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A Critical Review of Graphics for Subgroup Analyses in Clinical Trials

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A Critical Review of Graphics for Subgroup Analyses in Clinical Trials. / Ballarini, N.M.; Chiu, Yi-Da; Koenig, Franz; Posch, Martin; Jaki, Thomas.

In: Pharmaceutical Statistics, Vol. 19, No. 5, 01.09.2020, p. 541-560.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Ballarini, NM, Chiu, Y-D, Koenig, F, Posch, M & Jaki, T 2020, 'A Critical Review of Graphics for Subgroup Analyses in Clinical Trials', Pharmaceutical Statistics, vol. 19, no. 5, pp. 541-560. https://doi.org/10.1002/pst.2012

APA

Ballarini, N. M., Chiu, Y-D., Koenig, F., Posch, M., & Jaki, T. (2020). A Critical Review of Graphics for Subgroup Analyses in Clinical Trials. Pharmaceutical Statistics, 19(5), 541-560. https://doi.org/10.1002/pst.2012

Vancouver

Ballarini NM, Chiu Y-D, Koenig F, Posch M, Jaki T. A Critical Review of Graphics for Subgroup Analyses in Clinical Trials. Pharmaceutical Statistics. 2020 Sep 1;19(5):541-560. https://doi.org/10.1002/pst.2012

Author

Ballarini, N.M. ; Chiu, Yi-Da ; Koenig, Franz ; Posch, Martin ; Jaki, Thomas. / A Critical Review of Graphics for Subgroup Analyses in Clinical Trials. In: Pharmaceutical Statistics. 2020 ; Vol. 19, No. 5. pp. 541-560.

Bibtex

@article{b8339dd5ec4f4bb6a1c1fac1f40e54f1,
title = "A Critical Review of Graphics for Subgroup Analyses in Clinical Trials",
abstract = "Subgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.",
author = "N.M. Ballarini and Yi-Da Chiu and Franz Koenig and Martin Posch and Thomas Jaki",
year = "2020",
month = sep,
day = "1",
doi = "10.1002/pst.2012",
language = "English",
volume = "19",
pages = "541--560",
journal = "Pharmaceutical Statistics",
issn = "1539-1604",
publisher = "John Wiley and Sons Ltd",
number = "5",

}

RIS

TY - JOUR

T1 - A Critical Review of Graphics for Subgroup Analyses in Clinical Trials

AU - Ballarini, N.M.

AU - Chiu, Yi-Da

AU - Koenig, Franz

AU - Posch, Martin

AU - Jaki, Thomas

PY - 2020/9/1

Y1 - 2020/9/1

N2 - Subgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.

AB - Subgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.

U2 - 10.1002/pst.2012

DO - 10.1002/pst.2012

M3 - Journal article

VL - 19

SP - 541

EP - 560

JO - Pharmaceutical Statistics

JF - Pharmaceutical Statistics

SN - 1539-1604

IS - 5

ER -