arXiv subjects

Tim Mathes

Publications and source records attributed to Tim Mathes.

4 recordsLinked to original sources

arXiv2026

A beta-binomial model respecting randomization and its comparison to the standard beta-binomial model that ignores randomization for the meta-analysis of rare events

Background: One of the suggested models for meta-analysis with rare events is the beta-binomial model (BBM). The main advantage of this model compared to inverse-variance models, is that it uses information from zero cells without needing a continuity correction. A disadvantage of the standard BBM is that it ignores randomization. Here we introduce a BBM that respects randomization. Methods: The main idea to preserve randomization is using a common-beta BBM. We illustrate that randomization is preserved by conditioning on the total sum of counts in a studys four-fold table when estimating the model parameters. We perform a simulation study reflecting real-world meta-analyses to compare the models. In addition, we explore in which situations ignoring randomization could be problematic. Results: The BBM that respects randomization performs well in the simulation study that mirrors real meta-analyses in Cochrane and non-Cochrane reviews, respectively. Ignoring randomization appears to be problematic in situations with very different sample sizes of the studies included in the meta-analysis. However, the BBM ignoring randomization tended to perform better when heterogeneity was high. Conclusion: The results show that using the standard BBM, which ignores randomization is usually not biased when the randomization is balanced and the size of studies included in the meta-analysis is not very different. However, as possible ecological bias due to ignoring randomization is an inherent disadvantage of the model and the BBM that respects randomization shows very similar results in the simulation study, it may be generally preferred. Key words Beta-binomial model, generalized linear mixed models, meta-analyses, simulation study, rare events, zero events

stat.ME↗

arXiv2026

Identifying the potential of sample overlap in evidence synthesis of observational studies

Sample overlap is a common issue in evidence synthesis in the field of medical research, particularly when integrating findings from observational studies utilizing existing databases such as registries. Due to the general inaccessibility of unique identifiers for each observation, addressing sample overlap has been a complex problem, potentially biasing evidence synthesis outcomes and undermining their credibility. We developed a method to construct indicators for the degree of sample overlap in evidence synthesis of studies based on existing data. Our method is rooted in set theory and is based on the coding of the ranges of several well selected sample characteristics, offers a practical solution by focusing on making inference based on sample characteristics rather than on individual participant data. Useful information, such as the overlap-free sample set with the largest sample size in an evidence synthesis, can be derived from this method. We applied our model to several real-world evidence syntheses, demonstrating its effectiveness and flexibility. Our findings highlight the growing importance of addressing sample overlap in evidence synthesis, especially with the increasing relevance of secondary use of data, an area currently under-explored in research.

stat.ME↗

arXiv2024

A Review of EMA Public Assessment Reports where Non-Proportional Hazards were Identified

While well-established methods for time-to-event data are available when the proportional hazards assumption holds, there is no consensus on the best approach under non-proportional hazards. A wide range of parametric and non-parametric methods for testing and estimation in this scenario have been proposed. In this review we identified EMA marketing authorization procedures where non-proportional hazards were raised as a potential issue in the risk-benefit assessment and extract relevant information on trial design and results reported in the corresponding European Assessment Reports (EPARs) available in the database at paediatricdata.eu. We identified 16 Marketing authorization procedures, reporting results on a total of 18 trials. Most procedures covered the authorization of treatments from the oncology domain. For the majority of trials NPH issues were related to a suspected delayed treatment effect, or different treatment effects in known subgroups. Issues related to censoring, or treatment switching were also identified. For most of the trials the primary analysis was performed using conventional methods assuming proportional hazards, even if NPH was anticipated. Differential treatment effects were addressed using stratification and delayed treatment effect considered for sample size planning. Even though, not considered in the primary analysis, some procedures reported extensive sensitivity analyses and model diagnostics evaluating the proportional hazards assumption. For a few procedures methods addressing NPH (e.g.~weighted log-rank tests) were used in the primary analysis. We extracted estimates of the median survival, hazard ratios, and time of survival curve separation. In addition, we digitized the KM curves to reconstruct close to individual patient level data. Extracted outcomes served as the basis for a simulation study of methods for time to event analysis under NPH.

stat.AP↗

arXiv2023

Methods for non-proportional hazards in clinical trials: A systematic review

For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards (NPH) has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under NPH. Our literature search identified 907 abstracts, out of which we included 211 articles, mostly methodological ones. Review articles and applications were less frequently identified. The articles discuss effect measures, effect estimation and regression approaches, hypothesis tests, and sample size calculation approaches, which are often tailored to specific NPH situations. Using a unified notation, we provide an overview of methods available. Furthermore, we derive some guidance from the identified articles. We summarized the contents from the literature review in a concise way in the main text and provide more detailed explanations in the supplement.

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