Effects Of Delayed Treatment Research Articles

A Comparison of Additional Benefit Assessment Methods for Time-to-Event Endpoints Using Hazard Ratio Point Estimates or Confidence Interval Limits by Means of a Simulation Study.

For time-to-event endpoints, three additional benefit assessment methods have been developed aiming at an unbiased knowledge about the magnitude of clinical benefit of newly approved treatments. The American Society of Clinical Oncology (ASCO) defines a continuous score using the hazard ratio point estimate (HR-PE). The European Society for Medical Oncology (ESMO) and the German Institute for Quality and Efficiency in Health Care (IQWiG) developed methods with an ordinal outcome using lower and upper limits of the 95% HR confidence interval (HR-CI), respectively. We describe all three frameworks for additional benefit assessment aiming at a fair comparison across different stakeholders. Furthermore, we determine which ASCO score is consistent with which ESMO/IQWiG category. In a comprehensive simulation study with different failure time distributions and treatment effects, we compare all methods using Spearman's correlation and descriptive measures. For determination of ASCO values consistent with categories of ESMO/IQWiG, maximizing weighted Cohen's Kappa approach was used. Our research depicts a high positive relationship between ASCO/IQWiG and a low positive relationship between ASCO/ESMO. An ASCO score smaller than 17, 17 to 20, 20 to 24, and greater than 24 corresponds to ESMO categories. Using ASCO values of 21 and 38 as cutoffs represents IQWiG categories. We investigated the statistical aspects of the methods and hence implemented slightly reduced versions of all methods. IQWiG and ASCO are more conservative than ESMO, which often awards the maximal category independent of the true effect and is at risk of overcompensating with various failure time distributions. ASCO has similar characteristics as IQWiG. Delayed treatment effects and underpowered/overpowered studies influence all methods in some degree. Nevertheless, ESMO is the most liberal one. For the additional benefit assessment, the American Society of Clinical Oncology (ASCO) uses the hazard ratio point estimate (HR-PE) for their continuous score. In contrast, the European Society for Medical Oncology (ESMO) and the German Institute for Quality and Efficiency in Health Care (IQWiG) use the lower and upper 95% HR confidence interval (HR-CI) to specific thresholds, respectively. ESMO generously assigns maximal scores, while IQWiG is more conservative.This research provides the first comparison between IQWiG and ASCO and describes all three frameworks for additional benefit assessment aiming for a fair comparison across different stakeholders. Furthermore, thresholds for ASCO consistent with ESMO and IQWiG categories are determined, enabling a comparison of the methods in practice in a fair manner.IQWiG and ASCO are the more conservative methods, while ESMO awards high percentages of maximal categories, especially with various failure time distributions. ASCO has similar characteristics as IQWiG. Delayed treatment effects and under/-overpowered studies influence all methods. Nevertheless, ESMO is the most liberal one. An ASCO score smaller than 17, 17 to 20, 20 to 24, and greater than 24 correspond to the categories of ESMO. Using ASCO values of 21 and 38 as cutoffs represents categories of IQWiG.

Adaptive promising zone design for cancer immunotherapy with heterogeneous delayed treatment effect

ABSTRACT Indirect mechanisms of cancer immunotherapies result in delayed treatment effects that vary among patients. Consequently, the use of the log-rank test in trial design and analysis can lead to significant power loss and pose additional challenges for interim decisions in adaptive designs. In this paper, we describe patients’ survival using a piecewise proportional hazard model with random lag time and propose an adaptive promising zone design for cancer immunotherapy with heterogeneous delayed effects. We provide solutions for calculating conditional power and adjusting the critical value for the log-rank test with interim data. We divide the sample space into three zones – unfavourable, promising, and favourable –based on re-estimations of the survival parameters, the log-rank test statistic at the interim analysis, and the initial and maximum sample sizes. If the interim results fall into the promising zone, the sample size is increased; otherwise, it remains unchanged. We show through simulations that our proposed approach has greater overall power than the fixed sample design and similar power to the matched group sequential trial. Furthermore, we confirm that critical value adjustment effectively controls the type I error rate inflation. Finally, we provide recommendations on the implementation of our proposed method in cancer immunotherapy trials.

Therapeutic lag: Is treatment effect delayed in progressive MS?

