Separation and the information theory surrogate evaluation approach: a penalized likelihood solution

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Abstract / Description of output

Background: Surrogate evaluation is an important topic in clinical trials research, the use of a surrogate in place of a primary endpoint of interest is a common occurrence but also a contentious issue that is much debated. Statistical techniques to assess potential surrogates are closely scrutinised by the research community given the complexities of such an assessment. The information theory surrogate evaluation approach is well-established, practical and theoretically sound. In the context of discrete outcomes, we investigated issues of bias due to inefficiency, overfitting and separation (sparse data) that have not been recognised or addressed previously.

Methods: The most serious cause of bias is separation in trial information. We outline the concerns surrounding this bias and conduct a simulation study to investigate whether a penalized likelihood technique provides an appropriate solution.

Results: We found that removing trials with separation from surrogacy evaluation resulted in a large amount of discarded data. Conversely, the penalized likelihood technique allows retention of all trial information and enables precise and reliable surrogate estimation.

Conclusions: The information theory approach is a critical tool for conducting surrogate evaluation. This work strengthens the practical application of the information theory approach, allowing analyses to be adapted or the results summarised with appropriate caution to mitigate the biases highlighted. This is especially true where separation occurs. The adoption of the penalized likelihood technique into information theory surrogate evaluation is a useful addition that solves an issue likely to arise frequently in the context of categorical endpoints.
Original languageEnglish
JournalPharmaceutical Statistics
Early online date2 Aug 2021
Publication statusE-pub ahead of print - 2 Aug 2021


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