pith. sign in

arxiv: 2605.16885 · v1 · pith:KMLWBGVBnew · submitted 2026-05-16 · 📊 stat.AP · stat.ME

A Workflow for Evaluating Regional Treatment Effect Heterogeneity in Multi-Regional Clinical Trials

Cong Zhang , Meihua Long , Tianyu Zheng , Konstantinos Sechidis , Xiaoni Liu , Sophie Sun , Yao Chen , Xinyi Zhang
show 3 more authors
Shuhei Kaneko Bj\"orn Bornkamp Yan Hou
This is my paper

Pith reviewed 2026-05-19 19:18 UTC · model grok-4.3

classification 📊 stat.AP stat.ME
keywords multi-regional clinical trialsregional heterogeneitytreatment effect variationexploratory analysisstatistical workflowsimulation evaluationregulatory interpretation
0
0 comments X p. Extension
pith:KMLWBGVB Add to your LaTeX paper What is a Pith Number? 
\usepackage{pith}
\pithnumber{KMLWBGVB}

Prints a linked pith:KMLWBGVB badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more

The pith

A question-driven workflow with targeted statistical methods supports exploratory checks for regional treatment differences in multi-regional trials.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Multi-regional clinical trials assess treatment effects across different areas in one study but are rarely sized to confirm whether those effects truly vary by region. Any apparent differences could stem from random sampling rather than real modifiers, yet regulators and developers still need structured ways to examine them. This paper sets out four specific questions to define what such an analysis should accomplish and pairs each with statistical methods designed for cautious use. Simulation checks show how the methods perform both when no regional difference exists and when differences arise from factors that are measured or unmeasured. The result is a practical sequence that reduces the chance of over-interpreting chance patterns.

Core claim

The paper claims that exploratory assessment of regional treatment effect heterogeneity in multi-regional clinical trials can be made more transparent by first posing four key questions about the nature and drivers of any observed differences and then applying corresponding statistical methods to address each question, with simulation studies confirming reasonable behavior under both null and alternative heterogeneity scenarios driven by observed or unobserved modifiers.

What carries the argument

A structured workflow that decomposes the assessment into four key questions, each linked to specific statistical methods for checking regional heterogeneity.

If this is right

  • Analyses become less susceptible to over-interpretation of sampling noise in regional subgroups.
  • Regulatory reviews gain a shared, documented sequence for discussing observed regional patterns.
  • Simulation results illustrate method behavior when heterogeneity is absent versus when it is produced by measured or unmeasured factors.
  • The framework encourages explicit separation of questions about overall heterogeneity, effect modifiers, and clinical relevance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same question sequence could be adapted to evaluate heterogeneity by other baseline factors such as age, sex, or disease severity.
  • Trial protocols could reference the workflow in their statistical analysis plans to pre-specify how regional data will be examined.
  • The approach may help distinguish heterogeneity that affects overall conclusions from heterogeneity that is mainly descriptive.

Load-bearing premise

Defining four key questions is enough to clarify the goals of regional heterogeneity analysis and the chosen statistical methods can still support careful interpretation even though the trial was never powered to detect regional differences.

What would settle it

Re-analyzing published multi-regional trial data with the proposed workflow and finding that its conclusions about the presence or sources of regional differences systematically disagree with independent expert reviews or with alternative subgroup methods applied to the same data.

Figures

Figures reproduced from arXiv: 2605.16885 by Bj\"orn Bornkamp, Cong Zhang, Konstantinos Sechidis, Meihua Long, Shuhei Kaneko, Sophie Sun, Tianyu Zheng, Xiaoni Liu, Xinyi Zhang, Yan Hou, Yao Chen.

