Treatment switching in oncology trials and the acceptability of adjustment methods

Effectiveness and cost-effectiveness of radiofrequency denervation versus placebo for chronic and moderate to severe low back pain: study protocol for the RADICAL randomised controlled trial

INTRODUCTION

Low back pain (LBP) is the leading global cause of disability. Patients with moderate to severe LBP who respond positively to a diagnostic medial nerve branch block can be offered radiofrequency denervation (RFD). However, high-quality evidence on the effectiveness of RFD is lacking.

METHODS AND ANALYSIS

RADICAL (RADIofrequenCy denervAtion for Low back pain) is a double-blind, parallel-group, superiority randomised controlled trial. A total of 250 adults listed for RFD will be recruited from approximately 20 National Health Service (NHS) pain and spinal clinics. Recruitment processes will be optimised through qualitative research during a 12-month internal pilot phase. Participants will be randomised in theatre using a 1:1 allocation ratio to RFD or placebo. RFD technique will follow best practice guidelines developed for the trial. Placebo RFD will follow the same protocol, but the electrode tip temperature will not be raised. Participants who do not experience a clinically meaningful improvement in pain 3 months after randomisation will be offered the alternative intervention to the one provided at the outset without disclosing the original allocation. The primary clinical outcome will be pain severity, measured using a pain Numeric Rating Scale, at 3 months after randomisation. Secondary outcomes will be assessed up to 2 years after randomisation and include disability, health-related quality of life, psychological distress, time to pain recovery, satisfaction, adverse events, work outcomes and healthcare utilisation. The primary statistical analyses will be by intention to treat and will follow a prespecified analysis plan. The primary economic evaluation will take an NHS and social services perspective and estimate the discounted cost per quality-adjusted life-year and incremental net benefit of RFD over the 2-year follow-up period.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the London-Fulham Research Ethics Committee (21/LO/0471). Results will be disseminated in open-access publications and plain language summaries.

TRIAL REGISTRATION NUMBER

ISRCTN16473239.

Lenvatinib Plus Pembrolizumab Versus Sunitinib in First-Line Treatment of Advanced Renal Cell Carcinoma: Final Prespecified Overall Survival Analysis of CLEAR, a Phase III Study

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical trial updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.We present the final prespecified overall survival (OS) analysis of the open-label, phase III CLEAR study in treatment-naïve patients with advanced renal cell carcinoma (aRCC). With an additional follow-up of 23 months from the primary analysis, we report results from the lenvatinib plus pembrolizumab versus sunitinib comparison of CLEAR. Treatment-naïve patients with aRCC were randomly assigned to receive lenvatinib (20 mg orally once daily in 21-day cycles) plus pembrolizumab (200 mg intravenously once every 3 weeks) or sunitinib (50 mg orally once daily [4 weeks on/2 weeks off]). At this data cutoff date (July 31, 2022), the OS hazard ratio (HR) was 0.79 (95% CI, 0.63 to 0.99). The median OS (95% CI) was 53.7 months (95% CI, 48.7 to not estimable [NE]) with lenvatinib plus pembrolizumab versus 54.3 months (95% CI, 40.9 to NE) with sunitinib; 36-month OS rates (95% CI) were 66.4% (95% CI, 61.1 to 71.2) and 60.2% (95% CI, 54.6 to 65.2), respectively. The median progression-free survival (95% CI) was 23.9 months (95% CI, 20.8 to 27.7) with lenvatinib plus pembrolizumab and 9.2 months (95% CI, 6.0 to 11.0) with sunitinib (HR, 0.47 [95% CI, 0.38 to 0.57]). Objective response rate also favored the combination over sunitinib (71.3% v 36.7%; relative risk 1.94 [95% CI, 1.67 to 2.26]). Treatment-emergent adverse events occurred in >90% of patients who received either treatment. In conclusion, lenvatinib plus pembrolizumab achieved consistent, durable benefit with a manageable safety profile in treatment-naïve patients with aRCC.

A cautionary tale: an evaluation of the performance of treatment switching adjustment methods in a real world case study

BACKGROUND

Treatment switching in randomised controlled trials (RCTs) is a problem for health technology assessment when substantial proportions of patients switch onto effective treatments that would not be available in standard clinical practice. Often statistical methods are used to adjust for switching: these can be applied in different ways, and performance has been assessed in simulation studies, but not in real-world case studies. We assessed the performance of adjustment methods described in National Institute for Health and Care Excellence Decision Support Unit Technical Support Document 16, applying them to an RCT comparing panitumumab to best supportive care (BSC) in colorectal cancer, in which 76% of patients randomised to BSC switched onto panitumumab. The RCT resulted in intention-to-treat hazard ratios (HR) for overall survival (OS) of 1.00 (95% confidence interval [CI] 0.82-1.22) for all patients, and 0.99 (95% CI 0.75-1.29) for patients with wild-type KRAS (Kirsten rat sarcoma virus).

