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arxiv: 2511.04903 · v1 · submitted 2025-11-07 · 📊 stat.OT

Efficacy Analysis in Clinical Trials: A Comprehensive Review of Statistical and Machine Learning Approaches

Dhrubajyoti Ghosh , Samhita Pal This is my paper

Pith reviewed 2026-05-18 00:34 UTC · model grok-4.3

classification 📊 stat.OT
keywords efficacy analysisclinical trialsstatistical methodsmachine learningparametric methodsnonparametric methodsBayesian approachespersonalized methodologies
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The pith

Integrating statistical and machine learning methods can improve reliability and personalization of efficacy analysis in clinical trials.

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

This paper reviews parametric methods such as t-tests, ANOVA and linear mixed models, nonparametric techniques including the Friedman and Brunner-Munzel tests, Bayesian approaches, and machine learning tools like deep learning and reinforcement learning for testing intervention effects in clinical trials. It outlines how each category applies to trial data, their efficiency or robustness under different conditions, and their limitations with non-normal data or missing values. The review identifies remaining challenges in high-dimensional datasets, incomplete records, and fair performance across varied patient groups. It concludes that linking established statistical frameworks with newer computational methods supports progress toward dependable and individualized trial designs. A reader would care because more accurate efficacy testing influences which treatments are approved and reach patients.

Core claim

The paper claims that a comprehensive overview of parametric, nonparametric, Bayesian, and machine learning approaches to efficacy testing, along with their applications, strengths, limitations, and future directions, bridges traditional statistical frameworks with modern computational techniques and thereby advances the field toward more reliable and personalized clinical trial methodologies.

What carries the argument

The classification of efficacy analysis methods into parametric (t-tests, ANOVA, LMMs), nonparametric (Friedman test, Brunner-Munzel test, nparLD), Bayesian, and machine learning (deep learning, reinforcement learning) categories that organizes discussion of how each handles complexities in clinical trial data.

If this is right

  • Parametric methods deliver efficient results when data satisfy normality and other assumptions.
  • Nonparametric techniques supply robustness for skewed, ordinal, or non-normal data.
  • Bayesian methods incorporate prior information and provide uncertainty quantification.
  • Machine learning techniques support improved trial design and outcome prediction.
  • Filling gaps in high-dimensional data handling and missingness will support more equitable testing across populations.

Where Pith is reading between the lines

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

  • Hybrid statistical-ML models tailored to mixed trial data types could be tested on existing datasets to measure gains in accuracy.
  • Applying the reviewed methods to real multi-center trials with diverse demographics might expose equity issues the literature review did not fully capture.
  • Adaptive designs that update sample sizes using interim machine-learning predictions could be simulated to quantify efficiency improvements.

Load-bearing premise

The selected methods and identified gaps represent a balanced and sufficiently complete picture of current efficacy analysis without significant omissions or selection bias in the reviewed literature.

What would settle it

A concrete check would be to search recent clinical trial literature for a widely used efficacy analysis technique absent from the review or for studies demonstrating that the noted gaps in high-dimensional data and equity have already been resolved in practice.

Figures

Figures reproduced from arXiv: 2511.04903 by Dhrubajyoti Ghosh, Samhita Pal.

Figure 1
Figure 1. Figure 1: Schematic decision framework linking study design, outcome type, and analytic approach. The diagram guides readers from data structure (cross-sectional or longitudinal) through outcome characteristics (continuous, categorical, survival) to suitable parametric and nonparametric tests for two-arm, multi-arm, or paired comparisons. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_1.png] view at source ↗
read the original abstract

Efficacy testing is a cornerstone of clinical trials, ensuring that medical interventions achieve their intended therapeutic effects. Over the decades, a wide range of statistical methodologies have been developed to address the complexities of clinical trial data, including parametric, nonparametric, Bayesian, and machine learning approaches. Parametric methods, such as t-tests, ANOVA, and LMMs, have traditionally been the foundation of efficacy testing due to their efficiency under well-defined assumptions. Nonparametric techniques, including the Friedman test, Brunner-Munzel test, and modern extensions like nparLD, have emerged as robust alternatives, particularly for skewed, ordinal, or non-normal data. Bayesian methodologies have enabled the incorporation of prior information and uncertainty quantification, while machine learning techniques, such as deep learning and reinforcement learning, are revolutionizing trial designs and outcome predictions. Despite these advancements, significant gaps remain, including challenges in handling high-dimensional data, missingness, and ensuring equitable efficacy testing across diverse populations. This review provides a comprehensive overview of these statistical methods, highlighting their applications, strengths, limitations, and future directions. By bridging traditional statistical frameworks with modern computational techniques, the field can continue to advance toward more reliable and personalized clinical trial methodologies.

