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| Name | Class |
|---|---|
| Eli Lilly and Company | INDUSTRY |
The objective of this study is to identify EMR-based clinical covariates and quantify their association with the prescribing of each specific type 2 diabetes (T2DM) medication under investigation. This will include an assessment of how well these covariates are captured through claims data proxies, and their potential to confound comparative research of T2DM medications.
Purpose:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Linagliptin1 | T2DM patients initiating Linagliptin (DPP-4 comparison) |
| |
| Other DPP4 | T2DM patients initiating a non-linagliptin DPP-4 inhibitor | ||
| Linagliptin2 | T2DM patients initiating Linagliptin (glitizaone comparison) | ||
| Glitazones | T2DM patients initiating Thiazolidinediones (glitazones) | ||
| Sulfonylurea | T2DM patients initiating any medication in the Sulfonylurea class | ||
| Linagliptin3 | T2DM patients initiating Linagliptin (Sulfonylurea comparison) |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| linagliptin | Drug | non-randomized |
|
| Measure | Description | Time Frame |
|---|---|---|
| Missing EMR (Electronic Medical Record) Characteristic: Smoking | The missing EMR characteristic smoking defined as current, unknown, versus past/never smoker. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic smoking was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | Up to 20 months |
| Missing EMR Characteristic: Duration of Diabetes | The missing EMR characteristic duration of diabetes defined as >7, 5-6, 3-5, 1-3, <1 (in years) in duration. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic duration of diabetes was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | Up to 20 months |
| Missing EMR Characteristic: Duration of Diabetes (Continuous) | The missing EMR characteristic duration of diabetes defined as starting year/starting age of diabetes. Linear regression models were ran using a prioritized list of claims-based covariates as predictors and the value of select EMR-based clinical characteristics duration of diabetes as continuous outcomes. The estimated value represented is actually prediction accuracy defined by R-squared. | Up to 20 months |
| Missing EMR Characteristic: BMI (Body Mass Index) | The missing EMR characteristic BMI defined as not obese, overweight, obese, severe obesity. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic BMI was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. |
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Inclusion criteria:
Exclusion criteria:
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T2DM patients aged 18 or older, initiating antidiabetic treatment after at least 6 months of continuous enrollment
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| Name | Affiliation | Role |
|---|---|---|
| Boehringer Ingelheim | Boehringer Ingelheim | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Boehringer Ingelheim Investigational Site | Boston | Massachusetts | United States |
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Existing data cohort design using data from the MarketScan database from May 2011 through December 2012. 492963 potential patients were identified in the database, but after removing patients who violated inclusion and exclusion criteria 166613 patients were actually analysed in the study.
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| ID | Title | Description |
|---|---|---|
| FG000 | Linagliptin 1 | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication Linagliptin subjects matched to any other DPP-4 (Dipeptidyl peptidase-4 inhibitors). |
| FG001 | Any Other DPP-4 | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication DPP-4 (Dipeptidyl peptidase-4 inhibitors). |
| FG002 | Linagliptin 2 | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication Linagliptin subjects matched to pioglitazone. |
| FG003 | Pioglitazone | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication pioglitazone. |
| FG004 | Linagliptin 3 | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication Linagliptin subjects matched to second generation sulfonylurea. |
| FG005 | Second Generation Sulfonylurea | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication second generation sulfonylurea. |
| Title | Milestones | Reasons Not Completed | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
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All patients who did not violate any inclusion/exclusion criteria.
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| ID | Title | Description |
|---|---|---|
| BG000 | Linagliptin 1 | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication Linagliptin subjects matched to any other DPP-4 (Dipeptidyl peptidase-4 inhibitors). |
| BG001 | Any Other DPP-4 |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Missing EMR (Electronic Medical Record) Characteristic: Smoking | The missing EMR characteristic smoking defined as current, unknown, versus past/never smoker. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic smoking was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Number | Percentage of participants | Up to 20 months |
|
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Serious and other (non-serious) adverse events were not of interest in this study and therefore were not collected or assessed as part of the study, in addition individual patient data is not available therefore adverse event data is not presented.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | MarketScan | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication identified from the MarketScan database. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Boehringer Ingelheim Call Center | Boehringer Ingelheim (BI) | 1800-243-0127 | clintriage.rdg@boehringer-ingelheim.com |
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| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| ID | Term |
|---|---|
| D000069476 | Linagliptin |
| ID | Term |
|---|---|
| D011687 | Purines |
| D006574 | Heterocyclic Compounds, 2-Ring |
| D000072471 | Heterocyclic Compounds, Fused-Ring |
| D006571 | Heterocyclic Compounds |
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| Up to 20 months |
| Missing EMR Characteristic: BMI (Continuous) | The missing EMR characteristic BMI is BMI value. Linear regression models were ran using a prioritized list of claims-based covariates as predictors and the value of select EMR-based clinical characteristics BMI as continuous outcomes. The estimated value represented is actually prediction accuracy defined by R-squared. | Up to 20 months |
| Missing EMR Characteristic: HbA1c (Hemoglobin A1c (Glycosylated Hemoglobin)) | The missing EMR characteristic HbA1c defined as value in 6 months prior to and including index date. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic HbA1c was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | Up to 20 months |
