Review of the Reporting of Survival Analyses within Randomised Controlled Trials and the Implications for Meta-Analysis

The use and abuse of survival analysis and Kaplan-Meier curves in surgical trials

BACKGROUND

Survival analysis based on Cox regression and Kaplan-Meier curves, initially devised for oncology trials, have frequently been used in other contexts where fundamental statistical assumptions (such as a constant hazard ratio) are not satisfied. This is almost always the case in trials that compare surgery with medical management.

METHODS

We review a trial that compared extracranial-intracranial bypass surgery (EC-IC bypass) with medical management (MM) of patients with symptomatic occlusion of the carotid or middle cerebral artery, where it was claimed that surgery was of no benefit. We discuss a hypothetical study and review other neurovascular trials which have also used survival analysis to compare results.

RESULTS

The trial comparing EC-IC bypass and MM did not satisfy the fundamental proportional hazard assumption necessary for valid analyses. This was also the case for two prior EC-IC bypass trials, as well as for other landmark neurovascular studies, such as the trials comparing endarterectomy with MM for carotid stenoses, or for the trial that compared intervention and MM for unruptured brain arteriovenous malformations. While minor deviations may have little effect on large trials, it may be impossible to show the benefits of surgery when trial size is small and deviations large.

CONCLUSION

Survival analyses are inappropriate in RCTs comparing surgery with conservative management, unless survival is calculated after the postoperative period. Alternative ways to compare final clinical outcomes, using for example a fixed follow-up period, should be planned for preventive surgical trials that compare intervention with conservative management.

Heterogeneity in meta-analysis: a comprehensive overview

In recent years, meta-analysis has evolved to a critically important field of Statistics, and has significant applications in Medicine and Health Sciences. In this work we briefly present existing methodologies to conduct meta-analysis along with any discussion and recent developments accompanying them. Undoubtedly, studies brought together in a systematic review will differ in one way or another. This yields a considerable amount of variability, any kind of which may be termed heterogeneity. To this end, reports of meta-analyses commonly present a statistical test of heterogeneity when attempting to establish whether the included studies are indeed similar in terms of the reported output or not. We intend to provide an overview of the topic, discuss the potential sources of heterogeneity commonly met in the literature and provide useful guidelines on how to address this issue and to detect heterogeneity. Moreover, we review the recent developments in the Bayesian approach along with the various graphical tools and statistical software that are currently available to the analyst. In addition, we discuss sensitivity analysis issues and other approaches of understanding the causes of heterogeneity. Finally, we explore heterogeneity in meta-analysis for time to event data in a nutshell, pointing out its unique characteristics.

How is overall survival assessed in randomised clinical trials in cancer and are subsequent treatment lines considered? A systematic review

BACKGROUND

Overall survival is the "gold standard" endpoint in cancer clinical trials. It plays a key role in determining the clinical- and cost-effectiveness of a new intervention and whether it is recommended for use in standard of care. The assessment of overall survival usually requires trial participants to be followed up for a long period of time. In this time, they may stop receiving the trial intervention and receive subsequent anti-cancer treatments, which also aim to extend survival, during trial follow-up. This can potentially change the interpretation of overall survival in the context of the clinical trial. This review aimed to determine how overall survival has been assessed in cancer clinical trials and whether subsequent anti-cancer treatments are considered.

METHODS

Two searches were conducted using MEDLINE within OVID© on the 9th of November 2021. The first sought to identify papers publishing overall survival results from randomised controlled trials in eight reputable journals and the second to identify papers mentioning or considering subsequent treatments. Papers published since 2010 were included if presenting or discussing overall survival in the context of treating cancer.

RESULTS

One hundred and thirty-four papers were included. The majority of these were presenting clinical trial results (98, 73%). Of these, 45 (46%) reported overall survival as a (co-) primary endpoint. A lower proportion of papers including overall survival as a (co-) primary endpoint compared to a secondary endpoint were published in recent years. The primary analysis of overall survival varied across the papers. Fifty-nine (60%) mentioned subsequent treatments. Seven papers performed additional analysis, primarily when patients in the control arm received the experimental treatment during trial follow-up (treatment switching).