METHODS

We tested several applications of inverse probability of censoring weights (IPCW), rank preserving structural failure time models (RPSFTM) and simple and complex two-stage estimation (TSE) to estimate treatment effects that would have been observed if BSC patients had not switched onto panitumumab. To assess the performance of these analyses we ascertained the true effectiveness of panitumumab based on: (i) subsequent RCTs of panitumumab that disallowed treatment switching; (ii) studies of cetuximab that disallowed treatment switching, (iii) analyses demonstrating that only patients with wild-type KRAS benefit from panitumumab. These sources suggest the true OS HR for panitumumab is 0.76-0.77 (95% CI 0.60-0.98) for all patients, and 0.55-0.73 (95% CI 0.41-0.93) for patients with wild-type KRAS.

RESULTS

Some applications of IPCW and TSE provided treatment effect estimates that closely matched the point-estimates and CIs of the expected truths. However, other applications produced estimates towards the boundaries of the expected truths, with some TSE applications producing estimates that lay outside the expected true confidence intervals. The RPSFTM performed relatively poorly, with all applications providing treatment effect estimates close to 1, often with extremely wide confidence intervals.

CONCLUSIONS

Adjustment analyses may provide unreliable results. How each method is applied must be scrutinised to assess reliability.

GRADE concept 4: rating the certainty of evidence when study interventions or comparators differ from PICO targets

OBJECTIVES

This Grading of Recommendations Assessment, Development and Evaluation (GRADE) concept article offers systematic reviewers, guideline authors, and other users of evidence assistance in addressing randomized trial situations in which interventions or comparators differ from those in the target people, interventions, comparators, and outcomes. To clarify what GRADE considers under indirectness of interventions and comparators, we focus on a particular example: when comparator arm participants receive some or all aspects of the intervention management strategy (treatment switching).

STUDY DESIGN AND SETTING

An interdisciplinary panel of the GRADE working group members developed this concept article through an iterative review of examples in multiple teleconferences, small group sessions, and e-mail correspondence. After presentation at a GRADE working group meeting in November 2022, attendees approved the final concept paper, which we support with examples from systematic reviews and individual trials.

RESULTS

In the presence of safeguards against risk of bias, trials provide unbiased estimates of the effect of an intervention on the people as enrolled, the interventions as implemented, the comparators as implemented, and the outcomes as measured. Within the GRADE framework, differences in the people, interventions, comparators, and outcomes elements between the review or guideline recommendation targets and the trials as implemented constitute issues of indirectness. The intervention or comparator group management strategy as implemented, when it differs from the target comparator, constitutes one potential source of indirectness: Indirectness of interventions and comparators-comparator group receipt of the intervention constitutes a specific subcategory of said indirectness. The proportion of comparator arm participants that received the intervention and the apparent magnitude of effect bear on whether one should rate down, and if one does, to what extent.

CONCLUSION

Treatment switching and other differences between review or guideline recommendation target interventions and comparators vs. interventions and comparators as implemented in otherwise relevant trials are best considered issues of indirectness.

Maximizing the value of phase III trials in immuno-oncology: A checklist from the Society for Immunotherapy of Cancer (SITC)

The broad activity of agents blocking the programmed cell death protein 1 and its ligand (the PD-(L)1 axis) revolutionized oncology, offering long-term benefit to patients and even curative responses for tumors that were once associated with dismal prognosis. However, only a minority of patients experience durable clinical benefit with immune checkpoint inhibitor monotherapy in most disease settings. Spurred by preclinical and correlative studies to understand mechanisms of non-response to the PD-(L)1 antagonists and by combination studies in animal tumor models, many drug development programs were designed to combine anti-PD-(L)1 with a variety of approved and investigational chemotherapies, tumor-targeted therapies, antiangiogenic therapies, and other immunotherapies. Several immunotherapy combinations improved survival outcomes in a variety of indications including melanoma, lung, kidney, and liver cancer, among others. This immunotherapy renaissance, however, has led to many combinations being advanced to late-stage development without definitive predictive biomarkers, limited phase I and phase II data, or clinical trial designs that are not optimized for demonstrating the unique attributes of immune-related antitumor activity-for example, landmark progression-free survival and overall survival. The decision to activate a study at an individual site is investigator-driven, and generalized frameworks to evaluate the potential for phase III trials in immuno-oncology to yield positive data, particularly to increase the number of curative responses or otherwise advance the field have thus far been lacking. To assist in evaluating the potential value to patients and the immunotherapy field of phase III trials, the Society for Immunotherapy of Cancer (SITC) has developed a checklist for investigators, described in this manuscript. Although the checklist focuses on anti-PD-(L)1-based combinations, it may be applied to any regimen in which immune modulation is an important component of the antitumor effect.

