The use of restricted mean survival time to estimate the treatment effect in randomized clinical trials when the proportional hazards assumption is in doubt

2011 ◽  
Vol 30 (19) ◽  
pp. 2409-2421 ◽  
Author(s):  
Patrick Royston ◽  
Mahesh K. B. Parmar
Keyword(s):  
Clinical Trials ◽  
Survival Time ◽  
Treatment Effect ◽  
Randomized Clinical Trials ◽  
Proportional Hazards ◽  
Mean Survival Time ◽  
Proportional Hazards Assumption ◽  
Restricted Mean Survival Time

scholarly journals Restricted mean survival time: Does covariate adjustment improve precision in randomized clinical trials?

2018 ◽  
Vol 15 (2) ◽  
pp. 178-188 ◽  
Author(s):  
Theodore Karrison ◽  
Masha Kocherginsky
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Survival Time ◽  
Treatment Effect ◽  
Randomized Clinical Trials ◽  
Linear Models ◽  
Covariate Adjustment ◽  
Survival Times ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

Background: Restricted mean survival time is a measure of average survival time up to a specified time point. There has been an increased interest in using restricted mean survival time to compare treatment arms in randomized clinical trials because such comparisons do not rely on proportional hazards or other assumptions about the nature of the relationship between survival curves. Methods: This article addresses the question of whether covariate adjustment in randomized clinical trials that compare restricted mean survival times improves precision of the estimated treatment effect (difference in restricted mean survival times between treatment arms). Although precision generally increases in linear models when prognostic covariates are added, this is not necessarily the case in non-linear models. For example, in logistic and Cox regression, the standard error of the estimated treatment effect does not decrease when prognostic covariates are added, although the situation is complicated in those settings because the estimand changes as well. Because estimation of restricted mean survival time in the manner described in this article is also based on a model that is non-linear in the covariates, we investigate whether the comparison of restricted mean survival times with adjustment for covariates leads to a reduction in the standard error of the estimated treatment effect relative to the unadjusted estimator or whether covariate adjustment provides no improvement in precision. Chen and Tsiatis suggest that precision will increase if covariates are chosen judiciously. We present results of simulation studies that compare unadjusted versus adjusted comparisons of restricted mean survival time between treatment arms in randomized clinical trials. Results: We find that for comparison of restricted means in a randomized clinical trial, adjusting for covariates that are associated with survival increases precision and therefore statistical power, relative to the unadjusted estimator. Omitting important covariates results in less precision but estimates remain unbiased. Conclusion: When comparing restricted means in a randomized clinical trial, adjusting for prognostic covariates can improve precision and increase power.

Restricted mean survival time analysis in heart failure clinical trials

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
C Perego ◽  
M Sbolli ◽  
C Specchia ◽  
C Oriecuia ◽  
G Peveri ◽  
...  
Keyword(s):  
Heart Failure ◽  
Clinical Trials ◽  
Survival Time ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Statistical Testing ◽  
Secondary Outcome ◽  
Time To Event ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

Abstract Background The hazard ratio (HR) is the most common measure used to quantify treatment effects in heart failure (HF) clinical trials. However, the HR is only valid when the proportional hazards assumption is plausible, and the HR may be difficult to interpret for clinicians and laypeople. Restricted mean survival time (RMST), defined as the average time-to-event before a specific timepoint, is an intuitive summary of group-wise survival. The difference between two RMSTs measures treatment effects without model assumptions and may communicate more clinically interpretable results. Purpose To evaluate statistical and clinical properties of RMST-based statistics applied to clinical trial data for treatments of HF with reduced ejection fraction. Methods Patient time-to-event data was reconstructed from the published primary and secondary outcome Kaplan-Meier curves from landmark HF clinical trials. We estimated the RMST-differences between treatment groups as a measure of treatment effect with published data, and compared statistical testing results and effect size values to HR analysis results. Results We analyzed 7 HF clinical trials, including data from a total of 27,845 patients (Table 1). RMST should be interpreted as the average number of months that the outcome is avoided over the study period. As examples: On average, treatment with enalapril for 12 months extended each patient's life by 2.2 months compared to placebo, and treatment with spironolactone for 34 months extended each patient's life by 2.2 months compared to placebo. Conclusions RMST-difference test statistic has identical statistical conclusions as HRs but provided an intuitive estimate of each treatment effect. RMST-based data can potentially be used to better communicate treatment effects to patients, to assist in patient-preference discussions and shared decision-making Funding Acknowledgement Type of funding source: None