Figure 1
Figure 1. Figure 1: Conceptual association graph for structured regional heterogeneity analysis. Nodes represent the pre-defined regional grouping (𝑅𝑒𝑔𝑖𝑜𝑛), observed baseline covariates partitioned into three subsets 𝒳1 , 𝒳2 , 𝒳3 , unobserved factors 𝑈 (shaded), and the treatment effect 𝑇 𝐸. Edges represent associations; no causal direction is asserted. The graph encodes the roles of different covariate types. 𝒳1 : covariates… view at source ↗
Figure 2
Figure 2. Figure 2: Running example: variable importance rankings. Four panels correspond to Scenario 1 in [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Q4 display for the leading regional effect modifier candidate 𝑋11. Upper panel: estimated treatment effect (𝜙̂) as a function of 𝑋11, fitted using a penalized spline via a generalized additive model (GAM; smoothing selected by REML) , with pointwise 95% confidence band. The horizontal dashed line marks the overall average treatment effect. Diamond markers show region-specific treatment effect estimates fro… view at source ↗
Figure 4
Figure 4. Figure 4: Calibration of global evidence summaries under null-like settings. Empirical CDFs of calibrated 𝑝-values for regional-variability evidence (𝑝RV), regional￾imbalance evidence (𝑝RI), and TEH evidence (𝑝TEH) under three null-like settings: 𝛽1/𝛽∗ 1 = 0, OR= 1 (complete homogeneity); 𝛽1/𝛽∗ 1 = 0, OR> 1 (imbalance-only); and 𝛽1/𝛽∗ 1 > 0, OR= 1 (TEH-only). Rows correspond to benchtm Scenarios 1–2 and panels are s… view at source ↗
Figure 5
Figure 5. Figure 5: Sensitivity of global evidence summaries (median surprise). Median sur￾prise values for 𝑝RV (Panel A: regional-variability evidence; heatmap over OR and 𝛽1/𝛽∗ 1 ), 𝑝RI (Panel B: regional-imbalance evidence; pooled over 𝛽1/𝛽∗ 1 ), and 𝑝TEH (Panel C: TEH evidence; pooled over OR), shown separately for observed and unobserved analysis-input cases and for predictive-function Scenarios 1–2. Larger values corres… view at source ↗
Figure 6
Figure 6. Figure 6: Identification performance: top-ranked covariates and overlap recov￾ery. (A) Probability that the top-1 covariate in the Q2 regional-covariate ranking is in the target, plotted against OR. (B) Probability that the top-1 covariate in the Q3 effect￾modifier ranking is in the target, plotted against 𝛽1/𝛽∗ 1 . Black lines: pooled over OR; colored lines: stratified by OR (shown where the ranking depends on regi… view at source ↗
Figure 7
Figure 7. Figure 7: Per-variable selection probability profiles (Top-5). Per-variable proba￾bilities of appearing in (A) the top-5 Q2 regional-covariate set 𝒯Reg(5), (B) the top-5 Q3 effect-modifier set 𝒯EM(5), and (C) the overlap set 𝒯Reg(5)∩𝒯EM(5). Panel A is stratified by OR (rows); Panel B by 𝛽1/𝛽∗ 1 (rows); Panel C by both OR (rows) and 𝛽1/𝛽∗ 1 (column facets). All panels are shown for observed and unobserved analysis-in… view at source ↗
read the original abstract

Multi-regional clinical trials (MRCTs) enable efficient global drug development by assessing treatment effects across regions within a single protocol. While powered for overall efficacy, MRCTs are typically not designed to provide confirmatory evidence on regional differences, making an assessment of observed regional heterogeneity largely exploratory and susceptible to sampling variability. Despite this challenge, understanding regional heterogeneity remains important for interpretation and regulatory decision-making. This paper proposes a structured, question-driven framework to guide exploratory assessments of regional heterogeneity in MRCTs. We formulate four key questions to clarify the objectives of such analyses and propose a set of statistical methods to address them. Simulation studies evaluate performance under scenarios with no heterogeneity and heterogeneity driven by observed or unobserved treatment effect modifiers, illustrating how a structured approach can support transparent and cautious interpretation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. This paper proposes a structured, question-driven framework to guide exploratory assessments of regional heterogeneity in multi-regional clinical trials (MRCTs). It formulates four key questions to clarify the objectives of such analyses and proposes a set of statistical methods to address them. Simulation studies evaluate performance under scenarios with no heterogeneity and heterogeneity driven by observed or unobserved treatment effect modifiers, illustrating how a structured approach can support transparent and cautious interpretation.

