• CSR Summary
• NCT00968812
• Primary Citation
• Data Specification

Approved Data Requests Associated with this Trial

• 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)
• 2024-0060 : Using Synthetic Controls to Improve Randomised Controlled Trials for Rare Diseases
• 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
• 2022-5076 : The efficacy of canagliflozin in diabetes subgroups by using an unsupervised machine-learning method
• 2021-4849 : Minimax regret for data analysis with application to clinical treatment decisions
• 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
• 2021-4764 : Reporting of harms in SGLT2 clinical study reports compared to trial registries and publications: a methodological review
• 2021-4668 : An individual participant data meta-analysis to evaluate a new category of hypoglycemic drugs (SGLT-2Is) on type 2 diabetes mellitus patient subgroups
• 2020-4465 : Examination of implications of acute declines in kidney function with SGLTII inhibitors
• 2020-4211 : The effect of SGLT2 inhibitors in diabetes subgroups identified by data-driven clustering
• 2020-4165 : Exploring Novel Methods to Estimate Heterogeneous Treatment Effects
• 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: Efficacy and safety of SGLT2 inhibitors in the treatment of type 2 diabetes: systematic review incorporating unpublished clinical study reports
• 2019-3995 : Cardiovascular safety associated with SGLT-2 inhibitors across racial groups in patients with T2DM: a meta analysis and systematic review.
• 2019-3850 : A new randomization procedure: pairwise sequential randomization (PSR), properties and applications in both causal inference and clinical trials.
• 2017-2511 : Policy-aware evaluation of personalized treatment strategies
• 2017-1816 : MASTERMIND: Stratification of response to SGLT2 inhibitor glucose lowering therapy
• 2017-1746 : Heterogeneity in relative treatment efficacy by age, sex, socioeconomic status and comorbidity
• 2017-1466 : The use of clinical experimental data to build personalized medicine models and the evaluation of their effectiveness and statistical significance.
• 2016-1026 : Making Better Use of Randomized Trials: Assessing Applicability and Transporting Causal Effects
• 2015-0416 : Bone safety of canagliflozin, aSGLT2 inhibitor, in type 2 diabetes: A retrospective analysis to evaluate fracture risk
• 2014-0340 : The effects of SGLT-2 inhibitors in patients with type 2 diabetes: a systematic review with meta-analysis of randomised trials