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  ["project_title"]=>
  string(109) "Study proposal: Evaluating win statistics in longitudinal studies using data from the Bapineuzumab 302 trial."
  ["project_narrative_summary"]=>
  string(626) "In this study, we will use data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to evaluate the effect of Bapineuzumab on clinical outcomes, which include mortality, Alzheimer’s Disease Assessment Scale (ADAS) score and the Disability Assessment for Dementia (DAD) score. Assessment of the treatment effect will be carried and win measures will be estimated (along with the corresponding confidence intervals) at baseline and subsequently every 13 weeks (t = 13, 26, 39, 52, 65, 78 weeks). Then the overall test statistic and global win measures will be evaluated for the whole study duration. 



"
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    ["first_name"]=>
    string(6) "Roland"
    ["last_name"]=>
    string(9) "Matsouaka"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(15) "Duke University"
    ["email"]=>
    string(25) "roland.matsouaka@duke.edu"
    ["state_or_province"]=>
    string(2) "NC"
    ["country"]=>
    string(2) "US"
  }
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      ["p_pers_f_name"]=>
      string(6) "Huiman"
      ["p_pers_l_name"]=>
      string(8) "Barnhart"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
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      ["p_pers_l_name"]=>
      string(10) "Lokhnygina"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(15) "Duke University"
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  ["project_ext_grants"]=>
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    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
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  ["project_date_type"]=>
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  ["property_scientific_abstract"]=>
  string(1656) "Background: Alzheimer’s disease (AD) is a multifaceted disease, which requires multiples outcomes to ascertain the patient's experience.  AD clinical trials collect data on multiple endpoints to comprehensively evaluate treatment efficacy. 

Objective: To evaluate treatment efficacy through a hierarchical composite endpoints using win measures. The goal is to provide a combine treatment effect measure that encompasses the whole patient experience and a unique related test statistic  that does not require multiplicity adjustments. 

Study design: Posthoc observational study involving the original study of the  Bapineuzumab Trials (Apolipoprotein E4 Carriers and Non-Carriers)

Participants: Patients enrolled in the Bapineuzumab Trials (NCT 00574132 and NCT 00575055)

Primary outcome: composite of mortality, Alzheimer’s Disease Assessment Scale (ADAS) score and the Disability Assessment for Dementia (DAD) score.

Secondary outcome: scores on the Neuropsychological Test Battery,  the Clinical Dementia Rating–Sum of Boxes, the MMSE, and the Dependence Scale

Statistical analysis: Summary statistics will be estimated using the outcomes and patients' characteristics.  Treatment groups will be compared on outcomes and patients' characteristics using t-test, wilcoxon test, Chi-square test or fisher's exact test accordingly, depending on the nature of the variables (continuous,  ordinal, categorical) and their underlying disrtibutions (normal, non-normal, or count). Composite endpoints will be evaluated between treatment groups using the win measures and their confidence intervals.



"
  ["project_brief_bg"]=>
  string(1505) "Neurodegenerative disorders such as Alzheimer’s disease (AD) present a significant global health challenge, characterized by cognitive decline, functional impairment, and other debilitating effects. Current AD clinical trials often assess multiple longitudinal primary endpoints to comprehensively evaluate treatment efficacy. Often, competing risk data arise when non-terminal events are censored by terminal or fatal events. An abundant literature exists on model-based methods to analyze such data. Traditional methods, however, may fail to capture global treatment effects, require larger sample sizes due to multiplicity adjustments, and may not fully exploit multivariate longitudinal data. Specifications of the joint distribution is complex and not easy to provide as the measured outcomes have different natures, scales, and underlying distributions. To address these limitations, we will develop and promote the use of win statistics (win ratio, net treatment benefit, and win odds) for longitudinal data. The win statistics offer flexibility throughout various data distributions encountered in AD research and maximizes the utilization of longitudinal data. These are nonparametric rank-based measures of treatment effect on additive and multiplicative scale. This will enable a comprehensive assessment of the treatment efficacy across multiple endpoints and time points without the need for multiplicity adjustments, effectively controlling Type I error while enhancing statistical power. "
  ["project_specific_aims"]=>
  string(722) "To assess the treatment effect using win measures  (along with the corresponding confidence intervals) at baseline and subsequently every 13 weeks (t = 13, 26, 39, 52, 65, 78 weeks). Then the overall test statistic and global win measures will be evaluated for the whole study duration, based on the following aims:

Aims 1:  Evaluate win ratio and corresponding confidence intervals; 

Aim 2: Calculate the win odds and draw inference;  

Aim 3: Estimate the net treatment benefits. 

In all of these aims we will  use the Bapineuzumab trials data to illustrate the methods.



Our global hypothesis is that patients on Bapineuzumab have better outcomes overall compare to the placebo."
  ["project_study_design"]=>
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    ["label"]=>
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      ["value"]=>
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      ["label"]=>
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    }
    [1]=>
    array(2) {
      ["value"]=>
      string(37) "participant_level_data_only_from_yoda"
      ["label"]=>
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    }
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  ["project_research_methods"]=>
  string(691) "Participant inclusion criteria: We will use the same inclusion criteria as the original studies

Participation exclusion criteria: No systematic exclusions will be considered. However, in most of our analyses, the patients with missing outcome(s) measures or those who did not complied with the study treatments, as per the study-specific protocol, will be excluded. Thus, the analysis will be performed in the modified intention-to-treat population, which includes participants who received at least one dose of the study treatment and underwent a baseline and at least one post-baseline evaluation of the co-primary efficacy endpoints. 

We do not intend to input missing data."
  ["project_main_outcome_measure"]=>
  string(623) "Primary outcome: composite of mortality, Alzheimer’s Disease Assessment Scale (ADAS)-Cognitive Subscale/11 score, and the Disability Assessment for Dementia (DAD) score.

Secondary outcome: scores on the Neuropsychological Test Battery,  the Clinical Dementia Rating–Sum of Boxes, the Mini-Mental State Examination (MMSE), and the Dependence Scale.

The category will be defined based on the Bapineuzumab treatment versus the placebo. 

We will also consider subgroup analyses where the participants will be stratified by age subgroups, race (Black, Whites, Hispanic/Latino) and Sex (Male vs. Female)."
  ["project_main_predictor_indep"]=>
  string(681) "The predictors will represent the set of baseline patient characteristics defining the study populations and will be used to determine the descriptive statistics of patients. These include characteristics presented in Table 1 of the Bapineuzumab trial publication (N Engl J Med 2014; 370:322-333) and include age, sex, race (White/Black/Hispanic/Latino), APOE status, use of acetylchlolinesterase inhibitor or memantine, MMSE total score, ADAS-cog11 total score, DAD total score, total infusion received.



Particular interest will be reserved to the variables age, sex, race as we also consider to investigate the effect of treatment in these pre-specified subgroups."
  ["project_other_variables_interest"]=>
  string(483) " Additional predictors: APOE epsilon 4 status, use of acetylcholinesterase inhibitor or memantine, 



Additional outcomes: Clinical dementia rating scale--Sum of boxes total score, Neuropsychological Test Battery Total Score, Dependance scale, and total infusion received.



All variables will be defined according to the respective definition used in the original trials (as indicated in the Bapineuzumab trial publication (N Engl J Med 2014; 370:322-333))"
  ["project_stat_analysis_plan"]=>
  string(718) "we will develop and promote the use of win statistics (win ratio, net treatment benefit, and win odds) for longitudinal data. The win statistics offer flexibility throughout various data distributions encountered in AD research and maximizes the utilization of longitudinal data. These are nonparametric rank-based measures of treatment effect on additive and multiplicative scale. Overall test statistic and global win measures will be evaluated for the whole study duration. We will also extend the assessment of the win measures by also including the findings on positron-emission tomographic amyloid imaging with the use of Pittsburgh compound B (PIB-PET) and cerebrospinal fluid phosphorylated tau concentrations."
  ["project_software_used"]=>
  array(2) {
    ["value"]=>
    string(1) "r"
    ["label"]=>
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  ["project_timeline"]=>
  string(365) "Aim 1: Evaluate win ratio and corresponding confidence intervals (January--March 2025)