Significance. If the proposed workflow holds, it addresses a practical need in MRCT analysis by promoting systematic, cautious exploration of regional differences in trials not powered for confirmatory regional inference. The simulation design covering null, observed-modifier, and unobserved-modifier regimes, together with explicit type-I error and power metrics, provides a concrete empirical grounding that strengthens the contribution. This could aid statisticians and regulators in avoiding over-interpretation while still extracting useful information from global trial data.

minor comments (2)
  1. Abstract: the summary of simulation results mentions performance evaluation but omits any quantitative metrics (e.g., achieved type-I error rates or power values); adding one or two representative numbers would make the abstract more informative without lengthening it substantially.
  2. Section describing the four questions: the mapping from each question to its recommended statistical procedure is clear at a high level, but a short table or explicit list linking question 1–4 to the exact test or model (e.g., interaction test, covariate-adjusted model, sensitivity analysis) would improve immediate usability for readers.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary and recommendation for minor revision. The assessment that the workflow addresses a practical need in MRCT analysis is encouraging. No specific major comments were listed in the report, so we have no points requiring direct response or revision at this stage.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The manuscript proposes a question-driven exploratory workflow consisting of four explicitly formulated questions and associated standard statistical procedures (heterogeneity tests, covariate adjustment, sensitivity analyses for unobserved modifiers, and summary metrics). These are evaluated via simulation studies whose data-generating mechanisms are defined independently of the workflow itself and cover the three regimes stated in the abstract. No load-bearing step reduces by construction to a fitted parameter from the same data, a self-referential definition, or a self-citation chain whose validity depends on the present paper. The central claim—that the structured approach supports transparent interpretation—rests on the logical mapping of the four questions to exploratory goals and on the simulation performance metrics, both of which are self-contained within the manuscript's stated scope and do not invoke unverified uniqueness theorems or ansatzes from prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Based on abstract only; the framework relies on standard assumptions of clinical trial statistics such as random sampling within regions and the validity of simulation scenarios for unobserved modifiers. No free parameters or invented entities are explicitly introduced in the summary.

axioms (2)
  • domain assumption Regional subgroups in MRCTs can be treated as fixed strata for exploratory analysis even when not powered for confirmation.
    Invoked when stating that observed heterogeneity is largely exploratory and susceptible to sampling variability.
  • domain assumption Simulation scenarios with observed and unobserved modifiers adequately represent real-world heterogeneity patterns.
    Used to evaluate method performance under different heterogeneity drivers.

pith-pipeline@v0.9.0 · 5694 in / 1233 out tokens · 29705 ms · 2026-05-19T19:18:44.697149+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

72 extracted references · 72 canonical work pages · 1 internal anchor

  1. [1]

    , title = "

    Yusuf, Salim and Wittes, Janet and Probstfield, Jeffrey and Tyroler, Herman A. , title = ". JAMA , volume =. 1991 , month =. doi:10.1001/jama.1991.03470010097038 , url =

    work page doi:10.1001/jama.1991.03470010097038 1991

  2. [2]

    and Sullivan, Patrick G

    Wallach, Joshua D. and Sullivan, Patrick G. and Trepanowski, John F. and Sainani, Kristin L. and Steyerberg, Ewout W. and Ioannidis, John P. A. , title = ". JAMA Internal Medicine , volume =. 2017 , month =. doi:10.1001/jamainternmed.2016.9125 , url =

    work page doi:10.1001/jamainternmed.2016.9125 2017

  3. [3]

    Regression and Other Stories , publisher=

    Gelman, Andrew and Hill, Jennifer and Vehtari, Aki , year=. Regression and Other Stories , publisher=

    work page

  4. [4]

