Comparison of Treatment Effects Measured by the Hazard Ratio and by the Ratio of Restricted Mean Survival Times in Oncology Randomized Controlled Trials

The efficacy and safety of immunotherapy as first-line treatment for extensive-stage small cell lung cancer: evaluating based on reconstructed individual patient data

Objective

Selecting between programmed cell death ligand 1 (PD-L1) inhibitor or programmed cell death 1 (PD-1) inhibitor plus chemotherapy as first-line treatment for extensive-stage small cell lung cancer (ES-SCLC) patients urgently needs to be answered.

Methods

Eligible phase 3 randomized clinical trials evaluating regimens based on PD-1/PD-L1 inhibitors as first-line treatment in ES-SCLC patients were systematically searched on the PubMed and Cochrane Library databases and major international conferences from 01/01/2018 to 18/09/2023. The individual patient data (IPD) were recuperated from the Kaplan-Meier curves of the overall survival (OS) and progression-free survival (PFS) of the included studies using the IPDfromKM method. The reconstructed data were pooled into unified arms, including the PD-L1 inhibitor plus chemotherapy group (PD-L1 group), PD-1 inhibitor plus chemotherapy group (PD-1 group), and PD-1 (L1) inhibitor and chemotherapy plus other (anlotinib group, tiragolumab group, and tremelimumab group). Subsequently, the PD-L1 group was indirectly compared with the other groups. A standard statistical analysis was conducted using the "survival" package for the time-to-event endpoint. The primary outcomes were the OS and PFS of the PD-L1 group and the PD-1 inhibitor group. The secondary outcomes included safety and the 12- and 24-month restricted mean survival time (RMST) of the PD-L1 group and PD-1 group.

Results

A total of 9 studies including 11 immunotherapy cohorts were included. No significant difference in PFS (hazard ratio [HR]: 0.96, 95% confidence interval [CI]: 0.86-1.06), OS (HR: 0.94, 95% CI: 0.84-1.05), and 12-month and 24-month RMST for OS (P = 0.198 and P = 0.216, respectively) was observed between the PD-L1 group and the PD-1 group. In contrast, the anlotinib group showed significantly better OS (HR: 0.70, 95% CI: 0.55-0.89), PFS (HR: 0.69, 95% CI: 0.58-0.83), and RMST for OS compared to the PD-L1 group. The tiragolumab group showed similar efficacy to the PD-L1 group. However, the tremelimumab group exhibited inferior efficacy than the PD-L1 group. The incidence of ≥grade 3 treatment-emergent adverse events (TEAEs) was significantly higher in the PD-1 group compared to the PD-L1 group (85.4% vs. 69.6%, P <.001), whereas the incidence of irAEs was similar between the two groups.

Conclusion

This reconstructed IPD analysis revealed that PD-1 inhibitors plus chemotherapy achieved similar efficacy to PD-L1 inhibitors plus chemotherapy as first-line treatment in ES-SCLC patients, whereas PD-L1 inhibitors plus chemotherapy had a better safety profile.

Evaluation of the proportional hazards assumption and covariate adjustment methods in comparative surgical observational studies with time-to-event endpoints

INTRODUCTION

Comparative studies on surgical treatments with time-to-event endpoints have provided substantial evidence for clinical practice, but the accurate use of survival data analysis and the control of confounding bias remain big challenges.

METHODS

This was a survey of surgical studies with survival outcomes published in four general medical journals and five general surgical journals in 2021. The two most concerned statistical issues were evaluated, including confounding control by propensity score analysis (PSA) or multivariable analysis and testing of proportional hazards (PH) assumption in Cox model.

RESULTS

A total of 74 studies were included, comprising 63 observational studies and 11 randomized controlled trials. Among the observational studies, the proportion of studies utilizing PSA in surgical oncology and non-oncology studies was similar (40.9 % versus 36.8 %, P = 0.762). However, the former reported a significantly lower proportion of PH assumption assessments compared to the latter (13.6 % versus 42.1 %, P = 0.020). Twenty-five observational studies (25/63) used PSA methods, but two-thirds of them (17/25) showed unclear balance of baseline data after PSA. And the proportion of PH assumption testing after PSA was slightly lower than that before PSA, but the difference was not statistically significant (24.0 % versus 28.0 %, P = 0.317). Comprehensive suggestions were given on confounding control in survival analysis and alternative resolutions for non-compliance with PH assumption.

CONCLUSION

This study highlights suboptimal reporting of PH assumption evaluation in observational surgical studies both before and after PSA. Efforts and consensus are needed with respect to the underlying assumptions of statistical methods.

Individualized survival prediction and surgery recommendation for patients with glioblastoma

Background

There is a lack of individualized evidence on surgical choices for glioblastoma (GBM) patients.

Aim

This study aimed to make individualized treatment recommendations for patients with GBM and to determine the importance of demographic and tumor characteristic variables in the selection of extent of resection.

Methods

We proposed Balanced Decision Ensembles (BDE) to make survival predictions and individualized treatment recommendations. We developed several DL models to counterfactually predict the individual treatment effect (ITE) of patients with GBM. We divided the patients into the recommended (Rec.) and anti-recommended groups based on whether their actual treatment was consistent with the model recommendation.

Results

The BDE achieved the best recommendation effects (difference in restricted mean survival time (dRMST): 5.90; 95% confidence interval (CI), 4.40-7.39; hazard ratio (HR): 0.71; 95% CI, 0.65-0.77), followed by BITES and DeepSurv. Inverse probability treatment weighting (IPTW)-adjusted HR, IPTW-adjusted OR, natural direct effect, and control direct effect demonstrated better survival outcomes of the Rec. group.

Conclusion

The ITE calculation method is crucial, as it may result in better or worse recommendations. Furthermore, the significant protective effects of machine recommendations on survival time and mortality indicate the superiority of the model for application in patients with GBM. Overall, the model identifies patients with tumors located in the right and left frontal and middle temporal lobes, as well as those with larger tumor sizes, as optimal candidates for SpTR.

A transformer model for cause-specific hazard prediction

BACKGROUD

Modelling discrete-time cause-specific hazards in the presence of competing events and non-proportional hazards is a challenging task in many domains. Survival analysis in longitudinal cohorts often requires such models; notably when the data is gathered at discrete points in time and the predicted events display complex dynamics. Current models often rely on strong assumptions of proportional hazards, that is rarely verified in practice; or do not handle sequential data in a meaningful way. This study proposes a Transformer architecture for the prediction of cause-specific hazards in discrete-time competing risks. Contrary to Multilayer perceptrons that were already used for this task (DeepHit), the Transformer architecture is especially suited for handling complex relationships in sequential data, having displayed state-of-the-art performance in numerous tasks with few underlying assumptions on the task at hand.

RESULTS

Using synthetic datasets of 2000-50,000 patients, we showed that our Transformer model surpassed the CoxPH, PyDTS, and DeepHit models for the prediction of cause-specific hazard, especially when the proportional assumption did not hold. The error along simulated time outlined the ability of our model to anticipate the evolution of cause-specific hazards at later time steps where few events are observed. It was also superior to current models for prediction of dementia and other psychiatric conditions in the English longitudinal study of ageing cohort using the integrated brier score and the time-dependent concordance index. We also displayed the explainability of our model's prediction using the integrated gradients method.

CONCLUSIONS

Our model provided state-of-the-art prediction of cause-specific hazards, without adopting prior parametric assumptions on the hazard rates. It outperformed other models in non-proportional hazards settings for both the synthetic dataset and the longitudinal cohort study. We also observed that basic models such as CoxPH were more suited to extremely simple settings than deep learning models. Our model is therefore especially suited for survival analysis on longitudinal cohorts with complex dynamics of the covariate-to-outcome relationship, which are common in clinical practice. The integrated gradients provided the importance scores of input variables, which indicated variables guiding the model in its prediction. This model is ready to be utilized for time-to-event prediction in longitudinal cohorts.

Statistical primer: individual patient data meta-analysis and meta-analytic approaches in case of non-proportional hazards

Individual patient data (IPD) meta-analyses build upon traditional (aggregate data) meta-analyses by collecting IPD from the individual studies rather than using aggregated summary data. Although both traditional and IPD meta-analyses produce a summary effect estimate, IPD meta-analyses allow for the analysis of data to be performed as a single dataset. This allows for standardization of exposure, outcomes, and analytic methods across individual studies. IPD meta-analyses also allow the utilization of statistical methods typically used in cohort studies, such as multivariable regression, survival analysis, propensity score matching, uniform subgroup and sensitivity analyses, better management of missing data, and incorporation of unpublished data. However, they are more time-intensive, costly, and subject to participation bias. A separate issue relates to the meta-analytic challenges when the proportional hazards assumption is violated. In these instances, alternative methods of reporting time-to-event estimates, such as restricted mean survival time should be used. This statistical primer summarizes key concepts in both scenarios and provides pertinent examples.

