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  ["project_title"]=>
  string(104) "Longitudinal Analysis of Glycemic and Renal Outcomes in Canagliflozin Clinical Trials in Type 2 Diabetes"
  ["project_narrative_summary"]=>
  string(870) "Type 2 diabetes is a long-term disease in which blood sugar levels are too high. Over time, it can cause kidney disease, heart disease, stroke, and nerve damage. Canagliflozin is a medication used to treat type 2 diabetes by helping the body remove extra sugar through the urine.



In this study, we will use de-identified participant-level data from completed canagliflozin trials available through the YODA Project. We will describe the patients in each trial and examine how blood sugar, kidney-related measures, body weight, blood pressure, cardiovascular outcomes, and selected safety outcomes changed over time.



We will first analyze each trial and then compare trials. This study may show how canagliflozin trial outcomes differed across patient groups and trial settings, which may help researchers plan future type 2 diabetes trials."
  ["project_learn_source"]=>
  string(9) "colleague"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(10) "Seung Hyun"
    ["last_name"]=>
    string(4) "Baek"
    ["degree"]=>
    string(5) "Ph.D."
    ["primary_affiliation"]=>
    string(23) "Sungkyunkwan University"
    ["email"]=>
    string(17) "backtion@skku.edu"
    ["state_or_province"]=>
    string(11) "Gyeonggi-do"
    ["country"]=>
    string(17) "Republic of Korea"
  }
  ["project_key_personnel"]=>
  array(1) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(7) "Yoonsuk"
      ["p_pers_l_name"]=>
      string(3) "Cho"
      ["p_pers_degree"]=>
      string(5) "Ph.D."
      ["p_pers_pr_affil"]=>
      string(23) "Sungkyunkwan University"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
  }
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(2) "no"
    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1612) "Background:

Type 2 diabetes is associated with kidney and cardiovascular complications. Canagliflozin has been studied in multiple randomized trials, but participant-level outcome patterns across trials remain incompletely characterized.



Objective:

To evaluate baseline characteristics, longitudinal glycemic and renal outcomes, and treatment effect heterogeneity using de-identified participant-level data from completed canagliflozin trials.



Study Design:

Retrospective secondary analysis using participant-level data meta-analysis of randomized canagliflozin trials. Analyses will be conducted within each trial and, where comparable, across trials while preserving trial structure.



Participants:

Randomized adults with type 2 diabetes from the requested trials who have baseline and post-baseline outcome data.



Primary and Secondary Outcome Measure(s):

Primary outcomes include HbA1c and fasting plasma glucose. Secondary outcomes include renal measures, body weight, blood pressure, cardiovascular outcomes, and selected safety outcomes, where available.



Statistical Analysis:

Baseline characteristics will be summarized by trial and treatment arm. Longitudinal outcomes will be analyzed using mixed-effects or repeated-measures models. Treatment heterogeneity will be explored by baseline HbA1c, renal function, albuminuria, age, sex, and cardiovascular risk. Cross-trial analyses will use one-stage IPD models and/or two-stage meta-analysis. Missing data will be summarized descriptively."
  ["project_brief_bg"]=>
  string(2931) "Type 2 diabetes mellitus is a major chronic metabolic disease and an important public health problem worldwide. Poor glycemic control is associated with long-term complications, including chronic kidney disease, cardiovascular disease, retinopathy, neuropathy, reduced quality of life, and premature mortality. Because patients with type 2 diabetes vary widely in baseline glycemic control, kidney function, albuminuria, cardiovascular risk, background medications, and comorbidities, treatment outcomes observed in clinical trials may differ across patient populations and study settings.



Canagliflozin is a sodium-glucose cotransporter 2 inhibitor that has been evaluated in multiple randomized clinical trials in adults with type 2 diabetes. These trials include studies of glycemic efficacy and safety, as well as studies in populations with higher renal or cardiovascular risk. Published trial reports provide important evidence on the efficacy and safety of canagliflozin, but aggregate results cannot fully describe participant-level baseline distributions, longitudinal outcome trajectories, missing-data patterns, or heterogeneity of treatment response across trials.