Background: Randomized clinical trials (RCTs) in progressive multiple sclerosis (MS) often revealed non-significant treatment effects on disability progression. Objectives: To investigate whether the failure to detect a significant benefit from treatment may be motivated by a delay in treatment effect, possibly related to baseline characteristics. Methods: We re-analyzed data from two RCTs testing interferon-beta and glatiramer-acetate versus placebo in progressive MS with no significant effect on EDSS progression. We first designed a time-dependent Cox model with no treatment effect up to time = t0, and constant hazard ratio (HR) after time = t0. We selected the best-fitting t0 from 0 (standard Cox model) to 2.5 years. Furthermore, we modeled the delay as a function of baseline EDSS and fitted the resulting Cox model to the merged dataset. Results: The time-dependent Cox model revealed a significant benefit of treatment delayed by t0 = 2.5 years for the SPECTRIMS study (HR = 0.65 (0.43–0.98), p = 0.041), and delayed by t0 = 2 years for the PROMISE study (HR = 0.65, (0.42–0.99), p = 0.044). In the merged dataset, the HR for the EDSS-dependent delayed effect was 0.68 (0.56, 0.82), p < 0.001. Conclusion: The assumption of a delayed treatment effect improved the fit to the data of the two examined RCTs, uncovering a significant, although shifted, benefit of treatment.

Nonparametric analysis of delayed treatment effects using single-crossing constraints.

Clinical trials involving novel immuno-oncology therapies frequently exhibit survival profiles which violate the proportional hazards assumption due to a delay in treatment effect, and, in such settings, the survival curves in the two treatment arms may have a crossing before the two curves eventually separate. To flexibly model such scenarios, we describe a nonparametric approach for estimating the treatment arm-specific survival functions which constrains these two survival functions to cross at most once without making any additional assumptions about how the survival curves are related. A main advantage of our approach is that it provides an estimate of a crossing time if such a crossing exists, and, moreover, our method generates interpretable measures of treatment benefit including crossing-conditional survival probabilities and crossing-conditional estimates of restricted residual mean life. Our estimates of these measures may be used together with efficacy measures from a primary analysis to provide further insight into differences in survival across treatment arms. We demonstrate the use and effectiveness of our approach with a large simulation study and an analysis of reconstructed outcomes from a recent combination therapytrial.

Delayed treatment effect predicting (DTEP) model for guiding immuno-oncology trial designs

BackgroundThe presence of delayed treatment effects (DTE) is common in immuno-oncology trials. However, conventional trial designs often overlook the potential presence of DTE, which can result in an underestimation of the required sample size and loss of statistical power. Conversely, when there is actually no apparent delay in treatment effects, alternative trial designs for addressing DTE may lead to an overestimation of sample size and unnecessary prolongation of the trial duration. To mitigate this challenge, we propose the use of a DTE predicting (DTEP) model to better guide immuno-oncology trial designs. MethodsThe DTEP model was developed and validated using data from 147 published randomized immuno-oncology trials. The eligible trials were divided into a training set (approximately 75% of the trials) and a test set (approximately 25%). We employed linear discriminant analysis (LDA) to develop the DTEP model for predicting the DTE status using baseline characteristics available at the trial design stage. The receiver operating characteristic (ROC) curve was utilized to assess the ability of the model to distinguish between trials with and without DTE. We further re-conducted the JUPITER-02 trial in a simulation setting, employing three design approaches to assess the potential benefits of utilizing the DTEP model. ResultsBaseline characteristics available during the trial design stage, including cancer type, line of treatment, and experimental and control arm regimens were incorporated, and high accuracy in predicting the DTE status in both the training set (area under the operating characteristic curve (AUC), 0.79; 95% confidence interval (CI), 0.71–0.88) and test set (AUC, 0.78; 95% CI, 0.66–0.90) was achieved. Notably, the model successfully predicted the DTE status in two randomized trials among the test sets that were conducted by our team (ESCORT-1st (absence of DTE) and JUPITER-02 (presence of DTE)). In silico re-conduct of the JUPITER-02 trial further showed that the statistical power would be markedly improved when trial designs were guided by the DTEP model. ConclusionsThe DTEP model can significantly enhance the precision and effectiveness of immuno-oncology trial designs, thereby facilitating the discovery of effective immunotherapeutics in a more streamlined and expedited manner.

