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

Sullivan, Thomas; Latimer, Nicholas; Gray, Jodi; Sorich, Michael J.; Salter, Amy; Karnon, Jonathan

doi:10.1016/j.jval.2019.10.015

Cited by 25 publications

(22 citation statements)

References 35 publications

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“…Many of the included models adjusted for treatment switching, but none provided rationales for the adjustment methods selected. This lack of adequate justification for method choice is in line with the findings of an SLR of published trials and industry submissions in oncology [ 59 ], which claimed that the quality of reporting on implementation of a switching adjustment was generally poor. In agreement with the recommendations of Sullivan et al [ 59 ], future CEAs that employ treatment-switching adjustment should discuss whether the underlying assumptions corresponding to the chosen adjustment method are met in the specific application and should present a visual comparison of observed and adjusted OS curves.…”

Section: Discussionmentioning

confidence: 70%

“…This lack of adequate justification for method choice is in line with the findings of an SLR of published trials and industry submissions in oncology [ 59 ], which claimed that the quality of reporting on implementation of a switching adjustment was generally poor. In agreement with the recommendations of Sullivan et al [ 59 ], future CEAs that employ treatment-switching adjustment should discuss whether the underlying assumptions corresponding to the chosen adjustment method are met in the specific application and should present a visual comparison of observed and adjusted OS curves. A comparison of different switching adjustment methods and recommendations for their application is available in published literature [ 59 ] and in a NICE technical support document [ 60 ].…”

Section: Discussionmentioning

confidence: 70%

“…In agreement with the recommendations of Sullivan et al [59], future CEAs that employ treatment-switching adjustment should discuss whether the underlying assumptions corresponding to the chosen adjustment method are met in the specific application and should present a visual comparison of observed and adjusted OS curves. A comparison of different switching adjustment methods and recommendations for their application is available in published literature [59] and in a NICE technical support document [60]. The most appropriate adjustment method must be carefully evaluated against the assumptions required by each method, which depend on the data and characteristics of the trials [60].…”

Section: Switchmentioning

confidence: 82%

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Modeling Challenges in Cost-Effectiveness Analysis of First-Line Immuno-Oncology Therapies in Non-small Cell Lung Cancer: A Systematic Literature Review

et al. 2021

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Introduction The introduction of immuno-oncology (IO) therapies has changed the treatment landscape of non-small cell lung cancer (NSCLC). Numerous cost-effectiveness analyses (CEAs) and technology appraisals (TAs) evaluating IO therapies have been recently published. Objective We reviewed economic models of first-line (1L) IO therapies for previously untreated advanced or metastatic NSCLC to identify methodological challenges associated with modeling cost effectiveness from published literature and TAs and to make recommendations for future CEAs in this disease area. Methods A systematic literature review was conducted following Cochrane and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched MEDLINE, Embase, EconLit (January 2009-January 2020), and select conferences (since 2016) for CEAs of 1L IO treatments in patients with recurrent or metastatic, epidermal growth factor receptor (EGFR)/anaplastic lymphoma kinase (ALK) mutation-negative NSCLC, published in English. TAs from England, Scotland, Canada, Australia, Germany, and France were also examined. Two reviewers screened the results and extracted the data. The quality of the CEAs was described using the Drummond checklist. Results In total, 46 records reporting on 38 unique models met protocol-defined criteria and were included. Five models adjusted for treatment switching or crossover in base-case analyses, and the remainder considered treatment switching or crossover to represent clinical practice and made no adjustment. Seven models used external real-world data for survival modeling or extrapolation validation. Six models that assumed long-term treatment benefit stopped at 3 or 5 years after initiation. Seven models used the observed time-on-treatment distribution from the trial, and eight used progression-free survival for treatment duration. All models compared one or more IO monotherapies or combination therapies with chemotherapy. Only one study directly compared different IO agents but did not consider the concordance issue across programmed death-ligand 1 (PD-L1) testing methods. Utilities were modeled by health state in 12 models, four applied a time-to-death approach, and ten explored both. None applied cure models. Conclusion Variations in methodological challenges were seen across studies. Previous models took approaches that were followed in subsequent models, such as a 2-year stopping rule of IO duration or treatment-effect waning. Challenges such as heterogeneity in PD-L1 testing and survival extrapolation and validation using real-world data should be further considered for future models in advanced or metastatic NSCLC.