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

2 major / 2 minor

Summary. The manuscript reviews statistical and machine learning methods for efficacy analysis in clinical trials. It covers parametric approaches (t-tests, ANOVA, linear mixed models), nonparametric tests (Friedman, Brunner-Munzel, nparLD), Bayesian methods for prior incorporation and uncertainty, and ML techniques (deep learning, reinforcement learning) for trial design and prediction. The review discusses applications, strengths, limitations, and remaining gaps in high-dimensional data, missingness mechanisms, and equitable testing across populations, with the goal of bridging traditional and computational frameworks for more reliable, personalized methodologies.

Significance. If the literature coverage proves balanced and reproducible, the review could usefully synthesize disparate strands of work for clinical trial statisticians and data scientists, highlighting actionable gaps that future methodological research might address.

major comments (2)
  1. [Abstract] Abstract: the claim of a 'comprehensive overview' and 'bridging' of fields rests on an undocumented literature selection process. No search strategy, databases, keywords, inclusion/exclusion criteria, or PRISMA-style flow is described, making it impossible to verify whether representative work on causal inference frameworks, adaptive ML designs, or specific Bayesian nonparametrics has been included or systematically omitted.
  2. [Abstract / Introduction] The central claim that the selected methods and identified gaps represent a 'balanced and sufficiently complete picture' cannot be evaluated without evidence of reproducible selection criteria; this is load-bearing for the review's asserted value.
minor comments (2)
  1. Ensure every named method (e.g., nparLD, Brunner-Munzel) is accompanied by at least one key foundational citation so readers can locate primary sources.
  2. Clarify whether the discussion of machine learning extends beyond deep learning and reinforcement learning to include, for example, causal ML or ensemble methods commonly used in trial outcome modeling.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and constructive comments on our review. The concerns about transparency in literature selection are well-taken and directly affect the strength of our claims regarding comprehensiveness and balance. We address each major comment below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of a 'comprehensive overview' and 'bridging' of fields rests on an undocumented literature selection process. No search strategy, databases, keywords, inclusion/exclusion criteria, or PRISMA-style flow is described, making it impossible to verify whether representative work on causal inference frameworks, adaptive ML designs, or specific Bayesian nonparametrics has been included or systematically omitted.

    Authors: We agree that the manuscript does not document the literature selection process. The review was constructed as a narrative synthesis of prominent parametric, nonparametric, Bayesian, and machine-learning methods for efficacy analysis, drawing on well-established references and recent advances known to the authors. However, to make the coverage verifiable and to address potential omissions (such as explicit treatment of causal inference frameworks or adaptive ML designs), we will add a new subsection titled 'Literature Search and Selection' in the Introduction of the revised manuscript. This subsection will specify the primary databases consulted (PubMed, Web of Science, arXiv), core search terms, approximate time window, and high-level inclusion criteria focused on methods directly applicable to efficacy endpoints. We will also briefly note why certain specialized topics were treated at a summary level rather than exhaustively. revision: yes

  2. Referee: [Abstract / Introduction] The central claim that the selected methods and identified gaps represent a 'balanced and sufficiently complete picture' cannot be evaluated without evidence of reproducible selection criteria; this is load-bearing for the review's asserted value.