Adjusting Overall Survival Estimates of Macitentan in Pulmonary Arterial Hypertension After Treatment Switching: Results from the SERAPHIN Study

Introduction

Evaluating overall survival in randomized controlled trials (RCTs) can often be confounded by bias introduced by treatment switching. SERAPHIN was a large RCT that evaluated the effects of long-term treatment with the endothelin receptor antagonist macitentan in patients with pulmonary arterial hypertension. In an intent-to-treat (ITT) analysis, a non-significant decrease in the risk of all-cause mortality up to study closure was reported with macitentan 10 mg versus placebo. As patients could switch treatment when experiencing symptoms of disease progression, this analysis attempts to adjust for the confounding effects on overall survival.

Methods

The inverse probability of censoring weighted (IPCW) and rank-preserving structural failure time (RPSFT) models were used to estimate the treatment effect on overall mortality had there been no treatment switching in SERAPHIN. Time to all-cause death was evaluated up to study closure. Treatment switching was defined as patients in the placebo group switching to open-label macitentan 10 mg, and patients in the macitentan 10 mg group prematurely discontinuing macitentan.

Results

By study closure, 73.2% (183/250) of patients in the placebo group had switched to macitentan 10 mg. Among these patients, exposure time to macitentan 10 mg represented 28.2% of total study treatment exposure (cumulative exposure 134.6 patient-years). At study closure, 24.8% (60/242) of patients in the macitentan 10 mg group were not receiving open-label macitentan; mean time not receiving macitentan was 44.3 weeks. The adjusted hazard ratios (HR) for overall survival using the IPCW and RPSFT methods were lower (HR 0.42, 95% confidence interval [CI] 0.22, 0.81; p = 0.009, and HR 0.33, 95% CI 0.04, 2.83, respectively) than the ITT unadjusted HR (0.80, 95% CI 0.51, 1.24).

Conclusion

These results from the current analyses indicate that in SERAPHIN, the standard ITT analysis was confounded by treatment switching resulting in an underestimation of the benefit of macitentan 10 mg on overall survival. By adjusting for switching, the IPCW and RPSFT models estimated a 58% and 67% reduction in risk of mortality, respectively, with macitentan 10 mg versus placebo.

Trial registration

ClinicalTrials.gov identifier: NCT00660179.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12325-022-02253-8.

Estimating Treatment-Switching Bias in a Randomized Clinical Trial of Ovarian Cancer Treatment: Combining Causal Inference with Decision-Analytic Modeling

BACKGROUND

Bevacizumab is efficacious in delaying ovarian cancer progression and controlling ascites. The ICON7 trial showed a significant benefit in overall survival for bevacizumab, whereas the GOG-218 trial did not. GOG-218 allowed control group patients to switch to bevacizumab upon progression, which may have biased the results. Lack of data on switching behavior prevented the application of g-methods to adjust for switching. The objective of this study was to apply decision-analytic modeling to estimate the impact of switching bias on causal treatment-effect estimates.

METHODS

We developed a causal decision-analytic Markov model (CDAMM) to emulate the GOG-218 trial and estimate overall survival. CDAMM input parameters were based on data from randomized clinical trials and the published literature. Overall switching proportion was based on GOG-218 trial information, whereas the proportion switching with and without ascites was estimated using calibration. We estimated the counterfactual treatment effect that would have been observed had no switching occurred by denying switching in the CDAMM.

RESULTS

The survival curves generated by the CDAMM matched well with the ones reported in the GOG-218 trial. The survival curve correcting for switching showed an estimated bias such that 79% of the true treatment effect could not be observed in the GOG-218 trial. Results were most sensitive to changes in the proportion progressing with severe ascites and mortality.

LIMITATIONS

We used a simplified model structure and based model parameters on published data and assumptions. Robustness of the CDAMM was tested and model assumptions transparently reported.

CONCLUSIONS

Medical-decision science methods may be merged with empirical methods of causal inference to integrate data from other sources where empirical data are not sufficient. We recommend collecting sufficient information on switching behavior when switching cannot be avoided.

