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      string(184) "NCT01515423 - A Randomized, Multicenter, Double-Blind, Non-inferiority Study of Paliperidone Palmitate 3 Month and 1 Month Formulations for the Treatment of Subjects With Schizophrenia"
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  ["project_title"]=>
  string(78) "Novel Approaches to Address Analytical Challenges in the Non-Inferiority Trial"
  ["project_narrative_summary"]=>
  string(659) "Non-inferiority (NI) trials play a critical role in healthcare research. Rather than showing that a new treatment is better, NI trials aim to show that a new treatment is not meaningfully worse than an existing one. This is important when the new option offers other benefits—such as lower cost, easier access, or fewer side effects. These trials are especially valuable in behavioral, rehabilitation, and psychosocial interventions where traditional placebo-controlled trials may not be ethical or feasible. Our project will develop new, advanced statistical methods that make NI trials more reliable and relevant for patients, clinicians, and researchers."
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    ["first_name"]=>
    string(5) "Abdus"
    ["last_name"]=>
    string(6) "Sattar"
    ["degree"]=>
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    ["primary_affiliation"]=>
    string(31) "Case Western Reserve University"
    ["email"]=>
    string(15) "sattar@case.edu"
    ["state_or_province"]=>
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    ["country"]=>
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  ["property_scientific_abstract"]=>
  string(1465) "Background: Non-inferiority (NI) trials play a critical role in healthcare research. Rather than showing that a new treatment is better, NI trials aim to show that a new treatment is not meaningfully worse than an existing one. This is important when the new option offers other benefits—such as lower cost, easier access, or fewer side effects.

Objective: Our project will develop new, advanced statistical methods that make NI trials more reliable and relevant for patients, clinicians, and researchers.

Design: Non-inferiority trial design

Participants: All Schizophrenia patients in the Risperidone study (NCT00249223) and Paliperidone Palmitate study (NCT01515423) will be included.

Outcomes: Longitudinal continuous outcome variables such as Positive and Negative Syndrome Scale (PANSS) score, Clinical Global Impression Severity (CGI-S), Personal and Social Performance (PSP) score. 

Statistical Analysis: We plan to develop new statistical methods for NI testing.  Specifically, we will develop a likelihood-based approach for analyzing non-normal and non-linear longitudinal outcomes, incorporating flexible random effects distributions and addressing non-ignorable missing data to enable valid non-inferiority testing. Further, we will aim to develop and validate statistical methods that systematically account for real-world modifications in complex interventions (if any in the NCT00249223 and NCT01515423 trials). "
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  string(2393) "Non-inferiority (NI) trials are central to patient-centered care and comparative effectiveness research because they evaluate whether a new intervention preserves the effectiveness of an established treatment while offering practical advantages such as lower cost, greater convenience, or improved access. These designs are especially important in behavioral, psychosocial, and rehabilitation research, where placebo controls may be unethical or impractical. However, NI trials are highly vulnerable to bias when real-world complications arise, including protocol deviations, changes in intervention delivery, non-normal and non-linear longitudinal outcomes, and missing data. Existing methods do not adequately address these challenges within a unified framework, particularly for longitudinal studies.

This project will develop advanced likelihood-based statistical methods to improve the design and analysis of NI trials with longitudinal continuous outcomes. Specifically, the project will: (1) develop flexible models for non-normal and non-linear outcomes using multivariate skew-normal random effects and semiparametric spline regression; (2) create methods to account for protocol deviations and real-world delivery changes, including shifts from in-person to telehealth formats; and (3) establish a global likelihood-based test for non-inferiority across the full study period rather than at a single time point. These methods will also address non-ignorable missing data, a major source of bias in NI trials.

The significance of this work is that it will materially enhance generalizable scientific and medical knowledge by providing more valid, transparent, and interpretable methods for evaluating alternative interventions under realistic trial conditions. The methods are motivated by challenges observed in a completed three-arm NI trial of fatigue management interventions for people with multiple sclerosis, but they are intended to generalize broadly to patient-centered comparative effectiveness research in areas such as mental health, chronic disease management, rehabilitation, and telehealth. By improving the rigor and trustworthiness of NI analyses, this work will help researchers, clinicians, and policymakers make better-informed decisions about whether more accessible or scalable interventions can be used without unacceptable loss of effectiveness."
  ["project_specific_aims"]=>
  string(1504) "This project develops advanced statistical methods to address critical gaps in non-inferiority (NI) trials, particularly regarding real-world complexities and longitudinal data.