DISCUSSION

Overall survival has steadily moved from being the primary to a secondary endpoint. However, it is still of interest with papers presenting overall survival results with the caveat of subsequent treatments, but little or no investigation into their effect. This review shows that there is a methodological gap for what researchers should do when trial participants receive anti-cancer treatment during trial follow-up. Future research will identify the stakeholder opinions, on how this methodological gap should be addressed.

Meta-epidemiological review identified variable reporting and handling of time-to-event analyses in publications of trials included in meta-analyses of systematic reviews

OBJECTIVES

Previous findings indicate limited reporting of systematic reviews with meta-analyses of time-to-event (TTE) outcomes. We assessed corresponding available information in trial publications included in such meta-analyses.

STUDY DESIGN AND SETTING

We extracted data from all randomized trials in pairwise, hazard ratio (HR)-based meta-analyses of primary outcomes and overall survival of 50 systematic reviews systematically identified from the Cochrane Database and Core Clinical Journals. Data on methods and characteristics relevant for TTE analysis of reviews, trials, and outcomes were extracted.

RESULTS

Meta-analyses included 235 trials with 315 trial analyses. Most prominently assessed was overall survival (91%). Definitions (61%), censoring reasons (41%), and follow-up specifications (56%) for trial outcomes were often missing. Available TTE data per trial were most frequently survival curves (83%), log-rank P values (76%), and HRs (72%). When trial TTE data recalculation was reported, reviews mostly specified HRs or P values (each 5%). Reviews primarily included intention-to-treat analyses (64%) and analyses not adjusted for covariates (25%). Except for missing outcome data, TTE-relevant trial characteristics, for example, informative censoring, treatment switching, and proportional hazards, were sporadically addressed in trial publications. Reporting limitations in trial publications translate to the review level.

CONCLUSION

TTE (meta)-analyses, in trial and review publications, need clear reporting standards.

A Comparison of Alternative Network Meta-Analysis Methods in the Presence of Nonproportional Hazards: A Case Study in First-Line Advanced or Metastatic Renal Cell Carcinoma

OBJECTIVES

Network meta-analysis (NMA) of time-to-event outcomes based on constant hazard ratios can result in biased findings when the proportional hazards (PHs) assumption does not hold in a subset of trials. We aimed to summarize the published non-PH NMA methods for time-to-event outcomes, demonstrate their application, and compare their results.

METHODS

The following non-PH NMA methods were compared through an illustrative case study in oncology of 4 randomized controlled trials in terms of progression-free survival and overall survival: (1) 1-step or (2) 2-step multivariate NMAs based on traditional survival distributions or fractional polynomials, (3) NMAs with restricted cubic splines for baseline hazard, and (4) restricted mean survival NMA.

RESULTS

For progression-free survival, the PH assumption did not hold across trials and non-PH NMA methods better reflected the relative treatment effects over time. The most flexible models (fractional polynomials and restricted cubic splines) fit better to the data than the other approaches. Estimated hazard ratios obtained with different non-PH NMA methods were similar at 5 years of follow-up but differed thereafter in the extrapolations. Although there was no strong evidence of PH violation for overall survival, non-PH NMA methods captured this uncertainty in the relative treatment effects over time.

CONCLUSIONS

When the PH assumption is questionable in a subset of the randomized controlled trials, we recommend assessing alternative non-PH NMA methods to estimate relative treatment effects for time-to-event outcomes. We propose a transparent and explicit stepwise model selection process considering model fit, external constraints, and clinical validity. Given inherent uncertainty, sensitivity analyses are suggested.

Infant Circumcision for Sexually Transmitted Infection Risk Reduction Globally

Population-based studies in high-income countries have failed to find that male circumcision protects against sexually transmitted infections. Using evidence from several sources, we show that male circumcision does protect against HIV during insertive intercourse for men who have sex with men.

Lutetium in prostate cancer: Reconstruction of patient-level data from published trials and generation of a multi-trial Kaplan-Meier curve

BACKGROUND

Lutetium has been shown to be an important potential innovation in pre-treated metastatic castration-resistant prostate cancer. Two clinical trials have evaluated lutetium thus far (therap and vision with 99 and 385 patients, respectively), but their results are discordant.

AIM

To synthetize the available evidence on the effectiveness of lutetium in pre-treated metastatic castration-resistant prostate cancer; and to test the application of a new artificial intelligence technique that synthetizes effectiveness based on reconstructed patient-level data.