Estimands for overall survival in clinical trials with treatment switching in oncology

An addendum of the ICH E9 guideline on Statistical Principles for Clinical Trials was released in November 2019 introducing the estimand framework. This new framework aims to align trial objectives and statistical analyses by requiring a precise definition of the inferential quantity of interest, that is, the estimand. This definition explicitly accounts for intercurrent events, such as switching to new anticancer therapies for the analysis of overall survival (OS), the gold standard in oncology. Traditionally, OS in confirmatory studies is analyzed using the intention-to-treat (ITT) approach comparing treatment groups as they were initially randomized regardless of whether treatment switching occurred and regardless of any subsequent therapy (treatment-policy strategy). Regulatory authorities and other stakeholders often consider ITT results as most relevant. However, the respective estimand only yields a clinically meaningful comparison of two treatment arms if subsequent therapies are already approved and reflect clinical practice. We illustrate different scenarios where subsequent therapies are not yet approved drugs and thus do not reflect clinical practice. In such situations the hypothetical strategy could be more meaningful from patient's and prescriber's perspective. The cross-industry Oncology Estimand Working Group (www.oncoestimand.org) was initiated to foster a common understanding and consistent implementation of the estimand framework in oncology clinical trials. This paper summarizes the group's recommendations for appropriate estimands in the presence of treatment switching, one of the key intercurrent events in oncology clinical trials. We also discuss how different choices of estimands may impact study design, data collection, trial conduct, analysis, and interpretation.

Exploring the Impact of Treatment Switching on Overall Survival from the PROfound Study in Homologous Recombination Repair (HRR)-Mutated Metastatic Castration-Resistant Prostate Cancer (mCRPC)

Background

In oncology trials, treatment switching from the comparator to the experimental regimen is often allowed but may lead to underestimating overall survival (OS) of an experimental therapy.

Objective

This study evaluates the impact of treatment switching from control to olaparib on OS using the final survival data from the PROfound study and compares validated adjustment methods to estimate the magnitude of OS benefit with olaparib.

Patients and methods

The primary population from PROfound (Cohort A) was included, alongside two populations approved for treatment with olaparib by the European Medicines Agency and US Food and Drug Administration: BRCAm and Cohort A+B (excluding the PPP2R2A gene). Five methods were explored to adjust for switching: excluding or censoring patients in the control arm who receive subsequent olaparib, Rank Preserving Structural Failure Time Model (RPSFTM), Inverse Probability of Censoring Weights, and Two-Stage Estimation.

Results

The RPSFTM was considered the most appropriate approach for PROfound as the results were robust to sensitivity analysis testing of the common treatment effect assumption. For Cohort A, the final OS hazard ratio reduced from 0.69 (95% CI 0.5–0.97) to between 0.42 (0.18–0.90) and 0.52 (0.31–1.00) for olaparib versus control, depending on the RPSFTM selected. Median OS reduced from 14.7 months to between 11.73 and 12.63 months for control.

Conclusions

The magnitude of the statistically significant (P < 0.05) survival benefit of olaparib versus control observed in Cohort A of PROfound is likely to be underestimated if adjustment for treatment switching from control to olaparib is not conducted. The RPSFTM was considered the most plausible method, although further development and validation of robust methods to estimate the magnitude of impact of treatment switching is needed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11523-021-00837-y.

Critical review of oncology clinical trial design under non-proportional hazards

In trials of novel immuno-oncology drugs, the proportional hazards (PH) assumption often does not hold for the primary time-to-event (TTE) efficacy endpoint, likely due to the unique mechanism of action of these drugs. In practice, when it is anticipated that PH may not hold for the TTE endpoint with respect to treatment, the sample size is often still calculated under the PH assumption, and the hazard ratio (HR) from the Cox model is still reported as the primary measure of the treatment effect. Sensitivity analyses of the TTE data using methods that are suitable under non-proportional hazards (non-PH) are commonly pre-planned. In cases where a substantial deviation from the PH assumption is likely, we suggest designing the trial, calculating the sample size and analyzing the data, using a suitable method that accounts for non-PH, after gaining alignment with regulatory authorities. In this comprehensive review article, we describe methods to design a randomized oncology trial, calculate the sample size, analyze the trial data and obtain summary measures of the treatment effect in the presence of non-PH. For each method, we provide examples of its use from the recent oncology trials literature. We also summarize in the Appendix some methods to conduct sensitivity analyses for overall survival (OS) when patients in a randomized trial switch or cross-over to the other treatment arm after disease progression on the initial treatment arm, and obtain an adjusted or weighted HR for OS in the presence of cross-over. This is an example of the treatment itself changing at a specific point in time - this cross-over may lead to a non-PH pattern of diminishing treatment effect.

Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding

In oncology trials, control group patients often switch onto the experimental treatment during follow-up, usually after disease progression. In this case, an intention-to-treat analysis will not address the policy question of interest - that of whether the new treatment represents an effective and cost-effective use of health care resources, compared to the standard treatment. Rank preserving structural failure time models (RPSFTM), inverse probability of censoring weights (IPCW) and two-stage estimation (TSE) have often been used to adjust for switching to inform treatment reimbursement policy decisions. TSE has been applied using a simple approach (TSEsimp), assuming no time-dependent confounding between the time of disease progression and the time of switch. This is problematic if there is a delay between progression and switch. In this paper we introduce TSEgest, which uses structural nested models and g-estimation to account for time-dependent confounding, and compare it to TSEsimp, RPSFTM and IPCW. We simulated scenarios where control group patients could switch onto the experimental treatment with and without time-dependent confounding being present. We varied switching proportions, treatment effects and censoring proportions. We assessed adjustment methods according to their estimation of control group restricted mean survival times that would have been observed in the absence of switching. All methods performed well in scenarios with no time-dependent confounding. TSEgest and RPSFTM continued to perform well in scenarios with time-dependent confounding, but TSEsimp resulted in substantial bias. IPCW also performed well in scenarios with time-dependent confounding, except when inverse probability weights were high in relation to the size of the group being subjected to weighting, which occurred when there was a combination of modest sample size and high switching proportions. TSEgest represents a useful addition to the collection of methods that may be used to adjust for treatment switching in trials in order to address policy-relevant questions.

Adjusting Overall Survival Estimates for Treatment Switching in Metastatic, Castration-Sensitive Prostate Cancer: Results from the LATITUDE Study

Background

LATITUDE was the first phase 3 trial examining the survival benefit of adding abiraterone acetate (AA) + prednisone (P) to androgen-deprivation therapy (ADT) in newly diagnosed metastatic, castration-sensitive prostate cancer (mCSPC). Due to significant improvement in overall survival after the first interim analysis, patients in the placebos + ADT arm could switch to AA + P + ADT during an open-label extension. As in other studies where switching is allowed, statistical adjustments are needed to assess the real benefit of new drugs.

Patients and Methods

This was a post hoc analysis to estimate the true survival benefit of AA + P + ADT in patients with newly diagnosed mCSPC by applying statistical adjustments commonly used to adjust for treatment switching.

Results

Of 112 patients still receiving placebos + ADT at the first interim analysis, 72 switched to AA + P + ADT during the open-label extension. Final analysis was conducted after median follow-up of 51.8 months. Compared to the placebos + ADT arm, the risk of death in the AA + P + ADT arm was 34% lower [hazard ratio (HR) = 0.663 (95% confidence interval 0.566–0.778)] by unadjusted intent-to-treat analysis, 37% lower [HR = 0.629 (95% confidence interval 0.526–0.753)] by rank preserving structure failure time modeling, and 38% lower [HR = 0.616 (95% confidence interval 0.524–0.724)] by inverse probability of censoring weights.

Conclusions

Analyses adjusting for treatment switching using two different statistical approaches confirm the improved survival benefit of adding AA + P to ADT in patients with newly diagnosed mCSPC.

Trial Registration

ClinicalTrials.gov identifier NCT01715285.

Electronic supplementary material

The online version of this article (10.1007/s11523-019-00685-x) contains supplementary material, which is available to authorized users.

Adjusting for Treatment Switching in Oncology Trials: A Systematic Review and Recommendations for Reporting

OBJECTIVES

To systematically review the quality of reporting on the application of switching adjustment approaches in published oncology trials and industry submissions to the National Institute for Health and Care Excellence Although methods such as the rank preserving structural failure time model (RPSFTM) and inverse probability of censoring weights (IPCW) have been developed to address treatment switching, the approaches are not widely accepted within health technology assessment. This limited acceptance may partly be a consequence of poor reporting on their application.

METHODS

Published trials and industry submissions were obtained from searches of PubMed and nice.org.uk, respectively. The quality of reporting in these studies was judged against a checklist of reporting recommendations, which was developed by the authors based on detailed considerations of the methods.