A permutation test based on the restricted mean survival time for comparison of net survival distributions in non-proportional excess hazard settings

2019 ◽  
Vol 29 (6) ◽  
pp. 1612-1623
Author(s):  
Anna Wolski ◽  
Nathalie Grafféo ◽  
Roch Giorgi ◽  
Keyword(s):  
Survival Time ◽  
Proportional Hazards ◽  
Permutation Test ◽  
Survival Times ◽  
Test Statistic ◽  
Specific Test ◽  
Net Survival ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time ◽  
Type Test

Net survival is used in epidemiological studies to assess excess mortality due to a given disease when causes of death are unreliable. By correcting for the general population mortality, it allows comparisons between regions or periods and thus evaluation of health policies. The Pohar-Perme non-parametric estimator of net survival has been recently proposed, soon followed by an appropriate log-rank-type test. However, log-rank tests are known to be under-optimal in non-proportional settings (e.g. crossing of the hazard functions). In classical survival analysis, one solution is to compare the restricted mean survival times. A difference in restricted mean survival time represents a life benefit or loss over the studied period. In the present article the restricted mean net survival time was used to derive a specific test statistic to compare net survivals in proportional and non-proportional hazards settings. The new test was generalized to more than two groups and to stratified analysis. The test performance was assessed on simulation study, compared to the log-rank-type test, and its use illustrated on a population-based colorectal cancer registry. The new test for net survival comparisons proved robust to non-proportionality and well-performing in proportional hazards situations. Furthermore, it is also suited to the classical survival framework.

OP236 Evidence Synthesis Of Time-To-Event Outcomes In The Presence Of Non-Proportional Hazards

2021 ◽  
Vol 37 (S1) ◽  
pp. 8-8
Author(s):  
Suzanne Freeman ◽  
Nicola Cooper ◽  
Alex Sutton ◽  
Michael Crowther ◽  
James Carpenter ◽  
...  
Keyword(s):  
Survival Time ◽  
Simulation Study ◽  
Proportional Hazards ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Clinical Effectiveness ◽  
Time To Event ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

IntroductionSynthesis of clinical effectiveness is a well-established component of health technology assessment (HTA) combining data from multiple trials to obtain an overall pooled estimate of clinical effectiveness, which may inform an associated economic evaluation. Time-to-event outcomes are often synthesized using effect measures from Cox proportional hazards models assuming a constant hazard ratio over time. However, where treatment effects vary over time an assumption of proportional hazards is not always valid. Several methods have been proposed for synthesizing time-to-event outcomes in the presence of non-proportional hazards. However, guidance on choosing between these methods and the implications for HTA is lacking.MethodsWe applied five methods for estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption to a network of melanoma trials, reporting overall survival: restricted mean survival time, an accelerated failure time generalized gamma model, piecewise exponential, fractional polynomial and Royston-Parmar models. We conducted a simulation study to compare these five methods. Simulated individual patient data was generated from a mixture Weibull distribution assuming a treatment-time interaction. Each simulated meta-analysis consisted of five trials with varying numbers of patients and length of follow-up across trials. For each model fitted to each dataset, we calculated the restricted mean survival time at the end of observed follow-up and following extrapolation to a 20-year time horizon.ResultsAll models fitted the melanoma data reasonably well with some variation in the treatment rankings and differences in the survival curves. The simulation study demonstrated the potential for different conclusions from different modelling approaches.ConclusionsThe restricted mean survival time, generalized gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can all accommodate non-proportional hazards and differing lengths of trial follow-up within an evidence synthesis of time-to-event outcomes. Further work is needed in this area to extend the simulation study to the network meta-analysis setting and provide guidance on the key considerations for informing model choice for the purposes of HTA.

scholarly journals Are restricted mean survival time methods especially useful for noninferiority trials?