Comparative analysis of preoperative chemoradiotherapy and upfront surgery in the treatment of upper-half rectal cancer: oncological benefits, surgical outcomes, and cost implications.

The value of neoadjuvant chemoradiotherapy (CRT) is not absolutely clear for upper-half (> 7-15cm) rectal cancer. This study aimed to compare the efficacy and safety of radical surgery with preoperative CRT vs. upfront surgery (US) in Chinese patients with stage II and III upper-half rectal cancer. A total of 809 patients with locally advanced upper-half rectal cancer between 2017 and 2021 were enrolled retrospectively (280 treated with CRT and 529 treated with US). Through 1:1 propensity score matching, the CRT (172 patients) and US (172 patients) groups were compared for short-term postoperative results and long-term oncological and functional outcomes. In the entire cohort, patients in the CRT group had a younger age, lower distance from the anal verge (DAV), and higher rates of cT4 stage, cN2 stage, mrCRM positivity, EMVI positivity, CEA elevation, and CA-199 elevation than those in the US group. The 5-year disease-free survival (DFS) was lower in the CRT group than in the US group (76% vs. 84%, p = 0.022), while the 5-year overall survival (OS) was comparable between the two groups (85% and 88%, p = 0.084). The distant metastasis rate was higher in the CRT group than in the US group (12.5% vs. 7.8%, p = 0.028), though the local recurrence rate was similar between the two groups (1.1% and 1.3%, p = 1.000). After performing PSM, the 5-year OS (86% vs. 88% p = 0.312), the 5-year DFS (79% vs. 80%, p = 0.435), the local recurrence rate (1.2% vs. 1.7%, p = 1.000), and the distant metastasis rate (11.0% vs. 9.3%, p = 0.593) were comparable between the two groups. Notable pathological downstaging was observed in the CRT group, with a pathological complete response (PCR) rate of 14.5%. In addition, patients in the CRT group had a lower proportion of pT3 (61.6% vs. 77.9%, p < 0.001), pN + (pN1, 15.1% vs. 30.2%, pN2, 9.3% vs. 20.3%, p < 0.001), stage III (24.4% vs. 50.6%, p < 0.001), perineural invasion (19.8% vs. 32.0%, p = 0.014), and lymphovascular invasion (9.3% vs. 25.6%, p < 0.001) than those in the US group. Postoperative complications and long-term functional results were similar, yet there was a trend toward a higher conversion to laparotomy rate (5 (2.9%) vs. 0 (0.0%), p = 0.061) and higher rates of robotic surgery (11.6% vs. 4.7%, p < 0.001), open surgery (7.0% vs. 0.6%, p < 0.001), diverting stoma (47.1% vs. 25.6%, p < 0.001), and surgery costs (1473.6 ± 106.5 vs. 1140.3 ± 54.3$, p = 0.006) in the CRT group. In addition, EMVI (OR = 2.516, p = 0.001) was the only independent risk factor associated with poor response to CRT, and in subgroup analysis of EMVI + , CRT group patients presented a lower 5-year DFS (72.9% vs. 80.5%, p = 0.025) compared to US group patients. CRT prior to surgery has no additional oncological benefits over US in the treatment of upper-half rectal cancer. In contrast, CRT is associated with increased rates of conversion to laparotomy, stoma creation and higher surgery costs. Surgeons tend to favor robotic surgery in the treatment of complex cases such as radiated upper-half rectal cancers. Notably, EMVI + patients with upper-half rectal cancer should be encouraged to undergo upfront surgery, as preoperative CRT may not provide benefits and may lead to delayed treatment effects.

Metabolic-Related Gene Prognostic Index for Predicting Prognosis, Immunotherapy Response, and Candidate Drugs in Ovarian Cancer.

