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

Is inverse probability of censoring weighting a safer choice than per-protocol analysis in clinical trials?

Deviation from the treatment strategy under investigation occurs in many clinical trials. We term this intervention deviation. Per-protocol analyses are widely adopted to estimate a hypothetical estimand without the occurrence of intervention deviation. Per-protocol by censoring is prone to selection bias when intervention deviation is associated with time-varying confounders that also influence counterfactual outcomes. This can be corrected by inverse probability of censoring weighting, which gives extra weight to uncensored individuals who had similar prognostic characteristics to censored individuals. Such weights are computed by modelling selected covariates. Inverse probability of censoring weighting relies on the no unmeasured confounding assumption whose plausibility is not statistically testable. Suboptimal implementation of inverse probability of censoring weighting which violates the assumption will lead to bias. In a simulation study, we evaluated the performance of per-protocol and inverse probability of censoring weighting with different implementations to explore whether inverse probability of censoring weighting is a safe alternative to per-protocol. Scenarios were designed to vary intervention deviation in one or both arms with different prevalences, correlation between two confounders, effect of each confounder, and sample size. Results show that inverse probability of censoring weighting with different combinations of covariates outperforms per-protocol in most scenarios, except for an unusual case where selection bias caused by two confounders is in two directions, and 'cancels' out.

New Methods for Two-Stage Treatment Switching Estimation

Treatment switching is common in randomized trials of oncology treatments. For example, control group patients may receive the experimental treatment as a subsequent therapy. One possible estimand is the effect of trial treatment if this type of switching had instead not occurred. Two-stage estimation is an established approach for estimating this estimand. We argue that other estimands of interest instead describe the effect of trial treatments if the proportion of patients who switched was different. We give precise definitions of such estimands. By motivating estimands using real-world data, decision-making in universal health care systems is facilitated. Focusing on estimation, we show that an alternative choice of secondary baseline, the time of first subsequent treatment, is easily defined, and widely applicable, and makes alternative estimands amenable to two-stage estimation. We develop methodology using propensity scores, to adjust for confounding at a secondary baseline, and a new quantile matching technique that can be used to implement any parametric form of the post-secondary baseline survival model. Our methodology was motivated by a recent immuno-oncology trial where a substantial proportion of control group patients subsequently received a form of immunotherapy.

A multiple imputation approach in enhancing causal inference for overall survival in randomized controlled trials with crossover

Crossover or treatment-switching in randomized controlled trials presents notable challenges not only in the development and approval of new drugs but also poses a complex issue in their reimbursement, especially in oncology. When the investigational treatment is superior to control, crossover from control to investigational treatment upon disease progression or for other reasons will likely cause the underestimation of treatment benefit. Rank Preserving Structural Failure Time (RPSFT) and Two-Stage Estimation (TSE) methods are commonly employed to adjust for treatment switching by estimating counterfactual survival times. However, these methods may induce informative censoring by adjusting censoring times for switchers while leaving those for non-switchers unchanged. Existing approaches such as re-censoring or inverse probability of censoring weighting (IPCW) are often used alongside RPSFT or TSE to handle informative censoring, but may result in long-term information loss or suffer from model misspecification. In this paper, Kaplan-Meier multiple imputation with bootstrap procedure (KMIB) is proposed to address the informative censoring issues in adjustment methods for treatment switching. This approach can avoid information loss and is robust to model misspecification. In the scenarios that we investigate, simulation studies show that this approach performs better than other adjustment methods when the treatment effect is small, and behave similarly under other scenarios despite different switching probability. A case study in non-small cell lung cancer (NSCLC) is also provided to demonstrate the use of this method.

Treatment-Switching Adjustment of Overall Survival in CheckMate 227 Part 1 Evaluating First-Line Nivolumab Plus Ipilimumab Versus Chemotherapy for Metastatic Nonsmall Cell Lung Cancer

OBJECTIVES

CheckMate 227 (NCT02477826) evaluated first-line nivolumab-plus-ipilimumab versus chemotherapy in patients with metastatic nonsmall cell lung cancer (NSCLC) with programmed death ligand 1 (PD-L1) expression ≥ 1% or < 1% and no EGFR/ALK alterations. However, many patients randomized to chemotherapy received subsequent immunotherapy. Here, overall survival (OS) and relative OS benefit of nivolumab-plus-ipilimumab were adjusted for potential bias introduced by treatment switching.