Survival benefit of living donor kidney transplantation in patients on hemodialysis

BACKGROUND

Donors bravely donate their kidneys because they expect that living donor kidney transplantation (LKT) confers benefits to recipients. However, the magnitude of the survival benefit of LKT is uncertain.

METHODS

This prospective cohort study used two Japanese nationwide databases for dialysis and kidney transplantation and included 862 LKT recipients and 285,242 hemodialysis (HD) patients in the main model and 5299 LKT recipients and 151,074 HD patients in the supplementary model. We employed time-dependent model in the main model and assessed the hazard ratio and the difference in the restricted mean survival time (RMST) between LKT recipients and HD patients. In the main analysis of the main model (LKT, N = 675; HD, N = 675), we matched LKT recipients with HD patients by age, sex, dialysis vintage, and cause of renal failure and excluded HD patients with dementia or performance status grades 2, 3, or 4.

RESULTS

The median observational period was 8.00 (IQR 3.58-8.00) years. LKT was significantly associated with a lower risk of mortality (hazard ratios (95% confidence interval (CI)), 0.50 (0.35-0.72)) and an increase in life expectancy (7-year RMST differences (95% CI), 0.48 (0.35-0.60) years) compared with HD. In subgroup analysis, the survival benefit of LKT was greater in female patients than in male patients in the Cox model; whereas older patients gained longer life expectancy compared with younger patients.

CONCLUSIONS

LKT was associated with better survival benefits than HD, and the estimated increase in life expectancy was 0.48 years for 7 years.

Sample size calculation for multi-arm parallel design with restricted mean survival time

With the recent advances in oncology treatment, restricted mean survival time (RMST) is increasingly being used to replace the routine approach based on hazard ratios in randomized controlled trials for time-to-event outcomes. While RMST has been widely applied in single-arm and two-arm designs, challenges still exist in comparing RMST in multi-arm trials with three or more groups. In particular, it is unclear in the literature how to compare more than one intervention simultaneously or perform multiple testing based on RMST, and sample size determination is a major obstacle to its penetration to practice. In this paper, we propose a novel method of designing multi-arm clinical trials with right-censored survival endpoint based on RMST that can be applied in both phase II/III settings using a global χ 2 test as well as a modeling-based multiple comparison procedure. The framework provides a closed-form sample size formula built upon a multi-arm global test and a sample size determination procedure based on multiple-comparison in the phase II dose-finding study. The proposed method enjoys strong robustness and flexibility as it requires less a priori set-up than conventional work, and obtains a smaller sample size while achieving the target power. In the assessment of sample size, we also incorporate practical considerations, including the presence of non-proportional hazards and staggered patient entry. We evaluate the validity of our method through simulation studies under various scenarios. Finally, we demonstrate the accuracy and stability of our method by implementing it in the design of two real clinical trial examples.

Evaluation of dynamic recurrence risk for locally advanced gastric cancer in the clinical setting of adjuvant chemotherapy: a real-world study with IPTW-based conditional recurrence analysis

BACKGROUND

The long-term dynamic recurrence hazard of locally advanced gastric cancer (LAGC) in the clinical setting of adjuvant chemotherapy (ACT) remains unclear.

PURPOSE

This study aimed to investigate the dynamic recurrence risk of LAGC in patients who received ACT or not.

METHODS

The study assessed data from patients with LAGC who underwent radical gastrectomy between January, 2010 and October, 2015. Inverse probability of treatment weighting (IPTW) was performed to reduce selection bias between the ACT and observational (OBS) groups. Conditional recurrence-free survival (cRFS) and restricted mean survival time (RMST) were used to assess the survival differences.

RESULTS

In total, 1,661 LAGC patients were included (ACT group, n = 1,236 and OBS group, n = 425). The recurrence hazard gradually declined; in contrast, cRFS increased with RFS already accrued. Following IPTW adjustment, the cRFS rates were higher in the ACT group than those in the OBS group for patients at baseline or with accrued RFS of 1 and 2 years (p˂0.05). However, the cRFS rates of the ACT group were comparable with those of the OBS group for patients with accrued RFS of 3 or more years (p > 0.05). Likewise, the 5-year △RMST between the ACT and OBS groups demonstrated a similar trend. Moreover, the hematological metastasis rate of the ACT group was significantly lower than that of the OBS group for patients at baseline or with accrued RFS of 1 and 2 years, respectively (p˂0.05).

CONCLUSIONS

Although ACT could provide substantial benefits for patients with LAGC, the differences in recurrence hazard between the ACT and OBS groups may attenuate over time, which could help guide surveillance and alleviate patients' anxiety. Further prospective large-scale studies are warranted.

Restricted mean survival time to estimate an intervention effect in a cluster randomized trial

For time-to-event outcomes, the difference in restricted mean survival time is a measure of the intervention effect, an alternative to the hazard ratio, corresponding to the expected survival duration gain due to the intervention up to a predefined time t*. We extended two existing approaches of restricted mean survival time estimation for independent data to clustered data in the framework of cluster randomized trials: one based on the direct integration of Kaplan-Meier curves and the other based on pseudo-values regression. Then, we conducted a simulation study to assess and compare the statistical performance of the proposed methods, varying the number and size of clusters, the degree of clustering, and the magnitude of the intervention effect under proportional and non-proportional hazards assumption. We found that the extended methods well estimated the variance and controlled the type I error if there was a sufficient number of clusters (≥ 50) under both proportional and non-proportional hazards assumption. For cluster randomized trials with a limited number of clusters (< 50), a permutation test for pseudo-values regression was implemented and corrected the type I error. We also provided a procedure to estimate permutation-based confidence intervals which produced adequate coverage. All the extended methods performed similarly, but the pseudo-values regression offered the possibility to adjust for covariates. Finally, we illustrated each considered method with a cluster randomized trial evaluating the effectiveness of an asthma-control education program.

Smoking status and the association between patient-level factors and survival among lung cancer patients

BACKGROUND

Declines in the prevalence of cigarette smoking, advances in targeted therapies, and implementation of lung cancer screening have changed the clinical landscape for lung cancer. The proportion of lung cancer deaths is increasing in those who have never smoked cigarettes. To better understand contemporary patterns in survival among patients with lung cancer, a comprehensive evaluation of factors associated with survival, including differential associations by smoking status, is needed.

METHODS

Patients diagnosed with lung cancer between January 1, 2010, and September 30, 2019, were identified. We estimated all-cause and lung cancer-specific median, 5-year, and multivariable restricted mean survival time (RMST) to identify demographic, socioeconomic, and clinical factors associated with survival, overall and stratified by smoking status (never, former, and current).

RESULTS

Analyses included 6813 patients with lung cancer: 13.9% never smoked, 54.2% formerly smoked, and 31.9% currently smoked. All-cause RMST through 5 years for those who never, formerly, and currently smoked was 32.1, 25.9, and 23.3 months, respectively. Lung cancer-specific RMST was 36.3 months, 30.3 months, and 26.0 months, respectively. Across most models, female sex, younger age, higher socioeconomic measures, first-course surgery, histology, and body mass index were positively associated, and higher stage was inversely associated with survival. Relative to White patients, Black patients had increased survival among those who formerly smoked.

CONCLUSIONS

We identify actionable factors associated with survival between those who never, formerly, and currently smoked cigarettes. These findings illuminate opportunities to address underlying mechanisms driving lung cancer progression, including use of first-course treatment, and enhanced implementation of tailored smoking cessation interventions for individuals diagnosed with cancer.

A comparative study to alternatives to the log-rank test

BACKGROUND

Studies to compare the survival of two or more groups using time-to-event data are of high importance in medical research. The gold standard is the log-rank test, which is optimal under proportional hazards. As the latter is no simple regularity assumption, we are interested in evaluating the power of various statistical tests under different settings including proportional and non-proportional hazards with a special emphasis on crossing hazards. This challenge has been going on for many years now and multiple methods have already been investigated in extensive simulation studies. However, in recent years new omnibus tests and methods based on the restricted mean survival time appeared that have been strongly recommended in biometric literature.

METHODS

Thus, to give updated recommendations, we perform a vast simulation study to compare tests that showed high power in previous studies with these more recent approaches. We thereby analyze various simulation settings with varying survival and censoring distributions, unequal censoring between groups, small sample sizes and unbalanced group sizes.

RESULTS

Overall, omnibus tests are more robust in terms of power against deviations from the proportional hazards assumption.

CONCLUSION

We recommend considering the more robust omnibus approaches for group comparison in case of uncertainty about the underlying survival time distributions.

Percutaneous cryoablation versus partial nephrectomy for cT1b renal tumors: An inverse probability weight analysis

PURPOSE

To investigate differential clinical outcomes in patients treated with partial nephrectomy (PN) vs. percutaneous cryoablation (PCA) for cT1b renal tumors.