2021 ◽  
pp. 174077452097657
Author(s):  
Boris Freidlin ◽  
Chen Hu ◽  
Edward L Korn
Keyword(s):  
Survival Time ◽  
Proportional Hazards ◽  
Small Sample ◽  
Operating Characteristics ◽  
Mean Survival Time ◽  
Kaplan Meier ◽  
Noninferiority Trials ◽  
Restricted Mean Survival Time ◽  
Experimental Treatments ◽  
Event Rates

Background: Restricted mean survival time methods compare the areas under the Kaplan–Meier curves up to a time [Formula: see text] for the control and experimental treatments. Extraordinary claims have been made about the benefits (in terms of dramatically smaller required sample sizes) when using restricted mean survival time methods as compared to proportional hazards methods for analyzing noninferiority trials, even when the true survival distributions satisfy proportional hazardss. Methods: Through some limited simulations and asymptotic power calculations, the authors compare the operating characteristics of restricted mean survival time and proportional hazards methods for analyzing both noninferiority and superiority trials under proportional hazardss to understand what relative power benefits there are when using restricted mean survival time methods for noninferiority testing. Results: In the setting of low-event rates, very large targeted noninferiority margins, and limited follow-up past [Formula: see text], restricted mean survival time methods have more power than proportional hazards methods. For superiority testing, proportional hazards methods have more power. This is not a small-sample phenomenon but requires a low-event rate and a large noninferiority margin. Conclusion: Although there are special settings where restricted mean survival time methods have a power advantage over proportional hazards methods for testing noninferiority, the larger issue in these settings is defining appropriate noninferiority margins. We find the restricted mean survival time methods lacking in these regards.

Restricted Mean Survival Time for Analysis and Interpretation of Clinical Trials for Heart Failure Devices

2020 ◽  
Vol 26 (10) ◽  
pp. S64
Author(s):  
Carlotta Perego ◽  
Marco Sbolli ◽  
Claudia Specchia ◽  
Chiara Oriecuia ◽  
Giulia Peveri ◽  
...  
Keyword(s):  
Heart Failure ◽  
Clinical Trials ◽  
Survival Time ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

scholarly journals Time-dependent treatment effects of metronomic chemotherapy in unfit AML patients: a secondary analysis of a randomised controlled trial

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Phichayut Phinyo ◽  
Jayanton Patumanond ◽  
Saranya Pongudom
Keyword(s):  
Survival Time ◽  
Palliative Treatment ◽  
Proportional Hazards ◽  
Controlled Trial ◽  
Tertiary Care ◽  
Secondary Analysis ◽  
Metronomic Chemotherapy ◽  
Time Dependent ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

Abstract Objectives To examine the presence of the time-dependent effect of metronomic chemotherapy for the treatment of older patients with acute myeloid leukemia (AML) who were unfit for standard chemotherapy and to reanalyze the data using an appropriate statistical approach in the presence of non-proportional hazards, the restricted mean survival time (RMST). Results This was a secondary analysis of a multi-center, open-label, randomized controlled trial, which was conducted in seven tertiary care hospitals across Thailand. A total of 81 unfit AML patients were randomized into two treatment groups, metronomic chemotherapy and palliative treatment. The hazard ratio of metronomic chemotherapy over palliative treatment was time-dependent. At three landmark time points of 90, 180, 365 days, the restricted mean survival time differences were 13.3 (95% CI 1.9–24.7) days, 28.9 (95% CI 3.3–54.4) days, and 40.4 (95% CI − 1.3 to 82.0) days, respectively. With non-proportional hazards modeling and RMST analysis, we were able to conclude that metronomic chemotherapy is a potentially effective alternative treatment for elderly AML patients who were medically unfit for intensive chemotherapy. In the future clinical trials, non-proportional hazards should be carefully inspected and properly handled with appropriate statistical methods. Trial registration Randomized clinical trial TCTR20150918001; registration date: 15/09/2015. Retrospectively registered

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