METHODS

We employed a new artificial intelligence method (shiny method) to pool the survival data of these two trials and evaluate to what extent the lutetium cohorts differed from one another. The shiny technique employs an original reconstruction of individual patient data from the Kaplan-Meier curves. The progression-free survival graphs of the two lutetium cohorts were analyzed and compared.

RESULTS

The hazard ratio estimated was in favor of the vision trial; the difference was statistically significant (P < 0.001). These results indicate that further studies on lutetium are needed because the survival data of the two trials published thus far are conflicting.

CONCLUSION

Our study confirms the feasibility of reconstructing patient-level data from survival graphs in order to generate a survival statistics.

Synthetizing Published Evidence on Survival by Reconstruction of Patient-Level Data and Generation of a Multi-Trial Kaplan-Meier Curve

Introduction

In conducting a survival meta-analysis, the typical methodological approach analyses the hazard ratios (HRs) of individual trials and then combines them into a pooled meta-analytical estimate. The length of follow-up of individual trials is not generally accounted for. Recent techniques aimed at individual patient-data reconstruction from Kaplan-Meier graphs represent an important methodological innovation. These techniques permit the combination of the survival curves published in a single clinical trial but are also applicable to more than one trial. In the case of multiple trials, a meta-analysis can be conducted without using any statistical model of meta-analysis.

Methods

As an example of this new approach, we applied a technique of individual patient data reconstruction to the Kaplan-Meier graphs of overall survival reported in two phase-III trials, which were conducted on patients with locally advanced/advanced non-small cell lung cancer selected according to their PD-L1 expression status, not previously treated for their metastatic disease. Only subjects with PD-L1 ≥50% were considered for our analysis. The experimental arms received pembrolizumab monotherapy while the control arms were given platinum-based chemotherapy. The survival graphs were obtained for both trials. For each Kaplan-Meier curve, the graph was firstly digitalized. Then, the Shiny package was used to reconstruct patient-level data. Finally, the pooled survival curves were generated from the reconstructed patient-level data along with the relevant Cox statistics; for this purpose, we used three packages (“coxph”, “survfit”, and “ggsurvplot”) under the R-platform.

Results

In our pooled analysis based on this procedure, we compared 453 patients given pembrolizumab vs. 451 controls given chemotherapy. The HR estimated from reconstructed patient-level data was 0.670 (95% confidence interval [CI], 0.566 to 0.793).

Conclusion

The analysis described herein demonstrates the easy applicability of the Shiny technique. This technique was successful in generating a pooled survival graph for the experimental treatment groups vs. controls and efficiently estimated the pooled HR in which the results of the two trials were combined.

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.

Testing the proportional hazards assumption in cox regression and dealing with possible non-proportionality in total joint arthroplasty research: methodological perspectives and review

Background

Survival analysis and effect of covariates on survival time is a central research interest. Cox proportional hazards regression remains as a gold standard in the survival analysis. The Cox model relies on the assumption of proportional hazards (PH) across different covariates. PH assumptions should be assessed and handled if violated. Our aim was to investigate the reporting of the Cox regression model details and testing of the PH assumption in survival analysis in total joint arthroplasty (TJA) studies.

Methods

We conducted a review in the PubMed database on 28th August 2019. A total of 1154 studies were identified. The abstracts of these studies were screened for words “cox and “hazard*” and if either was found the abstract was read. The abstract had to fulfill the following criteria to be included in the full-text phase: topic was knee or hip TJA surgery; survival analysis was used, and hazard ratio reported. If all the presented criteria were met, the full-text version of the article was then read. The full-text was included if Cox method was used to analyze TJA survival. After accessing the full-texts 318 articles were included in final analysis.

Results

The PH assumption was mentioned in 114 of the included studies (36%). KM analysis was used in 281 (88%) studies and the KM curves were presented graphically in 243 of these (87%). In 110 (45%) studies, the KM survival curves crossed in at least one of the presented figures. The most common way to test the PH assumption was to inspect the log-minus-log plots (n = 59). The time-axis division method was the most used corrected model (n = 30) in cox analysis. Of the 318 included studies only 63 (20%) met the following criteria: PH assumption mentioned, PH assumption tested, testing method of the PH assumption named, the result of the testing mentioned, and the Cox regression model corrected, if required.