Ovarian cancer (OC) is a highly heterogeneous disease, with patients at different tumor staging having different survival times. Metabolic reprogramming is one of the key hallmarks of cancer; however, the significance of metabolism-related genes in the prognosis and therapy outcomes of OC is unclear. In this study, we used weighted gene coexpression network analysis and differential expression analysis to screen for metabolism-related genes associated with tumor staging. We constructed the metabolism-related gene prognostic index (MRGPI), which demonstrated a stable prognostic value across multiple clinical trial end points and multiple validation cohorts. The MRGPI population had its distinct molecular features, mutational characteristics, and immune phenotypes. In addition, we investigated the response to immunotherapy in MRGPI subgroups and found that patients with low MRGPI were prone to benefit from anti-PD-1 checkpoint blockade therapy and exhibited a delayed treatment effect. Meanwhile, we identified four candidate therapeutic drugs (ABT-737, crizotinib, panobinostat, and regorafenib) for patients with high MRGPI, and we evaluated the pharmacokinetics and safety of the candidate drugs. In summary, the MRGPI was a robust clinical feature that could predict patient prognosis, immunotherapy response, and candidate drugs, facilitating clinical decision making and therapeutic strategy of OC.

Sample size calculation for mixture model based on geometric average hazard ratio and its applications to nonproportional hazard.

With the advent of cancer immunotherapy, some special features including delayed treatment effect, cure rate, diminishing treatment effect and crossing survival are often observed in survival analysis. They violate the proportional hazard model assumption and pose a unique challenge for the conventional trial design and analysis strategies. Many methods like cure rate model have been developed based on mixture model to incorporate some of these features. In this work, we extend the mixture model to deal with multiple non-proportional patterns and develop its geometric average hazard ratio (gAHR) to quantify the treatment effect. We further derive a sample size and power formula based on the non-centrality parameter of the log-rank test and conduct a thorough analysis of the impact of each parameter on performance. Simulation studies showed a clear advantage of our new method over the proportional hazard based calculation across different non-proportional hazard scenarios. Moreover, the mixture modeling of two real trials demonstrates how to use the prior information on the survival distribution among patients with different biomarker and early efficacy results in practice. By comparison with a simulation-based design, the new method provided a more efficient way to compute the power and sample size with high accuracy of estimation. Overall, both theoretical derivation and empirical studies demonstrate the promise of the proposed method in powering future innovative trial designs.

Sequential monitoring of cancer immunotherapy trial with random delayed treatment effect

ABSTRACT Cancer immunotherapy trials are frequently characterized by a delayed treatment effect that violates the proportional hazards assumption. The log-rank test (LRT) suffers a substantial loss of statistical power under the nonproportional hazards model. Various group sequential designs using weighted LRTs (WLRTs) have been proposed under the fixed delayed treatment effect model. However, patients enrolled in immunotherapy trials are often heterogeneous, and the duration of the delayed treatment effect is a random variable. Therefore, we propose group sequential designs under the random delayed effect model using the random delayed distribution WLRT. The proposed group sequential designs are developed for monitoring the efficacy of the trial using the method of Lan-DeMets alpha-spending function with O’Brien-Fleming stopping boundaries or a gamma family alpha-spending function. The maximum sample size for the group sequential design is obtained by multiplying an inflation factor with the sample size for the fixed sample design. Simulations are conducted to study the operating characteristics of the proposed group sequential designs. The robustness of the proposed group sequential designs for misspecifying random delay time distribution and domain is studied via simulations.

The Inverse Log-Rank Test: A Versatile Procedure for Late Separating Survival Curves.

Often in the planning phase of a clinical trial, a researcher will need to choose between a standard versus weighted log-rank test (LRT) for investigating right-censored survival data. While a standard LRT is optimal for analyzing evenly distributed but distinct survival events (proportional hazards), an appropriately weighted LRT test may be better suited for handling non-proportional, delayed treatment effects. The "a priori" misspecification of this alternative may result in a substantial loss of power when determining the effectiveness of an experimental drug. In this paper, the standard unweighted and inverse log-rank tests (iLRTs) are compared with the multiple weight, default Max-Combo procedure for analyzing differential late survival outcomes. Unlike combination LRTs that depend on the arbitrary selection of weights, the iLRT by definition is a single weight test and does not require implicit multiplicity correction. Empirically, both weighted methods have reasonable flexibility for assessing continuous survival curve differences from the onset of a study. However, the iLRT may be preferable for accommodating delayed separating survival curves, especially when one arm finishes first. Using standard large-sample methods, the power and sample size for the iLRT are easily estimated without resorting to complex and timely simulations.

