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  string(91) "Individual Participant-Level Data, which includes Full CSR and all supporting documentation"
  ["property_scientific_abstract"]=>
  string(2357) "Background: One-in-six people aged 65-years or over has T2DM with over half of prevalent T2DM cases in this age group. Older adults have a higher risk of adverse cardiovascular and renal outcomes, and potentially more adverse effects from therapeutic agents. Evidence suggests that the relationships between therapy and outcome may differ between older and younger people ? therefore the common approach of trial participant selection (recruiting people with a wide range of ages) may not allow a full description of the responses in this age group.
Objective: To perform a systematic review and NMA using individual patient data of all relevant eligible randomized controlled trials to assess the long-term effects of second-line antidiabetic therapies on clinically important outcomes, in order to inform pharmacologic management in patients aged 65-years or over with type 2 diabetes.
Study design: A systematic review and NMA using individual patient data (IPD) from all relevant eligible randomized controlled trials.
Participants: We will include individuals included in all randomized controlled trials focused on type 2 diabetes with parallel or cross-over design, in which treatment was given for 24 weeks or longer, and within which we estimate there to be at least 100 persons aged 65 or over.
Main outcomes: Primary outcomes; change in haemoglobin A1c (HbA1c) from baseline, all-cause mortality. Secondary outcomes; myocardial infarction, stroke, heart failure, cardiovascular mortality, body weight, low-density lipoprotein cholesterol (LDL-C)/Dyslipidaemia, blood pressures (BP)/Hypertension, hypoglycaemia, kidney diseases, liver diseases, diabetic retinopathy, diabetic foot diseases/amputation, diabetic ketoacidosis, physical performance, frailty, patient-reported outcomes/quality of life.
Statistical analysis: The statistical analysis will be a two-stage process. Firstly, individual patient data will be analysed separately for each trial using appropriate statistical methods in order to compare the treatments in each study. Secondly, results will be combined using the statistical method of NMA. This will allow all treatments that have been studied to be compared with each other and to be ranked for different outcomes investigating both efficacy of therapeutic agents and safety within older adults." ["project_brief_bg"]=> string(2908) "Type 2 diabetes is a common chronic condition that is associated with elevation of the blood glucose concentration, and an increase in the risk of cardiovascular disease. It affects about 460 million individuals worldwide (2017 data). Almost half of people with type 2 diabetes are aged 65 years or over with one in six people in this age group having type 2 diabetes. This number is expected to increase as global life expectancy increases. Currently, metformin is recommended as the first drug of choice for people with type 2 diabetes. Where metformin alone does not provide therapeutic efficacy, an additional (or replacement) agent is required. This is termed second-line treatment. There are a number of drugs to choose from at this second-line stage, but currently available evidence does not clearly indicate which agent(s) might be the best option for people aged 65 years or over. Most research studies only include a small proportion of older people. Furthermore, few studies report results separately for participants aged 65 and over. This is unfortunate, as older people are more likely than younger people to have other health problems such as cardiovascular and kidney disease, which could influence the choice of second-line treatment; particularly now that newer drugs are available that both improve glucose levels and reduce incidence and progression of cardiovascular and kidney disease. However, older people may also more likely to experience side effects from therapeutic interventions, and this may influence drug choice decisions.
We will identify then combine all evidence for older people from different research studies comparing different second-line treatments. We will then perform a novel analysis using these data to identify the optimal second-line treatments for older patients with type 2 diabetes, in terms of their ability to improve blood sugar control and to prevent diabetes complications, minimizing the risk reduce side effects.
We will update the literature search in a recent network meta-analysis (NMA) (1), a model for comparing multiple treatments simultaneously in a single analysis by combing direct and indirect evidence within a network of randomized controlled trials. We will obtain individual patient data for all relevant studies using several different processes including available data repositories, contacting data owners, and authors of papers. The statistical analysis will be a two-stage process. Firstly, individual patient data will be analysed separately for each trial using appropriate statistical methods in order to compare the treatments in each study. Second, these results will be combined using the statistical method of NMA. This will allow all treatments that have been studied to be compared to each other and ranked for different outcomes to look at how good the treatments are and how safe they are for older adults." ["project_specific_aims"]=> string(724) "Aim: To perform a systematic review and NMA using individual patient data from all relevant eligible randomized controlled trials to assess the long-term effects of second-line antidiabetic therapies on clinically important outcomes to inform pharmacologic management in patients aged 65-years or over with type 2 diabetes.
