Four-year clinical update and treatment switching-adjusted outcomes with first-line nivolumab plus ipilimumab with chemotherapy for metastatic non-small cell lung cancer in the CheckMate 9LA randomized trial

BACKGROUND

In CheckMate 9LA, nivolumab plus ipilimumab with chemotherapy prolonged overall survival (OS) versus chemotherapy regardless of tumor PD-L1 expression or histology. We report updated efficacy and safety in all randomized patients with a minimum 4-year follow-up and an exploratory treatment-switching adjustment analysis in all treated patients who received chemotherapy and subsequent immunotherapy.

METHODS

Adults with stage IV/recurrent non-small cell lung cancer (NSCLC), no sensitizing EGFR/ALK alterations, and ECOG performance status ≤1 were randomized 1:1 to nivolumab 360 mg every 3 weeks plus ipilimumab 1 mg/kg every 6 weeks with chemotherapy (two cycles) or chemotherapy (four cycles, with optional maintenance pemetrexed for the nonsquamous population). Assessments included OS, progression-free survival, and objective response rate. Exploratory analyses included efficacy by tumor PD-L1 expression and histology and in patients who discontinued nivolumab plus ipilimumab with chemotherapy due to treatment-related adverse events (TRAEs), and a treatment-switching adjustment analysis using inverse probability of censoring weighting.

RESULTS

With a 47.9-month minimum follow-up for OS, nivolumab plus ipilimumab with chemotherapy continued to prolong OS over chemotherapy in all randomized patients (HR 0.74, 95% CI 0.63 to 0.87; 4-year OS rate: 21% versus 16%), regardless of tumor PD-L1 expression (HR (95% CI): PD-L1<1%, 0.66 (0.50 to 0.86) and ≥1%, 0.74 (0.60 to 0.92)) or histology (squamous, 0.64 (0.48 to 0.84) and non-squamous, 0.80 (0.66 to 0.97)). In patients who discontinued all components of nivolumab plus ipilimumab with chemotherapy due to TRAEs (n=61), the 4-year OS rate was 41%. With treatment-switching adjustment for the 36% of patients receiving subsequent immunotherapy in the chemotherapy arm, the estimated HR of nivolumab plus ipilimumab with chemotherapy versus chemotherapy was 0.66 (95% CI 0.55 to 0.80). No new safety signals were observed.

CONCLUSIONS

In this 4-year update, patients treated with nivolumab plus ipilimumab with chemotherapy continued to have long-term, durable efficacy benefit over chemotherapy regardless of tumor PD-L1 expression and/or histology. A greater estimated relative OS benefit was observed after adjustment for subsequent immunotherapy use in the chemotherapy arm. These results further support nivolumab plus ipilimumab with chemotherapy as a first-line treatment for patients with metastatic/recurrent NSCLC, including those with tumor PD-L1<1% or squamous histology, populations with high unmet needs.

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.

The net benefit for time-to-event outcome in oncology clinical trials with treatment switching

BACKGROUND

The net benefit is an effect measure for any type of endpoint, including the time-to-event outcome, and can provide intuitive and clinically meaningful interpretation. It is defined as the probability of a randomly selected subject from the experimental arm surviving by at least a clinically relevant time longer than a randomly selected subject from the control arm. In oncology clinical trials, an intercurrent event such as treatment switching is common, which potentially causes informative censoring; nevertheless, conventional methods for the net benefit are not able to deal with it. In this study, we proposed a new estimator using the inverse probability of censoring weighting (IPCW) method and illustrated an oncology clinical trial with treatment switching (the SHIVA study) to apply the proposed method under the estimand framework.

METHODS

The net benefit can be estimated using the survival functions of each treatment group. The proposed estimator was based on the survival functions estimated by the inverse probability of the censoring weighting method that can handle covariate-dependent censoring. The simulation study was undertaken to evaluate the operating characteristics of the proposed estimator under several scenarios; we varied the shapes of the survival curves, treatment effect, covariates effect on censoring, proportion of the censoring, threshold of the net benefit, and sample size. We also applied conventional methods (the scoring rules by Péron or Gehan) and the proposed method to the SHIVA study.