MATERIALS AND METHODS

We retrospectively analyzed the records of 119 patients who had undergone PN (n = 90) or PCA (n = 29) for cT1b renal tumors. Inverse probability weighting (IPW) was used for balancing patient demographics, including renal function and tumor complexity. Perioperative complications, renal function preservation rates, and oncological outcomes such as local recurrence-free, metastasis-free, cancer-specific, and overall survival were compared using IPW-adjusted restricted mean survival times (RMSTs).

RESULTS

PCA was more likely to be selected for octogenarians (odds ratio: 11.4, 95% confidence interval [CI]: 3.33-45.1). During the median follow-up of 43 months in the PCA group and 36.5 months in the PN group, unablated local residue or local recurrence was noted in 6 patients in the PCA group and local recurrence was noted in 4 patients in the PN groups. Of the 6 patients in the PCA group, 4 underwent salvage PCA, and local control had been achieved at the last visit. In the IPW-adjusted population, PCA had significantly worse local recurrence-free survival compared with PN (IPW-adjusted RMST difference: -22.7 months, 95% CI: -45.3 to -0.4, P = 0.046). IPW-adjusted RMST for metastasis-free survival (P = 0.23), cancer-specific survival (P = 0.77), and overall survival (P = 0.11) did not differ between PCA and PN. In addition, PN was not a predictor for local control failure at the last visit (odds ratio: 0.30, 95%CI: 0.05-1.29). There were no statistically significant differences between PN and PCA in renal function preservation or overall/severe complication rates.

CONCLUSIONS

In patients with cT1b renal tumor, although the local recurrence rate is higher for PCA than for PN, PCA provides comparable distant oncologic outcomes. PCA can be an alternative treatment option for elderly, comorbid patients, even those with cT1b renal tumors.

Sensitivity Analysis for Restricted Mean Survival Time When Survival Curves Have Divergent Tails

New immunotherapy methods are being developed to provide cancer patients with survival benefit. The tail effect of immuno-oncology (IO) therapy resulting in diverse tails of survival curves between treatment arms may provide important information for physicians to guide treatment decisions in clinical practice. The hazard ratio (HR) and the log-rank test may not be suitable for quantifying and interpreting the between-group difference in IO clinical trials because the underlying assumption that the HR is constant over time is not valid. As an alternative summary measure, the restricted mean survival time (RMST) has been attracting more attention for comparing survival curves. The RMST is defined as the mean survival time to a specific threshold timepoint τ and is calculated as the area under the curve within a specific time window from 0 to τ. Although physicians may wish to compare the RMST up to the end timepoint of a longer curve to elucidate the tail effect of the IO treatment, with the currently available statistical methods, τ is required to be set at the end timepoint of a shorter curve or before. To address this issue, we propose a sensitivity analysis approach to evaluating the between-group difference in the RMST at any timepoint that clinical investigators consider clinically relevant, thus being free from such a statistical constraint. Notably, this analysis can only provide complementary results; thus, it cannot function as the primary analysis.

Performance of Restricted Mean Survival Time Based Methods and Traditional Survival Methods: An Application in an Oncological Data

Objective

To compare restricted mean survival time- (RMST-) based methods with traditional survival methods when multiple covariates are of interest.

Methods

4405 osteosarcomas were captured from Surveillance, Epidemiology, and End Results Program Database. RMST-based methods included group comparison using Kaplan-Meier (KM) method, pseudovalue (PV) regression, and inverse probability of censoring probability (IPCW) regressions with group-specific and individual weights. Log-rank test, Wilcoxon test, Cox regression, and its extension with time-dependent variables were selected as traditional methods. Proportional hazard (PH) assumption and homogeneity of censoring mechanism assumption were assessed. We estimated hazard ratio (HR) and difference in RMST and explored their relationships.

Results

When covariate violated PH assumption, time-varying HR was inconvenient to report as a single value but PH assumption-free RMST allowed to report a single value of difference in RMST. In univariable analyses, using the difference in RMST calculated by KM method as reference, PV regressions (slope = 1.02 and R ² = 0.98) and IPCW regressions with group-specific weights (slope = 0.98 and R ² = 0.99) gave more consistent estimation than IPCW with individual weights (slope = 0.31 and R ² = 0.06), moreover, PV regressions presented more robust statistical power than IPCW regressions with group-specific weights. In multivariable analyses, IPCW regression with group-specific weights was limited when multiple covariates violated homogeneity of censoring mechanism assumption. For covariates met PH assumption, well-fitted logarithmic relationships between HR and difference in RMST estimated by PV regression were observed in both univariable and multivariable analyses (R ² = 0.97 and R ² = 0.94, respectively), which supported the robustness of PV regression and possible conversion between the two effect measures.

Conclusions

Difference in RMST is more interpretable than time-varying HR. The performance supports KM method and PV regression to be the preferred ones in RMST-based methods. IPCW regression can be an alternative sensitivity analysis. We encourage adoption of both traditional methods and RMST-based methods to present effects of covariates comprehensively.

Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population

In the presence of heterogeneity between the randomized controlled trial (RCT) participants and the target population, evaluating the treatment effect solely based on the RCT often leads to biased quantification of the real-world treatment effect. To address the problem of lack of generalizability for the treatment effect estimated by the RCT sample, we leverage observational studies with large samples that are representative of the target population. This article concerns evaluating treatment effects on survival outcomes for a target population and considers a broad class of estimands that are functionals of treatment-specific survival functions, including differences in survival probability and restricted mean survival times. Motivated by two intuitive but distinct approaches, i.e., imputation based on survival outcome regression and weighting based on inverse probability of sampling, censoring, and treatment assignment, we propose a semiparametric estimator through the guidance of the efficient influence function. The proposed estimator is doubly robust in the sense that it is consistent for the target population estimands if either the survival model or the weighting model is correctly specified and is locally efficient when both are correct. In addition, as an alternative to parametric estimation, we employ the nonparametric method of sieves for flexible and robust estimation of the nuisance functions and show that the resulting estimator retains the root-n consistency and efficiency, the so-called rate-double robustness. Simulation studies confirm the theoretical properties of the proposed estimator and show that it outperforms competitors. We apply the proposed method to estimate the effect of adjuvant chemotherapy on survival in patients with early-stage resected non-small cell lung cancer.

Contemporary trends in PGD incidence, outcomes, and therapies

BACKGROUND

We sought to describe trends in extracorporeal membrane oxygenation (ECMO) use, and define the impact on PGD incidence and early mortality in lung transplantation.

METHODS

Patients were enrolled from August 2011 to June 2018 at 10 transplant centers in the multi-center Lung Transplant Outcomes Group prospective cohort study. PGD was defined as Grade 3 at 48 or 72 hours, based on the 2016 PGD ISHLT guidelines. Logistic regression and survival models were used to contrast between group effects for event (i.e., PGD and Death) and time-to-event (i.e., death, extubation, discharge) outcomes respectively. Both modeling frameworks accommodate the inclusion of potential confounders.

RESULTS

A total of 1,528 subjects were enrolled with a 25.7% incidence of PGD. Annual PGD incidence (14.3%-38.2%, p = .0002), median LAS (38.0-47.7 p = .009) and the use of ECMO salvage for PGD (5.7%-20.9%, p = .007) increased over the course of the study. PGD was associated with increased 1 year mortality (OR 1.7 [95% C.I. 1.2, 2.3], p = .0001). Bridging strategies were not associated with increased mortality compared to non-bridged patients (p = .66); however, salvage ECMO for PGD was significantly associated with increased mortality (OR 1.9 [1.3, 2.7], p = .0007). Restricted mean survival time comparison at 1-year demonstrated 84.1 days lost in venoarterial salvaged recipients with PGD when compared to those without PGD (ratio 1.3 [1.1, 1.5]) and 27.2 days for venovenous with PGD (ratio 1.1 [1.0, 1.4]).

CONCLUSIONS

PGD incidence continues to rise in modern transplant practice paralleled by significant increases in recipient severity of illness. Bridging strategies have increased but did not affect PGD incidence or mortality. PGD remains highly associated with mortality and is increasingly treated with salvage ECMO.

Restricted Mean Survival Time Analysis to Estimate SGLT2i-Associated Heterogeneous Treatment Effects on Primary and Secondary Prevention of Cardiorenal Outcomes in Patients With Type 2 Diabetes in Taiwan

IMPORTANCE

Increasing numbers of post hoc analyses have applied restricted mean survival time (RMST) analysis on the aggregated-level data from clinical trials to report treatment effects, but studies that use individual-level claims data are needed to determine the feasibility of RMST analysis for quantifying treatment effects among patients with type 2 diabetes in routine clinical settings.

OBJECTIVES

To apply RMST analysis for assessing sodium-glucose cotransporter-2 inhibitor (SGLT2i)-associated cardiovascular (CV) events and estimating heterogenous treatment effects (HTEs) on CV and kidney outcomes in routine clinical settings.

