Rank-preserving Structural Failure Time Model Research Articles

Adjusting for subsequent therapies in the TOURMALINE-MM1 study shows clinically meaningful improvement in overall survival with addition of ixazomib to lenalidomide and dexamethasone.

TOURMALINE-MM1, the only blinded randomized study in patients with relapsed and/or refractory multiple myeloma (RRMM; ≥1 prior therapy) in the last 10 years, investigated ixazomib+lenalidomide+dexamethasone (IRd) versus lenalidomide+dexamethasone (Rd). Final overall survival (OS) data were based on a median follow-up of 85 months. In RRMM trials where patients have had 1-3 relapses after initial treatment, a high proportion receive subsequent therapy. Application of salvage therapies in blinded trials and newer modes of therapy can increasingly complicate the interpretation of OS. This analysis explores the impact of subsequent therapies on OS outcomes in TOURMALINE-MM1. The inverse probability of censoring weights (IPCW) method, marginal structural model (MSM), and rank preserving structural failure time model (RPSFTM) were utilized to adjust for confounding on OS, introduced by subsequent therapies. Analyses were conducted for the intentto-treat (ITT) population and ≥2 prior lines subgroup. Unadjusted hazard ratio (HR) for IRd versus Rd was 0.94 (95% confidence interval [CI]: 0.78-1.13) in the ITT population. After adjusting for the impact of subsequent therapies by the RPSFTM method, estimated HR for IRd versus Rd in the ITT population was 0.89 (95% CI: 0.74-1.07). Adjusting with IPCW and MSM methods also showed an improvement in HR, favoring IRd. IRd may be particularly beneficial in patients with ≥2 prior lines of therapy (IPCW and MSM HR=0.52, 95% CI: 0.30-0.88; RPSFTM HR=0.68, 95% CI: 0.51-0.91). These analyses highlight the growing challenge of demonstrating OS benefit in multiple myeloma patients and the importance of assessing confounding introduced by subsequent therapies when interpreting OS.

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

BackgroundTreatment 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).MethodsWe 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.ResultsSome 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.ConclusionsAdjustment analyses may provide unreliable results. How each method is applied must be scrutinised to assess reliability.

Survival analysis from the INCREASE study in PH-ILD: evaluating the impact of treatment crossover on overall mortality

ObjectiveA post-hoc analysis of the INCREASE trial and its open-label extension (OLE) was performed to evaluate whether inhaled treprostinil has a long-term survival benefit in patients with pulmonary hypertension associated...

Insights adjusting for non-adherence in randomized clinical trials: a reanalysis of an adjuvant trial of tamoxifen duration in early breast cancer

BackgroundSeveral randomized clinical trials provide evidence of the survival benefit of extended adjuvant tamoxifen in women with estrogen receptor (ER)-positive early breast cancer (BC). However, non-adherence may lead to underestimate treatment effects using intention to treat (ITT) methods. We reanalyzed a randomized trial using contemporary statistical methods adjusting for non-adherence.MethodsThe TAM01 study was a phase 3 trial including women with early BC, who had completed 2–3 years of adjuvant tamoxifen between 1986 and 1995. Participants were randomly assigned to continue tamoxifen up to 10 years or to discontinue the treatment at randomization. Invasive disease-free survival (iDFS) and overall survival (OS) were estimated using marginal structural models (MSM) and rank preserving structural failure time model (RPSFTM).ResultsOf 3830 patients enrolled, 2485 were randomized to extended tamoxifen, and 1345 to treatment discontinuation. The 10-year non-adherence rate in the extended group was 27.2%. Among women with ER-positive BC (n = 2402), extended tamoxifen was associated with a 45% and 21% relative improvement in iDFS by MSM and RPSFTM, respectively (Hazard Ratio (HR), 0.55; 95% Confidence Interval (CI), 0.48–0.64 and HR, 0.79; 95%CI, 0.67–0.95, respectively), a considerable greater benefit than in the ITT analysis (HR, 0.90; 95%CI, 0.81–0.99). The OS reanalysis revealed a substantial benefit of extended tamoxifen (MSM: HR, 0.70; 95%CI, 0.59–0.83; RPSFTM: HR, 0.85; 95%CI, 0.67–1.04), compared to the ITT analyses (HR, 0.94; 95%CI, 0.84–1.07).ConclusionThis analysis emphasizes both the importance of adherence to hormonotherapy in hormone-receptor positive early BC and the usefulness of more complex statistical analyses.

