On the relevance of prognostic information for clinical trials: A theoretical quantification

Siegfried, Sandra; Senn, Stephen; Hothorn, Torsten

doi:10.1002/bimj.202100349

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…However, it is clear that a particularly unfavorable scenario would be one, in which a linear model with a small number of covariates known from the literature is such a good model that attempts to create a "super-covariate" from moderately sized datasets cannot realistically improve-or may worsen-the efficiency of the trial analysis. With these uncertainties in mind, it has been suggested 52 to size trials ignoring that a "super-covariate" is being used and to consider any gain in power a bonus, or to check the performance of "supercovariates" with an interim analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Beyond that, more experience is needed to understand to what extent the effect on the standard error of the treatment effect estimate for an unseen study can be foreseen, as well as under which circumstances we expect the largest gain from the use of a “super‐covariate.” However, it is clear that a particularly unfavorable scenario would be one, in which a linear model with a small number of covariates known from the literature is such a good model that attempts to create a “super‐covariate” from moderately sized datasets cannot realistically improve—or may worsen—the efficiency of the trial analysis. With these uncertainties in mind, it has been suggested 52 to size trials ignoring that a “super‐covariate” is being used and to consider any gain in power a bonus, or to check the performance of “super‐covariates” with an interim analysis.…”

Section: Discussionmentioning

confidence: 99%

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“Super‐covariates”: Using predicted control group outcome as a covariate in randomized clinical trials

Holzhauer

Adewuyi

2023

Pharmaceutical Statistics

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The power of randomized controlled clinical trials to demonstrate the efficacy of a drug compared with a control group depends not just on how efficacious the drug is, but also on the variation in patients' outcomes. Adjusting for prognostic covariates during trial analysis can reduce this variation. For this reason, the primary statistical analysis of a clinical trial is often based on regression models that besides terms for treatment and some further terms (e.g., stratification factors used in the randomization scheme of the trial) also includes a baseline (pre‐treatment) assessment of the primary outcome. We suggest to include a “super‐covariate”—that is, a patient‐specific prediction of the control group outcome—as a further covariate (but not as an offset). We train a prognostic model or ensembles of such models on the individual patient (or aggregate) data of other studies in similar patients, but not the new trial under analysis. This has the potential to use historical data to increase the power of clinical trials and avoids the concern of type I error inflation with Bayesian approaches, but in contrast to them has a greater benefit for larger sample sizes. It is important for prognostic models behind “super‐covariates” to generalize well across different patient populations in order to similarly reduce unexplained variability whether the trial(s) to develop the model are identical to the new trial or not. In an example in neovascular age‐related macular degeneration we saw efficiency gains from the use of a “super‐covariate”.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“Super‐covariates”: Using predicted control group outcome as a covariate in randomized clinical trials

Holzhauer

Adewuyi

2023

Pharmaceutical Statistics

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On the relevance of prognostic information for clinical trials: A theoretical quantification

2022

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Digital twins and Bayesian dynamic borrowing: Two recent approaches for incorporating historical control data

Burman,

Hermansson,

Bock

et al. 2024

Pharmaceutical Statistics

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Recent years have seen an increasing interest in incorporating external control data for designing and evaluating randomized clinical trials (RCT). This may decrease costs and shorten inclusion times by reducing sample sizes. For small populations, with limited recruitment, this can be especially important. Bayesian dynamic borrowing (BDB) has been a popular choice as it claims to protect against potential prior data conflict. Digital twins (DT) has recently been proposed as another method to utilize historical data. DT, also known as PROCOVA™, is based on constructing a prognostic score from historical control data, typically using machine learning. This score is included in a pre‐specified ANCOVA as the primary analysis of the RCT. The promise of this idea is power increase while guaranteeing strong type 1 error control. In this paper, we apply analytic derivations and simulations to analyze and discuss examples of these two approaches. We conclude that BDB and DT, although similar in scope, have fundamental differences which need be considered in the specific application. The inflation of the type 1 error is a serious issue for BDB, while more evidence is needed of a tangible value of DT for real RCTs.

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On the relevance of prognostic information for clinical trials: A theoretical quantification

Abstract: This article has earned an open data badge "Reproducible Research"for making publicly available the code necessary to reproduce the reported results. The results reported in this article could fully be reproduced.

Cited by 4 publications

References 46 publications

“Super‐covariates”: Using predicted control group outcome as a covariate in randomized clinical trials

“Super‐covariates”: Using predicted control group outcome as a covariate in randomized clinical trials

On the relevance of prognostic information for clinical trials: A theoretical quantification

Digital twins and Bayesian dynamic borrowing: Two recent approaches for incorporating historical control data

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