RESULTS

Our simulation study showed that the proposed estimator provided less biased results under the covariate-dependent censoring than existing estimators. When applying the proposed method to the SHIVA study, we were able to estimate the net benefit by incorporating the information of the covariates with different estimand strategies to address the intercurrent event of the treatment switching. However, the estimates of the proposed method and those of the aforementioned conventional methods were similar under the hypothetical strategy.

CONCLUSIONS

We proposed a new estimator of the net benefit that can include covariates to account for the possibly informative censoring. We also provided an illustrative analysis of the proposed method for the oncology clinical trial with treatment switching using the estimand framework. Our proposed new estimator is suitable for handling the intercurrent events that can potentially cause covariate-dependent censoring.

Structural cumulative survival models for estimation of treatment effects accounting for treatment switching in randomized experiments

Treatment switching in a randomized controlled trial occurs when a patient in one treatment arm switches to another arm during follow-up. This can occur at the point of disease progression, whereby patients in the control arm may be offered the experimental treatment. It is widely known that failure to account for treatment switching can seriously bias the estimated treatment causal effect. In this paper, we aim to account for the potential impact of treatment switching in a reanalysis evaluating the treatment effect of nucleoside reverse transcriptase inhibitors (NRTIs) on a safety outcome (time to first severe or worse sign or symptom) in participants receiving a new antiretroviral regimen that either included or omitted NRTIs in the optimized treatment that includes or omits NRTIs trial. We propose an estimator of a treatment causal effect for a censored time to event outcome under a structural cumulative survival model that leverages randomization as an instrumental variable to account for selective treatment switching. We establish that the proposed estimator is uniformly consistent and asymptotically Gaussian, with a consistent variance estimator and confidence intervals given, whose finite-sample performance is evaluated via extensive simulations. An R package 'ivsacim' implementing all proposed methods is freely available on R CRAN. Results indicate that adding NRTIs versus omitting NRTIs to a new optimized treatment regime may increase the risk for a safety outcome.

Antidepressant Augmentation versus Switch in Treatment-Resistant Geriatric Depression

BACKGROUND

The benefits and risks of augmenting or switching antidepressants in older adults with treatment-resistant depression have not been extensively studied.

METHODS

We conducted a two-step, open-label trial involving adults 60 years of age or older with treatment-resistant depression. In step 1, patients were randomly assigned in a 1:1:1 ratio to augmentation of existing antidepressant medication with aripiprazole, augmentation with bupropion, or a switch from existing antidepressant medication to bupropion. Patients who did not benefit from or were ineligible for step 1 were randomly assigned in step 2 in a 1:1 ratio to augmentation with lithium or a switch to nortriptyline. Each step lasted approximately 10 weeks. The primary outcome was the change from baseline in psychological well-being, assessed with the National Institutes of Health Toolbox Positive Affect and General Life Satisfaction subscales (population mean, 50; higher scores indicate greater well-being). A secondary outcome was remission of depression.

RESULTS

In step 1, a total of 619 patients were enrolled; 211 were assigned to aripiprazole augmentation, 206 to bupropion augmentation, and 202 to a switch to bupropion. Well-being scores improved by 4.83 points, 4.33 points, and 2.04 points, respectively. The difference between the aripiprazole-augmentation group and the switch-to-bupropion group was 2.79 points (95% CI, 0.56 to 5.02; P = 0.014, with a prespecified threshold P value of 0.017); the between-group differences were not significant for aripiprazole augmentation versus bupropion augmentation or for bupropion augmentation versus a switch to bupropion. Remission occurred in 28.9% of patients in the aripiprazole-augmentation group, 28.2% in the bupropion-augmentation group, and 19.3% in the switch-to-bupropion group. The rate of falls was highest with bupropion augmentation. In step 2, a total of 248 patients were enrolled; 127 were assigned to lithium augmentation and 121 to a switch to nortriptyline. Well-being scores improved by 3.17 points and 2.18 points, respectively (difference, 0.99; 95% CI, -1.92 to 3.91). Remission occurred in 18.9% of patients in the lithium-augmentation group and 21.5% in the switch-to-nortriptyline group; rates of falling were similar in the two groups.