Impact of the treatment crossover design on comparative efficacy in EMPOWER-Lung 1: Cemiplimab monotherapy as first-line treatment of advanced non-small cell lung cancer.

In randomized-controlled crossover design trials, overall survival (OS) treatment effect estimates are often confounded by the control group benefiting from treatment received post-progression. We estimated the adjusted OS treatment effect in EMPOWER-Lung 1 (NCT03088540) by accounting for the potential impact of crossover to cemiplimab among controls and continued cemiplimab treatment post-progression. Patients were randomly assigned 1:1 to cemiplimab 350 mg every 3 weeks (Q3W) or platinum-doublet chemotherapy. Patients with disease progression while on or after chemotherapy could receive cemiplimab 350 mg Q3W for ≤108 weeks. Those who experienced progression on cemiplimab could continue cemiplimab at 350 mg Q3W for ≤108 additional weeks with four chemotherapy cycles added. Three adjustment methods accounted for crossover and/or continued treatment: simplified two-stage correction (with or without recensoring), inverse probability of censoring weighting (IPCW), and rank-preserving structural failure time model (RPSFT; with or without recensoring). In the programmed cell death-ligand 1 ≥50% population (N=563; median 10.8-month follow-up), 38.2% (n=107/280) crossed over from chemotherapy to cemiplimab (71.3%, n=107/150, among those with confirmed progression) and 16.3% (n=46/283) received cemiplimab treatment after progression with the addition of histology-specific chemotherapy (38.7%, n=46/119, among those with confirmed progression). The unadjusted OS hazard ratio (HR) with cemiplimab versus chemotherapy was 0.566 (95% confidence interval [CI]: 0.418, 0.767). Simplified two-stage correction-the most suitable method based on published guidelines and trial characteristics-produced an OS HR of 0.490 (95% CI: 0.365, 0.654) without recensoring and 0.493 (95% CI: 0.361, 0.674) with recensoring. The IPCW and RPSFT methods produced estimates generally consistent with simplified two-stage correction. After adjusting for treatment crossover and continued cemiplimab treatment after progression with the addition of histology-specific chemotherapy observed in EMPOWER-Lung 1, cemiplimab continued to demonstrate a clinically important and statistically significant OS benefit versus chemotherapy, consistent with the primary analysis.

Overall survival indirect treatment comparison between brigatinib and alectinib for the treatment of front-line anaplastic lymphoma kinase–positive non–small cell lung cancer using data from ALEX and final results from ALTA-1L

Background Second-generation anaplastic lymphoma kinase (ALK) gene targeted tyrosine kinase inhibitors (TKIs) alectinib and brigatinib have shown efficacy as front-line treatments for ALK-positive non–small cell lung cancer (NSCLC). No head-to-head data are currently available for brigatinib vs alectinib in the ALK-TKI–naive population. Objective To estimate the relative overall survival (OS) for brigatinib vs alectinib with indirect treatment comparisons (ITCs) using ALEX and ALTA-1L clinical trial data. Methods The latest aggregate data from the ALEX trial and final patient-level data from ALTA-1L were used. ITCs were conducted with/without treatment crossover adjustments to estimate relative OS. Bucher methods, anchored matching-adjusted indirect comparisons (MAICs) and unanchored MAICs were employed in ITCs without treatment crossover adjustments. An inverse probability of censoring weight Cox model, a marginal structure model and rank-preserving structural failure time models (with/without re-censoring) within an anchored MAIC were used in ITCs with treatment crossover adjustments. Hazard ratios (HRs) and 95% confidence intervals (CIs) were reported. Results HRs for brigatinib vs alectinib for relative OS generated from ITCs without treatment crossover adjustments ranged from 0.90 (95% CI: 0.59–1.38) in the unanchored MAIC to 1.20 (95% CI: 0.69–2.11) using the Bucher method. Methods employing treatment switching adjustments estimated HRs for relative OS ranging from 0.74 (95% CI: 0.38–1.45) to 1.11 (95% CI: 0.63–1.94). Results from all ITCs did not indicate statistically different survival profiles. Conclusion Regardless of ITC methodology, OS is comparable for brigatinib vs alectinib in patients with ALK+ NSCLC previously untreated with an ALK inhibitor.