Objective 1. Calculate the relative effectiveness of second-line therapies alone or in combination in patients aged 65 years or over
Objective 2. To compare the differential relative effectiveness of second-line treatment between those aged below 65 to those aged 65 years or older.
Objective 3. To explore the potential clusters of drug efficacy in patients aged 65 years or over." ["project_study_design"]=> string(0) "" ["project_study_design_exp"]=> string(0) "" ["project_purposes"]=> array(5) { [0]=> array(2) { ["value"]=> string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations" ["label"]=> string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations" } [1]=> array(2) { ["value"]=> string(49) "New research question to examine treatment safety" ["label"]=> string(49) "New research question to examine treatment safety" } [2]=> array(2) { ["value"]=> string(36) "Participant-level data meta-analysis" ["label"]=> string(36) "Participant-level data meta-analysis" } [3]=> array(2) { ["value"]=> string(69) "Meta-analysis using data from the YODA Project and other data sources" ["label"]=> string(69) "Meta-analysis using data from the YODA Project and other data sources" } [4]=> array(2) { ["value"]=> string(37) "Develop or refine statistical methods" ["label"]=> string(37) "Develop or refine statistical methods" } } ["project_purposes_exp"]=> string(0) "" ["project_software_used"]=> array(2) { ["value"]=> string(86) "not_analyzing_participant_level_data__plan_to_use_another_secure_data_sharing_platform" ["label"]=> string(92) "I am not analyzing participant-level data / plan to use another secure data sharing platform" } ["project_software_used_exp"]=> string(64) "We plan to analyze participant-level data in the Vivli platform." ["project_research_methods"]=> string(1236) "Data Source:
CENTRAL, MEDLINE, and EMBASE
Inclusion Criteria:
1. All RCTs for type 2 diabetes with parallel or cross-over design where treatment given for 24+ weeks and for whom we estimate least 100 persons aged 65 or over included.
2. Comparisons of the following drug classes: biguanide (metformin), sulfonylureas, thiazolidinediones, dipeptidyl peptidase-4 (DPP-4) inhibitors, sodium-glucose Cotransporter-2 (SGLT2) inhibitors, GLP-1 receptor (GLP-1) agonists, basal insulins, meglitinide, and ?-glucosidase inhibitor). Trials comparing an eligible intervention with another eligible intervention of a different class or placebo/standard therapy/no treatment. For GLP-1 agonists and SGLT2 inhibitors, trials comparing two drugs from the same drug class are included.
3. Any country
4. Any language
Exclusion criteria:
1. Studies comparing metformin and placebo only.
2. Ongoing studies.
List of other eligible studies is in the doc named 'Eligible trial list v4_PDF'. These are from CSDR, Vivli, Novo Nordisk, Merck, other independent. IPD analysis is separate for each trial and will be performed on the relevant server. Summary results then downloaded and pooled." ["project_main_outcome_measure"]=> string(837) "The primary outcomes are change in haemoglobin A1c (HbA1c) from baseline and all-cause mortality
Secondary outcomes are myocardial infarction, stroke, heart failure, cardiovascular mortality, body weight, low-density lipoprotein cholesterol (LDL-C)/Dyslipidaemia, blood pressures (BP)/Hypertension, hypoglycaemia, kidney diseases, liver diseases, diabetic retinopathy, diabetic foot diseases/amputation, diabetic ketoacidosis, physical performance, frailty, patient-reported outcomes/quality of life, hospitalization.
We accept each outcome element that was defined by the author for each trial. We will analyse continuous outcomes on a continuous scale. Please see our ?Statistical analysis plan? for further details.
We accept any outcome measurement that is no earlier than 24 weeks of treatment after randomization." ["project_main_predictor_indep"]=> string(310) "For each eligible trial, the main predictor variable will be a categorical variable representing the trial arm. This will be binary for 2 arm trials and categorical nominal for trials with 3 or more arms. Categorical indicator variables will be used to record the treatment contracts estimated from each trial." ["project_other_variables_interest"]=> string(1066) "Other variables of interest are demographic variables, co-morbidities, and the other prognostic factors. These include but are not limited to age, sex, ethnic groups, body mass index (BMI), diabetes duration, comorbidities, concurrent prescriptions of common non-antidiabetic drugs at baseline, information of background therapies (if there are), smoking status, alcohol consumption, baseline metabolic characteristics. The list will be modified depending on the availability of data in each trial.
Individuals? age and diabetes duration at baseline will be treated as continuous variables. Individuals? BMI will be classified using World Health Organization criteria as follows: underweight (BMI of < 18 kg/m2), normal weight (BMI of 18 kg/m2 to < 25 kg/m2), overweight (BMI of 25 kg/m2 to < 30 kg/m2), and obesity (BMI of ? 30 kg/m2). We accept ethnic groups, comorbid conditions, and the presence of medication prescriptions defined by researchers in each trial. Baseline metabolic measurements will be categorized based on clinically meaningful thresholds." ["project_stat_analysis_plan"]=> string(4048) "A two-stage NMA will be applied. (2)
