A guide on meta-analysis of time-to-event outcomes using aggregate data in vascular and endovascular surgery

Abstract Background The 2018 World Health Organization HIV guidelines were based on the results of a network meta-analysis (NMA) of published trials. This study employed individual patient-level data (IPD) and aggregate data (AgD) and meta-regression methods to assess the evidence supporting the WHO recommendations and whether they needed any refinements. Methods Access to IPD from three trials was granted through ClinicalStudyDataRequest.com (CSDR). Seven modelling approaches were applied and compared: 1) Unadjusted AgD network meta-analysis (NMA) – the original analysis; 2) AgD-NMA with meta-regression; 3) Two-stage IPD-AgD NMA; 4) Unadjusted one-stage IPD-AgD NMA; 5) One-stage IPD-AgD NMA with meta-regression (one-stage approach); 6) Two-stage IPD-AgD NMA with empirical-priors (empirical-priors approach); 7) Hierarchical meta-regression IPD-AgD NMA (HMR approach). The first two were the models used previously. Models were compared with respect to effect estimates, changes in the effect estimates, coefficient estimates, DIC and model fit, rankings and between-study heterogeneity. Results IPD were available for 2160 patients, representing 6.5% of the evidence base and 3 of 24 edges. The aspect of the model affected by the choice of modeling appeared to differ across outcomes. HMR consistently generated larger intervals, often with credible intervals (CrI) containing the null value. Discontinuations due to adverse events and viral suppression at 96 weeks were the only two outcomes for which the unadjusted AgD NMA would not be selected. For the first, the selected model shifted the principal comparison of interest from an odds ratio of 0.28 (95% CrI: 10.17, 0.44) to 0.37 (95% CrI: 0.23, 0.58). Throughout all outcomes, the regression estimates differed substantially between AgD and IPD methods, with the latter being more often larger in magnitude and statistically significant. Conclusions Overall, the use of IPD often impacted the coefficient estimates, but not sufficiently as to necessitate altering the final recommendations of the 2018 WHO Guidelines. Future work should examine the features of a network where adjustments will have an impact, such as how much IPD is required in a given size of network.

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Using aggregate data to estimate the standard error of a treatment-covariate interaction in an individual patient data meta-analysis

Biometrical Journal ◽

10.1002/bimj.201100167 ◽

2012 ◽

Vol 54 (3) ◽

pp. 370-384 ◽

Cited By ~ 3

Author(s):

Stephanie A. Kovalchik ◽

William G. Cumberland

Keyword(s):

Standard Error ◽

Individual Patient Data ◽

Meta Analysis ◽

Patient Data ◽

Aggregate Data

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Incorporating single-arm studies in meta-analysis of randomised controlled trials: a simulation study

BMC Medical Research Methodology ◽

10.1186/s12874-021-01301-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Janharpreet Singh ◽

Keith R. Abrams ◽

Sylwia Bujkiewicz

Keyword(s):

Simulation Study ◽

Randomised Controlled Trials ◽

Meta Analysis ◽

Health Technology ◽

Small Error ◽

Aggregate Data ◽

Controlled Trials ◽

Real World Data ◽

Study Heterogeneity ◽

Randomised Controlled

Abstract Background Use of real world data (RWD) from non-randomised studies (e.g. single-arm studies) is increasingly being explored to overcome issues associated with data from randomised controlled trials (RCTs). We aimed to compare methods for pairwise meta-analysis of RCTs and single-arm studies using aggregate data, via a simulation study and application to an illustrative example. Methods We considered contrast-based methods proposed by Begg & Pilote (1991) and arm-based methods by Zhang et al (2019). We performed a simulation study with scenarios varying (i) the proportion of RCTs and single-arm studies in the synthesis (ii) the magnitude of bias, and (iii) between-study heterogeneity. We also applied methods to data from a published health technology assessment (HTA), including three RCTs and 11 single-arm studies. Results Our simulation study showed that the hierarchical power and commensurate prior methods by Zhang et al provided a consistent reduction in uncertainty, whilst maintaining over-coverage and small error in scenarios where there was limited RCT data, bias and differences in between-study heterogeneity between the two sets of data. The contrast-based methods provided a reduction in uncertainty, but performed worse in terms of coverage and error, unless there was no marked difference in heterogeneity between the two sets of data. Conclusions The hierarchical power and commensurate prior methods provide the most robust approach to synthesising aggregate data from RCTs and single-arm studies, balancing the need to account for bias and differences in between-study heterogeneity, whilst reducing uncertainty in estimates. This work was restricted to considering a pairwise meta-analysis using aggregate data.

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Review of Methods for Combining Individual and Aggregate Data in a Meta-Analysis

Value in Health ◽

10.1016/j.jval.2016.09.091 ◽

2016 ◽

Vol 19 (7) ◽

pp. A362

Author(s):

R Kapso Kapnang ◽

K Thokagevistk ◽

A Vataire ◽

S Aballéa

Keyword(s):

Meta Analysis ◽

Aggregate Data

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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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