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.

Protective effect of intensive glucose lowering therapy on all-cause mortality, adjusted for treatment switching using G-estimation method, the ACCORD trial.

Previous analysis of the action to control cardiovascular risk in diabetes showed an increased risk of mortality among patients receiving intensive glucose lowering therapy using conventional regression method with intention to treat approach. This method is biased when time-varying confounder is affected by the previous treatment. We used 15 follow-up visits of ACCORD trial to compare the effect of time-varying intensive vs. standard treatment of glucose lowering drugs on cardiovascular and mortality outcomes in diabetic patients. The treatment effect was estimated using G-estimation and compared with accelerated failure time model using two modeling strategies. The first model adjusted for baseline confounders and the second adjusted for both baseline and time-varying confounders. While the hazard ratio of all-cause mortality for intensive compared to standard therapy in AFT model adjusted for baseline confounders was 1.17 (95% CI 1.01-1.36), the result of time-dependent AFT model  was compatible with both protective and risk effects. However, the hazard ratio estimated by G-estimation was 0.64 (95% CI 0.39-0.92). The results of this study revealed a protective effect of intensive therapy on all-cause mortality compared with standard therapy in ACCORD trial.

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.).

Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study.

BACKGROUND: Treatment switching, also called crossover, is common in clinical trials because of ethical concerns or other reasons. When it occurs and the primary objective is to identify treatment effects, the most widely used intention-to-treat analysis may lead to underpowered trials. Here, we presented an approach to preview power reductions and to estimate sample sizes required to achieve the desired power when treatment switching occurs in the intention-to-treat analysis. METHODS: We proposed a simulation-based approach and developed an R package to perform power and sample sizes estimation in clinical trials with treatment switching. RESULTS: We simulated a number of randomized trials incorporating treatment switching and investigated the impact of the relative effectiveness of the experimental treatment to the control, the switching probability, the switching time, and the deviation between the assumed and the real distributions for the survival time on power reductions and sample sizes estimation. The switching probability and the switching time are key determinants for significant power decreasing and thus sample sizes surging to maintain the desired power. The sample sizes required in randomized trials absence of treatment switching vary from around four-fifths to one-seventh of the sample sizes required in randomized trials allowing treatment switching as the switching probability increases. The power reductions and sample sizes increase with the decrease of switching time. CONCLUSIONS: The simulation-based approach not only provides a preview for power declining but also calculates the required sample size to achieve an expected power in the intention-to-treat analysis when treatment switching occurs. It will provide researchers and clinicians with useful information before randomized controlled trials are conducted.

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.

Bias and Type I error Control in Correcting Treatment Effect for Treatment Switching Using Marginal Structural Models in Phase III Oncology Trials.

This research focuses on the bias and type I error control issues when the marginal structural models (MSMs) are applied to evaluate the causal survival benefits of active intervention versus control in randomized clinical trials (RCTs) with treatment switching after disease progression. When MSMs are applied in the RCT setting, the question of interest, model specifications, strategies for type I error control, bias reduction, etc. differ somewhat from those for observational studies. This manuscript discusses the approaches used to accommodate these differences. Through Monte Carlo simulations and a case study, our research demonstrates that, with sufficient attention paid to issues applicable to RCTs in particular, MSMs may perform better than the inverse probability of censoring weighting (IPCW) method in analyzing the survival endpoint in RCTs with treatment switching because more information is used by the MSM.

Association Between CYP2D6 Metabolizer Status and Vortioxetine Exposure and Treatment Switching: A Retrospective, Naturalistic Cohort Study Using Therapeutic Drug Monitoring Data From 640 Patients.

PURPOSE: The antidepressant vortioxetine is mainly metabolized by the polymorphic enzyme CYP2D6. The aim of this study was to investigate the absolute serum concentrations of vortioxetine and frequency of switching to an alternative antidepressant in relation to CYP2D6 genotype in a naturalistic patient population. METHODS: The analyses included data from 640 CYP2D6 -genotyped patients treated with vortioxetine from a Norwegian therapeutic drug monitoring database. Serum concentration of vortioxetine was determined using ultrahigh-performance liquid chromatography-high-resolution mass spectrometry, whereas longitudinal reviews of therapeutic drug monitoring profiles were performed to identify cases of patients switching from vortioxetine to an alternative antidepressant. RESULTS: Compared with CYP2D6 normal metabolizers (n = 342), the median vortioxetine serum concentration (ng/mL) was 2.1-fold ( P < 0.001) increased in poor metabolizers (PMs) (n = 48), 1.5-fold ( P < 0.001) increased in intermediate metabolizers (n = 238), and not significantly changed in ultrarapid metabolizers (n = 12). Compared with CYP2D6 normal metabolizers, treatment switch from vortioxetine to alternative antidepressants was 5.1-fold (95% confidence interval, 1.6-15.4, P = 0.003) more frequent among PMs. The prescribed doses did not differ significantly between the subgroups ( P = 0.26). A possible explanation for the increased frequency of treatment switch among PMs is that concentration-dependent adverse events were more frequent in this group because of increased drug exposure. CONCLUSIONS: This naturalistic study provides novel data on the association between CYP2D6 genotype and treatment switch of vortioxetine, which likely reflects the significant effect of CYP2D6 genotype on vortioxetine exposure.