Survival analyses from the CENTAUR trial in amyotrophic lateral sclerosis: Evaluating the impact of treatment crossover on outcomes.

Introduction/AimsTrials incorporating placebo‐to‐active treatment crossover are encouraged in fatal conditions like amyotrophic lateral sclerosis (ALS) but may underestimate active treatment survival benefit. Here, we apply methods for modeling survival without crossover, including the rank‐preserving structural failure time model (RPSFTM), to data from the CENTAUR trial of sodium phenylbutyrate and taurursodiol (PB and TURSO) in ALS incorporating both randomized placebo‐controlled and open‐label extension (OLE) phases.MethodsIntent‐to‐treat (ITT) and RPSFTM survival analyses were performed with final data at a July 2020 cutoff date. Analyses of subgroups based on randomized treatment and OLE phase participation were also performed.ResultsHazard ratios (95% confidence intervals) of death for PB and TURSO versus participants initially on placebo were 0.57 (0.35–0.92) on ITT analysis and 0.39 (0.17–0.88) in the primary on‐treatment RPSFTM analysis (p = .023). Median ITT survival duration for PB and TURSO (25.8 mo) was 6.9 mo longer than placebo (18.9 mo) on ITT analysis and 10.6 mo longer than the median RPSFTM‐adjusted survival duration for placebo (15.2 mo). Median survival duration was 18.8 mo longer in the PB and TURSO–randomized subgroup who continued into the OLE phase versus the placebo‐randomized subgroup who did not continue into the OLE phase (p < .0001), although OLE phase selection bias may have potentially confounded these results.DiscussionSimilar to the prespecified ITT analysis, post hoc analyses adjusting for treatment crossover in CENTAUR showed a significant survival benefit for PB and TURSO. Such methods may provide clinical context for observed survival outcomes in future ALS crossover trials.

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).

BackgroundIn 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.ObjectiveThis 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 methodsThe 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.ResultsThe 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.ConclusionsThe 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 InformationThe online version contains supplementary material available at 10.1007/s11523-021-00837-y.

Estimation of treatment effect in presence of noncompliance with early or late switching: a simulation study

To assess the effect of treatment in survival studies, a randomized controlled trial (RCTs) is usually designed. Due to disease progression or other safety reasons, patients in the standard group may be allowed to switch to the experimental treatment. In such cases, estimation of the treatment effect is inevitably performed using naive methods such as intention-to-treat (ITT), per-protocol (PP), or more advanced methods such as the rank-preserving structural failure time (RPSFT) models or iterative parameter estimation (IPE). In some trials, people who progress early might be more likely to switch, and in other trials, people who progress late might be more likely to switch – depending on the characteristics of the disease and the drug being examined. So, this study simulated a new range of realistic scenarios with regard to patients’ prognosis and investigated the performance of the adjusted methods in the early and late switching with respect to the median of the survival time.

Final results from ClarIDHy, a global, phase 3, randomized, double-blind study of ivosidenib (IVO) versus placebo (PBO) in patients (pts) with previously treated cholangiocarcinoma (CCA) and an isocitrate dehydrogenase 1 (IDH1) mutation.

