• CSR Summary Not Yet Available
• NCT01032629
• Primary Citation
• Data Specification

Approved Data Requests Associated with this Trial

• 2025-0092 : Impact of SGLT2-inhibitors on frailty and physical function in older adults with type 2 diabetes mellitus: a systematic review and meta-analysis
• 2025-0088 : Tree-like representation of heterogeneity in response to canagliflozin in diabetes.
• 2025-0048 : SGLT-2 Inhibitors in Women and Men with Chronic Kidney Disease
• 2024-1056 : Meta-analysis on the regional differences of cardiovascular outcomes for patients treated with Sodium Glucose cotransporter 2 (SGLT-2) inhibitors
• 2024-0972 : Estimating Reliable Treatment Effects Despite Blinding Failures in Clinical Trials
• 2024-0716 : Validation of causal machine learning methods to identify heterogeneous treatment effects
• 2024-0688 : Trans-ethnic effect of Canagliflozin Cardiovascular risk in Type 2 Diabetes based on phenomapping derived too
• 2024-0600 : Bayesian machine learning for the identification of benefiting subgroups and treatment effect heterogeneity for canagliflozin in T2DM patients
• 2024-0580 : Developing approaches to accelerate clinical trials using real world data (RWD) from electronic health records (EHR)
• 2024-0572 : Meta-analysis on the effectiveness of canagliflozin in heart failure in elderly patients
• 2024-0516 : Identify treatment responders in patients with type 2 diabetes using a machine learning based dynamic cardiovascular risk assessment tool ML-CVD in clinical trials
• 2024-0296 : Validation of a novel clinical risk score for cardiovascular outcomes in type 2 diabetes
• 2024-0060 : Using Synthetic Controls to Improve Randomised Controlled Trials for Rare Diseases
• 2023-5534 : Quality tolerance limit and duplicated patient investigation in clinical trials
• 2023-5514 : Development of two predictive tools to estimate cardiovascular risk, renal risk and all-cause mortality risk in CKD patients using machine learning
• 2023-5367 : Efficacy and safety of canagliflozin in diabetic patients with erythrocytosis: A pooled analysis of the CANVAS Program and CREDENCE trial
• 2023-5296 : Prediction of Urinary Tract Infection Risk Using Urinary Dipstick Tests and Assessment of Their Impact on SGLT2 Inhibitors
• 2023-5195 : Predictive modeling of adverse events and symptoms patterns in large scale clinical trials
• 2022-5124 : Understanding frailty, multimorbidity and renal failure in clinical trials: Attrition, retention and heterogeneity of treatment effects in trials
• 2022-5077 : The efficacy of canagliflozin in diabetes subgroups by using an unsupervised machine-learning method
• 2022-5076 : The efficacy of canagliflozin in diabetes subgroups by using an unsupervised machine-learning method
• 2022-5045 : Network MA of RCTs to compare the effects of metabolic surgery vs. modern drug treatment on cardiovascular outcomes + mortality in patients with DMT2
• 2022-5033 : The effect of SLGT-2 inhibitors in patients with chronic kidney disease with or without type 2 DM: a systematic review and meta-analysis
• 2022-5007 : Risk stratification and responder identification for GLP-1RA and SGLT2i in T2DM: application of two machine learning based prediction models
• 2022-4996 : Risk stratification and responder identification for GLP-1RA and SGLT2i in T2DM: application of two machine learning based prediction models
• 2022-4860 : SGLT2 I in heart failure with non-reduced ejection fraction :a systematic review and meta analysis
• 2021-4850 : Cluster Analysis of Cardiovascular Phenotypes and SGLT2 Inhibition in Patients With Type 2 Diabetes and Established Cardiovascular Disease
• 2021-4814 : A systematic review and network meta-analysis (NMA) to determine the best second-line therapy for type 2 diabetes mellitus in people aged 65 and over
• 2021-4812 : Effects of new antidiabetic agents on cardiovascular outcomes in older adults: systemic review and meta-analysis
• 2021-4764 : Reporting of harms in SGLT2 clinical study reports compared to trial registries and publications: a methodological review
• 2020-4553 : Exploring heterogeneity in the cardiovascular and renal benefits of canagliflozin in type 2 diabetes mellitus
• 2020-4465 : Examination of implications of acute declines in kidney function with SGLTII inhibitors
• 2020-4417 : Vibration of effects in pooled analyses of Canagliflozin vs placebo, randomized, double blind trials
• 2020-4413 : Creatinine kinase levels among concomitant statin and SGLT2 use
• 2020-4409 : Canagliflozin and Non-Alcoholic Fatty Liver Disease in Type 2 Diabetes (CaNAFLD) A Post-Hoc Analysis of RCTs
• 2020-4310 : A Data-Zone Scoring System to Assess the Generalizability of Canagliflozin to Individual Patients
• 2020-4244 : Cardiorenal outcomes of second-line antidiabetic drugs in patients with Type 2 diabetes: a systematic review and network meta-analysis
• 2020-4213 : Validation of a Machine Learning-based Model to Predict the Risk of Heart Failure among Patients with Diabetes Mellitus: the WATCH-DM Risk Score
• 2020-4211 : The effect of SGLT2 inhibitors in diabetes subgroups identified by data-driven clustering
• 2019-4093 : GLP-1RAs and SGLT-2is reduce cardiovascular events in women with type 2 diabetes. A systematic review and meta-analysis
• 2019-4077 : Evaluation of debiased machine learning methods to estimate the average treatment effect in observational studies.
• 2019-4071 : Efficacy and safety of SGLT2 inhibitors in the treatment of type 2 diabetes: systematic review incorporating unpublished clinical study reports
• 2019-4035 : Exploration of regional heterogeneity in trials assessing the efficacy of recent non-insulin glucose lowering drugs on cardiovascular outcomes
• 2019-4023 : Correlation between anion gap and urine ketones with hospitalization for heart failure and cardiovascular death in patients receiving canagliflozin
• 2019-3995 : Cardiovascular safety associated with SGLT-2 inhibitors across racial groups in patients with T2DM: a meta analysis and systematic review.
• 2017-1746 : Heterogeneity in relative treatment efficacy by age, sex, socioeconomic status and comorbidity