Stage 1: Estimate relevant parameters independently from each trial.
The baseline characteristics for each trial will be summarized. Binary/categorical variables as numbers and percentages in each category. Continuous variables using the mean and standard deviation or median and inter-quartile range as appropriate.
Treatment effects will be estimated on an intention-to-treat basis using multivariable regression models. For continuous outcomes we will fit a linear regression model, adjusting for baseline values if appropriate. For binary outcomes we will fit a log-linear model to estimate risk ratios (RRs). If we encounter significant issues with model convergence, we will fit a logistic regression model using the Firth method. Time to event outcomes will be analysed using Cox regression. The linear predictor in all models will include an intercept, treatment parameter, and parameters for each of our list of prognostic factors. For analysis of cluster trials, we will use mixed effects multivariable regression models with a random intercept across clusters.
We will in general use the author definitions of the outcomes. However, where possible outcome measures will be converted to the same scale e.g. HbA1c and HbA1c%. Where this is not possible, we will consider using standardized mean differences.
To address different intercurrent events, five strategies recommended by the European Medicines Agency will be used wherever is appropriate. (3) Missing covariate data will be imputed using multiple imputation where appropriate.
Stage 2: We will fit a random effects NMA model to jointly synthesize the results from all of the included trials accounting for multi-arm trials. We will use in-built packages in STATA or R. These commands implement a multivariate meta-regression model fit using restricted maximum likelihood estimation (REML) to perform the NMA. We will assume a common between study variance parameter across the different treatment contrasts. The NMA will use the effect estimates and standard errors (on the natural log scale for RRs, ORs and HRs) and correlation coefficients for multi-arm trials calculated in stage 1.
We will assess the assumption of transitivity epidemiologically by considering the distributions of covariates that are potential effect modifiers across trials using graphical displays. Statistical tests will be used to assess evidence for global (using the design by treatment interaction model) and local (using node-splitting) inconsistency in the NMA.
Summary treatment effects with 95% confidence intervals will be reported for all treatment contrasts. We will calculate the probability that each treatment is of each rank and report the surface under the cumulative ranking curve (SUCRA) for each treatment. We will also report the mean and quantile ranks. The common between trial variance estimates will be reported. For primary outcomes we will report the percentage contribution of each trial and the Borrowing of Strength (BoS) statistic for each treatment contrast. We will also report network diagrams together with other appropriate graphs such as extended forest plots of summary estimates and rankograms.
We will perform the following sub-group analyses:
By splitting the comparisons into monotherapy, dual therapy, and triple therapy.
Where there are sufficient data available, we will consider the following covariates as potential effect modifiers: age; follow-up time; sex; body mass index; Cardiovascular disease at baseline; Chronic Kidney Disease at baseline.
We will use machine learning approaches (including neural network-based variational autoencoders, hierarchical clustering, embedding of individual time series data), applied to the pooled individual patient data from trials with the same comparisons, with the aim of (i) defining clusters of drug efficacy in patients aged 65 years or over and (ii) predicting individual response to therapies. (4,5)" ["project_timeline"]=> string(198) "Start date- Feb 2022
Analysis completion date- Feb 2023
Data Manuscript drafted- March 2023
First submission of manuscript- June 2023
Data results reporting to Yoda-June 2023" ["project_dissemination_plan"]=> string(1032) "Any data from this study will first be presented to the Public Patient group involved in the study. In particular, in collaboration with the Foundation for Diabetes Research in Older People we will present our work to the Older People Diabetes Network patient forum and also in their annual conference. We will publish our findings in peer-reviewed Scientific Journals such as Lancet Diabetes. Other routes of dissemination will include presentations to patients and patient support groups locally (including West Midlands Diabetes UK group, Birmingham and Solihull Clinical Commissioning Group Diabetes user group, and the Queen Elizabeth Hospital Birmingham Diabetes User Group) and nationally (via Diabetes UK), Association of British Clinical Diabetologists, Primary Care Diabetes Society, Royal College of General Practitioners and TREND-Diabetes. We will also present our work internationally at the (European Association for the Study of Diabetes), the International Diabetes Federation and the American Diabetes Association." ["project_bibliography"]=> string(1135) "

