NorthWest Biotherapeutics Inc (NWBO)

[hyperopia]

I see restricted mean survival time (RMST) being used in immunotherapy trials, and think it could be used in this one.

Quantifying the Survival Benefits of Oncology Drugs With a Focus on Immunotherapy Using Restricted Mean Survival Time

Background
In recent years, immunotherapy has emerged as an alternative to chemotherapy and has been commonly viewed as a “game changer” for its potential to improve survival in a small proportion of patients.1–4 Randomized controlled trials often report measures of absolute survival benefit as differences in median survival times or differences in survival rates at specific times on Kaplan-Meier (KM) curves, whereas measures of relative survival benefit are reported as hazard ratios (HRs). However, reporting survival benefits of immunotherapy using these measures is potentially challenging.

For example, differences in median survival times are not estimable when the median survival time has not been reached in at least one of the trial arms.5 Although median survival time is an absolute survival measure of central tendency, it ignores the rest of the shape of the survival curve and treats long-term survivors as outliers (in contrast to mean survival), especially in cases in which the survival curves diverge after the median times. For relative survival benefit measures, HRs rely on the proportional hazards assumption, which depends on the number of observed events and not directly on exposure times or sample size of the study.6 HRs also may vary with time and thus violate the proportional hazards assumption.7,8 When the proportional hazards assumption is violated, the HR estimate may not be a statistically valid measurement of the treatment effect and becomes difficult to interpret clinically.

Immunotherapy trials sometimes exhibit nonproportional hazards, often because of late postprogression benefit (ie, after median survival times) or nonuniform divergence of the survival curves with potential plateau of the immunotherapy arm, with some trials even exhibiting crossing of survival curves.9–13 Median survival time also does not account for the “tail of the curve,” where the benefit of immunotherapy is more pronounced. Conventional measures of survival benefit have clinical limitations that were previously overlooked in immunotherapy trials.7

An interpretable and robust measure of clinical efficacy in survival analyses is critical to capture the full context of the benefits of oncology drugs and to make more informed decisions about the cost versus benefit of treatment options. With various studies showing a steady increase in the cost and variety of novel oncology drugs, the benefits associated with these drugs are not necessarily following the same upward trend.14–16

Restricted mean survival time (RMST) is increasingly being recognized as a robust and clinically interpretable summary measure alternative to HR and median survival time because it directly quantifies information of the entire observed survival curve.7,17 RMST is defined as the area under the KM curve up to a specific time point (t), which is the follow-up time of the study.18,19 Because this measure takes the average (ie, mean, not median) to summarize the entire survival profile, it does not rely on the proportional hazards assumption and captures the benefit across the entire trial.6,19 RMST difference is the difference between the area under the KM curve of the treatment arm and the control arm, and has been proposed as a measure of absolute survival benefit as an alternative to median survival time.6 RMST ratio is the ratio of the RMSTs between the treatment arm and the control arm.7 An RMST ratio >1 shows an improvement in survival in the treatment arm. Unlike the HR, which is a ratio of the rates of death,20 RMST ratio is a ratio of survival, which is arguably clinically more meaningful. For example, if survival associated with a control arm were high, a “good” HR (such as 0.5) would still represent little relative survival gained (RMST ratio may be only 1.2 rather than 2.0 as one may incorrectly expect) (supplemental eFigure 1, available with this article at JNCCN.org). It has been shown that HR is a relative measure of the rate of death and tends to overestimate the relative survival benefit.4,8,21 Comparability of treatment effects of immunotherapy drugs between RMST ratios and HR estimates has also been shown to be in general agreement.21 It has been suggested that it may be useful to report RMST in immunotherapy trials, in which the benefit typically is at the tail of the curve.3,21,22

Because RMST methods seem to be a reasonable alternative approach to measure survival benefits to overcome limitations of conventional measures, our study aimed to quantify the magnitude of survival benefit in oncology drugs recently approved by the FDA, using RMST difference (absolute survival benefit) and RMST ratio (relative survival benefit) in a meta-analysis. We further aimed to compare whether immunotherapy drugs provide a greater survival benefit than nonimmunotherapy drugs. . . .

https://jnccn.org/view/journals/jnccn/18/3/article-p278.xml



Dynamic RMST curves for survival analysis in clinical trials

Background
The data from immuno-oncology (IO) therapy trials often show delayed effects, cure rate, crossing hazards, or some mixture of these phenomena. Thus, the proportional hazards (PH) assumption is often violated such that the commonly used log-rank test can be very underpowered. In these trials, the conventional hazard ratio for describing the treatment effect may not be a good estimand due to the lack of an easily understandable interpretation. To overcome this challenge, restricted mean survival time (RMST) has been strongly recommended for survival analysis in clinical literature due to its independence of the PH assumption as well as a more clinically meaningful interpretation. The RMST also aligns well with the estimand associated with the analysis from the recommendation in ICH E-9 (R1), and the test/estimation coherency. Currently, the Kaplan Meier (KM) curve is commonly applied to RMST related analyses. Due to some drawbacks of the KM approach such as the limitation in extrapolating to time points beyond the follow-up time, and the large variance at time points with small numbers of events, the RMST may be hindered.

Methods
The dynamic RMST curve using a mixture model is proposed in this paper to fully enhance the RMST method for survival analysis in clinical trials. It is constructed that the RMST difference or ratio is computed over a range of values to the restriction time t which traces out an evolving treatment effect profile over time.

Results
This new dynamic RMST curve overcomes the drawbacks from the KM approach. The good performance of this proposal is illustrated through three real examples.

Conclusions
The RMST provides a clinically meaningful and easily interpretable measure for survival clinical trials. The proposed dynamic RMST approach provides a useful tool for assessing treatment effect over different time frames for survival clinical trials. This dynamic RMST curve also allows ones for checking whether the follow-up time for a study is long enough to demonstrate a treatment difference. The prediction feature of the dynamic RMST analysis may be used for determining an appropriate time point for an interim analysis, and the data monitoring committee (DMC) can use this evaluation tool for study recommendation. . . .

https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-020-01098-5