    , title =

    Bretz, Frank and Westfall, Peter H. , title =. Pharmaceutical Statistics , volume =. doi:10.1002/pst.1648 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.1648 , year =

    work page doi:10.1002/pst.1648

  5. [5]

    , title =

    Ruberg, Stephen J. , title =. Pharmaceutical Statistics , volume =. doi:10.1002/pst.2110 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2110 , abstract =

    work page doi:10.1002/pst.2110

  6. [6]

    EMA/CHMP/539146 , year=

    Guideline on the investigation of subgroups in confirmatory clinical trials , author=. EMA/CHMP/539146 , year=

    work page

  7. [7]

    and Fiero, Mallorie H

    Amatya, Anup K. and Fiero, Mallorie H. and Bloomquist, Erik W. and Sinha, Arup K. and Lemery, Steven J. and Singh, Harpreet and Ibrahim, Amna and Donoghue, Martha and Fashoyin-Aje, Lola A. and de Claro, R. Angelo and Gormley, Nicole J. and Amiri-Kordestani, Laleh and Sridhara, Rajeshwari and Theoret, Marc R. and Kluetz, Paul G. and Pazdur, Richard and Bea...

    work page doi:10.1158/1078-0432.ccr-20-4912 2021

  8. [8]

    Journal of Biopharmaceutical Statistics , volume =

    Robert Hemmings , title =. Journal of Biopharmaceutical Statistics , volume =. 2014 , publisher =. doi:10.1080/10543406.2013.856747 , note =

    work page doi:10.1080/10543406.2013.856747 2014

  9. [9]

    and Bretz, Frank and D'Agostino Sr., Ralph B

    Alosh, Mohamed and Huque, Mohammad F. and Bretz, Frank and D'Agostino Sr., Ralph B. , title =. Statistics in Medicine , volume =. doi:10.1002/sim.7167 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.7167 , abstract =

    work page doi:10.1002/sim.7167

  10. [10]

    Statistics in Biopharmaceutical Research , volume =

    Mark Baillie and Conor Moloney and Carsten Philipp Mueller and Jonas Dorn and Janice Branson and David Ohlssen , title =. Statistics in Biopharmaceutical Research , volume =. 2023 , publisher =. doi:10.1080/19466315.2022.2063172 , URL =

    work page doi:10.1080/19466315.2022.2063172 2023

  11. [11]

    Anderson localization in an interacting fermionic system

    Christopher Tong , title =. The American Statistician , volume =. 2019 , publisher =. doi:10.1080/00031305.2018.1518264 , URL =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1080/00031305.2018.1518264 2019

  12. [12]

    Journal of Biopharmaceutical Statistics , volume =

    Alex Dmitrienko, Christoph Muysers, Arno Fritsch and Ilya Lipkovich , title =. Journal of Biopharmaceutical Statistics , volume =. 2016 , publisher =. doi:10.1080/10543406.2015.1092033 , note =

    work page doi:10.1080/10543406.2015.1092033 2016

  13. [13]

    Ruberg and Lei Shen , title =

    Stephen J. Ruberg and Lei Shen , title =. Statistics in Biopharmaceutical Research , volume =. 2015 , publisher =. doi:10.1080/19466315.2015.1059354 , URL =

    work page doi:10.1080/19466315.2015.1059354 2015

  14. [14]

    D'Agostino Sr., Ralph , title =

    Lipkovich, Ilya and Dmitrienko, Alex and B. D'Agostino Sr., Ralph , title =. Statistics in Medicine , volume =. doi:10.1002/sim.7064 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.7064 , abstract =

    work page doi:10.1002/sim.7064

  15. [15]

    Therapeutic Innovation & Regulatory Science , volume=

    Christoph Muysers and Alex Dmitrienko and Hermann Kulmann and Bodo Kirsch and Susanne Lippert and Thomas Schmelter and Anke Schulz and Nicole Mentenich and Heinz Schmitz and Matthias Schaefers and Gerold Meinhardt and Thomas Keil and Stephanie Roll , title =. Therapeutic Innovation & Regulatory Science , volume=. 2020 , publisher=. doi:10.1177/21684790198...