    Authors: We accept this assessment. The current text asserts balance without providing the reader with the means to evaluate it. In the revision we will integrate the new 'Literature Search and Selection' subsection referenced above and will explicitly link it to the discussion of remaining gaps (high-dimensional data, missingness, and equity across populations). This addition will allow readers to judge whether the identified gaps are representative and whether areas such as specific Bayesian nonparametric approaches or adaptive designs warrant further emphasis. We believe these changes will strengthen rather than weaken the manuscript's utility as a bridge between traditional and computational frameworks. revision: yes

Circularity Check

0 steps flagged

No circularity: review draws exclusively from external literature with no derivations or self-referential fits

full rationale

This is a literature review paper that summarizes parametric, nonparametric, Bayesian, and machine learning methods for clinical trial efficacy analysis without introducing any original equations, predictions, or derivations. The abstract and structure reference established external techniques and gaps in the field, with no self-citation chains, fitted parameters renamed as predictions, or ansatzes smuggled via prior author work. The claim of providing a comprehensive overview rests on literature selection rather than any internal reduction to the paper's own inputs by construction, making the derivation chain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a review paper the work introduces no new free parameters, axioms, or invented entities; it aggregates and describes methods from prior publications.

pith-pipeline@v0.9.0 · 5513 in / 1059 out tokens · 37728 ms · 2026-05-18T00:34:15.751385+00:00 · methodology

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    Parametric methods, such as t-tests, ANOVA, and LMMs, have traditionally been the foundation of efficacy testing due to their efficiency under well-defined assumptions. Nonparametric techniques, including the Friedman test, Brunner-Munzel test, and modern extensions like nparLD...

  • IndisputableMonolith/Foundation/ArithmeticFromLogic.lean LogicNat_equivNat unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    Bayesian methodologies have enabled the incorporation of prior information and uncertainty quantification, while machine learning techniques, such as deep learning and reinforcement learning...

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Reference graph

Works this paper leans on

165 extracted references · 165 canonical work pages · 2 internal anchors

  1. [1]

    The probable error of a mean.Biometrika, pages 1–25, 1908

    Student. The probable error of a mean.Biometrika, pages 1–25, 1908

    work page 1908

  2. [2]

    Fisher.Statistical methods for research workers

    R.A. Fisher.Statistical methods for research workers. Oliver and Boyd, 1925

    work page 1925

  3. [3]

    Laird and J

    N. Laird and J. Ware. Random-effects models for longitudinal data.Biometrics, 38:963–974, 1982

    work page 1982

  4. [4]

    Verbeke and G

    G. Verbeke and G. Molenberghs.Linear Mixed Models for Longitudinal Data. Springer, 2000

    work page 2000

  5. [5]

    Friedman

    M. Friedman. The use of ranks to avoid the assumption of normality implicit in the analysis of variance.Journal of the American Statistical Association, 1937

    work page 1937

  6. [6]

    Brunner and U

    E. Brunner and U. Munzel. The nonparametric behrens-fisher problem: Asymptotic theory and a small-sample approximation.Biometrics, 56(4):1173–1182, 2000

    work page 2000

  7. [7]

    Noguchi, Y .R

    K. Noguchi, Y .R. Gel, E. Brunner, and F. Konietschke. nparld: An r software package for the nonparametric analysis of longitudinal data in factorial experiments.Journal of Statistical Software, 50(1):1–23, 2012. 20 Running Title for Header

    work page 2012

  8. [8]

    A novel longitudinal rank-sum test for multiple primary endpoints in clinical trials: Applications to neurodegenerative disorders

    Xiaoming Xu, Dhrubajyoti Ghosh, Sheng Luo, and CPP Integrated Parkinson’s Database. A novel longitudinal rank-sum test for multiple primary endpoints in clinical trials: Applications to neurodegenerative disorders. Statistics in Biopharmaceutical Research, pages 1–11, 2025

    work page 2025

  9. [9]

    A non-parametric u-statistic testing approach for multi-arm clinical trials with multivariate longitudinal data.Journal of Multivariate Analysis, page 105447, 2025

    Dhrubajyoti Ghosh and Sheng Luo. A non-parametric u-statistic testing approach for multi-arm clinical trials with multivariate longitudinal data.Journal of Multivariate Analysis, page 105447, 2025

    work page 2025

  10. [10]

    Gelman and J

    A. Gelman and J. Hill.Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, 2013

    work page 2013

  11. [11]

    Rasmussen and C.K.I

    C.E. Rasmussen and C.K.I. Williams.Gaussian Processes for Machine Learning. MIT Press, 2006

    work page 2006

  12. [12]

    Optimizing clinical trials recruitment via deep learning.Journal of the American Medical Informatics Association, 26(11):1195–1202, 2019

