Time-To-Event Analysis, Or Who Gets Better Sooner? an Emerging Concept in Headache Study Methodology

Cephalalgia ◽  
1999 ◽  
Vol 19 (6) ◽  
pp. 552-556 ◽  
Author(s):  
C Allen ◽  
K Jiang ◽  
W Malbecq ◽  
PJ Goadsby
Keyword(s):  
Survival Analysis ◽  
Treatment Effect ◽  
Event Analysis ◽  
Time To Event ◽  
Time Points ◽  
Assessment Period ◽  
Headache Relief ◽  
Interesting Approach ◽  
Study Methodology ◽  
The Difference

Survival analysis, or, more generally, time-to-event analysis, is of interest when the data represent the time to a defined event. While well established in oncology, it has not been widely applied to migraine research, possibly because the data are usually collected intermittently, rather than continuously, and because of the awkwardness of interpreting treatment effect in survival terms. However, it represents an interesting approach for the analysis of time-to-headache relief, which addresses the clinically relevant question of who gets better sooner. The analysis uses data from all time-points to define the likelihood of headache relief following treatment throughout the entire assessment period. These data can then be used to quantify and test the difference between two therapies.

Survival analysis

2020 ◽  
pp. 181-218
Author(s):  
Bendix Carstensen
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Cox Model ◽  
Survival Function ◽  
Event Analysis ◽  
Time To Event ◽  
Poisson Models ◽  
Kaplan Meier ◽  
Analysis Survival ◽  
Event Rates

This chapter describes survival analysis. Survival analysis concerns data where the outcome is a length of time, namely the time from inclusion in the study (such as diagnosis of some disease) till death or some other event — hence the term 'time to event analysis', which is also used. There are two primary targets normally addressed in survival analysis: survival probabilities and event rates. The chapter then looks at the life table estimator of survival function and the Kaplan–Meier estimator of survival. It also considers the Cox model and its relationship with Poisson models, as well as the Fine–Gray approach to competing risks.

scholarly journals Germination Data Analysis by Time-to-Event Approaches

Plants ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 617
Author(s):  
Alessandro Romano ◽  
Piergiorgio Stevanato
Keyword(s):  
Survival Analysis ◽  
Data Analysis ◽  
Sugar Beet ◽  
Biological Data ◽  
Event Analysis ◽  
Time To Event ◽  
Advantages And Disadvantages ◽  
Regression Techniques ◽  
Parametric Functions ◽  
Scientific Fields

Germination data are analyzed by several methods, which can be mainly classified as germination indexes and traditional regression techniques to fit non-linear parametric functions to the temporal sequence of cumulative germination. However, due to the nature of germination data, often different from other biological data, the abovementioned methods may present some limits, especially when ungerminated seeds are present at the end of an experiment. A class of methods that could allow addressing these issues is represented by the so-called “time-to-event analysis”, better known in other scientific fields as “survival analysis” or “reliability analysis”. There is relatively little literature about the application of these methods to germination data, and some reviews dealt only with parts of the possible approaches such as either non-parametric and semi-parametric or parametric ones. The present study aims to give a contribution to the knowledge about the reliability of these methods by assessing all the main approaches to the same germination data provided by sugar beet (Beta vulgaris L.) seeds cohorts. The results obtained confirmed that although the different approaches present advantages and disadvantages, they could generally represent a valuable tool to analyze germination data providing parameters whose usefulness depends on the purpose of the research.

scholarly journals Time-to-event comparative effectiveness of NOACs vs VKAs in newly diagnosed non-valvular atrial fibrillation patients.

2021 ◽  
Author(s):  
Blanca Gallego Luxan ◽  
Jie Zhu
Keyword(s):  
Atrial Fibrillation ◽  
Major Bleeding ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Ischaemic Event ◽  
Newly Diagnosed ◽  
Time To Event ◽  
Oral Anticoagulant Treatment ◽  
Average Treatment ◽  
The Difference