A nonparametric test for comparing survival functions based on restricted distance correlation

Abstract In this article, we propose an omnibus test for comparing two survival functions under non-proportional hazards. The test statistic is based on a product-limit estimate of the restricted distance correlation, which is closely related to the L 2 {L}_{2} distance between survival curves. The strong consistency is established under mild regularity conditions. Our simulation studies show that the new test has satisfactory power under proportional hazard and various non-proportional hazards settings including delayed treatment effect, diminishing effect, and crossing survival curves; therefore, it can be a competitive alternative to the existing omnibus tests such as Kolmogorov-Smirnov test, Cramer-von Mises test, two-stage test, and the maxCombo test based on weighted log-rank statistics. Two extensions of the new test to one-sided alternatives and a Gaussian kernel are also discussed.

Comparative Analysis of Methods for Analyzing Clinical Results of Immuno-Oncology Therapy with Delayed Treatment Effects

Introduction: CAR-T therapy is a groundbreaking therapy that involves modifying patient's T cells to recognize and combat cancer cells. This therapy requires a period of CAR-T manufacture following apheresis, delaying the actual receipt of the CAR-T. During this period the patient may receive bridging therapy. CAR-T therapies are increasingly being studied in randomized controlled clinical trials and delays potentially affect the proportional hazards assumption, reduce log-rank test statistical power, and complicate reporting and interpretation. To address this, we assessed available statistical methods for analyzing immune-oncology outcomes, specifically CAR-T therapy's delayed treatment effects, with a focus on current and emerging tools to improve analysis and reporting. Methods: We systematically reviewed and compared existing and emerging tools to evaluate survival analysis methods suitable for assessing outcomes in CAR-T studies. These methods included standard, stratified, and weighted log-rank tests, cox proportional hazards (time dependent), parametric survival (weibull, etc.), time-dependent covariates, landmark analysis, joint modeling and restricted mean survival time (RMST). We simulated trial results, highlighted features, underlying assumptions, and interpretability. Results: Results were based on a simulated dataset (N=240) and revealed distinct characteristics and implications of each method used to analyze data (Table). Standard log-rank tests, which give equal weight to all time points and assumes proportional hazards, resulted in less discerning outcome measurements compared to weighted techniques such as censored weighted estimation/constant piecewise weighted (CWE/CPW) and Fleming-Harrington which do not assume proportional hazards and can be weighted towards early or late events. RMST summarized average time until progression, which may be a more intuitive measure for clinicians and patients and was presented along with other analytical techniques. Cox proportional hazards modeling provided ratios to quantify and compare efficacy across different time points. Parametric survival models captured survival dynamics and offered precise estimation of survival probabilities at specific intervals. Time-dependent covariates accounted for time-varying factors influencing treatment response, while landmark analysis evaluated treatment efficacy beyond a specific duration and was used in combination with other analytical techniques. Joint modeling simultaneously assessed treatment effects on both longitudinal and survival outcomes, accounting for their interdependencies. Stratification identified patient subgroups with differential treatment responses and outcomes, enabling precise reporting of categorical CAR-T treatment approaches. Conclusions: Our comparative analysis of analytical techniques underscores the critical role of selecting an appropriate approach in the context of CAR-T therapies. Conventional methods such cox regression and the standard log-rank test, while useful in most contexts, may underestimate key outcomes associated with CAR-T therapy due to violations of proportional hazard assumption. Weighted techniques, which can be weighted towards early or late events, may provide a more nuanced picture, and better accommodate CAR-T therapies. Similarly, the RMST offers an intuitive average time to progression, offering complementary insights to other methods. These findings highlight the importance of careful method selection in survival analysis for CAR-T therapy studies. Rigorous, tailored analytical approaches will be crucial in accurately determining the efficacy of these promising therapies and informing future clinical study designs. By considering these methods, researchers can gain deeper insights regarding CAR-T therapy and improve the design and analysis of clinical trials, ultimately advancing the understanding and application of this transformative therapy.

A change point-based analysis procedure for improving the success rate of decision-making in clinical trials with delayed treatment effects.