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Section: Discussionmentioning

confidence: 70%

Section: Discussionmentioning

confidence: 70%

Section: Switchmentioning

confidence: 82%

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Modeling Challenges in Cost-Effectiveness Analysis of First-Line Immuno-Oncology Therapies in Non-small Cell Lung Cancer: A Systematic Literature Review

et al. 2021

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“…Treatment switching (‘switching’) in oncology trials is common [ 9 ]. For the PROfound trial it was agreed with the steering committee and regulatory bodies (FDA and EMA) that switching from control to olaparib should be allowed upon radiographic progression (blinded independent central review (BICR)-confirmed up to data cutoff (DCO)1 and investigator-assessed beyond DCO1) if appropriate for the patient.…”

Section: Introductionmentioning

confidence: 99%

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)

Evans¹,

Hawkins²,

Dequen-O’Byrne³

et al. 2021

Targ Oncol

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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.

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“…Simulation studies have shown that these methods tend to produce more accurate estimates of the switching-adjusted estimand than simple adjustment methods or a standard intention to treat (ITT) analysis. See Jimenez et al (2017), Latimer, K. R. Abrams, et al (2018), and Sullivan et al (2020) for a systematic review of their applications. However, their performance can be compromised when the key underlying assumption of “no unmeasured confounding” is violated.…”

Section: Introductionmentioning

confidence: 99%

A New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model

Ying

Tchetgen

2021

Preprint

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Background: Treatment switching in a randomized controlled trial is said to occur when a patient randomized to one treatment arm switches to another treatment 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 dilute the estimated effect of treatment on overall survival. In this paper, we aim to account for the potential impact of treatment switching in a re-analysis 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 (OPTIONS) trial. Methods: We propose an estimator of a treatment causal effect under a structural cumulative survival model (SCSM) that leverages randomization as an instrumental variable to account for selective treatment switching. Unlike Robins' accelerated failure time model often used to address treatment switching, the proposed approach avoids the need for artificial censoring for estimation. We establish that the proposed estimator is uniformly consistent and asymptotically Gaussian under standard regularity conditions. A consistent variance estimator is also given and a simple resampling approach provides uniform confidence bands for the causal difference comparing treatment groups over time on the cumulative intensity scale. We develop an R package named ``ivsacim'' implementing all proposed methods, freely available to download from R CRAN. We examine the finite performance of the estimator via extensive simulations. Results: 357 participants in the OPTIONS trial were randomly assigned at baseline to add-NRTIs or omit-NRTIs treatment group; 93% subsequently completed a 48-week visit. Using the proposed methods, we found statistically significant evidence against the sharp null hypothesis of no treatment effect on the safety outcome (P value 0.034) and our SCSM estimator revealed an increased risk for a safety outcome in participants receiving a new antiretroviral regimen that included NRTIs when compared to participants receiving a regimen that omitted NRTIs. In fact, under an SCSM encoding a constant additive hazards model, we estimated a hazards difference equal to 0.0039 (95% CI 0.0002, 0.0075) over the 48-week follow-up. Conclusions: Treatment-experienced patients with HIV infection starting a new optimized regimen will experience a higher risk of severe or worse sign or symptom. Previous analyses concluded that treatment-experienced patients with HIV infection starting a new optimized regimen can safely omit NRTIs without compromising virologic efficacy. Our analysis suggests that adding NRTIs is not only unnecessary to achieve optimal outcomes but may increase the risk for a safety outcome.

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

Cited by 25 publications

References 35 publications

Modeling Challenges in Cost-Effectiveness Analysis of First-Line Immuno-Oncology Therapies in Non-small Cell Lung Cancer: A Systematic Literature Review

Modeling Challenges in Cost-Effectiveness Analysis of First-Line Immuno-Oncology Therapies in Non-small Cell Lung Cancer: A Systematic Literature Review

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)

A New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model

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