    Jelena Gligorijevic, Djordje Gligorijevic, Martin Pavlovski, Elizabeth Milkovits, Lucas Glass, Kevin Grier, Praveen Vankireddy, and Zoran Obradovic. Optimizing clinical trials recruitment via deep learning.Journal of the American Medical Informatics Association, 26(11):1195–1202, 2019

    work page 2019

  13. [13]

    Prediction of chronic kidney disease progression using recurrent neural network and electronic health records.Scientific Reports, 13(1):22091, 2023

    Yitan Zhu, Dehua Bi, Milda Saunders, and Yuan Ji. Prediction of chronic kidney disease progression using recurrent neural network and electronic health records.Scientific Reports, 13(1):22091, 2023

    work page 2023

  14. [14]

    Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach

    Shamim Nemati, Mohammad M Ghassemi, and Gari D Clifford. Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach. In2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pages 2978–2981. IEEE, 2016

    work page 2016

  15. [15]

    John Wiley & Sons, 2019

    Roderick JA Little and Donald B Rubin.Statistical analysis with missing data. John Wiley & Sons, 2019

    work page 2019

  16. [16]

    John Wiley & Sons, 2007

    Geert Molenberghs and Michael Kenward.Missing data in clinical studies. John Wiley & Sons, 2007

    work page 2007

  17. [17]

    Daniels and J

    M. Daniels and J. Hogan.Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis. CRC Press, 2008

    work page 2008

  18. [18]

    A.J. Vickers. The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient.Trials, 2001

    work page 2001

  19. [19]

    Pocock et al

    S.J. Pocock et al. Subgroup analysis, covariate adjustment, and baseline comparisons in clinical trial reporting. Statistics in Medicine, 2002

    work page 2002

  20. [20]

    The generalization of ‘student’s’problem when several different population varlances are involved.Biometrika, 34(1-2):28–35, 1947

    Bernard L Welch. The generalization of ‘student’s’problem when several different population varlances are involved.Biometrika, 34(1-2):28–35, 1947

    work page 1947

  21. [21]

    Testing a point null hypothesis: The irreconcilability of p values and evidence.Journal of the American statistical Association, 82(397):112–122, 1987

    James O Berger and Thomas Sellke. Testing a point null hypothesis: The irreconcilability of p values and evidence.Journal of the American statistical Association, 82(397):112–122, 1987

    work page 1987

  22. [22]

    Health outcomes associated with antihypertensive therapies used as first-line agents: a systematic review and meta-analysis.Jama, 277(9):739–745, 1997

    Bruce M Psaty, Nicholas L Smith, David S Siscovick, Thomas D Koepsell, Noel S Weiss, Susan R Heckbert, Rozenn N Lemaitre, Edward H Wagner, and Curt D Furberg. Health outcomes associated with antihypertensive therapies used as first-line agents: a systematic review and meta-analysis.Jama, 277(9):739–745, 1997

    work page 1997

  23. [23]

    Efficacy of metformin in type ii diabetes: results of a double-blind, placebo-controlled, dose-response trial.The American journal of medicine, 103(6):491–497, 1997

    Alan J Garber, Theodore G Duncan, Anita M Goodman, Donna J Mills, Jane L Rohlf, et al. Efficacy of metformin in type ii diabetes: results of a double-blind, placebo-controlled, dose-response trial.The American journal of medicine, 103(6):491–497, 1997

    work page 1997

  24. [24]

    Does physical activity prevent weight gain–a systematic review.Obesity reviews, 1(2):95–111, 2000

    Mikael Fogelholm and Katriina Kukkonen-Harjula. Does physical activity prevent weight gain–a systematic review.Obesity reviews, 1(2):95–111, 2000

    work page 2000

  25. [25]

    Zimmerman

    D.W. Zimmerman. A note on preliminary tests of equality of variances.British Journal of Mathematical and Statistical Psychology, 57(1):173–181, 2004

    work page 2004

  26. [26]

    The 15-item geriatric depression scale (gds-15) detects changes in depressive symptoms after a major negative life event