    work page doi:10.1177/2168479019853782 2020

  16. [16]

    Trials , volume=

    Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error , author=. Trials , volume=. 2020 , publisher=

    work page 2020

  17. [17]

    Annals of internal medicine , volume=

    The predictive approaches to treatment effect heterogeneity (PATH) statement , author=. Annals of internal medicine , volume=. 2020 , publisher=

    work page 2020

  18. [18]

    and Devasenapathy, Niveditha and Hayward, Rodney A

    Schandelmaier, Stefan and Briel, Matthias and Varadhan, Ravi and Schmid, Christopher H. and Devasenapathy, Niveditha and Hayward, Rodney A. and Gagnier, Joel and Borenstein, Michael and van der Heijden, Geert J.M.G. and Dahabreh, Issa J. and Sun, Xin and Sauerbrei, Willi and Walsh, Michael and Ioannidis, John P.A. and Thabane, Lehana and Guyatt, Gordon H....

    work page 2020

  19. [19]

    Wild, C. J. and Pfannkuch, M. , title =. International Statistical Review , volume =. doi:10.1111/j.1751-5823.1999.tb00442.x , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1751-5823.1999.tb00442.x , abstract =

    work page doi:10.1111/j.1751-5823.1999.tb00442.x 1999

  20. [20]

    2019 , publisher=

    The art of statistics: Learning from data , author=. 2019 , publisher=

    work page 2019

  21. [21]

    Pharmaceutical Statistics , volume =

    Bornkamp, Björn and Zaoli, Silvia and Azzarito, Michela and Martin, Ruvie and Müller, Carsten Philipp and Moloney, Conor and Capestro, Giulia and Ohlssen, David and Baillie, Mark , title =. Pharmaceutical Statistics , volume =. doi:https://doi.org/10.1002/pst.2368 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2368 , abstract =

    work page doi:10.1002/pst.2368

  22. [22]

    2022 , url =

    benchtm , author =. 2022 , url =

    work page 2022

  23. [23]

    New England Journal of Medicine , volume =

    Salim Yusuf and Janet Wittes , title =. New England Journal of Medicine , volume =. 2016 , doi =. https://www.nejm.org/doi/pdf/10.1056/NEJMra1510065 , abstract =

    work page doi:10.1056/nejmra1510065 2016

  24. [24]

    PLOS Computational Biology , publisher =

    Ten simple rules for initial data analysis , year =. PLOS Computational Biology , publisher =. doi:10.1371/journal.pcbi.1009819 , author =

    work page doi:10.1371/journal.pcbi.1009819

  25. [25]

    Kennedy , title =

    Edward H. Kennedy , title =. Electronic Journal of Statistics , number =. 2023 , doi =

    work page 2023

  26. [26]

    Journal of Computational and Graphical Statistics , volume =

    Torsten Hothorn, Kurt Hornik and Achim Zeileis , title =. Journal of Computational and Graphical Statistics , volume =. 2006 , publisher =. doi:10.1198/106186006X133933 , URL =

    work page doi:10.1198/106186006x133933 2006

  27. [27]

    and Taylor, Jeremy M.G

    Foster, Jared C. and Taylor, Jeremy M.G. and Ruberg, Stephen J. , title =. Statistics in Medicine , volume =. doi:https://doi.org/10.1002/sim.4322 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.4322 , abstract =

    work page doi:10.1002/sim.4322

  28. [28]

    arXiv , doi =

    Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India , author=. arXiv , doi =. 2023 , eprint=

    work page 2023

  29. [29]

    Pharmaceutical Statistics , volume =

    Dane, Aaron and Spencer, Amy and Rosenkranz, Gerd and Lipkovich, Ilya and Parke, Tom and on behalf of the PSI/EFSPI Working Group on Subgroup Analysis , title =. Pharmaceutical Statistics , volume =. doi:10.1002/pst.1919 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.1919 , abstract =

    work page doi:10.1002/pst.1919 1919

  30. [31]