RESULTS

Thirteen published trials and 8 submissions to nice.org.uk satisfied inclusion criteria. The quality of reporting around the implementation of the RPSFTM and IPCW methods was generally poor. Few studies stated whether the adjustment approach was prespecified, more than a third failed to provide any justification for the chosen method, and nearly half neglected to perform sensitivity analyses. Further, it was often unclear how the RPSFTM and IPCW methods were implemented.

CONCLUSIONS

Inadequate reporting on the application of switching adjustment methods increases uncertainty around results, which may contribute to the limited acceptance of these methods by decision makers. The proposed reporting recommendations aim to support the improved interpretation of analyses undertaken to adjust for treatment switching.

Assessing the Long-Term Effectiveness of Cladribine vs. Placebo in the Relapsing-Remitting Multiple Sclerosis CLARITY Randomized Controlled Trial and CLARITY Extension Using Treatment Switching Adjustment Methods

OBJECTIVES

Treatment switching adjustment methods are often used to adjust for switching in oncology randomized controlled trials (RCTs). In this exploratory analysis, we apply these methods to adjust for treatment changes in the setting of an RCT followed by an extension study in relapsing-remitting multiple sclerosis.

METHODS

The CLARITY trial evaluated cladribine tablets versus placebo over 96 weeks. In the 96-week CLARITY Extension, patients who received placebo in CLARITY received cladribine tablets; patients who received cladribine tablets in CLARITY were re-randomized to placebo or cladribine tablets. End points were time to first qualifying relapse (FQR) and time to 3- and 6-month confirmed disability progression (3mCDP, 6mCDP). We aimed to compare the effectiveness of cladribine tablets with placebo over CLARITY and the extension. The rank-preserving structural failure time model (RPSFTM) and iterative parameter estimation (IPE) were used to estimate what would have happened if patients had received placebo in CLARITY and the extension versus patients that received cladribine tablets and switched to placebo. To gauge whether treatment effect waned after the 96 weeks of CLARITY, we compared hazard ratios (HRs) from the adjustment analysis with HRs from CLARITY.

RESULTS

The RPSFTM resulted in an HR of 0.48 [95% confidence interval (CI) 0.36-0.62] for FQR, 0.62 (95% CI 0.46-0.84) for 3mCDP and 0.62 (95% CI 0.44-0.88) for 6mCDP. IPE algorithm results were similar. CLARITY HRs were 0.44 (95% CI 0.34-0.58), 0.60 (95% CI 0.41-0.87) and 0.58 (95% CI 0.40-0.83) for FQR, 3mCDP and 6mCDP, respectively.

CONCLUSIONS

Treatment switching adjustment methods are applicable in non-oncology settings. Adjusted CLARITY plus CLARITY Extension HRs were similar to the CLARITY HRs, demonstrating significant treatment benefits associated with cladribine tablets versus placebo.

FUNDING

EMD Serono, Inc. (a business of Merck KGaA, Darmstadt, Germany).

Performance of randomization-based causal methods with and without integrating external data sources for adjusting overall survival in case of extensive treatment switches in placebo-controlled randomized oncology phase 3 trials

In recent placebo-controlled randomized phase 3 oncology trials, evaluation of overall survival with frequent crossover is crucial for regulatory and pricing decisions. The problem is that an intention-to-treat based analysis causes a substantial loss of power to detect causal survival effect without crossover, and performance of existing methods is not satisfactory. In this article, our aims were to evaluate properties of the existing and a proposed Bayesian power prior method where data from an external trial is available. Simulation results suggested that proposed method was the most powerful under typical scenarios where patients with better prognosis are likely to crossover.

Impact of Nonrandomized Dropout on Treatment Switching Adjustment in the Relapsing-Remitting Multiple Sclerosis CLARITY Trial and the CLARITY Extension Study

OBJECTIVES

Statistical methods to adjust for treatment switching are commonly applied to randomized controlled trials (RCTs) in oncology. Nevertheless, RCTs with extension studies incorporating nonrandomized dropout require consideration of alternative adjustment methods. The current study used a recognized method and a novel method to adjust for treatment switching in relapsing-remitting multiple sclerosis (MS).

METHODS

The Cladribine Tablets Treating Multiple Sclerosis Orally (CLARITY) RCT evaluated the efficacy of cladribine versus placebo over 96 weeks. Many (but not all) CLARITY participants enrolled in the 96-week CLARITY extension study; placebo-treated patients from CLARITY received cladribine (PP→LL), and cladribine-treated patients were re-randomized to placebo (LL→PP) or continued cladribine (LL→LL). End points were time to first qualifying relapse (FQR) and time to 3-month and 6-month confirmed disability progression (3mCDP, 6mCDP). We aimed to estimate the effectiveness of the LL→PP treatment strategy compared with a counterfactual (unobserved) PP→PP strategy. We applied the commonly used rank-preserving structural failure time model (RPSFTM) and a novel approach that combined propensity score matching (PSM) with inverse probability of censoring weights (IPCW).