    David J Vinkers, Jacobijn Gussekloo, Max L Stek, Rudi GJ Westendorp, and Roos C Van Der Mast. The 15-item geriatric depression scale (gds-15) detects changes in depressive symptoms after a major negative life event. the leiden 85-plus study.International journal of geriatric psychiatry, 19(1):80–84, 2004

    work page 2004

  27. [27]

    Effect of angiotensin-converting enzyme inhibition and angiotensin ii receptor blockers on cardiac angiotensin-converting enzyme 2.Circulation, 111(20):2605–2610, 2005

    Carlos M Ferrario, Jewell Jessup, Mark C Chappell, David B Averill, K Bridget Brosnihan, E Ann Tallant, Debra I Diz, and Patricia E Gallagher. Effect of angiotensin-converting enzyme inhibition and angiotensin ii receptor blockers on cardiac angiotensin-converting enzyme 2.Circulation, 111(20):2605–2610, 2005

    work page 2005

  28. [28]

    G.D. Ruxton. The unequal variance t-test is an underused alternative to student’s t-test.Behavioral Ecology, 17(4):688–690, 2006

    work page 2006

  29. [29]

    Systematic review and meta-analysis of diagnostic and prognostic studies: a tutorial.Arquivos brasileiros de cardiologia, 92:241–251, 2009

    Marcos R de Sousa and Antonio Luiz P Ribeiro. Systematic review and meta-analysis of diagnostic and prognostic studies: a tutorial.Arquivos brasileiros de cardiologia, 92:241–251, 2009

    work page 2009

  30. [30]

    Fagerland et al

    M.W. Fagerland et al. Statistical analysis of contingency tables.BMC Medical Research Methodology, 11(1):47, 2011

    work page 2011

  31. [31]

    Rasch et al

    D. Rasch et al. The robustness of parametric statistical methods.Psychological Science, 22(9):1211–1213, 2011. 21 Running Title for Header

    work page 2011

  32. [32]

    De Winter

    J.C.F. De Winter. Using the student’s t-test with extremely small sample sizes.Practical Assessment, Research, and Evaluation, 18(10):1–12, 2013

    work page 2013

  33. [33]

    A randomized, controlled trial of 3.0 mg of liraglutide in weight management.New England Journal of Medicine, 373(1):11–22, 2015

    Xavier Pi-Sunyer, Arne Astrup, Ken Fujioka, Frank Greenway, Alfredo Halpern, Michel Krempf, David CW Lau, Carel W Le Roux, Rafael Violante Ortiz, Christine Bjørn Jensen, et al. A randomized, controlled trial of 3.0 mg of liraglutide in weight management.New England Journal of Medicine, 373(1):11–22, 2015

    work page 2015

  34. [34]

    Richard Beasley, Tim Harrison, Stefan Peterson, Per Gustafson, Angus Hamblin, Thomas Bengtsson, and Malin Fagerås. Evaluation of budesonide-formoterol for maintenance and reliever therapy among patients with poorly controlled asthma: a systematic review and meta-analysis.JAMA network open, 5(3):e220615–e220615, 2022

    work page 2022

  35. [35]

    A systematic assessment and optimization of photon-counting ct for lung density quantifications.Medical Physics, 51(4):2893–2904, 2024

    Saman Sotoudeh-Paima, W Paul Segars, Dhrubajyoti Ghosh, Sheng Luo, Ehsan Samei, and Ehsan Abadi. A systematic assessment and optimization of photon-counting ct for lung density quantifications.Medical Physics, 51(4):2893–2904, 2024

    work page 2024

  36. [36]

    C.L. Olson. On choosing a test statistic in multivariate analysis of variance.Psychological Bulletin, 1976

    work page 1976

  37. [37]

    R. Kay. The aft model in survival analysis: Theory and applications.Biometrics, 1977

    work page 1977

  38. [38]

    Pocock et al

    S.J. Pocock et al. Statistical considerations in the design of clinical trials: Beta-blockers in cardiovascular medicine.Statistics in Medicine, 1987

    work page 1987

  39. [39]

    S.P. Wright. Adjusting for baseline in longitudinal clinical trials.Journal of Clinical Epidemiology, 1992

    work page 1992

  40. [40]

    S. Senn. Repeated measures anova in clinical trials: Applications in weight loss and metabolic outcomes. Biometrika, 1993

    work page 1993

  41. [41]