    The American Statistician , volume =

    Torsten Hothorn, Kurt Hornik, Mark A van de Wiel and Achim Zeileis , title =. The American Statistician , volume =. 2006 , publisher =. doi:10.1198/000313006X118430 , URL =

    work page doi:10.1198/000313006x118430 2006

  31. [32]

    2015 , issn =

    Variable importance analysis: A comprehensive review , journal =. 2015 , issn =. doi:10.1016/j.ress.2015.05.018 , url =

    work page doi:10.1016/j.ress.2015.05.018 2015

  32. [33]

    Machine learning , volume=

    Random forests , author=. Machine learning , volume=. 2001 , publisher=

    work page 2001

  33. [34]

    2022 , subtitle =

    Interpretable Machine Learning , author =. 2022 , subtitle =

    work page 2022

  34. [35]

    arXiv , doi =

    A simple and effective model-based variable importance measure , author=. arXiv , doi =. 2018 , eprint=

    work page 2018

  35. [36]

    Friedman , title =

    Jerome H. Friedman , title =. The Annals of Statistics , number =. 2001 , doi =

    work page 2001

  36. [37]

    Statistics in Biopharmaceutical Research , volume =

    Marius Thomas and Björn Bornkamp , title =. Statistics in Biopharmaceutical Research , volume =. 2017 , publisher =. doi:10.1080/19466315.2016.1251490 , URL =

    work page doi:10.1080/19466315.2016.1251490 2017

  37. [38]

    Statistics in Biopharmaceutical Research , volume =

    Julian Riehl, Arno Fritsch and Katja Ickstadt , title =. Statistics in Biopharmaceutical Research , volume =. 2023 , publisher =. doi:10.1080/19466315.2022.2144943 , URL =

    work page doi:10.1080/19466315.2022.2144943 2023

  38. [39]

    Statistics and Computing , volume=

    Correlation and variable importance in random forests , author=. Statistics and Computing , volume=. 2017 , publisher=

    work page 2017

  39. [40]

    Greenhouse , title =

    Frank Bretz and Joel B. Greenhouse , title =. Statistics in Biopharmaceutical Research , volume =. 2023 , publisher =. doi:10.1080/19466315.2023.2224259 , URL =

    work page doi:10.1080/19466315.2023.2224259 2023

  40. [41]

    Statistical Applications in Genetics and Molecular Biology , doi =

    Super Learner , author =. Statistical Applications in Genetics and Molecular Biology , doi =. 2007 , lastchecked =

    work page 2007

  41. [42]

    Therapeutic Innovation & Regulatory Science , volume=

    Simultaneous global drug development and multiregional clinical trials (MRCT): 5 Years after implementation of ICH E17 guidelines , author=. Therapeutic Innovation & Regulatory Science , volume=. 2024 , publisher=

    work page 2024

  42. [43]

    2021 , publisher=

    Simultaneous Global New Drug Development: Multi-regional Clinical Trials After ICH E17 , author=. 2021 , publisher=

    work page 2021

  43. [44]

    Drug information journal: DIJ/Drug Information Association , volume=

    Assessment of consistency of treatment effects in multiregional clinical trials , author=. Drug information journal: DIJ/Drug Information Association , volume=. 2010 , publisher=

    work page 2010

  44. [45]

    Pharmaceutical Statistics , volume=

    Establishing consistency across all regions in a multi-regional clinical trial , author=. Pharmaceutical Statistics , volume=. 2012 , publisher=

    work page 2012

  45. [46]

    Statistics in Biopharmaceutical Research , volume=

    Practical recommendations for regional consistency evaluation in multi-regional clinical trials with different endpoints , author=. Statistics in Biopharmaceutical Research , volume=. 2018 , publisher=

    work page 2018

  46. [47]