Objective: To investigate the difference in the time-to-event probabilities of ischaemic events, major bleeding and death of NOAC vs VKAs in newly diagnosed non-valvular atrial fibrillation patients. Design: Retrospective observational cohort study. Setting: UK's Clinical Practice Research Data linked to the Hospital Episode Statistics inpatient and outpatient data, mortality data and the Patient Level Index of Multiple Deprivation. Participants: Patients over 18 years of age, with an initial diagnosis of atrial fibrillation between 1st-Mar-2011 and 31-July-2017, without a record for a valve condition, prosthesis or procedure previous to initial diagnosis, and without a record of oral anticoagulant treatment in the previous year. Intervention: Oral anticoagulant treatment with either vitamin K antagonists (VKAs) or the newer target-specific oral anticoagulants (NOACs). Main Outcome Measures: Ischaemic event, major bleeding event and death from 15 days from initial prescription up to two years follow-up. Statistical Analysis: Treatment effect was defined as the difference in time-to-event probability between NOAC and VKA treatment groups. Treatment and outcomes were modelled using an ensemble of parametric and non-parametric models, and the average and conditional average treatment effects were estimated using one-step Targeted Maximum Likelihood Estimation (TMLE). Heterogeneity of treatment effect was examined using variable importance methods in Bayesian Additive Regression Trees (BART). Results: The average treatment effect of NOAC vs VKA was consistently close to zero across all times, with a temporal average of $0.00[95\%0.00,0.00]$ for ischaemic event, $0.00\%[95\%-0.01,0.01]$ for major bleeding and $0.00[95\%-0.01,0.01]$ for death. Only history of major bleeding was found to influence the distribution of treatment effect for major bleeding, but its impact on the associated conditional average treatment effect was not significant. Conclusions: This study found no statistically significant difference between NOAC and VKA users up to two years of medication use for the prevention of ischaemic events, major bleeding or death.

scholarly journals Dynamic RMST Curves for Survival Analysis in Clinical Trials

2020 ◽  
Author(s):  
Jason Liao ◽  
G. Frank Liu ◽  
Wen-Chi Wu
Keyword(s):  
Clinical Trials ◽  
Survival Analysis ◽  
Treatment Effect ◽  
Proportional Hazards ◽  
Data Monitoring Committee ◽  
Rank Test ◽  
Evaluation Tool ◽  
Delayed Effects ◽  
Time Points

Abstract 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 τ 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.

scholarly journals Dynamic RMST Curves for Survival Analysis in Clinical Trials

2020 ◽  
Author(s):  
Jason Liao ◽  
G. Frank Liu ◽  
Wen-Chi Wu
Keyword(s):  
Clinical Trials ◽  
Survival Analysis ◽  
Treatment Effect ◽  
Proportional Hazards ◽  
Survival Curve ◽  
Rank Test ◽  
Evaluation Tool ◽  
Delayed Effects ◽  
Time Points

Abstract Background The data from immuno-oncology therapy trials often show delayed effects, cure rate, crossing hazards, or some mixture of these phenomena. Thus, the common assumption of proportional hazards 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 proportional hazard 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 To fully enhance the RMST method, the survival curve using a mixture model is proposed in this paper to construct dynamic RMST curves to evaluate and monitor survival analysis in clinical trials.Results This new dynamic RMST curve overcomes the drawbacks from the KM approach. The good performance of this new proposal is illustrated through three real examples.Conclusions 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 can also be useful 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 also be used for determining an appropriate time point for an interim analysis , and an evaluation tool for study recommendation from DMC .

Basic Time-to-Event Analyses of Online Educational Data

2019 ◽  
pp. 311-333
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Survival Analysis ◽  
Empirical Data ◽  
Group Membership ◽  
Event Analysis ◽  
Time To Event ◽  
Multiple Datasets ◽  
Time Patterns ◽  
Data Portal

This chapter introduces the use of basic time-to-event analysis (a variation of “survival analysis”) to identify time-series patterns from learning management system (LMS) data portal datasets to enable empirical-based theorizing and interpretation. This approach addresses questions such as How long does it usually take before a particular event occurs? What time patterns may be seen in empirical data? What sorts of analysis and decision making can be understood from the time patterns? This chapter uses multiple datasets—related to assignment submittals and their time to grading, learner enrollments and the updates to those enrollments, and group membership and how long groups last, and other data—to demonstrate this process.