Conclusions

Reporting and testing of the PH assumption and dealing with non-proportionality in hip and knee TJA studies was limited. More awareness and education regarding the assumptions behind the used statistical models among researchers, reviewers and editors are needed to improve the quality of TJA research. This could be achieved by better collaboration with methodologists and statisticians and introducing more specific reporting guidelines for TJA studies. Neglecting obvious non-proportionality undermines the overall research efforts since causes of non-proportionality, such as possible underlying pathomechanisms, are not considered and discussed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-021-04379-2.

A systematic review of the quality of conduct and reporting of survival analyses of tuberculosis outcomes in Africa

BACKGROUND

Survival analyses methods (SAMs) are central to analysing time-to-event outcomes. Appropriate application and reporting of such methods are important to ensure correct interpretation of the data. In this study, we systematically review the application and reporting of SAMs in studies of tuberculosis (TB) patients in Africa. It is the first review to assess the application and reporting of SAMs in this context.

METHODS

Systematic review of studies involving TB patients from Africa published between January 2010 and April 2020 in English language. Studies were eligible if they reported use of SAMs. Application and reporting of SAMs were evaluated based on seven author-defined criteria.

RESULTS

Seventy-six studies were included with patient numbers ranging from 56 to 182,890. Forty-three (57%) studies involved a statistician/epidemiologist. The number of published papers per year applying SAMs increased from two in 2010 to 18 in 2019 (P = 0.004). Sample size estimation was not reported by 67 (88%) studies. A total of 22 (29%) studies did not report summary follow-up time. The survival function was commonly presented using Kaplan-Meier survival curves (n = 51, (67%) studies) and group comparisons were performed using log-rank tests (n = 44, (58%) studies). Sixty seven (91%), 3 (4.1%) and 4 (5.4%) studies reported Cox proportional hazard, competing risk and parametric survival regression models, respectively. A total of 37 (49%) studies had hierarchical clustering, of which 28 (76%) did not adjust for the clustering in the analysis. Reporting was adequate among 4.0, 1.3 and 6.6% studies for sample size estimation, plotting of survival curves and test of survival regression underlying assumptions, respectively. Forty-five (59%), 52 (68%) and 73 (96%) studies adequately reported comparison of survival curves, follow-up time and measures of effect, respectively.

CONCLUSION

The quality of reporting survival analyses remains inadequate despite its increasing application. Because similar reporting deficiencies may be common in other diseases in low- and middle-income countries, reporting guidelines, additional training, and more capacity building are needed along with more vigilance by reviewers and journal editors.

GRADE Guidelines: 29. Rating the certainty in time-to-event outcomes-Study limitations due to censoring of participants with missing data in intervention studies

OBJECTIVES

To provide Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) guidance for the consideration of study limitations (risk of bias) due to missing participant outcome data for time-to-event outcomes in intervention studies.

STUDY DESIGN AND SETTING

We developed this guidance through an iterative process that included membership consultation, feedback, presentation, and iterative discussion at meetings of the GRADE working group.

RESULTS

The GRADE working group has published guidance on how to account for missing participant outcome data in binary and continuous outcomes. When analyzing time-to-event outcomes (e.g., overall survival and time-to-treatment failure) data of participants for whom the outcome of interest (e.g., death and relapse) has not been observed are dealt with through censoring. To do so, standard methods require that censored individuals are representative for those remaining in the study. Two types of censoring can be distinguished, end of study censoring and censoring because of missing data, commonly named loss to follow-up censoring. However, both types are not distinguishable with the usual information on censoring available to review authors. Dealing with individuals for whom data are missing during follow-up in the same way as individuals for whom full follow-up is available at the end of the study increases the risk of bias. Considerable differences in the treatment arms in the distribution of censoring over time (early versus late censoring), the overall degree of missing follow-up data, and the reasons why individuals were lost to follow-up may reduce the certainty in the study results. With often only very limited data available, review and guideline authors are required to make transparent and well-considered judgments when judging risk of bias of individual studies and then come to an overall grading decision for the entire body of evidence.

CONCLUSION

Concern for risk of bias resulting from censoring of participants for whom follow-up data are missing in the underlying studies of a body of evidence can be expressed in the study limitations (risk of bias) domain of the GRADE approach.