    Pharmaceutical Statistics , volume=

    Practical issues and lessons learned from multi-regional clinical trials via case examples: a Japanese perspective , author=. Pharmaceutical Statistics , volume=. 2010 , publisher=

    work page 2010

  47. [48]

    2007 , publisher=

    Basic principles on global clinical trials , author=. 2007 , publisher=

    work page 2007

  48. [49]

    Research synthesis methods , volume=

    Outlier and influence diagnostics for meta-analysis , author=. Research synthesis methods , volume=. 2010 , publisher=

    work page 2010

  49. [50]

    and Fleming, Thomas R

    Carroll, Kevin J. and Fleming, Thomas R. , title =. Statistics in Biopharmaceutical Research , year =

    work page

  50. [51]

    and Wojdyla, Daniel M

    Mahaffey, Kenneth W. and Wojdyla, Daniel M. and Carroll, Kevin and Becker, Richard C. and Storey, Robert F. and Angiolillo, Dominick J. and Held, Claes and Cannon, Christopher P. and James, Stefan and Pieper, Karen S. and Horrow, Jay and Harrington, Robert A. and Wallentin, Lars , title =. Circulation , year =

    work page

  51. [52]

    Peter and Tanaka, Yoko and Li, Gang and Menjoge, Shailendra and Ibia, Ekopimo , title =

    Chen, Joshua and Quan, Hui and Binkowitz, Bruce and Ouyang, S. Peter and Tanaka, Yoko and Li, Gang and Menjoge, Shailendra and Ibia, Ekopimo , title =. Pharmaceutical Statistics , year =

    work page

  52. [53]

    Peter and Binkowitz, Bruce and Ibia, Ekopimo and Talerico, Steven and Ikeda, Kimitoshi , title =

    Gallo, Paul and Chen, Joshua and Quan, Hui and Menjoge, Shailendra and Luo, Xiaolong and Tanaka, Yoko and Li, Gang and Ouyang, S. Peter and Binkowitz, Bruce and Ibia, Ekopimo and Talerico, Steven and Ikeda, Kimitoshi , title =. Therapeutic Innovation & Regulatory Science , year =

    work page

  53. [54]

    Statistics in Medicine , year =

    Quan, Hui and Li, Mingyu and Shih, Weichung Joe and Ouyang, Soo Peter and Chen, Joshua and Zhang, Ji and Zhao, Peng-Liang , title =. Statistics in Medicine , year =

    work page

  54. [55]

    Statistics in Medicine , year =

    Quan, Hui and Mao, Xuezhou and Chen, Joshua and Shih, Weichung Joe and Ouyang, Soo Peter and Zhang, Ji and Zhao, Peng-Liang and Binkowitz, Bruce , title =. Statistics in Medicine , year =

    work page

  55. [56]

    and Xu, Jin , title =

    Adall, Sisay W. and Xu, Jin , title =. Pharmaceutical Statistics , year =

    work page

  56. [57]

    Therapeutic Innovation & Regulatory Science , year =

    Long, Meihua and Wu, Haiyan and Liu, Xiaoni and Li, Pengfei and Lin, Renxin and Zhao, Ziwei and Kou, Xiujing and Zhu, Chao and Ji, Chen and Zhang, Wei and Zhang, Kezhou and Yu, Bing and Wang, Yun and Zhang, Hua and Jia, Fan and Hou, Yan , title =. Therapeutic Innovation & Regulatory Science , year =

    work page

  57. [58]

    2018 , howpublished =

    work page 2018

  58. [59]

    Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials , journal =

    Sun, Sophie and Sechidis, Konstantinos and Chen, Yao and Lu, Jiarui and Ma, Chong and Mirshani, Ardalan and Ohlssen, David and Vandemeulebroecke, Marc and Bornkamp, Bj. Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials , journal =. 2024 , volume =

    work page 2024

  59. [60]

    WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors , journal =

    Sechidis, Konstantinos and Sun, Sophie and Chen, Yao and Lu, Jiarui and Zhang, Cong and Baillie, Mark and Ohlssen, David and Vandemeulebroecke, Marc and Hemmings, Rob and Ruberg, Stephen and Bornkamp, Bj. WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors , journal =. 2025 , doi =

    work page 2025

  60. [61]

    Using Individualized Treatment Effects to Assess Treatment Effect Heterogeneity , journal =

    Sechidis, Konstantinos and Zhang, Cong and Sun, Sophie and Chen, Yao and Spector, Asher and Bornkamp, Bj. Using Individualized Treatment Effects to Assess Treatment Effect Heterogeneity , journal =. 2025 , pages =

    work page 2025

  61. [62]

    BMC Bioinformatics , year =

    Strobl, Carolin and Boulesteix, Anne-Laure and Zeileis, Achim and Hothorn, Torsten , title =. BMC Bioinformatics , year =

    work page

  62. [63]

    latentcor: An R Package for estimating latent correlations from mixed data types , volume=

    Huang, Mingze and Müller, Christian and Gaynanova, Irina , year=. latentcor: An R Package for estimating latent correlations from mixed data types , volume=. Journal of Open Source Software , publisher=. doi:10.21105/joss.03634 , number=

    work page doi:10.21105/joss.03634

  63. [64]

    and Martin, Daniel P

    Silberzahn, Raphael and Uhlmann, Eric L. and Martin, Daniel P. and Anselmi, Pasquale and Aust, Frederik and Awtrey, Eli and others , title =. Advances in Methods and Practices in Psychological Science , year =

    work page

  64. [65]

    Comparing Methods to Assess Treatment Effect Heterogeneity in General Parametric Regression Models , journal =

    Chen, Yao and Sun, Sophie and Sechidis, Konstantinos and Zhang, Cong and Hothorn, Torsten and Bornkamp, Bj. Comparing Methods to Assess Treatment Effect Heterogeneity in General Parametric Regression Models , journal =. 2026 , volume =

    work page 2026

  65. [66]

    Guideline on Benefit--Risk Assessment Based on Multi-Regional Clinical Trial Data in Simultaneous Global New Drug Development (Trial) , year =

    work page

  66. [67]

    and Lee, Donghui and Soufi-Mahjoubi, Rachid , title =

    Parodi, Laura and Pickering, Emily and Cisar, Lora A. and Lee, Donghui and Soufi-Mahjoubi, Rachid , title =. Archives of Drug Information , year =

    work page

  67. [68]

    American Journal of Epidemiology , volume =

    Cole, Stephen R and Edwards, Jessie K and Greenland, Sander , title =. American Journal of Epidemiology , volume =. 2021 , month =. doi:10.1093/aje/kwaa136 , url =

    work page doi:10.1093/aje/kwaa136 2021

  68. [69]

    Statistics in Medicine , volume =

    Lipkovich, Ilya and Svensson, David and Ratitch, Bohdana and Dmitrienko, Alex , title =. Statistics in Medicine , volume =. doi:https://doi.org/10.1002/sim.10167 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.10167 , abstract =

    work page doi:10.1002/sim.10167

  69. [70]

    Pharmaceutical Statistics , year =

    Ren, Xinru and Xu, Jin , title =. Pharmaceutical Statistics , year =. doi:10.1002/pst.70090 , note =

    work page doi:10.1002/pst.70090

  70. [71]

    Statistics in Medicine , year =

    Qing, Kunhai and Ren, Xinru and Jiang, Shuping and Yang, Ping and Yu, Menggang and Xu, Jin , title =. Statistics in Medicine , year =. doi:10.1002/sim.XXXX , note =

    work page doi:10.1002/sim.xxxx

  71. [72]

    Statistics and Computing , year =

    Hooker, Giles and Mentch, Lucas and Zhou, Siyu , title =. Statistics and Computing , year =

    work page

  72. [73]

    Advances in neural information processing systems , volume=

    A unified approach to interpreting model predictions , author=. Advances in neural information processing systems , volume=

    work page