MATERIALS AND METHODS

Treatment-switching adjustment analyses were conducted following the NICE Decision Support Unit Technical Support Document 16, for CheckMate 227 Part 1 OS data from treated patients (database lock, July 2, 2019). Inverse probability of censoring weighting (IPCW) was used in the base-case analysis; other methods were explored as sensitivity analyses.

RESULTS

Of 1166 randomized patients, 391 (PD-L1 ≥ 1%) and 185 (PD-L1 < 1%) patients received nivolumab-plus-ipilimumab; 387 (PD-L1 ≥ 1%) and 183 (PD-L1 < 1%) patients received chemotherapy, with 29.3-month minimum follow-up. Among chemotherapy-treated patients, 169/387 (43.7%; PD-L1 ≥ 1%) and 66/183 (36.1%; PD-L1 < 1%) switched to immunotherapy poststudy. Among treated patients, median OS was 17.4 months with nivolumab-plus-ipilimumab versus 14.9 months with chemotherapy (hazard ratio [HR], 0.80; 95% confidence interval [CI], 0.68-0.95) in the PD-L1 ≥ 1% subgroup and 17.1 versus 12.4 months (HR, 0.62; 95% CI, 0.49-0.80) in the PD-L1 < 1% subgroup. After treatment-switching adjustment using IPCW, the HR (95% CI) for OS for nivolumab-plus-ipilimumab versus chemotherapy was reduced to 0.68 (0.56-0.83; PD-L1 ≥ 1%) and 0.53 (0.40-0.69; PD-L1 < 1%). Sensitivity analyses supported the robustness of the results.

CONCLUSION

Treatment-switching adjustments resulted in a greater estimated relative OS benefit with first-line nivolumab-plus-ipilimumab versus chemotherapy in patients with metastatic NSCLC.

Time-to-event estimands and loss to follow-up in oncology in light of the estimands guidance

Time-to-event estimands are central to many oncology clinical trials. The estimands framework (addendum to the ICH E9 guideline) calls for precisely defining the treatment effect of interest to align with the clinical question of interest and requires predefining the handling of intercurrent events (ICEs) that occur after treatment initiation and "affect either the interpretation or the existence of the measurements associated with the clinical question of interest." We discuss a practical problem in clinical trial design and execution, that is, in some clinical contexts it is not feasible to systematically follow patients to an event of interest. Loss to follow-up in the presence of intercurrent events can affect the meaning and interpretation of the study results. We provide recommendations for trial design, stressing the need for close alignment of the clinical question of interest and study design, impact on data collection, and other practical implications. When patients cannot be systematically followed, compromise may be necessary to select the best available estimand that can be feasibly estimated under the circumstances. We discuss the use of sensitivity and supplementary analyses to examine assumptions of interest.

[Special issue PRO] Considering endpoints for comparative tolerability of cancer treatments using patient report given the estimand framework

Regulatory agencies are advancing the use of systematic approaches to collect patient experience data, including patient-reported outcomes (PROs), in cancer clinical trials to inform regulatory decision-making. Due in part to clinician under-reporting of symptomatic adverse events, there is a growing recognition that evaluation of cancer treatment tolerability should include the patient experience, both in terms of the overall side effect impact and symptomatic adverse events. Methodologies around implementation, analysis, and interpretation of "patient" reported tolerability are under development, and current approaches are largely descriptive. There is robust guidance for use of PROs as efficacy endpoints to compare cancer treatments, but it is unclear to what extent this can be relied-upon to develop tolerability endpoints. An important consideration when developing endpoints to compare tolerability between treatments is the linkage of trial design, objectives, and statistical analysis. Despite interest in and frequent collection of PRO data in oncology trials, heterogeneity in analyses and unclear PRO objectives mean that design, objectives, and analysis may not be aligned, posing substantial challenges for the interpretation of results. The recent ICH E9 (R1) estimand framework represents an opportunity to help address these challenges. Efforts to apply the estimand framework in the context of PROs have primarily focused on efficacy outcomes. In this paper, we discuss considerations for comparing the patient-reported tolerability of different treatments in an oncology trial context.