RESULTS

The RPSFTM resulted in LL→PP versus PP→PP hazard ratios (HRs) of 0.48 (95% confidence interval [CI] 0.36-0.62) for FQR, 0.62 (95% CI 0.46-0.84) for 3mCDP, and 0.62 (95% CI 0.44-0.88) for 6mCDP. The PSM+IPCW resulted in HRs of 0.47 (95% CI 0.38-0.63) for FQR, 0.61 (95% CI 0.43-0.86) for 3mCDP, and 0.63 (95% CI 0.40-0.87) for 6mCDP.

CONCLUSIONS

The PSM+IPCW HRs were consistent with those from the RPSFTM, suggesting that the results were not substantially biased by informative dropout, assuming that all relevant confounders were controlled for. There was no statistical evidence of a reduction in the cladribine treatment effect during the extension period.

Two-stage estimation to adjust for treatment switching in randomised trials: a simulation study investigating the use of inverse probability weighting instead of re-censoring

Background

Treatment switching is common in randomised trials of oncology treatments, with control group patients switching onto the experimental treatment during follow-up. This distorts an intention-to-treat comparison of the treatments under investigation. Two-stage estimation (TSE) can be used to estimate counterfactual survival times for patients who switch treatments – that is, survival times that would have been observed in the absence of switching. However, when switchers do not die during the study, counterfactual censoring times are estimated, inducing informative censoring. Re-censoring is usually applied alongside TSE to resolve this problem, but results in lost longer-term information – a major concern if the objective is to estimate long-term treatment effects, as is usually the case in health technology assessment. Inverse probability of censoring weights (IPCW) represents an alternative technique for addressing informative censoring but has not before been combined with TSE. We aim to determine whether combining TSE with IPCW (TSEipcw) represents a valid alternative to re-censoring.

Methods

We conducted a simulation study to compare TSEipcw to TSE with and without re-censoring. We simulated 48 scenarios where control group patients could switch onto the experimental treatment, with switching affected by prognosis. We investigated various switching proportions, treatment effects, survival function shapes, disease severities and switcher prognoses. We assessed the alternative TSE applications according to their estimation of control group restricted mean survival (RMST) that would have been observed in the absence of switching up to the end of trial follow-up.

Results

TSEipcw performed well when its weights had a low coefficient of variation, but performed poorly when the coefficient of variation was high. Re-censored analyses usually under-estimated control group RMST, whereas non-re-censored analyses usually produced over-estimates, with bias more serious when the treatment effect was high. In scenarios where TSEipcw performed well, it produced low bias that was often between the two extremes associated with the re-censoring and non-recensoring options.

Conclusions

Treatment switching adjustment analyses using TSE should be conducted with re-censoring, without re-censoring, and with IPCW to explore the sensitivity in results to these application options. This should allow analysts and decision-makers to better interpret the results of adjustment analyses.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0709-9) contains supplementary material, which is available to authorized users.

Cost-effectiveness of Pembrolizumab as Second-line Therapy for the Treatment of Locally Advanced or Metastatic Urothelial Carcinoma in Sweden

BACKGROUND

Urothelial carcinoma (UC) is the most common subtype of bladder cancer. The randomized phase 3 KEYNOTE-045 trial showed that pembrolizumab, used as second-line therapy significantly prolonged overall survival with fewer treatment-related adverse events than chemotherapy for advanced UC. Pembrolizumab has been approved by the European Medicines Agency for the treatment of locally advanced or metastatic UC in adults who have received platinum-containing chemotherapy. Many European countries use cost-effectiveness analysis to inform reimbursement decisions.

OBJECTIVE

To assess the cost-effectiveness of pembrolizumab as second-line therapy for the treatment of advanced UC from a Swedish health care perspective.

DESIGN, SETTING, AND PARTICIPANTS

We developed a partitioned-survival model to assess the costs and effectiveness of pembrolizumab compared with vinflunine (base case), paclitaxel, or docetaxel monotherapy in patients with advanced UC over a 15-yr time horizon. We obtained Kaplan-Meier estimates for survival endpoints, adverse events, and utility data from KEYNOTE-045.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We performed parametric extrapolations to estimate overall and progression-free survival beyond the clinical trial period. Swedish costs and utility weights were used to estimate total costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs). We performed deterministic and probabilistic sensitivity analyses to assess the robustness of the model results.