DESIGN, SETTING, AND PARTICIPANTS

This comparative effectiveness study of Taiwan's National Health Insurance Research Database examined 21 144 propensity score (PS)-matched pairs of patients with type 2 diabetes with SGLT2i and dipeptidyl peptidase-4 inhibitor (DPP4i) treatment for assessing CV outcomes, and 19 951 PS-matched pairs of patients with type 2 diabetes with SGLT2i and DPP4i treatment for assessing kidney outcomes. Patients were followed until December 31, 2018. Statistical analysis was performed from August 2021 to April 2022.

EXPOSURES

Newly stable SGLT2i or DPP4i use in 2017.

MAIN OUTCOMES AND MEASURES

Study outcomes were CV events including hospitalization for heart failure (HHF), 3-point major adverse CV events (3P-MACE: nonfatal myocardial infarction [MI], nonfatal stroke, and CV death), 4-point MACE (4P-MACE: HHF and 3P-MACE), and all-cause death, and chronic kidney disease (CKD). RMST and Cox modeling analyses were applied to estimate treatment effects on study outcomes.

RESULTS

After PS matching, the baseline patient characteristics were comparable between 21 144 patients with stable SGLT2i use (eg, mean [SD] age: 58.3 [10.7] years; 11 990 [56.7%] male) and 21 144 patients with stable DPP4i use (eg, mean [SD] age: 58.1 [11.6] years; 12 163 [57.5%] male) for assessing CV outcomes, and those were also comparable between 19 951 patients with stable SGLT2i use (eg, mean [SD] age: 58.1 [10.7] years; 11 231 [56.2%] male) and 19 951 patients with stable DPP4i use (eg, mean [SD] age: 57.9 [11.5] years; 11 340 [56.8%] male) for assessing kidney outcome. The 2-year difference in RMST between patients with SGLT2i use and patients with DPP4i use was 4.99 (95% CI, 3.56-6.42) days for HHF, 4.12 (95% CI, 2.72-5.52) days for 3P-MACE, 7.72 (95% CI, 5.83-9.61) days for 4P-MACE, 1.26 (95% CI, 0.47-2.04) days for MI, 2.70 (95% CI, 1.57-3.82) days for stroke, 0.69 (95% CI, 0.28-1.11) days for CV death, 6.05 (95% CI, 4.89-7.20) days for all-cause death, and 14.75 (95% CI, 12.99-16.52) days for CKD. Directions of hazard ratios from Cox modeling analyses were consistent with RMST estimates. No association was found between study treatment and the negative control outcome (dental visits for tooth care). Consistent results across sensitivity analyses using high-dimensional PS-matched and PS-weighting approaches supported the validity of primary analysis results. Largest difference in RMST of SGLT2i vs DPP4i use for HHF and CKD was found among patients with established heart failure (30.80 [95% CI, 5.08-56.51] days) and retinopathy (40.43 [95% CI, 31.74-49.13] days), respectively.

CONCLUSIONS AND RELEVANCE

In this comparative effectiveness study, RMST analysis was feasible for translating treatment effects into more clinical intuitive estimates and valuable for quantifying HTEs among diverse patients in routine clinical settings.

Intensive Versus Standard Blood Pressure Lowering and Days Free of Cardiovascular Events and Serious Adverse Events: a Post Hoc Analysis of Systolic Blood Pressure Intervention Trial

BACKGROUND

Communication of the benefits and harms of blood pressure lowering strategy is crucial for shared decision-making.

OBJECTIVES

To quantify the effect of intensive versus standard systolic blood pressure lowering in terms of the number of event-free days DESIGN: Post hoc analysis of the Systolic Blood Pressure Intervention Trial PARTICIPANTS: A total of 9361 adults 50 years or older without diabetes or stroke who had a systolic blood pressure of 130-180 mmHg and elevated cardiovascular risk INTERVENTIONS: Intensive (systolic blood pressure goal <120 mmHg) versus standard blood pressure lowering (<140 mmHg) MAIN MEASURES: Days free of major adverse cardiovascular events (MACE), serious adverse events (SAE), and monitored adverse events (hypotension, syncope, bradycardia, electrolyte abnormalities, injurious falls, or acute kidney injury) over a median follow-up of 3.33 years KEY RESULTS: The intensive treatment group gained 14.7 more MACE-free days over 4 years (difference, 14.7 [95% confidence interval: 5.1, 24.4] days) than the standard treatment group. The benefit of the intensive treatment varied by cognitive function (normal: difference, 40.7 [13.0, 68.4] days; moderate-to-severe impairment: difference, -15.0 [-56.5, 26.4] days; p-for-interaction=0.009) and self-rated health (excellent: difference, -22.7 [-51.5, 6.1] days; poor: difference, 156.1 [31.1, 281.2] days; p-for-interaction=0.001). The mean overall SAE-free days were not significantly different between the treatments (difference, -14.8 [-35.3, 5.7] days). However, the intensive treatment group had 28.5 fewer monitored adverse event-free days than the standard treatment group (difference, -28.5 [-40.3, -16.7] days), with significant variations by frailty status (non-frail: difference, 38.8 [8.4, 69.2] days; frail: difference, -15.5 [-46.6, 15.7] days) and self-rated health (excellent: difference, -12.9 [-45.5, 19.7] days; poor: difference, 180.6 [72.9, 288.4] days; p-for-interaction <0.001).

CONCLUSIONS

Over 4 years, intensive systolic blood pressure lowering provides, on average, 14.7 more MACE-free days than standard treatment, without any difference in SAE-free days. Whether this time-based effect summary improves shared decision-making remains to be elucidated.

TRIAL REGISTRATION

ClinicalTrials.gov Registration: NCT01206062.

Examining evidence of time-dependent treatment effects: an illustration using regression methods

BACKGROUND

For the design and analysis of clinical trials with time-to-event outcomes, the Cox proportional hazards model and the logrank test have been the cornerstone methods for many decades. Increasingly, the key assumption of proportionality-or time-fixed effects-that underpins these methods has been called into question. The availability of novel therapies with new mechanisms of action and clinical trials of longer duration mean that non-proportional hazards are now more frequently encountered.

METHODS

We compared several regression-based methods to model time-dependent treatment effects. For illustration purposes, we used selected endpoints from a large, community-based clinical trial of low dose daily aspirin in older persons. Relative and absolute estimands were defined, and analyses were conducted in all participants. Additional exploratory analyses were undertaken by selected subgroups of interest using interaction terms in the regression models.

DISCUSSION

In the trial with median 4.7 years follow-up, we found evidence for non-proportionality and a time-dependent treatment effect of aspirin on cancer mortality not previously reported in trial findings. We also found some evidence of time-dependence to an aspirin by age interaction for major adverse cardiovascular events. For other endpoints, time-fixed treatment effect estimates were confirmed as appropriate.

CONCLUSIONS

The consideration of treatment effects using both absolute and relative estimands enhanced clinical insights into potential dynamic treatment effects. We recommend these analytical approaches as an adjunct to primary analyses to fully explore findings from clinical trials.

Versatile tests for window mean survival time

Window mean survival time (WMST) evaluates the mean survival between a lower time horizon, τ 0 $$ {\tau}_0 $$ , and an upper time horizon, τ 1 $$ {\tau}_1 $$ . As a flexible extension of restricted mean survival time, specific clinically relevant windows of time can be assessed for survival difference accompanied by a communicable interpretation of estimates and tests. In its original application, WMST required the pre-specification of a window through the selection of appropriate window bounds, τ 0 $$ {\tau}_0 $$ and τ 1 $$ {\tau}_1 $$ . In the instance of severe window misspecification of τ 0 $$ {\tau}_0 $$ and τ 1 $$ {\tau}_1 $$ , the analysis may suffer from low power and a less meaningful interpretation. In this article, we introduce versatile tests whose procedures are based on the simultaneous use of multiple WMST test statistics that are asymptotically normal under the null hypothesis of no difference between two groups. Simulations are performed to examine the power of the tests in moderate sample sizes when the data are uncensored to heavily censored with a ramp-up enrollment period. The survival scenarios chosen for simulation are intended to imitate those which are commonly encountered in oncology, especially in trials involving immunotherapies. Implementation of the procedures is discussed in two real data examples for illustration. Functions for performing versatile WMST tests are provided in the survWMST package in R.