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.

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

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

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.

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

BACKGROUND: In oncology trials, treatment switching from the comparator to the experimental regimen is often allowed but may lead to underestimating overall survival (OS) of an experimental therapy. OBJECTIVE: This study evaluates the impact of treatment switching from control to olaparib on OS using the final survival data from the PROfound study and compares validated adjustment methods to estimate the magnitude of OS benefit with olaparib. PATIENTS AND METHODS: The primary population from PROfound (Cohort A) was included, alongside two populations approved for treatment with olaparib by the European Medicines Agency and US Food and Drug Administration: BRCAm and Cohort A+B (excluding the PPP2R2A gene). Five methods were explored to adjust for switching: excluding or censoring patients in the control arm who receive subsequent olaparib, Rank Preserving Structural Failure Time Model (RPSFTM), Inverse Probability of Censoring Weights, and Two-Stage Estimation. RESULTS: The RPSFTM was considered the most appropriate approach for PROfound as the results were robust to sensitivity analysis testing of the common treatment effect assumption. For Cohort A, the final OS hazard ratio reduced from 0.69 (95% CI 0.5-0.97) to between 0.42 (0.18-0.90) and 0.52 (0.31-1.00) for olaparib versus control, depending on the RPSFTM selected. Median OS reduced from 14.7 months to between 11.73 and 12.63 months for control. CONCLUSIONS: The magnitude of the statistically significant (P < 0.05) survival benefit of olaparib versus control observed in Cohort A of PROfound is likely to be underestimated if adjustment for treatment switching from control to olaparib is not conducted. The RPSFTM was considered the most plausible method, although further development and validation of robust methods to estimate the magnitude of impact of treatment switching is needed.

Repurposing of medicines – the underrated champion of sustainable innovation: policy brief

Drug repurposing (also called drug repositioning, reprofiling or retasking) is a strategy to identify new uses for approved or investigational medicines that are outside the scope of the original medical indication. New treatments are sought mainly from products that are already in use, but also from compounds that have been shelved, withdrawn or abandoned because they did not perform as expected in their primary designated indications or because better therapies emerged. This report discusses non-commercial establishment of new cancer treatments by using off-patent products relying on both “hard repurposing” (repurposing of non-cancer medicines for oncology use) and “soft repurposing” (adding new cancer indications for established cancer medicines) approaches.

Delayed treatment effects, treatment switching and heterogeneous patient populations: How to design and analyze RCTs in oncology.

In the analysis of survival times, the logrank test and the Cox model have been established as key tools, which do not require specific distributional assumptions. Under the assumption of proportional hazards, they are efficient and their results can be interpreted unambiguously. However, delayed treatment effects, disease progression, treatment switchers or the presence of subgroups with differential treatment effects may challenge the assumption of proportional hazards. In practice, weighted logrank tests emphasizing either early, intermediate or late event times via an appropriate weighting function may be used to accommodate for an expected pattern of non-proportionality. We model these sources of non-proportional hazards via a mixture of survival functions with piecewise constant hazard. The model is then applied to study the power of unweighted and weighted log-rank tests, as well as maximum tests allowing different time dependent weights. Simulation results suggest a robust performance of maximum tests across different scenarios, with little loss in power compared to the most powerful among the considered weighting schemes and huge power gain compared to unfavorable weights. The actual sources of non-proportional hazards are not obvious from resulting populationwise survival functions, highlighting the importance of detailed simulations in the planning phase of a trial when assuming non-proportional hazards.We provide the required tools in a software package, allowing to model data generating processes under complex non-proportional hazard scenarios, to simulate data from these models and to perform the weighted logrank tests.