Reanalysis of de-identified individual participant-level data from completed canagliflozin trials provides an opportunity to better understand how glycemic, renal, cardiometabolic, cardiovascular, and safety outcomes vary within and across trial populations. This project will characterize baseline participant profiles, evaluate longitudinal changes in HbA1c, fasting plasma glucose, kidney-related measures, body weight, blood pressure, and other available outcomes, and explore whether baseline characteristics are associated with differences in outcome trajectories or treatment effects.



The significance of this work is methodological and clinical. Scientifically, the study will help clarify how trial population characteristics, endpoint availability, visit schedules, and baseline risk factors influence observed outcomes in type 2 diabetes trials. Medically, the findings may improve understanding of which patient characteristics are associated with different glycemic or renal outcome patterns during treatment. The results may also inform future diabetes clinical trial design by identifying factors relevant to patient selection, subgroup definition, endpoint planning, follow-up schedules, and handling of missing data.



This study will not involve new patient recruitment, new interventions, or external data linkage. Only de-identified participant-level data available through the YODA Project will be used. Findings will be reported in aggregate form and are expected to contribute to generalizable knowledge on type 2 diabetes trial methodology, treatment response heterogeneity, and the interpretation of longitudinal glycemic and renal outcomes in randomized clinical trials."
  ["project_specific_aims"]=>
  string(1272) "Aim 1. To characterize baseline demographic and clinical profiles of participants enrolled in completed canagliflozin randomized trials in type 2 diabetes.

Hypothesis: Baseline characteristics, including glycemic control, renal function, albuminuria, cardiovascular risk, and background therapy, differ across trial populations and treatment contexts.



Aim 2. To evaluate longitudinal glycemic and renal outcome trajectories within each trial.

Hypothesis: Changes in HbA1c, fasting plasma glucose, eGFR, albuminuria-related measures, body weight, and blood pressure vary according to baseline clinical characteristics and trial setting.



Aim 3. To assess treatment effect heterogeneity and cross-trial differences while preserving trial structure.

Hypothesis: The magnitude and pattern of canagliflozin-associated outcomes differ across baseline risk strata and across trials, reflecting differences in participant characteristics, endpoint availability, follow-up duration, and study design.



This project will use de-identified individual participant-level data to generate evidence on longitudinal outcome patterns, subgroup differences, and trial design factors relevant to future type 2 diabetes studies."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(4) {
    [0]=>
    array(2) {
      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(22) "participant_level_data"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(37) "participant_level_data_only_from_yoda"
      ["label"]=>
      string(51) "Meta-analysis using only data from the YODA Project"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(34) "research_on_clinical_trial_methods"
      ["label"]=>
      string(34) "Research on clinical trial methods"
    }
  }
  ["project_research_methods"]=>
  string(2000) "The data source will consist exclusively of de-identified individual participant-level data from completed randomized clinical trials of canagliflozin in adults with type 2 diabetes mellitus made available through the YODA Project. No external individual-level datasets will be used, and no external data linkage will be performed.



The analytic population will include randomized participants from the requested canagliflozin trials based on the following criteria:



Inclusion Criteria



Randomized participants enrolled in one of the requested canagliflozin trials.

Adults with type 2 diabetes mellitus as defined by the original trial eligibility criteria.

Availability of baseline demographic and clinical data.

Availability of baseline value for at least one outcome of interest, such as HbA1c, fasting plasma glucose, eGFR, serum creatinine, urinary albumin-to-creatinine ratio, body weight, or blood pressure.

For longitudinal analyses, availability of at least one post-baseline assessment for the outcome being analyzed.



Exclusion Criteria



Participants without baseline data required for the specific analysis.

Participants without any post-baseline outcome assessment for longitudinal analyses.

Participants from treatment arms or study periods not relevant to the prespecified canagliflozin versus comparator/placebo analyses, if applicable.