Value of surgical resection compared to transarterial chemoembolization in the treatment of hepatocellular carcinoma with portal vein tumor thrombus: A meta-analysis of hazard ratios from five observational studies

Backgrounds/Aims

Although systemic therapy is recommended in advanced hepatocellular carcinoma (HCC), treatment options for advanced HCC with portal vein tumor thrombosis (PVTT) are debatable. Recent studies have recommended other treatments, such as surgical resection (SR) and transarterial chemoembolization (TACE). Therefore, we performed a meta-analysis of hazard ratio (HR) for overall survival (OS) between the two modalities using previous reports in order to compare the two treatment options.

Methods

A systematic review was performed on previously reported data that compared the survival benefits of SR and TACE in patients with advanced HCC with PVTT. Thereafter, the meta-analysis was performed to determine the cumulative HR between the two different treatment groups. We used the HR and 95% CI directly from the original data, when available; however, if these data were unavailable, reconstruction was performed with the secondary data from the original Kaplan-Meier survival curve.

Results

A total of seven studies were eligible; however, 2 were excluded from the meta-analysis. The remaining 5 studies that included 1422 patients (SR group=559, TACE group=863) were studied for the meta-analysis. The median OS was longer in the SR group (8.2-64 months in SR vs. 6.6-32 months in TACE), proving that SR offered survival benefits. Moreover, the HR for the OS in the TACE group was 1.64 (95% CI, 1.43-1.88) compared to SR group, depicting that TACE was a less favorable option compared to SR.

Conclusions

There is evidence that SR may be a better viable option for advanced HCC with PVTT.

Observed versus modelled lifetime overall survival of targeted therapies and immunotherapies for advanced non-small cell lung cancer patients - A systematic review

Outcomes used for the effectiveness (median) and cost-effectiveness (mean) on overall survival (OS) are different and can vary from one another. Therefore, we compared median and mean OS gains of targeted therapies and immunotherapies for stage IIIB/IV Non-small cell lung cancer and explored underlying aspect. Eligible trials were searched in PubMed, survival curves were digitized, and parametric survival models fitted to model the mean OS. Twenty-seven trials were found for targeted therapies (n = 17) and immunotherapies (n = 10). Differences between median and mean OS gains in months ranged from -2.8 to 6.8 and -4.9 to 0.3 for two different subgroups of targeted therapies, and -2.4 to 11.4 for immunotherapies. The mean OS gain was substantially larger for most immunotherapy trials, due to relatively long survival. Median and mean OS gains did not differ for targeted therapies. Our findings imply a potential discrepancy between the estimates of effectiveness and cost-effectiveness of cancer treatments.

Deviation from the Proportional Hazards Assumption in Randomized Phase 3 Clinical Trials in Oncology: Prevalence, Associated Factors, and Implications

PURPOSE

Deviations from proportional hazards (DPHs), which may be more prevalent in the era of precision medicine and immunotherapy, can lead to underpowered trials or misleading conclusions. We used a meta-analytic approach to estimate DPHs across cancer trials, investigate associated factors, and evaluate data-analysis approaches for future trials.Experimental Design: We searched PubMed for phase III trials in breast, lung, prostate, and colorectal cancer published in a preselected list of journals between 2014 and 2016 and extracted individual patient-level data (IPLD) from Kaplan-Meier curves. We re-analyzed IPLD to identify DPHs. Potential efficiency gains, when DPHs were present, of alternative statistical methods relative to standard log-rank based analysis were expressed as sample-size requirements for a fixed power level.

RESULTS

From 152 trials, we obtained IPLD on 129,401 patients. Among 304 Kaplan-Meier figures, 75 (24.7%) exhibited evidence of DPHs, including eight of 14 (57%) KM pairs from immunotherapy trials. Trial type [immunotherapy, odds ratio (OR), 4.29; 95% confidence interval (CI), 1.11-16.6], metastatic patient population (OR, 3.18; 95% CI, 1.26-8.05), and non-OS endpoints (OR, 3.23; 95% CI, 1.79-5.88) were associated with DPHs. In immunotherapy trials, alternative statistical approaches allowed for more efficient clinical trials with fewer patients (up to 74% reduction) relative to log-rank testing.

CONCLUSIONS

DPHs were found in a notable proportion of time-to-event outcomes in published clinical trials in oncology and was more common for immunotherapy trials and non-OS endpoints. Alternative statistical methods, without proportional hazards assumptions, should be considered in the design and analysis of clinical trials when the likelihood of DPHs is high.