    A 24-week, double- blind, placebo-controlled trial of donepezil in patients with alzheimer’s disease.Neurology, 50(1):136–145, 1998

    SL Rogers, MR Farlow, RS Doody, R Mohs, LT Friedhoff, and Donepezil Study Group*. A 24-week, double- blind, placebo-controlled trial of donepezil in patients with alzheimer’s disease.Neurology, 50(1):136–145, 1998

    work page 1998

  42. [42]

    Pinheiro and D.M

    J.C. Pinheiro and D.M. Bates.Mixed-Effects Models in S and S-PLUS. Springer, 2000

    work page 2000

  43. [43]

    Beaton et al

    D.E. Beaton et al. Measuring the burden of musculoskeletal conditions.Arthritis & Rheumatism, 2002

    work page 2002

  44. [44]

    Bradburn et al

    M.J. Bradburn et al. Survival analysis part ii: Multivariate data analysis—an introduction to concepts and methods.British Journal of Cancer, 2003

    work page 2003

  45. [45]

    Maxwell and H.D

    S.E. Maxwell and H.D. Delaney.Designing Experiments and Analyzing Data: A Model Comparison Perspective. Routledge, 2004

    work page 2004

  46. [46]

    Gueorguieva and J.H

    R. Gueorguieva and J.H. Krystal. Move over anova: Progress in analyzing repeated-measures data and its reflection in papers published in the archives of general psychiatry.Archives of General Psychiatry, 61(3):310– 317, 2004

    work page 2004

  47. [47]

    S. Senn. Change from baseline and analysis of covariance revisited.Statistics in Medicine, 2006

    work page 2006

  48. [48]

    Siddique et al

    J. Siddique et al. Missing data in randomized controlled trials for weight loss.Obesity, 2008

    work page 2008

  49. [49]

    THANOS: A predictive model of electoral campaigns using twitter data and opinion polls.Data Science in Science, 4(1):2484180, 2025

    Dhrubajyoti Ghosh, William A Boettcher, Rob Johnston, and Soumendra Lahiri. THANOS: A predictive model of electoral campaigns using twitter data and opinion polls.Data Science in Science, 4(1):2484180, 2025

    work page 2025

  50. [50]

    Henderson

    C.R. Henderson. Estimation of variance and covariance components.Biometrics, 9(2):226–252, 1954

    work page 1954

  51. [51]

    Liang and S.L

    K.Y . Liang and S.L. Zeger. Longitudinal data analysis using generalized linear models.Biometrika, 73(1):13–22, 1986

    work page 1986

  52. [52]

    Zeger and K.Y

    S.L. Zeger and K.Y . Liang. Longitudinal data analysis for discrete and continuous outcomes.Biometrics, 44(4):1049–1060, 1988

    work page 1988

  53. [53]

    Neuhaus et al

    J.M. Neuhaus et al. Estimation of covariate effects in generalized linear models for longitudinal data.Biometrics, 47(4):985–996, 1991

    work page 1991

  54. [54]

    Phase ii trial and pharmacokinetic evaluation of cytosine arabinoside for leptomeningeal metastases from breast cancer.Cancer chemotherapy and pharmacology, 46:382–386, 2000

    Francisco J Esteva, Lay-Tin Soh, Frankie Ann Holmes, William Plunkett, Christina A Meyers, Arthur D Forman, and Gabriel N Hortobagyi. Phase ii trial and pharmacokinetic evaluation of cytosine arabinoside for leptomeningeal metastases from breast cancer.Cancer chemotherapy and pharmacology, 46:382–386, 2000

    work page 2000

  55. [55]

    Applying linear mixed models to estimate reliability in clinical trial data with repeated measurements.Controlled clinical trials, 25(1):13–30, 2004

    Tony Vangeneugden, Annouschka Laenen, Helena Geys, Didier Renard, and Geert Molenberghs. Applying linear mixed models to estimate reliability in clinical trial data with repeated measurements.Controlled clinical trials, 25(1):13–30, 2004

    work page 2004

  56. [56]

    Fitzmaurice et al.Applied Longitudinal Analysis

    G.M. Fitzmaurice et al.Applied Longitudinal Analysis. Wiley, 2008

    work page 2008

  57. [57]

