Using median survival in meta-analysis of experimental time-to-event data

Abstract Background Time-to-event data is frequently reported in both clinical and preclinical research spheres. Systematic review and meta-analysis is a tool that can help to identify pitfalls in preclinical research conduct and reporting that can help to improve translational efficacy. However, pooling of studies using hazard ratios (HRs) is cumbersome especially in preclinical meta-analyses including large numbers of small studies. Median survival is a much simpler metric although because of some limitations, which may not apply to preclinical data, it is generally not used in survival meta-analysis. We aimed to appraise its performance when compared with hazard ratio-based meta-analysis when pooling large numbers of small, imprecise studies. Methods We simulated a survival dataset with features representative of a typical preclinical survival meta-analysis, including with influence of a treatment and a number of covariates. We calculated individual patient data-based hazard ratios and median survival ratios (MSRs), comparing the summary statistics directly and their performance at random-effects meta-analysis. Finally, we compared their sensitivity to detect associations between treatment and influential covariates at meta-regression. Results There was an imperfect correlation between MSR and HR, although the opposing direction of treatment effects between summary statistics appeared not to be a major issue. Precision was more conservative for HR than MSR, meaning that estimates of heterogeneity were lower. There was a slight sensitivity advantage for MSR at meta-analysis and meta-regression, although power was low in all circumstances. Conclusions We believe we have validated MSR as a summary statistic for use in a meta-analysis of small, imprecise experimental survival studies—helping to increase confidence and efficiency in future reviews in this area. While assessment of study precision and therefore weighting is less reliable, MSR appears to perform favourably during meta-analysis. Sensitivity of meta-regression was low for this set of parameters, so pooling of treatments to increase sample size may be required to ensure confidence in preclinical survival meta-regressions.

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Using Median Survival in Meta-analysis of Experimental Time-to-event Data

10.21203/rs.3.rs-172822/v1 ◽

2021 ◽

Author(s):

Theodore C Hirst ◽

Emily S. Sena ◽

Malcolm R. Macleod

Keyword(s):

Median Survival ◽

Meta Analysis ◽

Summary Statistics ◽

Event Data ◽

Time To Event ◽

Preclinical Research ◽

Time To Event Data ◽

Hazard Ratios ◽

Large Numbers ◽

Meta Regression

Abstract Background: Time-to-event data is frequently reported in both clinical and preclinical research spheres. Systematic review and meta-analysis is a tool that can help to identify pitfalls in preclinical research conduct and reporting that can help to improve translational efficacy. However, pooling of studies using hazard ratios (HR) is cumbersome especially in preclinical meta-analyses including large numbers of small studies. Median survival is a much simpler metric although because of some limitations, which may not apply to preclinical data, it is generally not used in survival meta-analysis. We aimed to appraise its performance when compared with hazard ratio-based meta-analysis when pooling large numbers of small, imprecise studies.Methods: We simulated a survival dataset with features representative of a typical preclinical survival meta-analysis, including with influence of a treatment and a number of covariates. We calculated individual patient data-based hazard ratios and median survival ratios (MSR), comparing the summary statistics directly and their performance at Random-effects meta-analysis. Finally, we compared their sensitivity to detect associations between treatment and influential covariates at meta-regression.Results: There was in imperfect correlation between MSR and HR, although opposing direction of treatment effects between summary statistics appeared not to be a major issue. Precision was more conservative for HR than MSR, meaning that estimates of heterogeneity were lower. There was a slight sensitivity advantage for MSR at meta-analysis and meta-regression, although power was low in all circumstances.Conclusions: MSR appears to be a valid summary statistic for use in meta-analysis of small, imprecise experimental studies. It is computationally more straightforward and quicker to approximate than HR. While assessment of study precision and therefore weighting is less reliable, MSR appears to perform favourably during meta-analysis. Sensitivity of meta-regression was low for this set of parameters, so pooling of treatments to increase sample size may be required to ensure confidence in preclinical survival meta-regressions.

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A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier

PLoS ONE ◽

10.1371/journal.pone.0259121 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0259121

Author(s):

Matthieu Faron ◽

Pierre Blanchard ◽

Laureen Ribassin-Majed ◽

Jean-Pierre Pignon ◽

Stefan Michiels ◽

...

Keyword(s):