Aim 1: Modeling Complex Longitudinal Outcomes. We will develop a likelihood-based framework that simultaneously handles non-normal random effects (multivariate skew-normal), non-linear trajectories (spline regression), and non-ignorable missing data (hybrid mixed-effects models). 

**Hypothesis:** Likelihood-based models incorporating skew-normal random effects will yield lower relative bias and more efficient parameter estimates compared to standard Gaussian models when longitudinal data is asymmetric.

Aim 2: Addressing Protocol Deviations. We will develop joint modeling and copula-based frameworks to account for intervention delivery modifications (e.g., shifts to telehealth).

**Hypothesis:** Jointly modeling longitudinal outcomes with protocol deviation indicators will correct shifts in location parameters, preventing biased NI inferences caused by delivery changes.

Aim 3: Global Non-Inferiority (NI) Testing. We will establish a global pseudo-likelihood ratio test ($LRT_p$) to evaluate NI over an entire study period rather than at cross-sectional points.

**Hypothesis:** A global $LRT_p$ utilizing gradient projection algorithms will provide a more comprehensive and valid assessment of treatment effects across repeated measures than current local hypothesis tests."
  ["project_study_design"]=>
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    ["label"]=>
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  ["project_purposes"]=>
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    [0]=>
    array(2) {
      ["value"]=>
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      ["label"]=>
      string(37) "Develop or refine statistical methods"
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  ["project_research_methods"]=>
  string(1854) "For the YODA trials NCT00249223 and NCT01515423, I will use the full participant cohorts provided through YODA and will not apply any additional post-randomization inclusion or exclusion criteria to define the analysis sample beyond data availability required for the planned statistical analyses. Thus, no extra analytic exclusion criteria will be imposed beyond the original trial eligibility criteria and available shared IPD.



In addition to YODA data, I will use our own independent multiple sclerosis trial dataset to validate newly developed non-inferiority testing methods. In our MS trial, eligibility criteria were: self-reported diagnosis of multiple sclerosis, age >18 years, moderate-to-severe fatigue (Fatigue Severity Scale >=4), and ability to speak and read English. Exclusion criteria were inability to understand the consent form or unwillingness to participate in study activities. 



Our dataset is described in: Plow M, Packer T, Mathiowetz VG, et al. “A Non-Inferiority Randomized Clinical Trial Comparing Three Delivery Formats of a Rehabilitation Intervention to Reduce Fatigue among Individuals With Multiple Sclerosis.” Archives of Physical Medicine and Rehabilitation (accepted/in press). 



I may also examine other relevant non-YODA datasets for external validation of the proposed methods. However, YODA trial data will not be pooled with our MS dataset or any other dataset for combined IPD or aggregated summary analyses. Each dataset will be analyzed separately for statistical method validation.



The inclusion/exclusion criteria pulled from your manuscript are on the recruitment and eligibility section: age >18, self-reported MS, FSS >=4, English-speaking/reading; exclusions were inability to understand consent or unwillingness to participate. "
  ["project_main_outcome_measure"]=>
  string(1872) "For the requested YODA trials, I will use repeated longitudinal continuous outcomes available in the shared datasets and appropriate for development and validation of non-inferiority methods for longitudinal continuous data. These outcomes will be analyzed in their original continuous form at all observed follow-up time points; I do not plan to categorize them into binary or ordinal responder variables. This is consistent with my project’s focus on longitudinal continuous outcomes, flexible modeling of non-normal/non-linear responses, and global non-inferiority testing over the full study period. 



NCT00249223: ClinicalTrials.gov identifies the primary efficacy outcome as change from baseline in PANSS total score. It also says additional efficacy testing includes CGI, but the snippet I found does not confirm PSP for this trial. 

NCT01515423: This trial clearly includes PANSS, CGI-S, and PSP as efficacy measures in the double-blind phase, although its primary endpoint is not PANSS-based in the sources I found; instead, PANSS/CGI-S/PSP appear among the secondary efficacy measures. 



Change will be operationalized as within-subject and between-group differences over time in the raw continuous scores. For PANSS and CGI-S, lower scores indicate improvement and higher scores indicate worsening. For PSP, higher scores indicate improvement and lower scores indicate worsening. I do not propose a single universal cutoff for positive or negative change across measures, because the goal is to evaluate statistical methods for continuous longitudinal outcomes rather than responder definitions. Non-inferiority will therefore be assessed using estimated longitudinal mean differences and the proposed global testing framework across time, rather than by categorizing patients according to a fixed change threshold. 