Doubly-robust estimator of the difference in restricted mean times lost with competing risks data

In the context of competing risks data, the subdistribution hazard ratio has limited clinical interpretability to measure treatment effects. An alternative is the difference in restricted mean times lost (RMTL), which gives the mean time lost to a specific cause of failure between treatment groups. In non-randomized studies, the average causal effect is conventionally used for decision-making about treatment and public health policies. We show how the difference in RMTL can be estimated by contrasting the integrated cumulative incidence functions from a Fine-Gray model. We also show how the difference in RMTL can be estimated by using inverse probability of treatment weighting and contrasts between weighted non-parametric estimators of the area below the cumulative incidence. We use pseudo-observation approaches to estimate both component models and we integrate them into a doubly-robust estimator. We demonstrate that this estimator is consistent when either component is correctly specified. We conduct simulation studies to assess its finite-sample performance and demonstrate its inherited consistency property from its component models. We also examine the performance of this estimator under varying degrees of covariate overlap and under a model misspecification of nonlinearity. We apply the proposed method to assess biomarker-treatment interaction in subpopulations of the POPLAR and OAK randomized controlled trials of second-line therapy for advanced non-small-cell lung cancer.

Clustered restricted mean survival time regression

For multicenter randomized trials or multilevel observational studies, the Cox regression model has long been the primary approach to study the effects of covariates on time-to-event outcomes. A critical assumption of the Cox model is the proportionality of the hazard functions for modeled covariates, violations of which can result in ambiguous interpretations of the hazard ratio estimates. To address this issue, the restricted mean survival time (RMST), defined as the mean survival time up to a fixed time in a target population, has been recommended as a model-free target parameter. In this article, we generalize the RMST regression model to clustered data by directly modeling the RMST as a continuous function of restriction times with covariates while properly accounting for within-cluster correlations to achieve valid inference. The proposed method estimates regression coefficients via weighted generalized estimating equations, coupled with a cluster-robust sandwich variance estimator to achieve asymptotically valid inference with a sufficient number of clusters. In small-sample scenarios where a limited number of clusters are available, however, the proposed sandwich variance estimator can exhibit negative bias in capturing the variability of regression coefficient estimates. To overcome this limitation, we further propose and examine bias-corrected sandwich variance estimators to reduce the negative bias of the cluster-robust sandwich variance estimator. We study the finite-sample operating characteristics of proposed methods through simulations and reanalyze two multicenter randomized trials.

Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network

BACKGROUND

Synthesis of clinical effectiveness from multiple trials is a well-established component of decision-making. Time-to-event outcomes are often synthesised using the Cox proportional hazards model assuming a constant hazard ratio over time. However, with an increasing proportion of trials reporting treatment effects where hazard ratios vary over time and with differing lengths of follow-up across trials, alternative synthesis methods are needed.

OBJECTIVES

To compare and contrast five modelling approaches for synthesis of time-to-event outcomes and provide guidance on key considerations for choosing between the modelling approaches.

METHODS

The Cox proportional hazards model and five other methods of estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption, were applied to a network of melanoma trials reporting overall survival: restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models.

RESULTS

All models fitted the melanoma network acceptably well. However, there were important differences in extrapolations of the survival curve and interpretability of the modelling constraints demonstrating the potential for different conclusions from different modelling approaches.

CONCLUSION

The restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can accommodate non-proportional hazards and differing lengths of trial follow-up within a network meta-analysis of time-to-event outcomes. We recommend that model choice is informed using available and relevant prior knowledge, model transparency, graphically comparing survival curves alongside observed data to aid consideration of the reliability of the survival estimates, and consideration of how the treatment effect estimates can be incorporated within a decision model.

Two-sample survival probability curves: A graphical approach for the analysis of time to event data in clinical trials

With the aim to improve the communication of trial results, we introduce a novel graphical approach that complements the analysis of time to event outcomes in two-arm randomized trials. We define the so-called two-sample survival probability curve and propose a nonparametric estimator of the curve based on a random walk using Kaplan-Meier survival estimates for the two arms. We then use the estimated curve to visualize treatment effect as well as potential effect modification of factors of interest. We also propose to estimate two-sample survival probability curves within the framework of the Cox model to graphically assess model fit. The proposed two-sample survival probability plot puts trials in a standardized [0,1] × [0,1] space, allowing for a simple visualization of the main effect, effect modification, and the adequacy of a model fit.

Visualizing Time-Varying Effect in Survival Analysis: 5 Complementary Plots to Kaplan-Meier Curve

Background

Kaplan-Meier (KM) curve has been widely used in the field of oxidative medicine and cellular longevity. However, time-varying effect might be presented in KM curve and cannot be intuitively observed. Complementary plots might promote clear insights in time-varying effect from KM curve.

Methods

Three KM curves were identified from published randomized control trials: (a) curves diverged immediately; (b) intersected curves with statistical significance; and (c) intersected curves without statistical significance. We reconstructed individual patient data, and plotted 5 complementary plots (difference in survival probability and risk difference, difference in restricted mean survival time, landmark analyses, and hazard ratio over time), along with KM curve.

Results

Entanglement and intersection of two KM curves would make the 5 complementary plots to fluctuate over time intuitively. Absolute effects were presented in the 3 plots of difference in survival probability, risk, and restricted mean survival time. Changed P values from landmark analyses were used to inspect conditional treatment effect; the turning points could be identified for further landmark analysis. When proportional hazard assumption was not met, estimated hazard ratio from traditional Cox regression was not appropriate, and time-varying hazard ratios could be presented instead of an average and single value.

Conclusions

The 5 complementary plots with KM curve give a broad and straightforward picture of potential time-varying effect. They will provide clear insight in treatment effect and assist clinicians to make decision comprehensively.

Randomized two-stage optimal design for interval-censored data

Interval-censored data occur in a study where the exact event time of each participant is not observed but it is known to be within a certain time interval. Multiple tests were proposed for such data, including the logrank test by Sun, the proportional hazard test by Finkelstein, and the Wilcoxon-type test by Peto and Peto. We propose sample size calculations based on these tests for a parallel one-stage or two-stage design. When the proportional hazard assumption is met, the proportional hazard test and the logrank test need smaller sample sizes than the Wilcoxon-type test, and the sample size savings are substantial. But this trend is reversed when the proportional hazard assumption does not hold, and the sample size savings using the Wilcoxon-type test are sizable. An example from a lung cancer clinical trial is used to illustrate the application of the proposed sample size calculations.

Impact of postoperative chemotherapy on survival for oesophagogastric adenocarcinoma after preoperative chemotherapy and surgery

BACKGROUND

Perioperative chemotherapy is widely used in the treatment of oesophagogastric adenocarcinoma (OGAC) with a substantial survival benefit over surgery alone. However, the postoperative part of these regimens is given in less than half of patients, reflecting uncertainty among clinicians about its benefit and poor postoperative patient fitness. This study estimated the effect of postoperative chemotherapy after surgery for OGAC using a large population-based data set.

METHODS

Patients with adenocarcinoma of the oesophagus, gastro-oesophageal junction or stomach diagnosed between 2012 and 2018, who underwent preoperative chemotherapy followed by surgery, were identified from a national-level audit in England and Wales. Postoperative therapy was defined as the receipt of systemic chemotherapy within 90 days of surgery. The effectiveness of postoperative chemotherapy compared with observation was estimated using inverse propensity treatment weighting.

RESULTS

Postoperative chemotherapy was given to 1593 of 4139 patients (38.5 per cent) included in the study. Almost all patients received platinum-based triplet regimens (4004 patients, 96.7 per cent), with FLOT used in 3.3 per cent. Patients who received postoperative chemotherapy were younger, with a lower ASA grade, and were less likely to have surgical complications, with similar tumour characteristics. After weighting, the median survival time after postoperative chemotherapy was 62.7 months compared with 50.4 months without chemotherapy (hazard ratio 0.84, 95 per cent c.i. 0.77 to 0.94; P = 0.001).

CONCLUSION

This study has shown that postoperative chemotherapy improves overall survival in patients with OGAC treated with preoperative chemotherapy and surgery.

Efficacy of Niclosamide vs Placebo in SARS-CoV-2 Respiratory Viral Clearance, Viral Shedding, and Duration of Symptoms Among Patients With Mild to Moderate COVID-19: A Phase 2 Randomized Clinical Trial

IMPORTANCE

Oral anthelmintic niclosamide has potent in vitro antiviral activity against SARS-CoV-2. Repurposed niclosamide could be a safe and efficacious COVID-19 therapy.

OBJECTIVE

To investigate whether niclosamide decreased SARS-CoV-2 shedding and duration of symptoms among patients with mild to moderate COVID-19.

DESIGN, SETTING, AND PARTICIPANTS

This randomized, placebo-controlled clinical trial enrolled individuals testing positive for SARS-CoV-2 by polymerase chain reaction with mild to moderate symptoms of COVID. All trial participants, investigators, staff, and laboratory personnel were kept blind to participant assignments. Enrollment was among individuals reporting at Tufts Medical Center and Wellforce Network in Massachusetts for outpatient COVID-19 testing. The trial opened to accrual on October 1, 2020; the last participant enrolled on April 20, 2021. Trial exclusion criteria included hospitalization at time of enrollment or use of any experimental treatment for COVID-19, including vaccination. Enrollment was stopped before attaining the planned sample size when COVID-19 diagnoses decreased precipitously in Massachusetts. Data were analyzed from July through September 2021.

