Alternatives to the hazard ratio in summarizing efficacy in time-to-event studies: an example from influenza trials

2002 ◽  
Vol 21 (23) ◽  
pp. 3687-3700 ◽  
Author(s):  
Oliver N. Keene
Keyword(s):  
Hazard Ratio ◽  
Event Studies ◽  
Time To Event

The Average Hazard Ratio – A Good Effect Measure for Time-to-event Endpoints when the Proportional Hazard Assumption is Violated?

2018 ◽  
Vol 57 (03) ◽  
pp. 089-100 ◽  
Author(s):  
Werner Brannath ◽  
Matthias Brückner ◽  
Meinhard Kieser ◽  
Geraldine Rauch
Keyword(s):  
Proportional Hazards ◽  
Weighting Function ◽  
Hazard Ratio ◽  
Group Differences ◽  
Proportional Hazard ◽  
Time To Event ◽  
Logrank Test ◽  
Proportional Hazard Assumption ◽  
Effect Measure ◽  
Average Hazard Ratio

Summary Background: In many clinical trial applications, the endpoint of interest corresponds to a time-to-event endpoint. In this case, group differences are usually expressed by the hazard ratio. Group differences are commonly assessed by the logrank test, which is optimal under the proportional hazard assumption. However, there are many situations in which this assumption is violated. Especially in applications were a full population and several subgroups or a composite time-to-first-event endpoint and several components are considered, the proportional hazard assumption usually does not simultaneously hold true for all test problems under investigation. As an alternative effect measure, Kalbfleisch and Prentice proposed the so-called ‘average hazard ratio’. The average hazard ratio is based on a flexible weighting function to modify the influence of time and has a meaningful interpretation even in the case of non-proportional hazards. Despite this favorable property, it is hardly ever used in practice, whereas the standard hazard ratio is commonly reported in clinical trials regardless of whether the proportional hazard assumption holds true or not. Objectives: There exist two main approaches to construct corresponding estimators and tests for the average hazard ratio where the first relies on weighted Cox regression and the second on a simple plug-in estimator. The aim of this work is to give a systematic comparison of these two approaches and the standard logrank test for different time-toevent settings with proportional and nonproportional hazards and to illustrate the pros and cons in application. Methods: We conduct a systematic comparative study based on Monte-Carlo simulations and by a real clinical trial example. Results: Our results suggest that the properties of the average hazard ratio depend on the underlying weighting function. The two approaches to construct estimators and related tests show very similar performance for adequately chosen weights. In general, the average hazard ratio defines a more valid effect measure than the standard hazard ratio under non-proportional hazards and the corresponding tests provide a power advantage over the common logrank test. Conclusions: As non-proportional hazards are often met in clinical practice and the average hazard ratio tests often outperform the common logrank test, this approach should be used more routinely in applications.

scholarly journals Early Cessation of Breastfeeding and Determinants: Time to Event Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Ebrahim Babaee ◽  
Babak Eshrati ◽  
Mehran Asadi-Aliabadi ◽  
Majid Purabdollah ◽  
Marzieh Nojomi
Keyword(s):  
Hazard Ratio ◽  
Breastfeeding Duration ◽  
Event Analysis ◽  
Time To Event ◽  
Starting Time ◽  
Factors Influencing ◽  
The Status ◽  
The Mean ◽  
Breastfeeding Rates ◽  
Analyze Data

Background. The onset of breastfeeding has a high success rate in most countries, but the time for termination of breastfeeding varies between countries. Objective. This survey was aimed to determine the effective factors on the early termination of breastfeeding. Methods. This study was conducted in 2018, in Iran. About 410 mothers were enrolled in the study. All considered factors were evaluated as factors influencing the continuity of breastfeeding. Survival analysis was used to analyze data. Results. The mean age of the mothers was equal to 29.48 ± 5.8 years. The frequency of termination of breastfeeding before the first 2 years was equal to 34%. The mean of breastfeeding duration was equal to 21.49 ± 5.3 months. The percentage of infants who had been breastfed for 24 months was equal to 65.8%. An infant’s birth weight (2500–4000 gr) (hazard ratio: 0.54), neonatal birth order (hazard ratio: 0.69), neonatal pathologic jaundice (hazard ratio: 1.52), starting time of using complementary food (hazard ratio: 2.45), using pacifier (hazard ratio: 2.82), and the status of using artificial milk (hazard ratio: 3.29) were significantly associated with cessation of breastfeeding before 24 months of age. The probability of termination of breastfeeding at 6, 12, 18, and 24 months of age was reported by 6%, 8%, 15%, and 34%, respectively. Conclusions. There were notifiable variations in breastfeeding rates both in national and international levels. Nevertheless, in this study, the mean of breastfeeding duration was longer compared to a number of countries and previous national studies.

