The restricted mean survival time as a replacement for the hazard ratio and the number needed to treat in long-term studies

Mitraclip Versus Medical Therapy or Surgery in Patients With Mitral Regurgitation: Long-Term Outcomes Determined by the Reconstruction of Individual Patient Data

Although MitraClip has been studied in numerous trials, its evidence in the long term is based on a few original studies. We used an original technique of evidence synthesis to review long-term comparative trials evaluating MitraClip. We searched the PubMed database to select long-term comparative trials of MitraClip. The endpoint was all-cause mortality (minimum follow-up, one year). Included trials were analyzed using the IPDfromKM (reconstruct Individual Patient Data from published Kaplan-Meier survival curves) method to reconstruct individual patient data from Kaplan-Meier curves. Standard survival statistics were used to interpret these long-term efficacy data. The survival benefit per patient was estimated from the restricted mean survival time (RMST). Six comparative studies of MitraClip were included; 973 patients were treated with MitraClip (six arms), 717 with medical therapy (five arms), and 80 with surgical repair or replacement (one arm). In our main analysis, the outcomes observed in patients treated with MitraClip were significantly better than those of medical therapy (hazard ratio for all-cause mortality, 0.5276; 95% confidence interval, 0.4412 to 0.6309; p < 0.001); the number of patients treated with surgery was too small to make reliable comparisons. Median survival was 30.4 months for medical therapy versus not reached for the other two groups. RMST was 43.931 and 33.756 months for MitraClip and controls, respectively, yielding a gain per patient of 10.17 months (95% confidence interval, 7.47 to 12.88). In our simplified cost-effectiveness evaluation, a gain of approximately 10 months per patient compared favorably with the device cost. Our analysis provided an original interpretation of the long-term evidence available on MitraClip.

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.

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.

The restricted mean survival time as a replacement for the hazard ratio and the number needed to treat in long-term studies

Aims

We applied the restricted mean survival time (RMST) to analyse the survival data reported in the PARADIGM‐HT trial in which sacubitril + valsartan was studied in comparison with enalapril in patients with heart failure. The estimates of this parameter were compared with the published values of hazard ratio (HR).

Methods

Two endpoints were evaluated: a composite of death or hospitalization and cardiovascular death. Our analyses were performed by considering the original follow‐up of 41.4 months and on the basis of a lifetime perspective. All statistical calculations were carried out using specific packages developed under the R‐platform.

Results

According to our RMST analysis, the results for the composite endpoint in the comparison of sacubitril + valsartan vs. enalapril showed an improvement from 32.9 to 34.2 months (gain of 1.25 months). This result is based on a time horizon of 41.4 months. The results for the cardiovascular mortality endpoint showed a RMST of 37.2 months for sacubitril + valsartan vs. 36.2 for enalapril (gain of 0.96 months). In the two lifetime analyses, the improvements were much more relevant and yielded a gain of 25.8 months for the composite endpoint and 27.6 months for survival free from cardiovascular death.

Conclusions

Using the data of the PARADIGM‐HT trial, our analysis confirmed that the RMST has documented advantages over the HR, particularly when the clinical study is characterized by a long follow‐up. The number needed to treat (NNT) has a more specific methodological role and cannot be replaced by the RMST.