1. Palmer SC, Mavridis D, Nicolucci A, Johnson DW, Tonelli M, Craig JC, et al. Comparison of clinical outcomes and adverse events associated with glucose-lowering drugs in patients with type 2 diabetes: a meta-analysis. Jama. 2016;316(3):313-24.
2. Riley RD, Tierney JF, Stewart LA. Individual Participant Data Meta?Analysis: A Handbook for Healthcare Research. West Sussex: Wiley; 2021.
3. European Medicines Agency. ICH E9 (R1) addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials 2020 [updated 30 July 2020. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e9-r1-ad….
4. Karwath A, Bunting KV, Gill SK, Tica O, Pendleton S, Aziz F, et al. Redefining ?-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis. The Lancet. 2021;398(10309):1427-35.
5. Liu R, Wei L, Zhang P. When deep learning meets causal inference: a computational framework for drug repurposing from real-world data. arXiv preprint arXiv:200710152. 2020.

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2021-4814

General Information

How did you learn about the YODA Project?: Colleague

Conflict of Interest

Request Clinical Trials

Associated Trial(s):
  1. NCT01064414 - A Randomized, Double-Blind, Placebo-Controlled, 3-Arm, Parallel-Group, 26-Week, Multicenter Study With a 26-Week Extension, to Evaluate the Efficacy, Safety and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus Who Have Moderate Renal Impairment
  2. NCT01081834 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin as Monotherapy in the Treatment of Subjects With Type 2 Diabetes Mellitus Inadequately Controlled With Diet and Exercise
  3. NCT01106677 - A Randomized, Double-Blind, Placebo and Active-Controlled, 4-Arm, Parallel Group, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin Monotherapy
  4. NCT00968812 - A Randomized, Double-Blind, 3-Arm Parallel-Group, 2-Year (104-Week), Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of JNJ-28431754 Compared With Glimepiride in the Treatment of Subjects With Type 2 Diabetes Mellitus Not Optimally Controlled on Metformin Monotherapy
  5. NCT01106651 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin Compared With Placebo in the Treatment of Older Subjects With Type 2 Diabetes Mellitus Inadequately Controlled on Glucose Lowering Therapy
  6. NCT01137812 - A Randomized, Double-Blind, Active-Controlled, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin Versus Sitagliptin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin and Sulphonylurea Therapy
  7. NCT01809327 - A Randomized, Double-Blind, 5-Arm, Parallel-Group, 26-Week, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in Combination With Metformin as Initial Combination Therapy in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control With Diet and Exercise
  8. NCT01032629 - A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of JNJ-28431754 on Cardiovascular Outcomes in Adult Subjects With Type 2 Diabetes Mellitus
  9. NCT01989754 - A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of Canagliflozin on Renal Endpoints in Adult Subjects With Type 2 Diabetes Mellitus
  10. NCT02065791 - A Randomized, Double-blind, Event-driven, Placebo-controlled, Multicenter Study of the Effects of Canagliflozin on Renal and Cardiovascular Outcomes in Subjects With Type 2 Diabetes Mellitus and Diabetic Nephropathy
What type of data are you looking for?:

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Data Request Status

Status: Approved Pending DUA Signature

Research Proposal

Project Title: 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

Scientific Abstract: Background: One-in-six people aged 65-years or over has T2DM with over half of prevalent T2DM cases in this age group. Older adults have a higher risk of adverse cardiovascular and renal outcomes, and potentially more adverse effects from therapeutic agents. Evidence suggests that the relationships between therapy and outcome may differ between older and younger people ? therefore the common approach of trial participant selection (recruiting people with a wide range of ages) may not allow a full description of the responses in this age group.
Objective: To perform a systematic review and NMA using individual patient data of all relevant eligible randomized controlled trials to assess the long-term effects of second-line antidiabetic therapies on clinically important outcomes, in order to inform pharmacologic management in patients aged 65-years or over with type 2 diabetes.
Study design: A systematic review and NMA using individual patient data (IPD) from all relevant eligible randomized controlled trials.
Participants: We will include individuals included in all randomized controlled trials focused on type 2 diabetes with parallel or cross-over design, in which treatment was given for 24 weeks or longer, and within which we estimate there to be at least 100 persons aged 65 or over.
Main outcomes: Primary outcomes; change in haemoglobin A1c (HbA1c) from baseline, all-cause mortality. Secondary outcomes; myocardial infarction, stroke, heart failure, cardiovascular mortality, body weight, low-density lipoprotein cholesterol (LDL-C)/Dyslipidaemia, blood pressures (BP)/Hypertension, hypoglycaemia, kidney diseases, liver diseases, diabetic retinopathy, diabetic foot diseases/amputation, diabetic ketoacidosis, physical performance, frailty, patient-reported outcomes/quality of life.
Statistical analysis: The statistical analysis will be a two-stage process. Firstly, individual patient data will be analysed separately for each trial using appropriate statistical methods in order to compare the treatments in each study. Secondly, results will be combined using the statistical method of NMA. This will allow all treatments that have been studied to be compared with each other and to be ranked for different outcomes investigating both efficacy of therapeutic agents and safety within older adults.

Brief Project Background and Statement of Project Significance: Type 2 diabetes is a common chronic condition that is associated with elevation of the blood glucose concentration, and an increase in the risk of cardiovascular disease. It affects about 460 million individuals worldwide (2017 data). Almost half of people with type 2 diabetes are aged 65 years or over with one in six people in this age group having type 2 diabetes. This number is expected to increase as global life expectancy increases. Currently, metformin is recommended as the first drug of choice for people with type 2 diabetes. Where metformin alone does not provide therapeutic efficacy, an additional (or replacement) agent is required. This is termed second-line treatment. There are a number of drugs to choose from at this second-line stage, but currently available evidence does not clearly indicate which agent(s) might be the best option for people aged 65 years or over. Most research studies only include a small proportion of older people. Furthermore, few studies report results separately for participants aged 65 and over. This is unfortunate, as older people are more likely than younger people to have other health problems such as cardiovascular and kidney disease, which could influence the choice of second-line treatment; particularly now that newer drugs are available that both improve glucose levels and reduce incidence and progression of cardiovascular and kidney disease. However, older people may also more likely to experience side effects from therapeutic interventions, and this may influence drug choice decisions.
We will identify then combine all evidence for older people from different research studies comparing different second-line treatments. We will then perform a novel analysis using these data to identify the optimal second-line treatments for older patients with type 2 diabetes, in terms of their ability to improve blood sugar control and to prevent diabetes complications, minimizing the risk reduce side effects.
We will update the literature search in a recent network meta-analysis (NMA) (1), a model for comparing multiple treatments simultaneously in a single analysis by combing direct and indirect evidence within a network of randomized controlled trials. We will obtain individual patient data for all relevant studies using several different processes including available data repositories, contacting data owners, and authors of papers. The statistical analysis will be a two-stage process. Firstly, individual patient data will be analysed separately for each trial using appropriate statistical methods in order to compare the treatments in each study. Second, these results will be combined using the statistical method of NMA. This will allow all treatments that have been studied to be compared to each other and ranked for different outcomes to look at how good the treatments are and how safe they are for older adults.

Specific Aims of the Project: Aim: To perform a systematic review and NMA using individual patient data from all relevant eligible randomized controlled trials to assess the long-term effects of second-line antidiabetic therapies on clinically important outcomes to inform pharmacologic management in patients aged 65-years or over with type 2 diabetes.
Objective 1. Calculate the relative effectiveness of second-line therapies alone or in combination in patients aged 65 years or over
Objective 2. To compare the differential relative effectiveness of second-line treatment between those aged below 65 to those aged 65 years or older.
Objective 3. To explore the potential clusters of drug efficacy in patients aged 65 years or over.