4069 Background: CCA is a rare cancer for which there are limited effective therapies. IDH1 mutations occur in ̃20% of intrahepatic CCAs, resulting in production of the oncometabolite D-2-hydroxyglutarate, which promotes oncogenesis. IVO (AG-120) is a first-in-class, oral, small-molecule inhibitor of mutant IDH1 (mIDH1). ClarIDHy aimed to demonstrate the efficacy of IVO vs PBO in pts with unresectable or metastatic m IDH1 CCA. The primary endpoint was met with significant improvement in progression-free survival (PFS) by independent radiology center (IRC) with IVO vs PBO (hazard ratio [HR] = 0.37, p &lt; 0.0001). Objective response rate (ORR) and stable disease for IVO were 2.4% (3 partial responses) and 50.8% (n = 63) vs 0% and 27.9% (n = 17) for PBO. IVO pts experienced significantly less decline in physical and emotional functioning domains of quality of life at cycle 2 day 1 vs PBO pts (nominal p &lt; 0.05). Methods: Pts with m IDH1 CCA were randomized 2:1 to IVO (500 mg PO QD) or matched PBO and stratified by prior systemic therapies (1 or 2). Key eligibility: unresectable or metastatic m IDH1 CCA based on central testing; ECOG PS 0–1; measurable disease (RECIST v1.1). Crossover from PBO to IVO was permitted at radiographic progression. Primary endpoint: PFS by IRC. Secondary endpoints included overall survival (OS; by intent-to-treat), ORR, PFS (by investigator), safety, and quality of life. The planned crossover-adjusted OS was derived using the rank-preserving structural failure time (RPSFT) model. Results: As of 31 May 2020, ̃780 pts were prescreened for an IDH1 mutation and 187 were randomized to IVO (n = 126) or PBO (n = 61); 13 remain on IVO. Median age 62 y; M/F 68/119; 91% intrahepatic CCA; 93% metastatic disease; 47% had 2 prior therapies. 70% of PBO pts crossed over to IVO. OS data were mature, with 79% OS events in IVO arm and 82% in PBO. Median OS (mOS) was 10.3 months for IVO and 7.5 months for PBO (HR = 0.79; 95% CI 0.56–1.12; one-sided p = 0.093). The RPSFT-adjusted mOS was 5.1 months for PBO (HR = 0.49; 95% CI 0.34–0.70; p &lt; 0.0001). Common all-grade treatment emergent adverse events (TEAEs, ≥ 15%) in the IVO arm: nausea 41%, diarrhea 35%, fatigue 31%, cough 25%, abdominal pain 24%, decreased appetite 24%, ascites 23%, vomiting 23%, anemia 18%, and constipation 15%. Grade ≥ 3 TEAEs were reported in 50% of IVO pts vs 37% of PBO pts, with grade ≥ 3 treatment-related AEs in 7% of IVO pts vs 0% in PBO. 7% of IVO pts experienced an AE leading to treatment discontinuation vs 9% of PBO pts. There were no treatment-related deaths. Conclusions: IVO was well tolerated and resulted in a favorable OS trend vs PBO despite a high rate of crossover. These data – coupled with statistical improvement in PFS, supportive quality of life data, and favorable safety profile – demonstrate the clinical benefit of IVO in advanced m IDH1 CCA. Clinical trial information: NCT02989857.

Teasing Out the Overall Survival Benefit With Adjustment for Treatment Switching to Multiple Treatments

In oncology clinical trials, characterizing the long-term overall survival (OS) benefit for an experimental treatment is often unobservable if some patients in the control group switch to drugs in the experimental group and/or other cancer treatments after disease progression. A key question often raised by payers and reimbursement agencies is how to estimate the true benefit of the experimental treatment on OS that would have been estimated if there were no treatment switches. Several commonly used statistical methods are available to estimate OS benefit while adjusting for treatment switching, ranging from naive exclusion or censoring approaches to more advanced methods including inverse probability of censoring weighting (IPCW), two-stage model (2-stage), iterative parameter estimation (IPE) algorithm or rank-preserving structural failure time models (RPSFTM). However, many clinical trials now have patients in the control group switching to different treatment regimens other than the experimental treatments. While some existing methods including RPSFTM and IPE cannot handle complicated scenarios such as multilevel switching, other methods such as IPCW and two-stage methods are restricted with strong assumptions. To address this challenge, we propose two additional methods: stratified RPSFTM and random-forest-based prediction. A simulation study is conducted to assess the performance of the existing methods along with the two newly proposed approaches.

Final results from ClarIDHy, a global, phase III, randomized, double-blind study of ivosidenib (IVO) versus placebo (PBO) in patients (pts) with previously treated cholangiocarcinoma (CCA) and an isocitrate dehydrogenase 1 (IDH1) mutation.