Original trial eligibility criteria, treatment assignments, visit schedules, and endpoint definitions will be preserved. No additional clinical inclusion or exclusion criteria will be imposed beyond data availability requirements necessary for the planned analyses. Individual trials will first be analyzed separately. Where variables and endpoints are sufficiently comparable, harmonized participant-level cross-trial analyses will be conducted within the YODA secure research environment using only YODA Project data."
  ["project_main_outcome_measure"]=>
  string(1386) "The primary outcome measures will be longitudinal glycemic outcomes:



HbA1c: baseline value, scheduled post-baseline values, and change from baseline, analyzed as a continuous variable.

Fasting plasma glucose: baseline value, scheduled post-baseline values, and change from baseline, analyzed as a continuous variable where available.



Secondary outcome measures will include renal, cardiometabolic, cardiovascular, and safety outcomes where available:



Renal outcomes: eGFR, serum creatinine, urinary albumin-to-creatinine ratio, albuminuria category, and albuminuria progression.

Cardiometabolic outcomes: body weight, systolic blood pressure, and diastolic blood pressure.

Cardiovascular outcomes: cardiovascular death, myocardial infarction, stroke, or composite cardiovascular outcomes as defined in the original trials.

Safety outcomes: adverse events, serious adverse events, and selected safety events of interest.



Continuous outcomes will be analyzed as observed values and change from baseline. Categorical, binary, and time-to-event outcomes will follow the original trial definitions where applicable. For cross-trial analyses, outcomes will be harmonized only when definitions, units, and visit timing are sufficiently comparable. Otherwise, outcomes will be analyzed separately by trial."
  ["project_main_predictor_indep"]=>
  string(1381) "The main independent variable will be randomized treatment assignment within each trial. Treatment groups will be categorized according to the original trial definitions, such as canagliflozin dose groups and placebo or active comparator groups. Randomization will be preserved, and treatment groups will not be reclassified except for prespecified harmonization of comparable canagliflozin doses or comparator categories where appropriate.



For longitudinal analyses, key model variables will include treatment, visit/time, and the treatment-by-time interaction. The treatment-by-time interaction will be used to estimate whether outcome trajectories differ between canagliflozin and comparator groups over follow-up.



For heterogeneity analyses, prespecified baseline variables will be evaluated as potential effect modifiers, including baseline HbA1c, renal function/eGFR category, albuminuria status, age, sex, baseline body weight, blood pressure, cardiovascular risk status, and background antihyperglycemic therapy, where available.



For cross-trial analyses, trial identifier will be included as a categorical variable to account for between-trial differences in study design, population, follow-up duration, and endpoint structure. Cross-trial treatment effects will be interpreted within the context of trial-level heterogeneity."
  ["project_other_variables_interest"]=>
  string(1890) "Other Variables of Interest



Other variables of interest will be used for descriptive summaries, covariate adjustment, subgroup analyses, and assessment of cross-trial heterogeneity, where available.



Demographic variables will include age, sex, race, and ethnicity. Age will be analyzed as a continuous variable and may also be categorized into clinically relevant age groups.



Baseline clinical variables will include baseline HbA1c, fasting plasma glucose, diabetes duration, body weight, body mass index, systolic and diastolic blood pressure, renal function, serum creatinine, eGFR, urinary albumin-to-creatinine ratio, and albuminuria category. These variables will be analyzed as continuous measures and/or categorized using original trial definitions or clinically relevant thresholds.



Medical history and risk factors may include history of cardiovascular disease, hypertension, dyslipidemia, chronic kidney disease, smoking status, and other comorbidities recorded in the trial datasets.



Background treatment variables will include baseline antihyperglycemic therapy, such as metformin, insulin, sulfonylureas, DPP-4 inhibitors, GLP-1 receptor agonists, or other diabetes medications, where available. These will be categorized according to original trial coding and treatment context.



Trial-level variables will include trial identifier, treatment context, scheduled visit timing, follow-up duration, study period, endpoint availability, and missing-data patterns. Trial identifier will be included as a categorical variable in cross-trial analyses.