"
  ["project_main_predictor_indep"]=>
  string(106) "Main predictor: Binary treatment indicator variable those used in the NCT00249223 and NCT01515423 trials. "
  ["project_other_variables_interest"]=>
  string(830) "For the requested YODA trials, I plan to account for patient demographic and clinical characteristics that may influence longitudinal continuous outcomes and/or modify non-inferiority assessment, subject to availability in each shared dataset. Anticipated variables include age, sex, race/ethnicity, baseline disease severity, baseline value of the longitudinal outcome, treatment group, study visit/time, and treatment-by-time interaction terms. If available, I will also examine other clinically relevant covariates such as illness duration, prior treatment exposure, comorbidity burden, concomitant medication use, and protocol deviation indicators. These variables will be used as adjustment covariates, confounders, or potential effect modifiers in the longitudinal models, rather than as criteria for excluding participants."
  ["project_stat_analysis_plan"]=>
  string(1149) "My project develops and compares new likelihood-based models for longitudinal non-inferiority trials. I will evaluate model performance using both simulation studies and application to the requested YODA trial data. Performance measures will include bias of parameter estimates, mean squared error, standard error estimation, confidence interval performance, and the stability and interpretability of estimated non-inferiority effects over time. For hypothesis-testing methods, I will evaluate operating characteristics such as Type I error control, power, and the behavior of the global likelihood ratio test under relevant data-generating conditions. Competing models will be compared based on goodness of fit, computational feasibility, robustness to non-normal outcomes and missing data assumptions, and sensitivity analyses under alternative modeling assumptions. For models addressing protocol deviations, I will also compare feasibility and computational burden of the joint-modeling approach. The goal is to determine which methods provide the most valid, efficient, and robust inference for longitudinal continuous non-inferiority outcomes."
  ["project_software_used"]=>
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      ["label"]=>
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      ["label"]=>
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  ["project_timeline"]=>
  string(752) "I am re-submitting my grant application to PCORI. LOI is due end of April, and the mail application is due end of August. So, I would like to do some preliminary analysis for the LOI submission. For example, I would like to review the outcome variables distributions and its trend over the study period. I will also be interested to examine the missing data pattern and percentages in the datasets. If there is any protocol modification, I would like to examine its effects on the outcome variables. These are the aspects I would like to do at this time, prior to funding. Once it is funded, I will start the project. Hoping to start the project in May 2027. If the funding is approved for three years, then the project will continue until April 2030. "
  ["project_dissemination_plan"]=>
  string(198) "Publish papers in statistical journals and develop software which I mentioned above. I will be interested to publish in Biometrics, Biostatistics, Statistics in Medicine, or similar other journals. "
  ["project_bibliography"]=>
  string(1392) "
Here are some relevant citations,

Azzalini A, Dalla Valle A. The multivariate skew-normal distribution. Article. Biometrika. 1996;83(4):715-726. doi:10.1093/biomet/83.4.715
Azzalini A, Capitanio A. Statistical applications of the multivariate skew normal distribution. Article. Journal of the Royal Statistical Society Series B: Statistical Methodology. 1999;61(3):579-602. doi:10.1111/1467-9868.00194
Sattar A, Sinha SK. INFERENCE WITH JOINT MODELS UNDER MISSPECIFIED RANDOM EFFECTS DISTRIBUTIONS. Article. Journal of Statistical Research. 2021;55(1):187-205. doi:10.47302/jsr.2021550113
Wu L. Mixed effects models for complex data. CRC Press; 2010.
Sinha SK, Sattar A. Inference in semi-parametric spline mixed models for longitudinal data. Article. Metron. 2015;73(3):377-395. doi:10.1007/s40300-015-0059-2
Li W, Zhang Y, Tang N. Non-Parametric Non-Inferiority Assessment in a Three-Arm Trial with Non-Ignorable Missing Data. Article. Mathematics. 2023;11(1)246. doi:10.3390/math11010246
Li H, Tian G, Tang N, Cao H. Assessing non-inferiority for incomplete paired-data under non-ignorable missing mechanism. Article. Computational Statistics and Data Analysis. 2018;127:69-81. doi:10.1016/j.csda.2018.05.009
Little R. Selection and pattern-mixture models. Longitudinal Data Analysis. 2008:409-431.

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