Survival Analysis In Clinical Trials Research Articles

Dynamic RMST curves for survival analysis in clinical trials

BackgroundThe 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.MethodsThe 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 τ which traces out an evolving treatment effect profile over time.ResultsThis new dynamic RMST curve overcomes the drawbacks from the KM approach. The good performance of this proposal is illustrated through three real examples.ConclusionsThe 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.

Bayesian survival analysis in clinical trials: What methods are used in practice?

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.

Progression-free survival, patient-reported outcomes and the Holy Grail.

In oncology clinical trials, overall survival (OS) remains the gold standard for clinical beneft. However, because there are so many available treatment options for most types of cancer, survival analysis in clinical trials is often confounded by subsequent therapies. Progression-free survival (PFS) is an endpoint not so confounded, and one that requires fewer patients and less time to arrive at a conclusion about a new therapy. As a result, an ever-increasing number of oncology clinical trials are launched in which PFS is used as the primary endpoint. Several years ago, Pazdur emphasized that the time-to-progression endpoint must use the same evaluation techniques and schedules for all treatment arms and he recommended blinding of trials or at minimum, the use of an external blinded radiographic review committee. He also noted that improvement in disease-related symptoms qualifes as clinical beneft and may therefore be an appropriate endpoint for drug approval.

Survival analysis in clinical trials: Basics and must know areas

Many clinical trials involve following patients for a long time. The primary event of interest in those studies is death, relapse, adverse drug reaction or development of a new disease. The follow-up time for the study may range from few weeks to many years. A different set of statistical procedures are employed to analyze the data, which involves time to event an analysis. It is a very useful tool in clinical research and provides invaluable information about an intervention. This article introduces the researcher to the different tools of survival analysis.

Survival analysis in clinical trials: Old tools or new techniques

Almost 40 years have passed since the publication by D.R. Cox in 1972 of the most widely used method of life table analysis [1]. The same year R. Peto and J. Peto published the logrank test statistic [2]. These approaches are now recognized as the two major cornerstones of survival data analyses. In the meantime, numerous statisticians have developed or updated tools for survival analysis. In the journal, Maetani and Gamel [3] question the importance and the applicability of these classical approaches and wonder just how useful some new techniques can be in clinical research. In particular, are they still relevant when a fraction of the population is cured, which is hopefully more and more frequent with the advance of surgery and systemic treatments. We surely do not have the definitive answer. In this communication, we give a brief overview of the strengths and limitations of the ‘classical’ tools. We argue that the logrank test and the Cox model provide reliable results even when in the presence of a non-negligible cure rate. However, they depend on the proportional hazard assumption. If this assumption is not verified, we will comment on how the cure models reported by Maetani and Gamel [3] can help in analysing survival data.

Survival analysis in clinical trials: past developments and future directions.

The field of survival analysis emerged in the 20th century and experienced tremendous growth during the latter half of the century. The developments in this field that have had the most profound impact on clinical trials are the Kaplan-Meier (1958, Journal of the American Statistical Association 53, 457-481) method for estimating the survival function, the log-rank statistic (Mantel, 1966, Cancer Chemotherapy Report 50, 163-170) for comparing two survival distributions, and the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model for quantifying the effects of covariates on the survival time. The counting-process martingale theory pioneered by Aalen (1975, Statistical inference for a family of counting processes, Ph.D. dissertation, University of California, Berkeley) provides a unified framework for studying the small- and large-sample properties of survival analysis statistics. Significant progress has been achieved and further developments are expected in many other areas, including the accelerated failure time model, multivariate failure time data, interval-censored data, dependent censoring, dynamic treatment regimes and causal inference, joint modeling of failure time and longitudinal data, and Baysian methods.

A simultaneous inference procedure for clinical trials

The problem of multiple treatment comparisons in multiple, possibly overlapping subgroups is considered for survival analysis in clinical trials. The joint asymptotic distribution of the log rank statistics from different subgroups for pairwise comparisons of the treatments is derived. A method of using simulation to find the distribution of the statistics is proposed. The method is applied to data from a clinical trial in the treatment of breast cancer.