CONCLUSIONS

In older adults with treatment-resistant depression, augmentation of existing antidepressants with aripiprazole improved well-being significantly more over 10 weeks than a switch to bupropion and was associated with a numerically higher incidence of remission. Among patients in whom augmentation or a switch to bupropion failed, changes in well-being and the occurrence of remission with lithium augmentation or a switch to nortriptyline were similar. (Funded by the Patient-Centered Outcomes Research Institute; OPTIMUM ClinicalTrials.gov number, NCT02960763.).

An Analysis of 5-Level Version of EQ-5D Adjusting for Treatment Switching: The Case of Patients With Epidermal Growth Factor Receptor T790M-Positive Nonsmall Cell Lung Cancer Treated With Osimertinib

OBJECTIVES

Treatment switching from control to treatment after disease progression is common in oncology trials. Analyses of survival data typically adjust for this bias, but such adjustments are rarely performed in analyses of patient-reported outcomes. This analysis aimed to examine the impact of adjusting for treatment switching on estimated treatment effects on 5-level version of EQ-5D (EQ-5D-5L) utilities and quality-adjusted life-years (QALYs). The AURA3 trial (NCT02151981) was a randomized controlled trial comparing osimertinib with platinum-based doublet chemotherapy (standard care) in patients with locally advanced or metastatic epidermal growth factor receptor mutant- and T790M-positive nonsmall cell lung cancer whose disease has progressed with previous epidermal growth factor receptor tyrosine kinase inhibitor therapy.

METHODS

Descriptive analyses were used to compare treatment arms. The primary analysis used a 2-stage least squares instrumental variable regression to estimate treatment effect adjusting for treatment crossover. Time to deterioration, defined from baseline to minimally important deterioration in EQ-5D-5L utility, was assessed using a rank preserving structural failure time model.

RESULTS

Intention-to-treat analysis of imputed data showed incremental QALYs for osimertinib of 0.23 at 60 weeks. Accounting for treatment switching increased this to 0.52 in the primary analysis and to 0.63 QALYs in sensitivity analysis at 150 weeks. Time to deterioration analysis showed longer health-related quality of life maintenance with osimertinib, of 12.76 weeks, although this was at the borderline of statistical significance (acceleration factor, ψ = -0.275; 95% confidence interval -0.50 to 0.00).

CONCLUSIONS

This analysis demonstrates methods to adjust for treatment switching in the analysis of EQ-5D-5L from clinical trials. Failure to account for crossover substantially underestimated the QALY gain for osimertinib.

Addressing Posttreatment Selection Bias in Comparative Effectiveness Research, Using Real-World Data and Simulation

To examine methodologies that address imbalanced treatment switching and censoring, 6 different analytical approaches were evaluated under a comparative effectiveness framework: intention-to-treat, as-treated, intention-to-treat with censor-weighting, as-treated with censor-weighting, time-varying exposure, and time-varying exposure with censor-weighting. Marginal structural models were employed to address time-varying exposure, confounding, and possibly informative censoring in an administrative data set of adult patients who were hospitalized with acute coronary syndrome and treated with either clopidogrel or ticagrelor. The effectiveness endpoint included first occurrence of death, myocardial infarction, or stroke. These methodologies were then applied across simulated data sets with varying frequencies of treatment switching and censoring to compare the effect estimate of each analysis. The findings suggest that implementing different analytical approaches has an impact on the point estimate and interpretation of analyses, especially when censoring is highly unbalanced.

A survey of physicians' treatment switching practice in people on long-term prophylaxis for hemophilia in five European countries

OBJECTIVE

Recombinant factor VIII and factor IX Fc fusion proteins (rFVIIIFc and rFIXFc) were developed with an extended half-life (EHL) to improve the management of people with hemophilia A (PwHA) and B (PwHB), respectively.

METHODS

This survey gathered physician-reported treatment decisions and physician views on outcomes in PwHA or PwHB who switched to rFVIIIFc or rFIXFc in the 12 months prior to study completion.

RESULTS

Physicians (N = 37) considered bleeds, pharmacokinetic parameters, joint health and adherence the most important factors to assess both in routine care and when deciding to switch to an EHL therapy. In the 12 months prior to study completion, 37 physicians switched 113 PwHA to rFVIIIFc and 25 physicians switched 36 PwHB to rFIXFc. Most PwH (>90%) had moderate or severe hemophilia and many (>60%) switched within 6 months of the survey. The main reason for switching PwHA to rFVIIIFc was to allow fewer injections (49%), while the main reason for switching PwHB to rFIXFc was the product becoming available for use (36%). Overall, 96% of PwHA and 89% of PwHB who were switched remained on these EHL products at the time of survey. Mean total weekly dose, injection frequency and annualized bleeding rate were reported to have reduced following switching.