All variable definitions will follow the original trial data dictionaries and protocols. Harmonization across trials will be performed only when variables are sufficiently comparable in definition, unit, and measurement timing."
  ["project_stat_analysis_plan"]=>
  string(4385) "Statistical Analysis Plan



Analyses will use de-identified individual participant-level data from the requested randomized canagliflozin trials. Each trial will first be analyzed separately to preserve the original study design, eligibility criteria, treatment arms, visit schedule, and endpoint definitions. Cross-trial analyses will be conducted only for variables and outcomes that can be harmonized appropriately.



Descriptive analyses:

Baseline demographic and clinical characteristics will be summarized by trial and treatment arm using means and standard deviations, medians and interquartile ranges, and counts and percentages, as appropriate. Variables will include age, sex, race/ethnicity, baseline HbA1c, fasting plasma glucose, diabetes duration, body weight, BMI, blood pressure, renal function, albuminuria, cardiovascular risk factors, and background diabetes medications. Outcome availability, follow-up duration, discontinuation, and missing-data patterns will be summarized by trial, arm, visit, and outcome.



Primary longitudinal analyses:

Primary glycemic outcomes, including HbA1c and fasting plasma glucose, will be analyzed within each trial using mixed-effects models or repeated-measures models. Models will include treatment, visit/time, treatment-by-time interaction, baseline outcome value, and prespecified baseline covariates where available. Results will be reported as estimated mean changes from baseline and treatment-group differences at scheduled visits with 95% confidence intervals.



Secondary outcome analyses:

Renal outcomes, including eGFR, serum creatinine, urinary albumin-to-creatinine ratio, albuminuria category, and albuminuria progression, will be analyzed using continuous, categorical, or time-to-event methods depending on the original endpoint definition and data structure. Body weight and blood pressure will be analyzed similarly to continuous longitudinal outcomes. Cardiovascular outcomes, if available, will be summarized descriptively and may be analyzed using Kaplan-Meier methods and Cox proportional hazards models where event timing is available. Safety outcomes will be summarized as counts, percentages, and event rates by trial and treatment arm.



Bivariate and subgroup analyses:

Bivariate analyses will describe relationships between baseline characteristics and outcome changes using appropriate methods, including correlation analyses, group comparisons, and stratified summaries. Prespecified subgroup analyses will be conducted by baseline HbA1c, renal function/eGFR category, albuminuria status, age, sex, cardiovascular risk status, and background antihyperglycemic therapy, where sample size and data availability permit.



Treatment effect heterogeneity:

Treatment effect heterogeneity will be explored using interaction terms between treatment assignment and prespecified baseline variables. For longitudinal outcomes, treatment-by-subgroup and treatment-by-time-by-subgroup interactions may be evaluated. These analyses will be interpreted as exploratory, with attention to limited power and multiplicity.



Cross-trial participant-level meta-analysis:

For harmonized outcomes, cross-trial analyses will be conducted using one-stage and/or two-stage IPD meta-analysis approaches. One-stage models will include trial indicators and trial-by-treatment terms to account for between-trial heterogeneity. Two-stage analyses will estimate trial-specific effects first and then combine estimates using fixed-effect or random-effects meta-analysis depending on heterogeneity. Results will emphasize trial-specific and endpoint-specific findings rather than a single pooled effect when populations, endpoints, or follow-up schedules differ.



Missing data and sensitivity analyses:

Missing data will be described by trial, treatment arm, visit, and outcome. Primary repeated-measures models will use available observed data under standard missing-at-random assumptions. Sensitivity analyses may include alternative covariance structures, alternative visit windows, complete-case analyses, and multiple imputation for key covariates or outcomes where feasible. All analyses will be conducted within the YODA secure research environment using R and/or Python."
  ["project_software_used"]=>
  array(3) {
    [0]=>
    array(2) {
      ["value"]=>
      string(6) "python"
      ["label"]=>
      string(6) "Python"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(7) "rstudio"
      ["label"]=>
      string(7) "RStudio"
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  ["project_timeline"]=>
  string(1531) "The project will begin after YODA approval, Data Use Agreement execution, and data access.



Month 0–1: Access trial datasets and documentation; review data dictionaries, protocols, endpoint definitions, treatment arms, and visit schedules; finalize the harmonization and analysis plan.