    Temporal and gender trends in concordance of urine drug screens and self-reported use in cocaine treatment studies.Journal of addiction medicine, 3(4):211–217, 2009

    Megan S Schuler, William V Lechner, Rickey E Carter, and Robert Malcolm. Temporal and gender trends in concordance of urine drug screens and self-reported use in cocaine treatment studies.Journal of addiction medicine, 3(4):211–217, 2009. 22 Running Title for Header

    work page 2009

  58. [58]

    Linear mixed-effects modeling approach to fmri group analysis.Neuroimage, 73:176–190, 2013

    Gang Chen, Ziad S Saad, Jennifer C Britton, Daniel S Pine, and Robert W Cox. Linear mixed-effects modeling approach to fmri group analysis.Neuroimage, 73:176–190, 2013

    work page 2013

  59. [59]

    The treatment of self-efficacy among psychology and management scholars.Journal of Applied Social Psychology, 43(4):811–822, 2013

    Steven M Elias, Chet E Barney, and James W Bishop. The treatment of self-efficacy among psychology and management scholars.Journal of Applied Social Psychology, 43(4):811–822, 2013

    work page 2013

  60. [60]

    A preliminary rct of cbt-ad for adherence and depression among hiv-positive latinos on the us-mexico border: the nuevo dia study.AIDS and Behavior, 17:2816–2829, 2013

    Jane M Simoni, John S Wiebe, John A Sauceda, David Huh, Giselle Sanchez, Virginia Longoria, C An- dres Bedoya, and Steven A Safren. A preliminary rct of cbt-ad for adherence and depression among hiv-positive latinos on the us-mexico border: the nuevo dia study.AIDS and Behavior, 17:2816–2829, 2013

    work page 2013

  61. [61]

    James Y Dai, Peter B Gilbert, James P Hughes, and Elizabeth R Brown. Estimating the efficacy of preexposure prophylaxis for hiv prevention among participants with a threshold level of drug concentration.American journal of epidemiology, 177(3):256–263, 2013

    work page 2013

  62. [62]

    NJ Wiles, K Fischer, P Cowen, D Nutt, TJ Peters, G Lewis, and IR White. Allowing for non-adherence to treatment in a randomized controlled trial of two antidepressants (citalopram versus reboxetine): an example from the genpod trial.Psychological medicine, 44(13):2855–2866, 2014

    work page 2014

  63. [63]

    Models for measuring anthelmintic drug efficacy for parasitologists.Trends in parasitology, 30(11):528–537, 2014

    Martin Walker, Thomas S Churcher, and María-Gloria Basáñez. Models for measuring anthelmintic drug efficacy for parasitologists.Trends in parasitology, 30(11):528–537, 2014

    work page 2014

  64. [64]

    Increases and decreases in drug use attributed to housing status among street-involved youth in a canadian setting.Harm reduction journal, 11:1–6, 2014

    Tessa Cheng, Evan Wood, Paul Nguyen, Thomas Kerr, and Kora DeBeck. Increases and decreases in drug use attributed to housing status among street-involved youth in a canadian setting.Harm reduction journal, 11:1–6, 2014

    work page 2014

  65. [65]

    Linear mixed-effects model of qtc prolongation for olmesartan medoxomil.Journal of Clinical Pharmacology, 56(1):96, 2015

    SaeHeum Song, Nobuko Matsushima, James Lee, and Jeanne Mendell. Linear mixed-effects model of qtc prolongation for olmesartan medoxomil.Journal of Clinical Pharmacology, 56(1):96, 2015

    work page 2015

  66. [66]

    Julia B Halder, Joanne Benton, Amélie M Julé, Phillipe J Guérin, Piero L Olliaro, María-Gloria Basáñez, and Martin Walker. Systematic review of studies generating individual participant data on the efficacy of drugs for treating soil-transmitted helminthiases and the case for data-sharing.PLoS Neglected Tropical Diseases, 11(10):e0006053, 2017

    work page 2017

  67. [67]

    Statistical analyses of chicken intestinal lesion scores in battery cage studies of anti-coccidial drugs.Veterinary parasitology, 272:83–94, 2019