A delayed treatment effect is a commonly observed phenomenon in tumor immunotherapy clinical trials. It can cause a loss of statistical power and complicate the interpretation of the analytical findings. This phenomenon also poses challenges for interim analysis in the context of phase II/III seamless design or group sequential design. It shows potential to lead researchers to make incorrect go/no-go decisions. Despite its significance, rare research has explored the impact of delayed treatment effects on the decision success rate of the interim analysis and the methods to compensate for this loss. In this study, we propose an analysis procedure based on change points for improving the decision success rate at the interim analysis in the presence of delayed treatment effects. This procedure primarily involves three steps: I. detecting and testing the number and locations of change points; II. estimating treatment efficacy; and III. making go/no-go decisions. Simulation results demonstrate that when there is a delayed treatment effect with a single change point, using the proposed analysis procedure significantly improves the decision success rate while controlling the type I error rate. Moreover, the proposed method exhibits very little disparity compared to the unadjusted method when the proportional hazards assumption holds. Therefore, the proposed analysis procedure provides a feasible approach for decision-making at the interim analysis when delayed treatment effects are present.

Determining the late effect parameter in the Fleming-Harrington test using asymptotic relative efficiency in cancer immunotherapy clinical trials

ABSTRACT The delayed treatment effect, which manifests as a separation of survival curves after a change point, has often been observed in immunotherapy clinical trials. A late effect of this kind may violate the proportional hazards assumption, resulting in the non-negligible loss of statistical power of an ordinary log-rank test when comparing survival curves. The Fleming-Harrington (FH) test, a weighted log-rank test, is configured to mitigate the loss of power by incorporating a weight function with two parameters, one each for early and late treatment effects. The two parameters need to be appropriately determined, but no helpful guides have been fully established. Since the late effect is expected in immunotherapy trials, we focus on the late effect parameter in this study. We consider parameterizing the late effect in a readily interpretable fashion and determining the optimal late effect parameter in the FH test to maintain statistical power in reference to the asymptotic relative efficiency (ARE). The optimization is carried out under three lag models (i.e. linear, threshold, and generalized linear lag), where the optimal weights are proportional to the lag functions characterized by the change points. Extensive simulation studies showed that the FH test with the selected late parameter reliably provided sufficient power even when the change points in the lag models were misspecified. This finding suggests that the FH test with the ARE-guided late parameter may be a reasonable and practical choice for the primary analysis in immunotherapy clinical trials.

Restricted Mean Survival Time-Can It Be a New Tool in Assessing the Survival of Non-Small Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors?

Lung cancer (LC) is the first and most lethal cancer in the world; identifying new methods to treat it, such as immune checkpoint inhibitors (ICIs), is needed. ICIs treatment is very effective, but it comes bundled with a series of immune-related adverse events (irAEs). Restricted mean survival time (RMST) is an alternative tool for assessing the patients' survival when the proportional hazard assumption (PH) fails. We included in this analytical cross-sectional observational survey patients with metastatic non-small-cell lung cancer (NSCLC), treated for at least 6 months with ICIs in the first- and second-line settings. Using RMST, we estimated the overall survival (OS) of patients by dividing them into two groups. A multivariate Cox regression analysis was performed to determine the impact of the prognostic factors on OS. Seventy-nine patients were included (68.4% men, mean age 63.8), and 34/79 (43%) presented irAEs. The OS RMST of the entire group was 30.91 months, with a survival median of 22 months. Thirty-two out of seventy-nine (40.5%) died before we ended our study. The OS RMST and death percentage favored the patients who presented irAEs (long-rank test, p = 0.036). The OS RMST of patients with irAEs was 35.7 months, with a number of deaths of 12/34 (35.29%), while the OS RMST of the patients without irAEs was 17 months, with a number of deaths of 20/45 (44.44%). The OS RMST by the line of treatment favored the first line of treatment. In this group, the presence of irAEs significantly impacted the survival of these patients (p = 0.0083). Moreover, patients that experienced low-grade irAEs had a better OS RMST. This result has to be cautiously regarded because of the small number of patients stratified according to the grades of irAEs. The prognostic factors for the survival were: the presence of irAEs, Eastern Cooperative Oncology Group (ECOG) performance status and the number of organs affected by metastasis. The risk of dying was 2.13 times higher for patients without irAEs than for the patients who presented irAEs, (CI) 95% of 1.03 to 4.39. Moreover, by increasing the ECOG performance status by one point, the risk of death increased by 2.28 times, with a CI 95% of 1.46 to 3.58, while the involvement of more metastatic organs was associated with a 1.60 times increase in the death risk, with a CI 95% of 1.09 to 2.36. Age and the type of tumor were not predictive for this analysis. The RMST is a new tool that helps researchers to better address the survival in studies with ICIs treatment where the PH fails, and the long-rank test is less efficient due to the existence of the long-term responses and delayed treatment effects. Patients with irAEs have a better prognosis than those without irAEs in the first-line settings. The ECOG performance status and the number of organs affected by metastasis must be considered when selecting patients for ICIs treatment.