266 Background: CCA is a rare cancer for which there are limited effective therapies. IDH1 mutations occur in ~20% of intrahepatic CCAs, resulting in production of the oncometabolite D-2-hydroxyglutarate, which promotes oncogenesis. IVO (AG-120) is a first-in-class, oral, small-molecule inhibitor of mutant IDH1 (m IDH1). ClarIDHy aimed to demonstrate the efficacy of IVO vs PBO in pts with unresectable or metastatic m IDH1 CCA. The primary endpoint was met with significant improvement in progression-free survival (PFS) by independent radiology center (IRC) with IVO vs PBO (hazard ratio [HR] = 0.37, p &lt; 0.0001). Objective response rate (ORR) and stable disease for IVO were 2.4% (3 partial responses) and 50.8% (n = 63) vs 0% and 27.9% (n = 17) for PBO. IVO pts experienced significantly less decline in physical and emotional functioning domains of quality of life at cycle 2 day 1 vs PBO pts (nominal p &lt; 0.05). Methods: Pts with m IDH1 CCA were randomized 2:1 to IVO (500 mg PO QD) or matched PBO and stratified by prior systemic therapies (1 or 2). Key eligibility: unresectable or metastatic m IDH1 CCA based on central testing; ECOG PS 0–1; measurable disease (RECIST v1.1). Crossover from PBO to IVO was permitted at radiographic progression. Primary endpoint: PFS by IRC. Secondary endpoints included overall survival (OS; by intent-to-treat), ORR, PFS (by investigator), safety, and quality of life. The planned crossover-adjusted OS was derived using the rank-preserving structural failure time (RPSFT) model. Results: As of 31 May 2020, ~780 pts were prescreened for an IDH1 mutation and 187 were randomized to IVO (n = 126) or PBO (n = 61); 13 remain on IVO. Median age 62 y; M/F 68/119; 91% intrahepatic CCA; 93% metastatic disease; 47% had 2 prior therapies. 70% of PBO pts crossed over to IVO. OS data were mature, with 79% OS events in IVO arm and 82% in PBO. Median OS (mOS) was 10.3 months for IVO and 7.5 months for PBO (HR = 0.79; 95% CI 0.56–1.12; one-sided p = 0.093). The RPSFT-adjusted mOS was 5.1 months for PBO (HR = 0.49; 95% CI 0.34–0.70; p &lt; 0.0001). Common all-grade treatment emergent adverse events (TEAEs, ≥ 15%) in the IVO arm: nausea 41%, diarrhea 35%, fatigue 31%, cough 25%, abdominal pain 24%, decreased appetite 24%, ascites 23%, vomiting 23%, anemia 18%, and constipation 15%. Grade ≥ 3 TEAEs were reported in 50% of IVO pts vs 37% of PBO pts, with grade ≥ 3 treatment-related AEs in 7% of IVO pts vs 0% in PBO. 7% of IVO pts experienced an AE leading to treatment discontinuation vs 9% of PBO pts. There were no treatment-related deaths. Conclusions: IVO was well tolerated and resulted in a favorable OS trend vs PBO despite a high rate of crossover. These data – coupled with statistical improvement in PFS, supportive quality of life data, and favorable safety profile – demonstrate the clinical benefit of IVO in advanced m IDH1 CCA. Clinical trial information: NCT02989857.

Assessing the Influence of Subsequent Immunotherapy on Overall Survival in Patients with Unresectable Stage III Non–Small Cell Lung Cancer from the PACIFIC Study