Are non-constant rates and non-proportional treatment effects accounted for in the design and analysis of randomised controlled trials? A review of current practice

Background

Most clinical trials with time-to-event primary outcomes are designed assuming constant event rates and proportional hazards over time. Non-constant event rates and non-proportional hazards are seen increasingly frequently in trials. The objectives of this review were firstly to identify whether non-constant event rates and time-dependent treatment effects were allowed for in sample size calculations of trials, and secondly to assess the methods used for the analysis and reporting of time-to-event outcomes including how researchers accounted for non-proportional treatment effects.

Methods

We reviewed all original reports published between January and June 2017 in four high impact medical journals for trials for which the primary outcome involved time-to-event analysis. We recorded the methods used to analyse and present the main outcomes of the trial and assessed the reporting of assumptions underlying these methods. The sample size calculation was reviewed to see if the effect of either non-constant hazard rates or anticipated non-proportionality of the treatment effect was allowed for during the trial design.

Results

From 446 original reports we identified 66 trials with a time-to-event primary outcome encompassing trial start dates from July 1995 to November 2014. The majority of these trials (73%) had sample size calculations that used standard formulae with a minority of trials (11%) using simulation for anticipated changing event rates and/or non-proportional hazards. Well-established analytical methods, Kaplan-Meier curves (98%), the log rank test (88%) and the Cox proportional hazards model (97%), were used almost exclusively for the main outcome. Parametric regression models were considered in 11% of the reports. Of the trials reporting inference from the Cox model, only 11% reported any results of testing the assumption of proportional hazards.

Conclusions

Our review confirmed that when designing trials with time-to-event primary outcomes, methodologies assuming constant event rates and proportional hazards were predominantly used despite potential efficiencies in sample size needed or power achieved using alternative methods. The Cox proportional hazards model was used almost exclusively to present inferential results, yet testing and reporting of the pivotal assumption underpinning this estimation method was lacking.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0749-1) contains supplementary material, which is available to authorized users.

Measuring Survival Benefit in Health Technology Assessment in the Presence of Nonproportional Hazards

BACKGROUND

Proportional hazards (PH) is an assumption often made by researchers, despite evidence of nonproportionality in a significant proportion of clinical trials. In the presence of non-PH, the interpretation of hazard ratios, medians, and landmark survival as summary measures of treatment effect can become problematic. Several recent studies have recommended restricted mean survival time (RMST) as an alternative metric for survival analysis, particularly where non-PH may apply.

OBJECTIVES

To determine the current approaches of health technology assessment (HTA) agencies to value assessment in the presence of non-PH, and the extent to which RMST is accepted as an alternative measure of treatment benefit.

METHODS

Methodological guidelines published by 10 HTA agencies were reviewed to establish recommended approaches for presenting survival benefit from clinical trials. Published HTA reports for 23 oncology agents approved by the US Food and Drug Administration and the European Medicines Agency since 2014 were reviewed to determine how guidelines are implemented in practice and identify instances where the PH assumption was tested and RMST analyses reported.

RESULTS

Testing for non-PH is not widely incorporated into HTA except by the UK National Institute for Health and Care Excellence. RMST is used infrequently but has been used in a number of countries, particularly by agencies that focus on cost effectiveness.

CONCLUSIONS

HTA agencies vary in their approaches to non-PH. Most do not routinely check the PH assumption. RMST has played a role in assessing clinical benefit within HTA, although not consistently within countries (across drugs) or across countries (for the same drug).

Combination of Intravesical Bacille Calmette-Guérin and Chemotherapy vs. Bacille Calmette-Guérin Alone in Non-muscle Invasive Bladder Cancer: A Meta-Analysis