INTERVENTIONS

In addition to receiving current standard of care, participants were randomly assigned on a 1:1 basis to receive niclosamide 2 g by mouth daily for 7 days or identically labeled placebo at the same dosing schedule.

MAIN OUTCOMES AND MEASURES

Oropharyngeal and fecal samples were self-collected for viral shedding measured by reverse-transcriptase-polymerase-chain-reaction on days 3, 7, 10, and 14, and an additional fecal sample was collected on day 21. A telehealth platform was developed to conduct remote study visits, monitor symptoms, and coordinate sample collection via couriers. The primary end point was the proportion of participants with viral clearance in respiratory samples at day 3 based on the intention-to-treat sample. Mean times to viral clearance and symptom resolution were calculated as restricted mean survival times and accounted for censored observations.

RESULTS

Among 73 participants, 36 individuals were enrolled and randomized to niclosamide and 37 individuals to placebo. Participant characteristics were similar across treatment groups; among 34 patients receiving placebo and 33 patients receiving niclosamide in the intention-to-treat sample, mean (SD) age was 36.0 (13.3) years vs 36.8 (12.9) years and there were 21 (61.8%) men vs 20 (60.6%) men. The overall mean (SD) age was 36.4 (13.0) years. For the primary end point, 66.67% (95% CI, 50.74% to 81.81%) of participants receiving niclosamide and 55.88% (95% CI, 40.27% to 72.73%) of participants receiving placebo had oropharyngeal SARS-CoV-2 clearance at day 3 (P = .37). Among 63 participants with symptoms, niclosamide did not significantly shorten symptom duration, which was 12.01 (95% CI, 8.82 to 15.2) days in the niclosamide group vs 14.61 (95% CI, 11.25 to 17.96) days in the placebo group (mean difference, -2.6 [95% CI, -7.23 to 2.03] days). Niclosamide was well-tolerated; the most commonly reported adverse events in the placebo and niclosamide groups were headaches (11 patients [32.4%] vs 7 patients [21.2%]; P = .31) and cough (8 patients [23.5%] vs 7 patients [21.2%]; P = .82).

CONCLUSIONS AND RELEVANCE

In this randomized clinical trial, there was no significant difference in oropharyngeal clearance of SARS-CoV-2 at day 3 between placebo and niclosamide groups. Confirmation in larger studies is warranted.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04399356.

Which test for crossing survival curves? A user’s guideline

Background

The exchange of knowledge between statisticians developing new methodology and clinicians, reviewers or authors applying them is fundamental. This is specifically true for clinical trials with time-to-event endpoints. Thereby, one of the most commonly arising questions is that of equal survival distributions in two-armed trial. The log-rank test is still the gold-standard to infer this question. However, in case of non-proportional hazards, its power can become poor and multiple extensions have been developed to overcome this issue. We aim to facilitate the choice of a test for the detection of survival differences in the case of crossing hazards.

Methods

We restricted the review to the most recent two-armed clinical oncology trials with crossing survival curves. Each data set was reconstructed using a state-of-the-art reconstruction algorithm. To ensure reproduction quality, only publications with published number at risk at multiple time points, sufficient printing quality and a non-informative censoring pattern were included. This article depicts the p-values of the log-rank and Peto-Peto test as references and compares them with nine different tests developed for detection of survival differences in the presence of non-proportional or crossing hazards.

Results

We reviewed 1400 recent phase III clinical oncology trials and selected fifteen studies that met our eligibility criteria for data reconstruction. After including further three individual patient data sets, for nine out of eighteen studies significant differences in survival were found using the investigated tests. An important point that reviewers should pay attention to is that 28% of the studies with published survival curves did not report the number at risk. This makes reconstruction and plausibility checks almost impossible.

Conclusions

The evaluation shows that inference methods constructed to detect differences in survival in presence of non-proportional hazards are beneficial and help to provide guidance in choosing a sensible alternative to the standard log-rank test.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01520-0.

Radiation therapy dose and androgen deprivation therapy in localized prostate cancer: a meta-regression of 5-year outcomes in phase III randomized controlled trials

BACKGROUND

While multiple randomized trials have evaluated the benefit of radiation therapy (RT) dose escalation and the use and prolongation of androgen deprivation therapy (ADT) in the treatment of prostate cancer, few studies have evaluated the relative benefit of either form of treatment intensification with each other. Many trials have included treatment strategies that incorporate either high or low dose RT, or short-term or long-term ADT (STADT or LTADT), in one or more trial arms. We sought to compare different forms of treatment intensification of RT in the context of localized prostate cancer.

METHODS

Using preferred reporting items for systemic reviews and meta-analyses (PRISMA) guidelines, we collected over 40 phases III clinical trials comparing different forms of RT for localized prostate cancer. We performed a meta-regression of 40 individual trials with 21,429 total patients to allow a comparison of the rates and cumulative proportions of 5-year overall survival (OS), prostate cancer-specific mortality (PCSM), and distant metastasis (DM) for each treatment arm of every trial.

RESULTS

Dose-escalation either in the absence or presence of STADT failed to significantly improve any 5-year outcome. In contrast, adding LTADT to low dose RT significantly improved 5-year PCSM (Odds ratio [OR] 0.34, 95% confidence interval [CI] 0.22-0.54, p < 0.001) and DM (OR 0.35, 95% CI 0.20-0.63. p < 0.001) over low dose RT alone. Adding STADT also significantly improved 5-year PCSM over low dose RT alone (OR 0.55, 95% CI 0.41-0.75, p < 0.001).

CONCLUSION

While limited by between-study heterogeneity and a lack of individual patient data, this meta-analysis suggests that adding ADT, versus increasing RT dose alone, offers a more consistent improvement in clinical endpoints.

Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time-to-event outcomes in randomized controlled trials

In cancer randomized controlled trials, surrogate endpoints are frequently time-to-event endpoints, subject to the competing risk from the time-to-event clinical outcome. In this context, we introduce a counterfactual-based mediation analysis for a causal assessment of surrogacy. We use a multistate model for risk prediction to account for both direct transitions towards the clinical outcome and indirect transitions through the surrogate outcome. Within the counterfactual framework, we define natural direct and indirect effects with a causal interpretation. Based on these measures, we define the proportion of the treatment effect on the clinical outcome mediated by the surrogate outcome. We estimate the proportion for both the cumulative risk and restricted mean time lost. We illustrate our approach by using 18-year follow-up data from the SPCG-4 randomized trial of radical prostatectomy for prostate cancer. We assess time to metastasis as a surrogate outcome for prostate cancer-specific mortality.

Restricted survival benefit with right-censored data

The hazard ratio is widely used to quantify treatment effects. However, it may be difficult to interpret for patients and practitioners, especially when the hazard ratio is not constant over time. Alternative measures of the treatment effects have been proposed such as the difference of the restricted mean survival times, the difference in survival proportions at some fixed follow-up time, or the net chance of a longer survival. In this paper, we propose the restricted survival benefit (RSB), a quantity that can incorporate multiple useful measurements of treatment effects. Hence, it provides a framework for a comprehensive assessment of the treatment effects. We provide estimation and inference procedures for the RSB that accommodate censored survival outcomes, using methods of the inverse-probability-censoring-weighted U -statistic and the jackknife empirical likelihood. We conduct extensive simulation studies to examine the numerical performance of the proposed method, and we analyze data from a randomized Phase III clinical trial (SWOG S0777) using the proposed method.

Bayesian multivariate network meta-analysis model for the difference in restricted mean survival times

Network meta-analysis (NMA) is essential for clinical decision-making. NMA enables inference for all pair-wise comparisons between interventions available for the same indication, by using both direct evidence and indirect evidence. In randomized trials with time-to event outcome data, such as lung cancer data, conventional NMA methods rely on the hazard ratio and the proportional hazards assumption, and ignore the varying follow-up durations across trials. We introduce a novel multivariate NMA model for the difference in restricted mean survival times (RMST). Our model synthesizes all the available evidence from multiple time points simultaneously and borrows information across time points through within-study covariance and between-study covariance for the differences in RMST. We propose an estimator of the within-study covariance and we then assume it to be known. We estimate the model under the Bayesian framework. We evaluated our model by conducting a simulation study. Our multiple-time-point model yields lower mean squared error over the conventional single-time-point model at all time points, especially when the availability of evidence decreases. We illustrated the model on a network of randomized trials of second-line treatments of advanced non-small-cell lung cancer. Our multiple-time-point model yielded increased precision and detected evidence of benefit at earlier time points as compared to the single-time-point model. Our model has the advantage of providing clinically interpretable measures of treatment effects.

