-effect NMAs for a large initial set of candidate models took less than 1 second with standard GLM routines. This allowed for model selection from a large range of hazard ratio structures by comparing a set of criteria including Akaike information criterion/Bayesian information criterion, visual inspection of goodness-of-fit, and long-term extrapolations. The "best" models were then refitted in a Bayesian framework. Estimates agreed very closely.

CONCLUSIONS

NMA models with time-varying hazard ratios can be explored efficiently with a stepwise approach. A frequentist fixed-effect framework enables rapid exploration of different models. The best model can then be assessed further in a Bayesian framework to capture and propagate uncertainty for decision-making.

QIN Benchmarks for Clinical Translation of Quantitative Imaging Tools

The Quantitative Imaging Network of the National Cancer Institute is in its 10th year of operation, and research teams within the network are developing and validating clinical decision support software tools to measure or predict the response of cancers to various therapies. As projects progress from development activities to validation of quantitative imaging tools and methods, it is important to evaluate the performance and clinical readiness of the tools before committing them to prospective clinical trials. A variety of tests, including special challenges and tool benchmarking, have been instituted within the network to prepare the quantitative imaging tools for service in clinical trials. This article highlights the benchmarking process and provides a current evaluation of several tools in their transition from development to validation.

Treatment-Free Survival: A Novel Outcome Measure of the Effects of Immune Checkpoint Inhibition-A Pooled Analysis of Patients With Advanced Melanoma

PURPOSE

Outcome measures that comprehensively capture attributes of immuno-oncology agents, including prolonged treatment-free time and persistent treatment-related adverse events (TRAEs), are needed to complement conventional survival end points.

METHODS

We pooled data from the CheckMate 067 and 069 clinical trials of nivolumab and ipilimumab, as monotherapies or in combination, for patients with advanced melanoma. Treatment-free survival (TFS) was defined as the area between Kaplan-Meier curves for two conventional time-to-event end points, each defined from random assignment: time to immune checkpoint inhibitor (ICI) protocol therapy cessation and time to subsequent systemic therapy initiation or death. TFS was partitioned as time with and without toxicity by a third end point, time to cessation of both ICI therapy and toxicity. Toxicity included persistent and late-onset grade 3 or higher TRAEs. The area under each Kaplan-Meier curve was estimated by the 36-month restricted mean time.

RESULTS

At 36 months, many of the 1,077 patients who initiated ICI therapy were surviving free of subsequent therapy initiation (47% nivolumab plus ipilimumab, 37% nivolumab, 15% ipilimumab). The restricted mean TFS was longer for nivolumab plus ipilimumab (11.1 months) compared with nivolumab (4.6 months; difference, 6.5 months; 95% CI, 5.0 to 8.0 months) or ipilimumab (8.7 months; difference, 2.4 months; 95% CI, 0.8 to 4.1 months); restricted mean TFS represented 31% (3% with and 28% without toxicity), 13% (1% and 11%), and 24% (less than 1% and 23%) of the 36-month period, respectively, in the three treatment groups. TFS without toxicity was longer for nivolumab plus ipilimumab than nivolumab (difference, 6.0 months) or ipilimumab (difference, 1.7 months).

CONCLUSION

The analysis of TFS between ICI cessation and subsequent therapy initiation revealed longer TFS without toxicity for patients with advanced melanoma who received nivolumab plus ipilimumab compared with nivolumab or ipilimumab. Regardless of treatment, a small proportion of the TFS involved grade 3 or higher TRAEs.

Design and monitoring of survival trials based on restricted mean survival times

Background/Aims:

Restricted mean survival time has become a popular treatment effect measurement because of its nice interpretability. However, study design based on restricted mean survival times often requires extensive simulation studies as the variance structure is hard to obtain analytically. This article aims to provide a flexible approach to conduct study design and monitoring based on the restricted mean survival times without resorting to simulation.

Methods:

We assume that both the event time and censoring time distributions are piecewise exponential, and the accrual distribution is piecewise uniform, with which the restricted mean survival times and their variance–covariance structure can be conveniently computed.

Results:

Since we allow arbitrary number of pieces in the piecewise exponential and uniform distributions, the resulting model can handle a wide range of scenarios. The usefulness of the approach is demonstrated via an example.

Conclusion:

The proposed approach is flexible and useful in the design and monitoring of survival trials based on restricted mean survival times.

Pseudo- or real progression? An ovarian cancer patient under nivolumab: A case report

BACKGROUND

Checkpoint-Inhibition has revolutionized the treatment for several entities such as melanoma and renal cell carcinoma. The first encouraging experience in ovarian cancer was reported for nivolumab, a fully humanized anti-programmed death-1 antibody. Pseudoprogression is a new phenomenon associated with these novel immuno-oncologic agents. It can be explained by infiltrating leucocytes and edema that result in a temporary increase in tumor size and delayed subsequent shrinkage due to tumor cell destruction.

CASE SUMMARY

We report on a 47-year old patient with platinum-resistant ovarian cancer that was treated off-label with nivolumab 3mg/kg iv d1q14d. She first experienced classic pseudoprogression with inguinal lymph node swelling after cycle two and subsequent shrinkage. After 6 cycles she presented with rectal bleeding and progressive disease was diagnosed due to new tumor infiltration into the rectum.

CONCLUSION

Clinicians should be aware of pseudoprogression, its underlying mechanisms and strategies to discriminate pseudo- from real progression in ovarian cancer.

Trial Reporting in Immuno-Oncology (TRIO): An American Society of Clinical Oncology-Society for Immunotherapy of Cancer Statement

PURPOSE

To develop recommendations for clinical trial reporting that address the unique efficacy, toxicity, and combination and sequencing aspects of immuno-oncology (IO) treatments.

METHODS

ASCO and the Society for Immunotherapy of Cancer (SITC) convened a working group that consisted of practicing medical oncologists, immunologists, clinical researchers, biostatisticians, and representatives from industry and government to develop Trial Reporting in Immuno-Oncology (TRIO) recommendations. These recommendations are based on expert consensus, given that existing data to support evidence-based recommendations are limited.

CONCLUSION

The TRIO recommendations are intended to improve the reporting of IO clinical trials and thus provide more complete evidence on the relative benefits and risks of an IO therapeutic approach. Given the rapid expansion of the number of IO clinical trials and ongoing improvements to the evidence base supporting the use of IO treatments in clinical care, these recommendations will likely need regular revision as the IO field develops.

Trial Reporting in Immuno-Oncology (TRIO): an American society of clinical oncology-society for immunotherapy of cancer statement

Purpose

To develop recommendations for clinical trial reporting that address the unique efficacy, toxicity, and combination and sequencing aspects of immuno-oncology (IO) treatments.

Methods

ASCO and the Society for Immunotherapy of Cancer (SITC) convened a working group that consisted of practicing medical oncologists, immunologists, clinical researchers, biostatisticians, and representatives from industry and government to develop Trial Reporting in Immuno-Oncology (TRIO) recommendations. These recommendations are based on expert consensus, given that existing data to support evidence-based recommendations are limited.

Conclusion

The TRIO recommendations are intended to improve the reporting of IO clinical trials and thus provide more complete evidence on the relative benefits and risks of an IO therapeutic approach. Given the rapid expansion of the number of IO clinical trials and ongoing improvements to the evidence base supporting the use of IO treatments in clinical care, these recommendations will likely need regular revision as the IO field develops.