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.

Statistical considerations in long-term efficacy evaluation of anti-cancer therapies

Anti-cancer therapy has been a significant focus of research. Developing and marketing various types and mechanisms of anti-cancer therapies benefit a variety of patients significantly. The long-term benefit to patients in evaluating the risk-benefit ratio of anti-cancer therapy has become a significant concern. This paper discusses the evaluation of long-term efficacy within the estimand framework and summarizes the various strategies for addressing potential intercurrent events. Non-proportional hazards of survival data may arise with novel anti-cancer therapies, leading to potential bias in conventional evaluation methods. This paper reviews statistical methods for addressing this issue, including novel endpoints, hypothesis testing, and efficacy estimation methods. We also discuss the influences of treatment switching. Although advanced methods have been developed to address the non-proportional hazard, they still have limitations that require continued collaborative efforts to resolve issues.

Defining estimands for efficacy assessment in single arm phase 1b or phase 2 clinical trials in oncology early development

The addendum of the ICH E9 guideline on the statistical principles for clinical trials introduced the estimand framework. The framework is designed to strengthen the dialog between different stakeholders, to introduce greater clarity in the clinical trial objectives and to provide alignment between the estimand and statistical analysis. Estimand framework related publications thus far have mainly focused on randomized clinical trials. The intention of the Early Development Estimand Nexus (EDEN), a task force of the cross-industry Oncology Estimand Working Group (www.oncoestimand.org), is to apply it to single arms Phase 1b or Phase 2 trials designed to detect a treatment-related efficacy signal, typically measured by objective response rate. Key recommendations regarding the estimand attributes include that in a single arm early clinical trial, the treatment attribute should start when the first dose is received by the participant. Focusing on the estimation of an absolute effect, the population-level summary measure should reflect only the property used for the estimation. Another major component introduced in the ICH E9 addendum is the definition of intercurrent events and the associated possible ways to handle them. Different strategies reflect different clinical questions of interest that can be answered based on the journeys an individual subject can take during a trial. We provide detailed strategy recommendations for intercurrent events typically seen in early-stage oncology. We highlight where implicit assumptions should be made transparent as whenever follow-up is suspended, a while-on-treatment strategy is implied.

Multistate Pharmacometric Model to Define the Impact of Second-Line Immunotherapies on the Survival Outcome of the IMpower131 Study

Overall survival is defined as the time since randomization into the clinical trial to event of death or censor (end of trial or follow-up), and is considered to be the most reliable cancer end point. However, the introduction of second-line treatment after disease progression could influence survival and be considered a confounding factor. The aim of the current study was to set up a multistate model framework, using data from the IMpower131 study, to investigate the influence of second-line immunotherapies on overall survival analysis. The model adequately described the transitions between different states in patients with advanced squamous non-small cell lung cancer treated with or without atezolizumab plus nab-paclitaxel and carboplatin, and characterized the survival data. High PD-L1 expression at baseline was associated with a decreased hazard of progression, while the presence of liver metastasis at baseline was indicative of a high risk of disease progression after initial response. The hazard of death after progression was lower for participants who had longer treatment response, i.e., longer time to progression. The simulations based on the final multistate model showed that the addition of atezolizumab to the nab-paclitaxel and carboplatin regimen had significant improvement in the patients' survival (hazard ratio = 0.75, 95% prediction interval: 0.61-0.90 favoring the atezolizumab + nab-paclitaxel and carboplatin arm). The developed modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, and investigate the benefit of primary therapy in survival while accounting for the switch to alternative treatment in the case of disease progression.