Aim 2:  calculate the win odds and draw inference (April--June 2025)  

Aim 3:  estimate the net treatment benefits (July--September 2025)

Analysis completion date: 30 September  2025

Results reported back to the YODA Project: October -- November 2024"
  ["project_dissemination_plan"]=>
  string(688) "First manuscript writing and publication: March 2025--June 2025; 

Publication target journal: Biostatistics, Statistics in Medicine, Journal of Biopharmaceutical statistics.

 

Second manuscript writing and publication: July 2025--September 2025

Publication target journal:  Biostatistics, Statistical Methods in Medical Research, Journal of Biopharmaceutical statistics.



Third manuscript writing and publication: Oct. 2025--December 2025

Publication target journal:  Biostatistics, Statistical Methods in Medical Research, Journal of Biopharmaceutical statistics.



Presentation at the Joint Statistical Meetings: August 2025"
  ["project_bibliography"]=>
  string(1925) "

Tang, DI, Geller, NL and Pocock, SJ. (1993). On the design and analysis of randomized clinical trials with multiple endpoints. Biometrics 49(1), 23–30.
Umlauft, M, Placzek, M, F, Konietschke. and Pauly, M. (2019). Wild bootstrapping rank-based procedures: Multiple testing in nonparametric factorial repeated measures designs. Journal of Multivariate Analysis 171, 176–192.
Wei, LJ and Johnson, WE. (1985). Combining dependent tests with incomplete repeated measurements. Biometrika 72(2), 359–364.
Wei, LJ, Lin, DY and Weissfeld, L. (1989). Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. Journal of the American Statistical Association 84(408), 1065–1073.
Yu, JC, Hl´avka, JP, Joe, E, Richmond, FJ and Lakdawalla, DN. (2022). Impact of non-binding FDA guidances on primary endpoint selection in Alzheimer’s disease trials. Alzheimer’s & Dementia: Translational Research & Clinical Interventions 8(1), e12280.
Zhang, W, Liu, A, Tang, LL and Li, Q. (2019). A cluster-adjusted rank-based test for a clinical trial concerning multiple endpoints with application to dietary intervention assessment. Biometrics 75(3), 821–830.
Zhu, H, Chen, K, Luo, X, Yuan, Y and Wang, JL. (2019). Fmem: Functional mixed effects models for longitudinal functional responses. Statistica Sinica 29(4), 2007–33.
Zhuang, Y, Guan, Y, Qiu, L, Lai, M, Tan, MT and Chen, P. (2018). A novel rank-based non-parametric method for longitudinal ordinal data. Statistical Methods in Medical Research 27(9), 2775–2794.
Salloway, S, Sperling, R, Fox, NC, Blennow, K, Klunk, W, Raskind, M, Sabbagh, M, Honig, LS, Porsteinsson, AP, Ferris, S and others. (2014). Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. New England Journal of Medicine 370(4), 322–333.

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    string(34) "yoda-project-review-2024-0940_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(15934)
    ["date"]=>
    string(19) "2025-03-06 17:06:57"
    ["modified"]=>
    string(19) "2025-03-06 17:06:57"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(15) "johnson-johnson"
  ["request_overridden_res"]=>
  string(1) "3"
  ["human_research_protection_training"]=>
  bool(true)
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(1) {
  [0]=>
  string(3) "n-a"
}


num of trials
array(1) {
  [0]=>
  string(1) "2"
}


res
array(1) {
  [0]=>
  string(1) "3"
}