CONCLUSION

This survey provides valuable insight into reasons for, and challenges to, the use of EHL products in clinical practice. Physicians perceived that switching to treatment with rFVIIIFc or rFIXFc can improve quality of life, treatment burden, disease control and adherence.

A modified weighted log-rank test for confirmatory trials with a high proportion of treatment switching

In confirmatory cancer clinical trials, overall survival (OS) is normally a primary endpoint in the intention-to-treat (ITT) analysis under regulatory standards. After the tumor progresses, it is common that patients allocated to the control group switch to the experimental treatment, or another drug in the same class. Such treatment switching may dilute the relative efficacy of the new drug compared to the control group, leading to lower statistical power. It would be possible to decrease the estimation bias by shortening the follow-up period but this may lead to a loss of information and power. Instead we propose a modified weighted log-rank test (mWLR) that aims at balancing these factors by down-weighting events occurring when many patients have switched treatment. As the weighting should be pre-specified and the impact of treatment switching is unknown, we predict the hazard ratio function and use it to compute the weights of the mWLR. The method may incorporate information from previous trials regarding the potential hazard ratio function over time. We are motivated by the RECORD-1 trial of everolimus against placebo in patients with metastatic renal-cell carcinoma where almost 80% of the patients in the placebo group received everolimus after disease progression. Extensive simulations show that the new test gives considerably higher efficiency than the standard log-rank test in realistic scenarios.

Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study

Treatment switching frequently occurs in clinical trials due to ethical reasons. Intent-to-treat analysis without adjusting for switching yields biased and inefficient estimates of the treatment effects. In this paper, we propose a class of semiparametric semi-competing risks transition survival models to accommodate two-way time-varying switching. Theoretical properties of the proposed method are examined. An efficient expectation-maximization algorithm is derived to obtain maximum likelihood estimates and model diagnostic tools. Existing software is used to implement the algorithm. Simulation studies are conducted to demonstrate the validity of the model. The proposed method is further applied to data from a clinical trial with patients having recurrent or metastatic squamous-cell carcinoma of head and neck.

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.

Plasma Thymidine Kinase Activity as a Biomarker in Patients with Luminal Metastatic Breast Cancer Treated with Palbociclib within the TREnd Trial

PURPOSE

Thymidine kinase 1 (TK1) is downstream to the CDK4/6 pathway, and TK activity (TKa) measured in blood is a dynamic marker of outcome in patients with advanced breast cancer (ABC). This study explores TK1 as a biomarker of palbociclib response, both in vitro and in patients with ABC.

EXPERIMENTAL DESIGN

Modulation of TK1 levels and activity by palbociclib were studied in seven estrogen receptor-positive breast cancer cell lines: sensitive (PDS) and with palbociclib acquired resistance (PDR). TKa was assayed in plasma obtained at baseline (T0), after one cycle (T1), and at disease progression on palbociclib (T2) in patients enrolled in the "To Reverse ENDocrine Resistance" (TREnd) trial (n = 46).

RESULTS

Among E2F-dependent genes, TK1 was significantly downregulated after short-term palbociclib. Early TKa reduction by palbociclib occurred in PDS but not in PDR cells. In patients, median TKa (mTKa) at T0 was 75 DiviTum units per liter (Du/L), with baseline TKa not proving prognostic. At T1, mTKa decreased to 35 Du/L, with a minority of patients (n = 8) showing an increase-correlating with a worse outcome than those with decreased/stable TKa (n = 33; mPFS 3.0 vs 9.0 months; P = 0.002). At T2, mTKa was 251 Du/L; patients with TKa above the median had worse outcomes on post-study treatment compared with those with lower TKa (2.9 vs 8.7 months; P = 0.05).

CONCLUSIONS

TK is a dynamic marker of resistance to palbociclib which may lead to early identification of patients in whom treatment escalation may be feasible. In addition, TKa may stratify prognosis in patients with acquired resistance to palbociclib.