Meta Analysis ◽

Event Data ◽

Time To Event ◽

Time To Event Data ◽

Step Model ◽

Node Network ◽

Treatment Interaction ◽

One Step ◽

Meta Regression ◽

Better Than

Introduction Individual patient data (IPD) present particular advantages in network meta-analysis (NMA) because interactions may lead an aggregated data (AD)-based model to wrong a treatment effect (TE) estimation. However, fewer works have been conducted for IPD with time-to-event contrary to binary outcomes. We aimed to develop a general frequentist one-step model for evaluating TE in the presence of interaction in a three-node NMA for time-to-event data. Methods One-step, frequentist, IPD-based Cox and Poisson generalized linear mixed models were proposed. We simulated a three-node network with or without a closed loop with (1) no interaction, (2) covariate-treatment interaction, and (3) covariate distribution heterogeneity and covariate-treatment interaction. These models were applied to the NMA (Meta-analyses of Chemotherapy in Head and Neck Cancer [MACH-NC] and Radiotherapy in Carcinomas of Head and Neck [MARCH]), which compared the addition of chemotherapy or modified radiotherapy (mRT) to loco-regional treatment with two direct comparisons. AD-based (contrast and meta-regression) models were used as reference. Results In the simulated study, no IPD models failed to converge. IPD-based models performed well in all scenarios and configurations with small bias. There were few variations across different scenarios. In contrast, AD-based models performed well when there were no interactions, but demonstrated some bias when interaction existed and a larger one when the modifier was not distributed evenly. While meta-regression performed better than contrast-based only, it demonstrated a large variability in estimated TE. In the real data example, Cox and Poisson IPD-based models gave similar estimations of the model parameters. Interaction decomposition permitted by IPD explained the ecological bias observed in the meta-regression. Conclusion The proposed general one-step frequentist Cox and Poisson models had small bias in the evaluation of a three-node network with interactions. They performed as well or better than AD-based models and should also be undertaken whenever possible.

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Prognostic review and time-to-event data meta-analysis of low skeletal muscle mass in patients with peripheral arterial disease of the lower limbs undergoing revascularization

International Angiology ◽

10.23736/s0392-9590.19.04248-2 ◽

2020 ◽

Vol 39 (1) ◽

Author(s):

Madeleine Sharpe ◽

Emeka Okoye ◽

George A. Antoniou

Keyword(s):

Skeletal Muscle ◽

Peripheral Arterial Disease ◽

Skeletal Muscle Mass ◽

Meta Analysis ◽

Arterial Disease ◽

Lower Limbs ◽

Event Data ◽

Time To Event ◽

Time To Event Data ◽

Peripheral Arterial

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Mitigation of biases in estimating hazard ratios under non-sensitive and non-specific observation of outcomes–applications to influenza vaccine effectiveness

Emerging Themes in Epidemiology ◽

10.1186/s12982-020-00091-z ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Ulrike Baum ◽

Sangita Kulathinal ◽

Kari Auranen

Keyword(s):

False Positive ◽

False Positive Rate ◽

Vaccine Effectiveness ◽

Partial Likelihood ◽

Event Data ◽

Time To Event ◽

Positive Events ◽

Time To Event Data ◽

Hazard Ratios ◽

Positive Rate

Abstract Background Non-sensitive and non-specific observation of outcomes in time-to-event data affects event counts as well as the risk sets, thus, biasing the estimation of hazard ratios. We investigate how imperfect observation of incident events affects the estimation of vaccine effectiveness based on hazard ratios. Methods Imperfect time-to-event data contain two classes of events: a portion of the true events of interest; and false-positive events mistakenly recorded as events of interest. We develop an estimation method utilising a weighted partial likelihood and probabilistic deletion of false-positive events and assuming the sensitivity and the false-positive rate are known. The performance of the method is evaluated using simulated and Finnish register data. Results The novel method enables unbiased semiparametric estimation of hazard ratios from imperfect time-to-event data. False-positive rates that are small can be approximated to be zero without inducing bias. The method is robust to misspecification of the sensitivity as long as the ratio of the sensitivity in the vaccinated and the unvaccinated is specified correctly and the cumulative risk of the true event is small. Conclusions The weighted partial likelihood can be used to adjust for outcome measurement errors in the estimation of hazard ratios and effectiveness but requires specifying the sensitivity and the false-positive rate. In absence of exact information about these parameters, the method works as a tool for assessing the potential magnitude of bias given a range of likely parameter values.

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ODACH: A One-shot Distributed Algorithm for Cox model with Heterogeneous Multi-center Data

10.1101/2021.04.18.21255694 ◽

2021 ◽

Author(s):

Chongliang Luo ◽

Rui Duan ◽

Yong Chen

Keyword(s):

Likelihood Function ◽

Cox Model ◽

Meta Analysis ◽

Event Data ◽

Time To Event ◽

Hazard Functions ◽

Patient Level ◽

Time To Event Data ◽

Level Data ◽

Center Time

Objective: We developed and evaluated a privacy-preserving One-shot Distributed Algorithm for Cox model to analyze multi-center time-to-event data without sharing patient-level information across sites, while accounting for heterogeneity across sites by allowing site-specific baseline hazard functions and feature distributions. Materials and Methods: We constructed a surrogate likelihood function to approximate the Cox log partial likelihood function which is stratified by site, using patient-level data from a single site and aggregated information from other sites. The ODAC estimator was obtained by maximizing the surrogate likelihood function. We evaluated and compare the performance of ODACH with meta-analysis by extensive numerical studies. Results: The simulation study showed that ODACH provided estimates close to the pooled estimator, which is obtained by directly analyzing patient-level data from all sites via a stratified Cox model. The relative bias was <1% across all scenarios. As a comparison, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 12% when the event rate is 1%. Conclusions: ODACH is a privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data, which allows the baseline hazard functions as well as the distribution of covariate variables to vary across sites. It provides estimates that is close to the pooled estimator and substantially outperforms the meta-analysis estimator when the event is rare. It is thus extremely suitable for studying rare events with heterogeneous baseline hazards across sites in a distributed manner.

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