Study Design:

What is the purpose of the analysis being proposed? Please select all that apply.: New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations New research question to examine treatment safety Participant-level data meta-analysis Meta-analysis using data from the YODA Project and other data sources Develop or refine statistical methods

Software Used: I am not analyzing participant-level data / plan to use another secure data sharing platform

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: Data Source:
CENTRAL, MEDLINE, and EMBASE
Inclusion Criteria:
1. All RCTs for type 2 diabetes with parallel or cross-over design where treatment given for 24+ weeks and for whom we estimate least 100 persons aged 65 or over included.
2. Comparisons of the following drug classes: biguanide (metformin), sulfonylureas, thiazolidinediones, dipeptidyl peptidase-4 (DPP-4) inhibitors, sodium-glucose Cotransporter-2 (SGLT2) inhibitors, GLP-1 receptor (GLP-1) agonists, basal insulins, meglitinide, and ?-glucosidase inhibitor). Trials comparing an eligible intervention with another eligible intervention of a different class or placebo/standard therapy/no treatment. For GLP-1 agonists and SGLT2 inhibitors, trials comparing two drugs from the same drug class are included.
3. Any country
4. Any language
Exclusion criteria:
1. Studies comparing metformin and placebo only.
2. Ongoing studies.
List of other eligible studies is in the doc named 'Eligible trial list v4_PDF'. These are from CSDR, Vivli, Novo Nordisk, Merck, other independent. IPD analysis is separate for each trial and will be performed on the relevant server. Summary results then downloaded and pooled.

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: The primary outcomes are change in haemoglobin A1c (HbA1c) from baseline and all-cause mortality
Secondary outcomes are myocardial infarction, stroke, heart failure, cardiovascular mortality, body weight, low-density lipoprotein cholesterol (LDL-C)/Dyslipidaemia, blood pressures (BP)/Hypertension, hypoglycaemia, kidney diseases, liver diseases, diabetic retinopathy, diabetic foot diseases/amputation, diabetic ketoacidosis, physical performance, frailty, patient-reported outcomes/quality of life, hospitalization.
We accept each outcome element that was defined by the author for each trial. We will analyse continuous outcomes on a continuous scale. Please see our ?Statistical analysis plan? for further details.
We accept any outcome measurement that is no earlier than 24 weeks of treatment after randomization.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: For each eligible trial, the main predictor variable will be a categorical variable representing the trial arm. This will be binary for 2 arm trials and categorical nominal for trials with 3 or more arms. Categorical indicator variables will be used to record the treatment contracts estimated from each trial.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: Other variables of interest are demographic variables, co-morbidities, and the other prognostic factors. These include but are not limited to age, sex, ethnic groups, body mass index (BMI), diabetes duration, comorbidities, concurrent prescriptions of common non-antidiabetic drugs at baseline, information of background therapies (if there are), smoking status, alcohol consumption, baseline metabolic characteristics. The list will be modified depending on the availability of data in each trial.
Individuals? age and diabetes duration at baseline will be treated as continuous variables. Individuals? BMI will be classified using World Health Organization criteria as follows: underweight (BMI of < 18 kg/m2), normal weight (BMI of 18 kg/m2 to < 25 kg/m2), overweight (BMI of 25 kg/m2 to < 30 kg/m2), and obesity (BMI of ? 30 kg/m2). We accept ethnic groups, comorbid conditions, and the presence of medication prescriptions defined by researchers in each trial. Baseline metabolic measurements will be categorized based on clinically meaningful thresholds.