N°169 – Better effect of early vs delayed treatment after the first seizure: A brain connectivity study

N°169 – Better effect of early vs delayed treatment after the first seizure: A brain connectivity study

Outcome measures of phase III anticancer drug trials in China.

Outcome measures of phase III anticancer drug trials in China.

Group sequential designs for cancer immunotherapy trial with delayed treatment effect

ABSTRACT Cancer immunotherapy trials are frequently characterized by delayed treatment effects such that the proportional hazards assumption is violated and the log-rank test suffers a substantial loss of statistical power. To increase the efficacy of the trial design, a variety of weighted log-rank tests have been proposed for fixed sample and group sequential trial designs. However, in such a group sequential design, it is often not recommended for futility interim monitoring due to possible delayed treatment effect which could result a high false-negative rate. To resolve this problem, we propose a group sequential design using a piecewise weighted log-rank test which provides an event-driven approach based on number of events after the delayed time. That is, the interim looks will not be conducted until the planned number of events observed after the delay time. Thus, it avoids the possibility of false-negative rate due to the delayed treatment effect. Furthermore, with an event-driven approach, the proposed group sequential design is robust against the underlying survival, accrual and censoring distributions. The group sequential designs using Fleming-Harrington-( ρ , γ ) weighted log-rank test and a new weighted log-rank test are also discussed.

Sample size calculation for the combination test under nonproportional hazards.

For sample size calculation in clinical trials with survival endpoints, the logrank test, which is the optimal method under the proportional hazard (PH) assumption, is predominantly used. In reality, the PH assumption may not hold. For example, in immuno-oncology trials, delayed treatment effects are often expected. The sample size without considering the potential violation of the PH assumption may lead to an underpowered study. In recent years, combination tests such as the maximum weighted logrank test have received great attention because of their robust performance in various hazards scenarios. In this paper, we propose a flexible simulation-free procedure to calculate the sample size using combination tests. The procedure extends the Lakatos' Markov model and allows for complex situations encountered in a clinical trial, like staggered entry, dropouts, etc. We evaluate the procedure using two maximum weighted logrank tests, one projection-type test, and three other commonly used tests under various hazards scenarios. The simulation studies show that the proposed method can achieve the target power for all compared tests in most scenarios. The combination tests exhibit robust performance under correct specification and misspecification scenarios and are highly recommended when the hazard-changing patterns are unknown beforehand. Finally, we demonstrate our method using two clinical trial examples and provide suggestions about the sample size calculations under nonproportionalhazards.

On assessing survival benefit of immunotherapy using long-term restricted mean survival time.

The pattern of the difference between two survival curves we often observe in randomized clinical trials for evaluating immunotherapy is not proportional hazards; the treatment effect typically appears several months after the initiation of the treatment (ie, delayed difference pattern). The commonly used logrank test and hazard ratio estimation approach will be suboptimal concerning testing and estimation for those trials. The long-term restricted mean survival time (LT-RMST) approach is a promising alternative for detecting the treatment effect that potentially appears later in the study. A challenge in employing the LT-RMST approach is that it must specify a lower end of the time window in addition to a truncation time point that the RMST requires. There are several investigations and suggestions regarding the choice of the truncation time point for the RMST. However, little has been investigated to address the choice of the lower end of the time window. In this paper, we propose a flexible LT-RMST-based test/estimation approach that does not require users to specify a lower end of the time window. Numerical studies demonstrated that the potential power loss by adopting this flexibility was minimal, compared to the standard LT-RMST approach using a prespecified lower end of the time window. The proposed method is flexible and can offer higher power than the RMST-based approach when the delayed treatment effect is expected. Also, it provides a robust estimate of the magnitude of the treatment effect and its confidence interval that corresponds to the test result.