    Qing Kang, Christopher I Vahl, Huihao Fan, Thomas Geurden, Keith A Ameiss, and Lucas P Taylor. Statistical analyses of chicken intestinal lesion scores in battery cage studies of anti-coccidial drugs.Veterinary parasitology, 272:83–94, 2019

    work page 2019

  68. [68]

    The design, analysis and application of mouse clinical trials in oncology drug development.BMC cancer, 19:1–14, 2019

    Sheng Guo, Xiaoqian Jiang, Binchen Mao, and Qi-Xiang Li. The design, analysis and application of mouse clinical trials in oncology drug development.BMC cancer, 19:1–14, 2019

    work page 2019

  69. [69]

    In vivo efficacy of anti-malarial drugs against clinical plasmodium vivax malaria in ethiopia: a systematic review and meta-analysis.Malaria Journal, 20:1–19, 2021

    Tsige Ketema, Ketema Bacha, Kefelegn Getahun, and Quique Bassat. In vivo efficacy of anti-malarial drugs against clinical plasmodium vivax malaria in ethiopia: a systematic review and meta-analysis.Malaria Journal, 20:1–19, 2021

    work page 2021

  70. [70]

    Evaluation of the effects of skin-to-skin contact on newborn sucking, and breastfeeding abilities: a quasi- experimental study design.Nutrients, 14(9):1846, 2022

    Jia-Zhen Huang, Chi-Nien Chen, Chih-Ping Lee, Chien-Huei Kao, Heng-Cheng Hsu, and An-Kuo Chou. Evaluation of the effects of skin-to-skin contact on newborn sucking, and breastfeeding abilities: a quasi- experimental study design.Nutrients, 14(9):1846, 2022

    work page 2022

  71. [71]

    Randomized clinical trial of a digital integrative medicine intervention among patients undergoing active cancer treatment.npj Digital Medicine, 8(1):29, 2025

    Jun J Mao, Karolina Bryl, Erin F Gillespie, Angela Green, Tony KW Hung, Raymond Baser, Katherine Panageas, Michael A Postow, and Bobby Daly. Randomized clinical trial of a digital integrative medicine intervention among patients undergoing active cancer treatment.npj Digital Medicine, 8(1):29, 2025

    work page 2025

  72. [72]

    Routledge, 2017

    Marie Davidian.Nonlinear models for repeated measurement data. Routledge, 2017

    work page 2017

  73. [73]

    chapman and hall/CRC, 2017

    Simon N Wood.Generalized additive models: an introduction with R. chapman and hall/CRC, 2017

    work page 2017

  74. [74]

    Semi-parametric joint modeling of survival and longitudinal data: the r package jsm.Journal of Statistical Software, 93:1–29, 2020

    Cong Xu, Pantelis Z Hadjipantelis, and Jane-Ling Wang. Semi-parametric joint modeling of survival and longitudinal data: the r package jsm.Journal of Statistical Software, 93:1–29, 2020

    work page 2020

  75. [75]

    Farewell

    V .T. Farewell. The use of mixture models for the analysis of survival data with long-term survivors.Biometrics, 1982

    work page 1982

  76. [76]

    L.J. Wei. The accelerated failure time model: A useful alternative to the cox regression model in survival analysis. Statistics in Medicine, 1992

    work page 1992

  77. [77]

    Tsiatis and M

    A.A. Tsiatis and M. Davidian. Joint modeling of longitudinal and time-to-event data: An overview.Statistica Sinica, 14(3):809–834, 2004

    work page 2004

  78. [78]

    Proust-Lima et al

    C. Proust-Lima et al. Joint latent class models for longitudinal and time-to-event data: A review.Statistical Methods in Medical Research, 18(2):147–166, 2009

    work page 2009

  79. [79]

    Rizopoulos.Joint Models for Longitudinal and Time-to-Event Data: With Applications in R

    D. Rizopoulos.Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. Chapman and Hall/CRC, 2012. 23 Running Title for Header

    work page 2012

  80. [80]

    Assessing time-by-covariate interactions in proportional hazards regression models using cubic spline functions.Statistics in medicine, 13(10):1045–1062, 1994

    Kenneth R Hess. Assessing time-by-covariate interactions in proportional hazards regression models using cubic spline functions.Statistics in medicine, 13(10):1045–1062, 1994

    work page 1994

Showing first 80 references.