Background: About 75% of newly diagnosed bladder cancer cases suffer from non-muscle invasive bladder cancer (NMIBC), which used to recur and progress despite transurethral resection of bladder tumor (TURBT). This meta-analysis was conducted to examine if combined application of intravesical bacille Calmette-Guérin (BCG) with chemotherapy is associated with better prognosis. Methods: Systematic searches of randomized controlled trials (RCTs) concerning NMIBC were performed in PubMed, EMbase, CENTRAL, CNKI, WanFang, VIP, CBM databases, and some specialized websites. Two researchers independently implemented study selection, quality assessment and data extraction. Hazard ratios (HRs) and their 95% confidence intervals (CIs) for treatment effects on recurrence-free survival (RFS), progression-free survival (PFS), overall survival (OS) and disease-specific survival (DSS) were directly extracted, if available, or estimated using relevant data from included studies. Side effects, such as fever, gastrointestinal reaction, cystitis, irritative bladder symptoms and hematuria, were also extracted as outcome measurements, and associated relative risks (RRs) were calculated to assess treatment safety. RevMan 5.3 software was used to perform statistical analyses. Results: Thirteen RCTs containing 1,754 patients with NMIBC were included in this meta-analysis. Compared with BCG alone, the combination therapy significantly improved RFS (HR = 0.53, 95% CI: 0.43-0.66, P < 0.01), OS (HR = 0.66, 95%CI: 0.50-0.86, P = 0.002), and DSS (HR = 0.48, 95%CI: 0.29-0.80, P = 0.005). While PFS showed no obvious difference between combination therapy and BCG alone (HR = 0.65, 95%CI: 0.25-1.68, P = 0.38). The rate of fever (RR = 0.50, 95%CI: 0.27-0.91, P = 0.02), irritative bladder symptoms (RR = 0.69, 95%CI: 0.52-0.90, P = 0.007) and hematuria (RR = 0.50, 95%CI: 0.28-0.89, P = 0.02) were significantly decreased in patients treated with combination therapy compared to those with BCG alone. There were no statistically significant differences between combination therapy and BCG alone in toxicity (RR = 0.69, 95%CI: 0.34-1.40, P = 0.30), gastrointestinal reaction (RR = 2.54, 95%CI: 0.61-10.60, P = 0.20) or cystitis (RR = 0.67, 95%CI: 0.29-1.54, P = 0.34). Conclusions: Combined application of intravesical BCG and chemotherapy appears to be an effective treatment for patients with intermediate- to high-risk NMIBC, but not for those with tumor in situ alone or recurrent bladder cancer.

Frontline treatment for transplant-eligible multiple myeloma: A 6474 patients network meta-analysis

Autologous transplantation continues to be the cornerstone of younger and fit multiple myeloma patients. It is known that frontline induction therapy before transplantation can influence post-transplant results. Therefore, best frontline treatment for transplant-eligible patients should be based on best available evidence to guide therapy. Furthermore, until now due to data scarcity, it was not possible to thoroughly compare lenalidomide to other regimens in this setting. We performed a systematic review and network (mixed treatment comparison) meta-analysis of 21 clinical trial publications, enrolling 6474 patients and comparing 11 different treatment frontline setting regimens regarding survival, response, and safety outcomes. OS analysis showed superiority of CRD (cyclophosphamide-lenalidomide-dexamethasone) over TD-based (thalidomide-dexamethasone, HR = 0.76,0.62-0.90), VAD-based (HR = 0.71,0.52-0.90), and Z-Dex (idarubicin-dexamethasone, HR = 0.37,0.17-0.76) regimens. Concerning PFS, VTD (bortezomib-thalidomide-dexametasone) showed superior results when compared with TD-based (HR = 0.66,0.51-0.84), VAD-based (HR = 0.61,0.46-0.82), Z-Dex (HR = 0.42,0.22-0.78), and high dose dexamethasone (Dex, HR = 0.62,0.41-0.90) regimens. Bortezomib/thalidomide regimens were not superior to lenalidomide, considering these outcomes. Also, concerning complete and overall response, VTD ranked first among other regimens, showing clear superiority over thalidomide-only containing protocols. Safety outcome evaluated infectious, cardiac, gastrointestinal, neurological, thrombotic, and hematological grade 3 to 4 adverse events. Risk of thrombotic events was higher with TAD (thalidomide-doxorubicin-dexamethasone), neurological with PAD (bortezomib-doxorubicin-dexamethasone), infectious with Dex, hematological with Z-Dex, gastrointestinal with VTD, and cardiac with PAD regimens. Our study endorses current recommendations on combined immunomodulatory drugs and proteasome inhibitors frontline regimens (in triplets) in transplant-eligible multiple myeloma patients, but also formally demonstrates the favorable performance of lenalidomide in overall and progression-free survival, when compared with bortezomib/thalidomide protocols.