BackgroundHistorically, the standard of care for patients with unresectable, Stage III non–small cell lung cancer had been concurrent chemoradiotherapy. However, outcomes had been poor, with approximately 15% to 32% of patients alive at 5 years. In the placebo-controlled Phase III A PACIFIC trial, consolidation treatment with durvalumab after concurrent chemoradiotherapy significantly improved overall survival (OS) and progression-free survival in patients with unresectable, Stage III non–small cell lung cancer, establishing this regimen as a new standard of care in this setting. In the PACIFIC trial, crossover between treatment arms (durvalumab or placebo) was not permitted. However, after discontinuation from study treatment, patients from both arms of PACIFIC could switch to subsequent anticancer therapy, including durvalumab and other immunotherapies, which is known to influence standard intention-to-treat analysis of OS, potentially underestimating the effect of an experimental drug. Moreover, the introduction of immunotherapies has demonstrated marked improvements in the postprogression, metastatic non–small cell lung cancer setting.ObjectiveTo examine the influence of subsequent immunotherapy on OS in the PACIFIC trial.MethodsBoth a Rank Preserving Structural Failure Time Model (RPSFTM) and modified 2-stage method were used. RPSFTM assumes that a patient's survival time with no immunotherapy (counterfactual survival time) is equal to the observed time influenced by immunotherapy, multiplied by an acceleration factor, plus the time not influenced. The modified 2-stage method estimates the effect of immunotherapy by comparing postsubsequent-treatment-initiation survival times between patients with and without subsequent immunotherapy. In both models, OS was adjusted to reflect a hypothetical scenario in which no patients received subsequent immunotherapy. RPSFTM was also used for scenarios in which subsequent immunotherapy was received by increasing proportions of placebo patients but none of the durvalumab patients.ResultsIn the intention-to-treat analysis (3-year follow-up), durvalumab improved OS versus placebo (stratified hazard ratio = 0.69; 95% CI, 0.55–0.86). Overall, 10% and 27% of durvalumab and placebo patients, respectively, received subsequent immunotherapy. With subsequent immunotherapy removed from both arms, estimated hazard ratio was 0.66 (95% CI, 0.53–0.84) with RPSFTM and 0.68 (95% CI, 0.54–0.85) with the modified 2-stage method. With subsequent immunotherapy removed from the durvalumab arm only (RPSFTM), estimated hazard ratio increased as the proportion of placebo patients receiving subsequent immunotherapy increased, up to 0.75 (95% CI, 0.60–0.94) maximum (assuming all placebo patients with subsequent treatment received immunotherapy).ConclusionsResults were consistent with the intention-to-treat analysis, supporting the conclusion that durvalumab after chemoradiotherapy provides substantial OS benefit in patients with Stage III, unresectable non–small cell lung cancer. ClinicalTrials.gov identifier: NCT02125461 (Curr Ther Res Clin Exp. 2021; 82:XXX–XXX)

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.

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.

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. The CLARITY trial evaluated cladribine tablets versus placebo over 96weeks. 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 96weeks of CLARITY, we compared hazard ratios (HRs) from the adjustment analysis with HRs from CLARITY. 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. 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. EMD Serono, Inc. (a business of Merck KGaA, Darmstadt, Germany).

PCN372 METHODOLOGIES FOR ADJUSTMENT OF THE TRIAL OVERALL SURVIVAL FOR TREATMENT SWITCHING: A REVIEW OF NICE SUBMISSIONS

Frequently, technology appraisals (TAs) submitted to the National Institute for Health and Care excellence (NICE) include methodologies that adjust trial overall survival (OS) for treatment switching. The aim is to evaluate how NICE responds to these methods, their critique and recommendations for future submissions. This NICE HTA review included all oncology submissions that were completed as of December 2001. All reports published on the NICE website on TAs including naïve and more complex methodologies that adjust for treatment switch such as inverse probability of censoring weights (IPCW), rank-preserving structural failure time (RPSFT) models, iterative parametric estimation (IPE) and the 2-stage method were included. The included extraction items were: methodology used, acceptance by NICE of the adjusted outcomes, and overall criticism/recommendations. Out of 218 submissions, 38 were included in this review. Most commonly, more than one method was used. The RPSFT and IPCW methods were combined in 12 submissions. Among all submissions, the most common method was RPSFT (32 submissions) followed by the IPCW (15 submissions) and the 2-stage method (11 submissions). IPE and naïve methods were presented in six and five submissions, respectively. In twelve appraisals, only a RPSFT model was presented. NICE considered RPSFT a preferred methodology if the common treatment effect assumption was met. Otherwise, the 2-stage method was recognized as a better fit (5 submissions). IPCW was considered unsuitable when the percentage of patients switching treatments was high. Using more than one adjustment method was recommended. However, it appears that the choice of adjustment method(s) used or their number was not crucial for the reimbursement decisions. Treatment switching adjustment methods appear to be well accepted by NICE. Currently, NICE recommends for the future submissions to report treatment switching methodologies in a transparent way and to explore multiple adjustment methods.