Month 2–3: Construct trial-specific analysis datasets; summarize baseline characteristics, treatment arms, outcome availability, follow-up duration, and missing-data patterns.



Month 3–5: Conduct within-trial longitudinal analyses of glycemic, renal, cardiometabolic, cardiovascular, and safety outcomes, where available.



Month 5–7: Conduct subgroup and treatment effect heterogeneity analyses; perform harmonized cross-trial participant-level meta-analysis where variables and endpoints are comparable.



Month 7–8: Complete sensitivity analyses, finalize tables and figures, and prepare the results summary.



Month 8–9: Draft manuscript and circulate for internal review.



Month 9–10: Submit the manuscript to a peer-reviewed journal and/or submit an abstract to a scientific conference.



Month 10–12: Respond to reviewer or conference feedback as applicable, finalize the written results report, and report study status and results back to the YODA Project before the end of the 12-month access period. An extension will be requested if additional time is needed for manuscript revision or publication activities."
  ["project_dissemination_plan"]=>
  string(1518) "The results of this study will be disseminated through peer-reviewed publication, scientific conference presentations, and a written results report to the YODA Project. The primary anticipated product will be a manuscript describing baseline participant characteristics, longitudinal glycemic and renal outcome trajectories, treatment effect heterogeneity, and cross-trial differences across canagliflozin clinical trials in type 2 diabetes.



The target audiences include clinical researchers, diabetologists, endocrinologists, nephrologists, cardiovascular outcome researchers, biostatisticians, clinical trial methodologists, and stakeholders involved in diabetes drug development and trial design.



Potential target journals include Diabetes Care, Diabetologia, Diabetes, Obesity and Metabolism, The Lancet Diabetes & Endocrinology, Clinical Trials, Trials, Pharmaceutical Statistics, and other relevant peer-reviewed journals depending on the final emphasis of the analysis. Potential conferences include the American Diabetes Association Scientific Sessions, European Association for the Study of Diabetes Annual Meeting, International Diabetes Federation Congress, and methods-focused meetings such as the Society for Clinical Trials Annual Meeting.



Only aggregate results will be reported, and no attempt will be made to identify individual participants. All dissemination activities will follow YODA Project data use, publication, and acknowledgment requirements."
  ["project_bibliography"]=>
  string(1807) "

Neal B, Perkovic V, Mahaffey KW, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. New England Journal of Medicine. 2017;377(7):644-657. doi:10.1056/NEJMoa1611925.
Perkovic V, Jardine MJ, Neal B, et al. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. New England Journal of Medicine. 2019;380(24):2295-2306. doi:10.1056/NEJMoa1811744.
Stenlöf K, Cefalu WT, Kim KA, et al. Efficacy and safety of canagliflozin monotherapy in subjects with type 2 diabetes mellitus inadequately controlled with diet and exercise. Diabetes, Obesity and Metabolism. 2013;15(4):372-382. doi:10.1111/dom.12054.
Bode B, Stenlöf K, Sullivan D, Fung A, Usiskin K. Efficacy and safety of canagliflozin treatment in older subjects with type 2 diabetes mellitus: a randomized trial. Hospital Practice. 2013;41(2):72-84. doi:10.3810/hp.2013.04.1020.
Karagiannis T, Bekiari E, Tsapas A. Canagliflozin in the treatment of type 2 diabetes: an evidence-based review of its place in therapy. Core Evidence. 2017;12:1-10. doi:10.2147/CE.S109654.
Stewart LA, Clarke M, Rovers M, et al. Preferred reporting items for a systematic review and meta-analysis of individual participant data: the PRISMA-IPD Statement. JAMA. 2015;313(16):1657-1665. doi:10.1001/jama.2015.3656.
Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ. 2010;340:c221. doi:10.1136/bmj.c221.

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    string(7) "inherit"
    ["uploaded_to"]=>
    int(19300)
    ["date"]=>
    string(19) "2026-06-17 15:22:51"
    ["modified"]=>
    string(19) "2026-06-17 15:22:51"
    ["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(0) ""
}
data partner
array(1) {
  [0]=>
  string(0) ""
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(0) {
}


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


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