Publication: Graphing and reporting heterogeneous treatment effects through reference classes
Issued Date
2020-05-07
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ISSN
17456215
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2-s2.0-85084382226
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Mahidol University
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SCOPUS
Bibliographic Citation
Trials. Vol.21, No.1 (2020)
Suggested Citation
James A. Watson, Chris C. Holmes Graphing and reporting heterogeneous treatment effects through reference classes. Trials. Vol.21, No.1 (2020). doi:10.1186/s13063-020-04306-1 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/56238
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Title
Graphing and reporting heterogeneous treatment effects through reference classes
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Abstract
© 2020 The Author(s). Background: Exploration and modelling of heterogeneous treatment effects as a function of baseline covariates is an important aspect of precision medicine in randomised controlled trials (RCTs). Randomisation generally guarantees the internal validity of an RCT, but heterogeneity in treatment effect can reduce external validity. Estimation of heterogeneous treatment effects is usually done via a predictive model for individual outcomes, where one searches for interactions between treatment allocation and important patient baseline covariates. However, such models are prone to overfitting and multiple testing and typically demand a transformation of the outcome measurement, for example, from the absolute risk in the original RCT to log-odds of risk in the predictive model. Methods: We show how reference classes derived from baseline covariates can be used to explore heterogeneous treatment effects via a two-stage approach. We first estimate a risk score which captures on a single dimension some of the heterogeneity in outcomes of the trial population. Heterogeneity in the treatment effect can then be explored via reweighting schemes along this axis of variation. This two-stage approach bypasses the search for interactions with multiple covariates, thus protecting against multiple testing. It also allows for exploration of heterogeneous treatment effects on the original outcome scale of the RCT. This approach would typically be applied to multivariable models of baseline risk to assess the stability of average treatment effects with respect to the distribution of risk in the population studied. Case study: We illustrate this approach using the single largest randomised treatment trial in severe falciparum malaria and demonstrate how the estimated treatment effect in terms of absolute mortality risk reduction increases considerably in higher risk strata. Conclusions: 'Local' and 'tilting' reweighting schemes based on ranking patients by baseline risk can be used as a general approach for exploring, graphing and reporting heterogeneity of treatment effect in RCTs. Trial registration: ISRCTN clinical trials registry: ISRCTN50258054. Prospectively registered on 22 July 2005.