Challenges needed to be overcome in multi-institutional surgical trials: accumulated experience in the JCOG Colorectal Cancer Study Group (CCSG)

JCOG-CCSG has been conducting several surgical trials and experienced several challenges. The first point is the appropriate timing of conducting the trial. Once a certain number of surgeons acquire the new technique and its utility is accepted, it suddenly becomes difficult to maintain 'equipoise' between the standard and new treatment, which may lead to poor patient accrual. Smooth preparation and commencement of the trial at an appropriate timing is necessary for its success. Second is the appropriate quality assurance of surgery. High-level quality assurance will strengthen the comparability of randomized control trials and minimize the heterogeneity among hospitals. On the other hand, it may impair the generalizability of the trial. Large observational studies help to bridge the gap of heterogeneity among hospitals. Third is the selection of an appropriate endpoint. Overall survival (OS) is the gold-standard primary endpoint; however, the number of events is much less due to more effective treatment. JCOG0212 and JCOG0404 were unable to demonstrate the non-inferiority of omission of lateral lymph node dissection and laparoscopic surgery partly due to a lack of power. Disease-free survival (DFS) is also a promising candidate for primary endpoint, but as in JCOG0603, special attention must be paid when DFS does not correlate with OS. Although careful discussion is required because the precision of the hazard ratio depends on the number of events, an alternative population-level summary of variables, including restricted mean survival time, can be considered as the primary endpoint. Future surgical trials should be planned considering these points.

Assessment of Treatment Effects and Long-term Benefits in Immune Checkpoint Inhibitor Trials Using the Flexible Parametric Cure Model: A Systematic Review

IMPORTANCE

Compared with standard cytotoxic therapies, randomized immune checkpoint inhibitor (ICI) phase 3 trials reveal delayed benefits in terms of patient survival and/or long-term response. Such outcomes generally violate the assumption of proportional hazards, and the classical Cox proportional hazards regression model is therefore unsuitable for these types of analyses.

OBJECTIVE

To evaluate the ability of the flexible parametric cure model (FPCM) to estimate treatment effects and long-term responder fractions (LRFs) independently of prespecified time points.

EVIDENCE REVIEW

This systematic review used reconstructed individual patient data from ICI advanced or metastatic melanoma and lung cancer phase 3 trials extracted from the literature. Trials published between January 1, 2010, and October 1, 2019, with long-term follow-up periods (maximum follow-up, ≥36 months in first line and ≥30 months otherwise) were selected to identify LRFs. Individual patient data for progression-free survival were reconstructed from the published randomized ICI phase 3 trial results. The FPCM was applied to estimate treatment effects on the overall population and on the following components of the population: LRF and progression-free survival in non-long-term responders. Results obtained were compared with treatment effects estimated using the Cox proportional hazards regression model.

FINDINGS

In this systematic review, among the 23 comparisons studied using the FPCM, a statistically significant association between the time-to-event component and experimental treatment was observed in the main analyses and confirmed in the sensitivity analyses of 18 comparisons. Results were discordant for 4 comparisons that were not significant by the Cox proportional hazards regression model. The LRFs varied from 1.5% to 12.7% for the control arms and from 4.6% to 38.8% for the experimental arms. Differences in LRFs varied from 2% to 29% and were significantly increased in the experimental compared with the control arms, except for 4 comparisons.

CONCLUSIONS AND RELEVANCE

This systematic review of reconstructed individual patient data found that the FPCM was a complementary approach that provided a comprehensive and pertinent evaluation of benefit and risk by assessing whether ICI treatment was associated with an increased probability of patients being long-term responders or with an improved progression-free survival in patients who were not long-term responders.

Prediction of long-term survival after gastrectomy using random survival forests

BACKGROUND

No well validated and contemporaneous tools for personalized prognostication of gastric adenocarcinoma exist. This study aimed to derive and validate a prognostic model for overall survival after surgery for gastric adenocarcinoma using a large national dataset.

METHODS

National audit data from England and Wales were used to identify patients who underwent a potentially curative gastrectomy for adenocarcinoma of the stomach. A total of 2931 patients were included and 29 clinical and pathological variables were considered for their impact on survival. A non-linear random survival forest methodology was then trained and validated internally using bootstrapping with calibration and discrimination (time-dependent area under the receiver operator curve (tAUC)) assessed.

RESULTS

The median survival of the cohort was 69 months, with a 5-year survival of 53.2 per cent. Ten variables were found to influence survival significantly and were included in the final model, with the most important being lymph node positivity, pT stage and achieving an R0 resection. Patient characteristics including ASA grade and age were also influential. On validation the model achieved excellent performance with a 5-year tAUC of 0.80 (95 per cent c.i. 0.78 to 0.82) and good agreement between observed and predicted survival probabilities. A wide spread of predictions for 3-year (14.8-98.3 (i.q.r. 43.2-84.4) per cent) and 5-year (9.4-96.1 (i.q.r. 31.7-73.8) per cent) survival were seen.

CONCLUSIONS

A prognostic model for survival after a potentially curative resection for gastric adenocarcinoma was derived and exhibited excellent discrimination and calibration of predictions.

Individual Patient Data Meta-Analysis and Network Meta-Analysis

Meta-analyses are often conducted using trial-level summary data. However, when individual patient data (IPD ) is available, there is greater flexibility in the analysis and a wider range of statistical models that can be fitted. There are two approaches to fitting IPD models. The traditional two-stage approach involves analyzing each trial individually in the first stage and then combining trial estimates of treatment effectiveness in the second stage using methods developed for aggregate data meta-analysis. Growing in popularity is the one-stage approach in which trials are analyzed and synthesized within one statistical model whilst the clustering of patients within trials is accounted for. This chapter outlines both fixed effect and random effects one- and two-stage meta-analysis models for continuous, binary, and time-to-event outcomes. The meta-analysis framework is then extended to the scenario where there are more than two treatments and network meta-analysis models are described.The availability of IPD provides greater statistical power for investigating interactions between treatments and covariates. Treatment-covariate interactions contain both within- and across-trial information where the across-trial information may be subject to ecological bias. This chapter presents network meta-analysis models separating out the within- and across-trial information and finishes by considering practical solutions for dealing with missing covariate data, assessing the consistency assumption, combining IPD and aggregate data and specific considerations for time-to-event outcomes.

Impact of a non-constant baseline hazard on detection of time-dependent treatment effects: a simulation study

Background

Non-proportional hazards are common with time-to-event data but the majority of randomised clinical trials (RCTs) are designed and analysed using approaches which assume the treatment effect follows proportional hazards (PH). Recent advances in oncology treatments have identified two forms of non-PH of particular importance - a time lag until treatment becomes effective, and an early effect of treatment that ceases after a period of time. In sample size calculations for treatment effects on time-to-event outcomes where information is based on the number of events rather than the number of participants, there is crucial importance in correct specification of the baseline hazard rate amongst other considerations. Under PH, the shape of the baseline hazard has no effect on the resultant power and magnitude of treatment effects using standard analytical approaches. However, in a non-PH context the appropriateness of analytical approaches can depend on the shape of the underlying hazard.

Methods

A simulation study was undertaken to assess the impact of clinically plausible non-constant baseline hazard rates on the power, magnitude and coverage of commonly utilized regression-based measures of treatment effect and tests of survival curve difference for these two forms of non-PH used in RCTs with time-to-event outcomes.

Results

In the presence of even mild departures from PH, the power, average treatment effect size and coverage were adversely affected. Depending on the nature of the non-proportionality, non-constant event rates could further exacerbate or somewhat ameliorate the losses in power, treatment effect magnitude and coverage observed. No single summary measure of treatment effect was able to adequately describe the full extent of a potentially time-limited treatment benefit whilst maintaining power at nominal levels.

Conclusions

Our results show the increased importance of considering plausible potentially non-constant event rates when non-proportionality of treatment effects could be anticipated. In planning clinical trials with the potential for non-PH, even modest departures from an assumed constant baseline hazard could appreciably impact the power to detect treatment effects depending on the nature of the non-PH. Comprehensive analysis plans may be required to accommodate the description of time-dependent treatment effects.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01372-0.

Dynamic prediction and analysis based on restricted mean survival time in survival analysis with nonproportional hazards

BACKGROUND AND OBJECTIVE

In the process of clinical diagnosis and treatment, the restricted mean survival time (RMST), which reflects the life expectancy of patients up to a specified time, can be used as an appropriate outcome measure. However, the RMST only calculates the mean survival time of patients within a period of time after the start of follow-up and may not accurately portray the change in a patient's life expectancy over time.

METHODS

The life expectancy can be adjusted for the time the patient has already survived and defined as the conditional restricted mean survival time (cRMST). A dynamic RMST model based on the cRMST can be established by incorporating time-dependent covariates and covariates with time-varying effects. We analyzed data from a study of primary biliary cirrhosis (PBC) to illustrate the use of the dynamic RMST model, and a simulation study was designed to test the advantages of the proposed approach. The predictive performance was evaluated using the C-index and the prediction error.