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

ObjectivesStatistical 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). MethodsThe 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). ResultsThe 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. ConclusionsThe 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.

Survival Analysis from the CALGB Study of Lenalidomide Maintenance Therapy in Newly Diagnosed Multiple Myeloma Post-Autologous Stem Cell Transplantation Adjusted for Crossover (Alliance 100104)

Survival Analysis from the CALGB Study of Lenalidomide Maintenance Therapy in Newly Diagnosed Multiple Myeloma Post-Autologous Stem Cell Transplantation Adjusted for Crossover (Alliance 100104)

PRM233 - THE USE OF THE TWO-STAGE METHOD IN ADJUSTING FOR TREATMENT CROSSOVER. A SIMULATION STUDY.

A subsequent therapy that is applied in a clinical trial, but not registered in clinical practice, might hamper how reflective the reported overall survival (OS) is of clinical practice. For this, methods like two-stage approach and rank preserving structural failure time models (RPSFTM) have been developed. However, there might also be situations that the treatment switch is reflective of clinical practice but that the percentage of patients receiving a subsequent therapy differs. The objective is to present a methodology using the two-stage method that adjusts survival for differences in frequency of subsequent therapy between clinical trials and clinical practice. PFS and OS data of a trial with two treatment arms was simulated (an active and a placebo arm). It was assumed that in the trial 45% of patients in the placebo arm switched treatment. However, theoretically in clinical practice 80% of patients would receive active therapy after progression on placebo. We applied the two-stage method, where first the counterfactual survival times were estimated to reflect a scenario where nobody switches to an active subsequent therapy. Then we estimated counterfactual survival times assuming that everybody switched. Subsequently, Weibull models, which had best fit, were fitted on both datasets and a weighted average Weibull, was estimated, reflecting the 80% treatment switch pattern in clinical practice. The unadjusted average OS was 25.5 months. Average OS of patients on placebo, assuming nobody switched was 24 months and assuming everybody switched was 28.4 months. The weighted OS based on the theoretical clinical practice percentage of switchers was 27.5 months. This method can be applied to adjust the OS outcomes of trial such that they reflect treatment switching patterns in real clinical practice. The methodology can be further tested on methods like the RPSFTM and other datasets.

Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times?

Treatment switching often has a crucial impact on estimates of effectiveness and cost-effectiveness of new oncology treatments. Rank preserving structural failure time models (RPSFTM) and two-stage estimation (TSE) methods estimate ‘counterfactual’ (i.e. had there been no switching) survival times and incorporate re-censoring to guard against informative censoring in the counterfactual dataset. However, re-censoring causes a loss of longer term survival information which is problematic when estimates of long-term survival effects are required, as is often the case for health technology assessment decision making. We present a simulation study designed to investigate applications of the RPSFTM and TSE with and without re-censoring, to determine whether re-censoring should always be recommended within adjustment analyses. We investigate a context where switching is from the control group onto the experimental treatment in scenarios with varying switch proportions, treatment effect sizes, treatment effect changes over time, survival function shapes, disease severity and switcher prognosis. Methods were assessed according to their estimation of control group restricted mean survival that would be observed in the absence of switching, up to the end of trial follow-up. We found that analyses which re-censored usually produced negative bias (i.e. underestimating control group restricted mean survival and overestimating the treatment effect), whereas analyses that did not re-censor consistently produced positive bias which was often smaller in magnitude than the bias associated with re-censored analyses, particularly when the treatment effect was high and the switching proportion was low. The RPSFTM with re-censoring generally resulted in increased bias compared to the other methods. We believe that analyses should be conducted with and without re-censoring, as this may provide decision-makers with useful information on where the true treatment effect is likely to lie. Incorporating re-censoring should not always represent the default approach when the objective is to estimate long-term survival times and treatment effects.