scholarly journals Decline in Literacy and Incident AD Dementia Among Community-Dwelling Older Persons

2017 ◽  
Vol 30 (9) ◽  
pp. 1389-1405 ◽  
Author(s):  
Lei Yu ◽  
Robert S. Wilson ◽  
S. Duke Han ◽  
Sue Leurgans ◽  
David A. Bennett ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Older Persons ◽  
Hazard Ratio ◽  
Community Dwelling ◽  
Cognitive Outcomes ◽  
Longitudinal Change ◽  
Time To Event ◽  
Time To Event Data ◽  
Clinical Diagnoses

Objective: To quantify longitudinal change in financial and health literacy and examine the associations of declining literacy with incident Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI). Method: Data came from 799 participants of an ongoing cohort study. Literacy was measured using a battery of 32 questions. Clinical diagnoses were made annually following uniform structured procedures. The associations of declining literacy with incident AD dementia and MCI were tested using a joint model for longitudinal and time-to-event data. Results: We observed an overall decline in total literacy score over up to 6 years of follow-up ( p < .001). Faster decline in literacy was associated with higher risks for incident AD dementia (hazard ratio = 4.526, 95% confidence interval = [2.993, 6.843], p < .001) and incident MCI (hazard ratio = 2.971, 95% confidence interval = [1.509, 5.849], p = .002). Discussion: Declining literacy among community-dwelling older persons predicts adverse cognitive outcomes and serves as an early indicator of impending dementia.

Pitfalls inherent in retrospective time-to-event studies: The example of time to pregnancy

1993 ◽  
Vol 12 (9) ◽  
pp. 867-879 ◽  
Author(s):  
Clarice R. Weinberg ◽  
Donna Day Baird ◽  
Andrew S. Rowland
Keyword(s):  
Event Studies ◽  
Time To Event ◽  
Time To Pregnancy

TWISTER PLOTS FOR TIME-TO-EVENT STUDIES

2021 ◽  
Author(s):  
Paul N Zivich ◽  
Stephen R Cole ◽  
Alexander Breskin
Keyword(s):  
Event Studies ◽  
Time To Event

scholarly journals Design of non-inferiority randomized trials using the difference in restricted mean survival times

2018 ◽  
Vol 15 (5) ◽  
pp. 499-508 ◽  
Author(s):  
Isabelle R Weir ◽  
Ludovic Trinquart
Keyword(s):  
Sample Size ◽  
Survival Time ◽  
Trial Design ◽  
Time Horizon ◽  
Hazard Ratio ◽  
Sample Sizes ◽  
Time To Event ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time ◽  
The Difference

Background/aims Non-inferiority trials with time-to-event outcomes are becoming increasingly common. Designing non-inferiority trials is challenging, in particular, they require very large sample sizes. We hypothesized that the difference in restricted mean survival time, an alternative to the hazard ratio, could lead to smaller required sample sizes. Methods We show how to convert a margin for the hazard ratio into a margin for the difference in restricted mean survival time and how to calculate the required sample size under a Weibull survival distribution. We systematically selected non-inferiority trials published between 2013 and 2016 in seven major journals. Based on the protocol and article of each trial, we determined the clinically relevant time horizon of interest. We reconstructed individual patient data for the primary outcome and fit a Weibull distribution to the comparator arm. We converted the margin for the hazard ratio into the margin for the difference in restricted mean survival time. We tested for non-inferiority using the difference in restricted mean survival time and hazard ratio. We determined the required sample size based on both measures, using the type I error risk and power from the original trial design. Results We included 35 trials. We found evidence of non-proportional hazards in five (14%) trials. The hazard ratio and the difference in restricted mean survival time were consistent regarding non-inferiority testing, except in one trial where the difference in restricted mean survival time led to evidence of non-inferiority while the hazard ratio did not. The median hazard ratio margin was 1.43 (Q1–Q3, 1.29–1.75). The median of the corresponding margins for the difference in restricted mean survival time was −21 days (Q1–Q3, −36 to −8) for a median time horizon of 2.0 years (Q1–Q3, 1–3 years). The required sample size according to the difference in restricted mean survival time was smaller in 71% of trials, with a median relative decrease of 8.5% (Q1–Q3, 0.4%–38.0%). Across all 35 trials, about 25,000 participants would have been spared from enrollment using the difference in restricted mean survival time compared to hazard ratio for trial design. Conclusion The margins for the hazard ratio may seem large but translate to relatively small differences in restricted mean survival time. The difference in restricted mean survival time offers meaningful interpretation and can result in considerable reductions in sample size. Restricted mean survival time-based measures should be considered more widely in the design and analysis of non-inferiority trials with time-to-event outcomes.

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