Statistical Analysis Plan: A two-stage NMA will be applied. (2)
Stage 1: Estimate relevant parameters independently from each trial.
The baseline characteristics for each trial will be summarized. Binary/categorical variables as numbers and percentages in each category. Continuous variables using the mean and standard deviation or median and inter-quartile range as appropriate.
Treatment effects will be estimated on an intention-to-treat basis using multivariable regression models. For continuous outcomes we will fit a linear regression model, adjusting for baseline values if appropriate. For binary outcomes we will fit a log-linear model to estimate risk ratios (RRs). If we encounter significant issues with model convergence, we will fit a logistic regression model using the Firth method. Time to event outcomes will be analysed using Cox regression. The linear predictor in all models will include an intercept, treatment parameter, and parameters for each of our list of prognostic factors. For analysis of cluster trials, we will use mixed effects multivariable regression models with a random intercept across clusters.
We will in general use the author definitions of the outcomes. However, where possible outcome measures will be converted to the same scale e.g. HbA1c and HbA1c%. Where this is not possible, we will consider using standardized mean differences.
To address different intercurrent events, five strategies recommended by the European Medicines Agency will be used wherever is appropriate. (3) Missing covariate data will be imputed using multiple imputation where appropriate.
Stage 2: We will fit a random effects NMA model to jointly synthesize the results from all of the included trials accounting for multi-arm trials. We will use in-built packages in STATA or R. These commands implement a multivariate meta-regression model fit using restricted maximum likelihood estimation (REML) to perform the NMA. We will assume a common between study variance parameter across the different treatment contrasts. The NMA will use the effect estimates and standard errors (on the natural log scale for RRs, ORs and HRs) and correlation coefficients for multi-arm trials calculated in stage 1.
We will assess the assumption of transitivity epidemiologically by considering the distributions of covariates that are potential effect modifiers across trials using graphical displays. Statistical tests will be used to assess evidence for global (using the design by treatment interaction model) and local (using node-splitting) inconsistency in the NMA.
Summary treatment effects with 95% confidence intervals will be reported for all treatment contrasts. We will calculate the probability that each treatment is of each rank and report the surface under the cumulative ranking curve (SUCRA) for each treatment. We will also report the mean and quantile ranks. The common between trial variance estimates will be reported. For primary outcomes we will report the percentage contribution of each trial and the Borrowing of Strength (BoS) statistic for each treatment contrast. We will also report network diagrams together with other appropriate graphs such as extended forest plots of summary estimates and rankograms.
We will perform the following sub-group analyses:
By splitting the comparisons into monotherapy, dual therapy, and triple therapy.
Where there are sufficient data available, we will consider the following covariates as potential effect modifiers: age; follow-up time; sex; body mass index; Cardiovascular disease at baseline; Chronic Kidney Disease at baseline.
We will use machine learning approaches (including neural network-based variational autoencoders, hierarchical clustering, embedding of individual time series data), applied to the pooled individual patient data from trials with the same comparisons, with the aim of (i) defining clusters of drug efficacy in patients aged 65 years or over and (ii) predicting individual response to therapies. (4,5)

Narrative Summary: Experts recommend metformin to treat people with type-2-diabetes to control their blood sugar. When metformin alone does not work, we need to give an additional drug or replace metformin. There are many drugs to choose from, but doctors do not know which is best for people aged over 65 years as most research studies include too few such patients. Older people are more likely to have other health problems like heart disease. They are also more likely to experience side-effects. These issues affect choice of drug. We will identify all evidence in older people from research studies comparing treatments and perform a new analysis using all these data to identify the best second-line treatments.

Project Timeline: Start date- Feb 2022
Analysis completion date- Feb 2023
Data Manuscript drafted- March 2023
First submission of manuscript- June 2023
Data results reporting to Yoda-June 2023

Dissemination Plan: Any data from this study will first be presented to the Public Patient group involved in the study. In particular, in collaboration with the Foundation for Diabetes Research in Older People we will present our work to the Older People Diabetes Network patient forum and also in their annual conference. We will publish our findings in peer-reviewed Scientific Journals such as Lancet Diabetes. Other routes of dissemination will include presentations to patients and patient support groups locally (including West Midlands Diabetes UK group, Birmingham and Solihull Clinical Commissioning Group Diabetes user group, and the Queen Elizabeth Hospital Birmingham Diabetes User Group) and nationally (via Diabetes UK), Association of British Clinical Diabetologists, Primary Care Diabetes Society, Royal College of General Practitioners and TREND-Diabetes. We will also present our work internationally at the (European Association for the Study of Diabetes), the International Diabetes Federation and the American Diabetes Association.

Bibliography:

1. Palmer SC, Mavridis D, Nicolucci A, Johnson DW, Tonelli M, Craig JC, et al. Comparison of clinical outcomes and adverse events associated with glucose-lowering drugs in patients with type 2 diabetes: a meta-analysis. Jama. 2016;316(3):313-24.
2. Riley RD, Tierney JF, Stewart LA. Individual Participant Data Meta?Analysis: A Handbook for Healthcare Research. West Sussex: Wiley; 2021.
3. European Medicines Agency. ICH E9 (R1) addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials 2020 [updated 30 July 2020. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e9-r1-ad….
4. Karwath A, Bunting KV, Gill SK, Tica O, Pendleton S, Aziz F, et al. Redefining ?-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis. The Lancet. 2021;398(10309):1427-35.
5. Liu R, Wei L, Zhang P. When deep learning meets causal inference: a computational framework for drug repurposing from real-world data. arXiv preprint arXiv:200710152. 2020.