RESULTS

Considering both the example results and the simulation results, the proposed dynamic RMST model, which can explore the dynamic effects of prognostic factors on survival time, has better predictive performance than the RMST model. Three PBC patient examples were used to illustrate how the predicted cRMST changed at different prediction times during follow-up.

CONCLUSIONS

The use of the dynamic RMST model based on the cRMST allows for the optimization of evidence-based decision-making by updating personalized dynamic life expectancy for patients.

Treatment effect measures for culture conversion endpoints in phase IIb tuberculosis treatment trials

Phase IIb trials of tuberculosis therapy rely on early biomarkers of treatment effect. Despite limited predictive ability for clinical outcomes, culture conversion, the event in which an individual previously culture positive for Mycobacterium tuberculosis yields a negative culture after initiating treatment, is a commonly used endpoint. Lack of consensus on how to define the outcome and corresponding measure of treatment effect complicates interpretation and limits between-trial comparisons. We review common analytic approaches to measuring treatment effect and introduce difference in restricted mean survival times as an alternative to identify faster times to culture conversion and express magnitude of effect on the time scale. Findings from the PanACEA MAMSTB trial are reanalyzed as an illustrative example. In a systematic review we demonstrate variability in analytic approaches, sampling strategies, and outcome definitions in phase IIb tuberculosis trials. Harmonization would allow for larger meta-analyses, and may help expedite advancement of new TB therapeutics.

Window mean survival time

We propose a class of alternative estimates and tests to restricted mean survival time (RMST) which improves power in numerous survival scenarios while maintaining a level of interpretability. The industry standards for interpretable hypothesis tests in survival analysis, RMST and logrank tests (LRTs), can suffer from low power in cases where the proportional hazards assumption fails. In particular, when late differences occur between survival curves, our proposed estimate and class of tests, window mean survival time (WMST), outperforms both RMST and LRT without sacrificing interpretability, unlike weighted rank tests (WRTs). WMST has the added advantage of maintaining high power when the proportional hazards assumption is met, while WRTs do not. With testing methods often being chosen in advance of data collection, WMST can ensure adequate power without distributional assumptions and is robust to the choice of its restriction parameters. Functions for performing WMST analysis are provided in the survWM2 package in R.

Sintilimab Plus Platinum and Gemcitabine as First-Line Treatment for Advanced or Metastatic Squamous NSCLC: Results From a Randomized, Double-Blind, Phase 3 Trial (ORIENT-12)

INTRODUCTION

The standard chemotherapy for squamous NSCLC (sqNSCLC) includes platinum plus gemcitabine. Sintilimab, an anti-programmed cell death protein 1 antibody, plus platinum and gemcitabine (GP) has revealed encouraging efficacy as first-line therapy for sqNSCLC in a phase 1b study. We conducted a randomized, double-blind, phase 3 study to further compare the efficacy and safety of sintilimab with placebo, both in combination with GP.

METHODS

ORIENT-12, a randomized, double-blind, phase 3 study, was conducted at 42 centers in the People's Republic of China (ClinicalTrials.gov, number NCT03629925). Patients with locally advanced or metastatic sqNSCLC and without EGFR-sensitive mutations or ALK rearrangements were enrolled in the study. The stratification factors included clinical stage, choice of platinum, and programmed death-ligand 1 tumor proportion score. The patients, investigators, research staff, and sponsor team were masked to treatment assignment. Eligible patients were randomized 1:1, using an integrated web-response system, to receive sintilimab 200 mg or placebo plus GP every 3 weeks for four or six cycles, followed by sintilimab or placebo as maintenance therapy until disease progression or 2 years. The primary end point was progression-free survival (PFS), assessed by an independent radiographic review committee.

RESULTS

Between September 25, 2018 and July 26, 2019, a total of 543 patients were screened, of whom 357 patients were randomized to the sintilimab-GP group (n = 179) and the placebo-GP group (n = 178). After a median follow-up period of 12.9 months, sintilimab-GP continued to reveal a meaningful improvement in PFS than placebo-GP (hazard ratio = 0.536 [95% confidence interval: 0.422-0.681], p < 0.00001). Treatment-emergent adverse events of grade 3 or worse occurred in 86.6% patients in the sintilimab-GP group and in 83.1% in the placebo-GP group. The incidence of treatment-emergent adverse event leading to death was 4.5% and 6.7% in the two treatment groups, respectively.

CONCLUSIONS

Regarding PFS, sintilimab plus GP reveals clinical benefit than GP alone as first-line therapy in patients with locally advanced or metastatic sqNSCLC. The toxicity was acceptable, and no new unexpected safety signals were observed.

The Restricted Mean Survival Time as a Tool for Ranking Comparative Outcomes in a Narrative Review that Evaluates a Network of Randomized Trials: An Example Based on PCSK9 Inhibitors

INTRODUCTION

On the basis of two randomized trials, evolocumab and alirocumab have been approved in patients with cardiovascular disease. The evidence on these two agents has been studied through different methods of analysis that span from narrative approaches to network meta-analysis. In the present study, we assessed the performance of a narrative approach combined with the application of the restricted mean survival time (RMST).

METHODS

We studied the two pivotal placebo-controlled trials focused on evolocumab and alirocumab. Our original framework of comparative assessment employed the RMST. Our objective was to show that in the context of a narrative review, the RMST can be an efficient although simple tool to make indirect comparisons. The endpoint was event-free survival, expressed in months.

RESULTS

For each cohort of patients (13,784 patients administered evolocumab, 9462 patients administered alirocumab, 23,242 controls), we determined the RMST values with 95% confidence intervals (CI) [evolocumab: 33.60 months, 95% CI 33.46-33.74; alirocumab: 34.07 months, 95% CI 33.92-34.22]. These results, along with those of the control groups, were analyzed and interpreted narratively. Univariate statistics were conducted, but no network meta-analysis was performed.

CONCLUSION

The experience presented herein indicates that a framework of evidence assessment focused on the RMST is a worthwhile option. Our study is in line with the growing literature that has recently emphasized the methodological advantages of the RMST.

Nonparametric estimation in an illness-death model with component-wise censoring

In disease settings where study participants are at risk for death and a serious nonfatal event, composite endpoints defined as the time until the earliest of death or the nonfatal event are often used as the primary endpoint in clinical trials. In practice, if the nonfatal event can only be detected at clinic visits and the death time is known exactly, the resulting composite endpoint exhibits "component-wise censoring." The standard method used to estimate event-free survival in this setting fails to account for component-wise censoring. We apply a kernel smoothing method previously proposed for a marker process in a novel way to produce a nonparametric estimator for event-free survival that accounts for component-wise censoring. The key insight that allows us to apply this kernel method is thinking of nonfatal event status as an intermittently observed binary time-dependent variable rather than thinking of time to the nonfatal event as interval-censored. We also propose estimators for the probability in state and restricted mean time in state for reversible or irreversible illness-death models, under component-wise censoring, and derive their large-sample properties. We perform a simulation study to compare our method to existing multistate survival methods and apply the methods on data from a large randomized trial studying a multifactor intervention for reducing morbidity and mortality among men at above average risk of coronary heart disease.

Effect of PARP Inhibitors as Maintenance Treatment on Restricted Mean Survival Time in Platinum-Sensitive Recurrent Ovarian Cancer: A Systematic Review and Meta-analysis

BACKGROUND

Earlier trials on the efficacy of poly (ADP-ribose) polymerase (PARP) inhibitors in platinum-sensitive relapsed ovarian cancer used the hazard ratio (HR) as an efficacy parameter.

OBJECTIVE

The present meta-analysis was focused on improving the robustness and clinical interpretability of the efficacy evaluation of PARP inhibitors using the restricted mean survival time (RMST).

METHODS

A search for relevant studies published up to July 31, 2020, was performed in electronic databases to identify eligible trials comparing PARP inhibitors with placebo. The difference in RMST was used as a PARP inhibitor efficacy parameter. Combined differences in RMST with 95% CIs across studies were calculated using a random-effects model.

RESULTS

Four trials (6 articles) were assessed, including 1079 patients treated with PARP inhibitors and 598 with placebo. The combined RMST differences for up to 360 days (PARP inhibitors minus placebo: point estimate and 95% CI) among all patients and the patients of subgroups with BRCA mutations, homologous recombination-deficient (HRD) carcinoma, and BRCA wild-type carcinoma were 87 days (95% CI = 71, 102), 112 days (95% CI = 96, 129), 99 days (95% CI = 80, 119), and 69 days (95% CI = 47, 92), respectively. The combined RMST differences for up to 660 and 720 days were also larger among patients with BRCA mutations than among those with HRD carcinoma.

CONCLUSION AND RELEVANCE

Based on using the RMST difference as an alternative measure to the HR, this meta-analysis suggests that PARP inhibitors are the most effective for patients with BRCA mutations, followed by patients with HRD carcinoma.