Comparison of Treatment Effects Measured by the Hazard Ratio and by the Ratio of Restricted Mean Survival Times in Oncology Randomized Controlled Trials

2016 ◽  
Vol 34 (15) ◽  
pp. 1813-1819 ◽  
Author(s):  
Ludovic Trinquart ◽  
Justine Jacot ◽  
Sarah C. Conner ◽  
Raphaël Porcher
Keyword(s):  
Overall Survival ◽  
Randomized Controlled Trials ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Controlled Trials ◽  
Survival Times ◽  
Time To Event ◽  
Average Ratio ◽  
Randomized Controlled ◽  
The Difference

Purpose We aimed to compare empirically the treatment effects measured by the hazard ratio (HR) and by the difference (and ratio) of restricted mean survival times (RMST) in oncology randomized trials. Methods We selected oncology randomized controlled trials from five leading journals during the last 6 months of 2014. We reconstructed individual patient data for one time-to-event outcome from each trial, preferably the primary outcome. We reanalyzed each trial and compared the treatment effect estimated by the HR with that by the difference (and ratio) of RMST. We estimated an average ratio of the HR to the ratio of RMST; an average ratio less than one indicates more optimistic assessments with HRs. Results We analyzed 54 randomized controlled trials totaling 33,212 patients. The selected outcome was overall survival in 21 (39%) trials. There was evidence of nonproportionality of hazards in 13 (24%) trials. The HR and RMST-based measures were in agreement regarding the statistical significance of the effect, except in one case. The median HR was 0.84 (Q1 to Q3 range, 0.67 to 0.97) and the median difference in RMST was 1.12 months (range, 0.22 to 2.75 months). The average ratio of the HR to the ratio of RMST was 1.11 (95% CI, 1.07 to 1.15), with substantial between-trial variability (I2 = 86%). Results were consistent by outcome type (overall survival v other outcomes) and whether the proportional hazard assumption held or not. Conclusion On average, the HR provided significantly larger treatment effect estimates than the ratio of RMST. The HR may seem large when the absolute effect is small. RMST-based measures should be routinely reported in randomized trials with time-to-event outcomes.

scholarly journals Dynamic RMST Curves for Survival Analysis in Clinical Trials

2020 ◽  
Author(s):  
Jason Liao ◽  
G. Frank Liu ◽  
Wen-Chi Wu
Keyword(s):  
Clinical Trials ◽  
Survival Analysis ◽  
Treatment Effect ◽  
Proportional Hazards ◽  
Data Monitoring Committee ◽  
Rank Test ◽  
Evaluation Tool ◽  
Delayed Effects ◽  
Time Points

Abstract 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 τ 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 an evaluation tool for study recommendation from data monitoring committee (DMC).

Quantifying abortion rates of reproductive organs and effects of contributing factors using time-to-event analysis

2011 ◽  
Vol 38 (5) ◽  
pp. 431 ◽  
Author(s):  
A. M. Wubs ◽  
E. Heuvelink ◽  
L. F. M. Marcelis ◽  
L. Hemerik
Keyword(s):  
Survival Analysis ◽  
Reproductive Organs ◽  
Abortion Rate ◽  
Planting Density ◽  
Sink Strength ◽  
Event Analysis ◽  
Time To Event ◽  
Contributing Factors ◽  
Advantages And Disadvantages ◽  
Source And Sink

Time-to-event analysis, or survival analysis, is a method to analyse the timing of events and to quantify the effects of contributing factors. We apply this method to data on the timing of abortion of reproductive organs. This abortion often depends on source and sink strength. We hypothesise that the effect of source and sink strength on abortion rate can be quantified with a statistical model, obtained via survival analysis. Flower and fruit abortion in Capsicum annuum L., observed in temperature and planting density experiments, were analysed. Increasing the source strength as well as decreasing the sink strength decreased the abortion rate. The effect was non-linear, e.g. source strengths above 6 g CH2O per plant per d did not decrease abortion rates further. The maximum abortion rate occurred around 100 degree-days after anthesis. Analyses in which sink strength was replaced with the number of fruits in a specified age category had an equal or better fit to the data. We discuss the advantages and disadvantages of using survival analyses for this kind of data. The technique can also be used for other crops showing reproductive organ abortion (e.g. soybean (Glycine max L.), cucumber (Cucumis sativus L.)), but also on other event types like bud break or germination.

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