RESULTS AND LIMITATIONS

In the base-case analysis, pembrolizumab resulted in a mean survival gain of 1.66 years (1.38 QALYs) at an incremental cost of €69852 and an ICER of €50529/QALY gained versus vinflunine monotherapy. ICERs for other chemotherapies were €81356/QALY for pembrolizumab versus paclitaxel or docetaxel monotherapy, and €71924/QALY for pembrolizumab versus paclitaxel, docetaxel, or vinflunine monotherapy. Long-term follow-up from KEYNOTE-045 and real-world data are needed to validate the extrapolations.

CONCLUSIONS

The results indicate that pembrolizumab improves survival, increases QALYs, and is cost-effective as second-line therapy at a willingness-to-pay threshold of €100000/QALY for the treatment of advanced UC.

PATIENT SUMMARY

To date, pembrolizumab is the only treatment associated with a significant overall survival benefit compared with chemotherapy in a randomized controlled trial as second-line therapy for advanced urothelial carcinoma. Our trial-based cost-effectiveness analysis suggests that pembrolizumab is a cost-effective option over chemotherapy in patients with advanced urothelial carcinoma after platinum-based therapy in Sweden.

Accounting for Uncertainty in Decision Analytic Models Using Rank Preserving Structural Failure Time Modeling: Application to Parametric Survival Models

OBJECTIVES

Rank Preserving Structural Failure Time models are one of the most commonly used statistical methods to adjust for treatment switching in oncology clinical trials. The method is often applied in a decision analytic model without appropriately accounting for additional uncertainty when determining the allocation of health care resources. The aim of the study is to describe novel approaches to adequately account for uncertainty when using a Rank Preserving Structural Failure Time model in a decision analytic model.

METHODS

Using two examples, we tested and compared the performance of the novel Test-based method with the resampling bootstrap method and with the conventional approach of no adjustment. In the first example, we simulated life expectancy using a simple decision analytic model based on a hypothetical oncology trial with treatment switching. In the second example, we applied the adjustment method on published data when no individual patient data were available.

RESULTS

Mean estimates of overall and incremental life expectancy were similar across methods. However, the bootstrapped and test-based estimates consistently produced greater estimates of uncertainty compared with the estimate without any adjustment applied. Similar results were observed when using the test based approach on a published data showing that failing to adjust for uncertainty led to smaller confidence intervals.

CONCLUSIONS

Both the bootstrapping and test-based approaches provide a solution to appropriately incorporate uncertainty, with the benefit that the latter can implemented by researchers in the absence of individual patient data.

Adjusting Overall Survival Estimates after Treatment Switching: a Case Study in Metastatic Castration-Resistant Prostate Cancer

BACKGROUND

If patients in oncology trials receive subsequent therapy, standard intention-to-treat (ITT) analyses may inaccurately estimate the overall survival (OS) effect of the investigational product. In this context, a post-hoc analysis of the phase 3 PREVAIL study was performed with the aim to compare enzalutamide with placebo in terms of OS, adjusting for potential confounding from switching to antineoplastic therapies that are not part of standard metastatic castration-resistant prostate cancer (mCRPC) treatment pathways in some jurisdictions.

METHODS

The PREVAIL study, which included 1717 chemotherapy-naïve men with mCRPC randomized to treatment with enzalutamide 160 mg/day or placebo, was stopped after a planned interim survival analysis revealed a benefit in favor of enzalutamide. Data from this cutoff point were confounded by switching from both arms and so were evaluated in terms of OS using two switching adjustment methods: the two-stage accelerated failure time model (two-stage method) and inverse probability of censoring weights (IPCW).

RESULTS

Following adjustment for switching to nonstandard antineoplastic therapies by 14.8 (129/872 patients) and 21.3% (180/845 patients) of patients initially randomized to enzalutamide and placebo, respectively, the two-stage and IPCW methods both resulted in numerical reductions in the hazard ratio (HR) for OS [HR 0.66, 95% confidence interval (CI) 0.57-0.81 and HR 0.63, 95% CI 0.52-0.75, respectively] for enzalutamide compared to placebo versus the unadjusted ITT analysis (HR 0.71, 95% CI 0.60-0.84). These results suggest a slightly greater effect of enzalutamide on OS than originally reported.

CONCLUSION

In the PREVAIL study, switching to nonstandard antineoplastic mCRPC therapies resulted in the ITT analysis of primary data underestimating the benefit of enzalutamide on OS.