| Missing EMR Characteristic: eGFR (Glomerular Filtration Rate) | The missing EMR characteristic eGFR defined as value in 6 months prior to and including index date. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic eGFR was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | Upto 20 months |
| Missing EMR Characteristic: Total Cholesterol | The missing EMR characteristic total cholesterol defined as value in 6 months prior to and including index date. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic total cholesterol was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | Up to 20 months |
| Missing EMR Characteristic: Systolic BP (Blood Pressure) | The missing EMR characteristic systolic BP defined as value in 6 months prior to and including index date. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic systolic BP was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | Up to 20 months |
| Missing EMR Characteristic: Diastolic BP | The missing EMR characteristic diastolic BP defined as value in 6 months prior to and including index date. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic diastolic BP was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | Up to 20 months |
| Binary EMR Characteristic: Neuropathy | The missing EMR characteristic neuropathy defined as participants with any note of diabetic neuropathy. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic neuropathy was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | Up to 20 months |
| Binary EMR Characteristic: Nephropathy | The missing EMR characteristic nephropathy defined as participants with any note of diabetic nephropathy. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic nephropathy was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | Upto 20 months |
| Binary EMR Characteristic: Retinopathy | The missing EMR characteristic retinopathy defined as participants with any note of diabetic retinopathy. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic retinopathy was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | Up to 20 months |
| Binary EMR Characteristic: Pancreatitis | The missing EMR characteristic pancreatitis defined as participants with any note of prior pancreatitis. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic pancreatitis was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | Up to 20 months |
Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication DPP-4 (Dipeptidyl peptidase-4 inhibitors). |
| BG002 | Linagliptin 2 | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication Linagliptin subjects matched to pioglitazone. |
| BG003 | Pioglitazone | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication pioglitazone. |
| BG004 | Linagliptin 3 | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication Linagliptin subjects matched to second generation sulfonylurea. |
| BG005 | Second Generation Sulfonylurea | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication second generation sulfonylurea. |
| BG006 | Total | Total of all reporting groups |
| Years |
|
| Gender | Count of Participants | Participants |
|
Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication Linagliptin subjects matched to any other DPP-4 (Dipeptidyl peptidase-4 inhibitors). |
| OG001 | Any Other DPP-4 | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication DPP-4 (Dipeptidyl peptidase-4 inhibitors). |
| OG002 | Linagliptin 2 | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication Linagliptin subjects matched to pioglitazone. |
| OG003 | Pioglitazone | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication pioglitazone. |
| OG004 | Linagliptin 3 | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication Linagliptin subjects matched to second generation sulfonylurea. |
| OG005 | Second Generation Sulfonylurea | Patients had a recorded diagnosis of type 2 diabetes mellitus (T2DM) using an oral and non-insulin injected glucose-lowering medication second generation sulfonylurea. |
|
|
|
| Primary | Missing EMR Characteristic: Duration of Diabetes | The missing EMR characteristic duration of diabetes defined as >7, 5-6, 3-5, 1-3, <1 (in years) in duration. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic duration of diabetes was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Number | Percentage of participants | Up to 20 months |
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|
|
| Primary | Missing EMR Characteristic: Duration of Diabetes (Continuous) | The missing EMR characteristic duration of diabetes defined as starting year/starting age of diabetes. Linear regression models were ran using a prioritized list of claims-based covariates as predictors and the value of select EMR-based clinical characteristics duration of diabetes as continuous outcomes. The estimated value represented is actually prediction accuracy defined by R-squared. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Mean | Standard Deviation | Months | Up to 20 months |
|
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|
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| Primary | Missing EMR Characteristic: BMI (Body Mass Index) | The missing EMR characteristic BMI defined as not obese, overweight, obese, severe obesity. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic BMI was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Number | Percentage of participants | Up to 20 months |
|
|
|
|
| Primary | Missing EMR Characteristic: BMI (Continuous) | The missing EMR characteristic BMI is BMI value. Linear regression models were ran using a prioritized list of claims-based covariates as predictors and the value of select EMR-based clinical characteristics BMI as continuous outcomes. The estimated value represented is actually prediction accuracy defined by R-squared. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Mean | Standard Deviation | Kg/m^2 | Up to 20 months |
|
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| Primary | Missing EMR Characteristic: HbA1c (Hemoglobin A1c (Glycosylated Hemoglobin)) | The missing EMR characteristic HbA1c defined as value in 6 months prior to and including index date. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic HbA1c was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Mean | Standard Deviation | Percentage | Up to 20 months |
|
|
|
|
| Primary | Missing EMR Characteristic: eGFR (Glomerular Filtration Rate) | The missing EMR characteristic eGFR defined as value in 6 months prior to and including index date. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic eGFR was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Mean | Standard Deviation | ml/min per 1.73 m^2 | Upto 20 months |
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| Primary | Missing EMR Characteristic: Total Cholesterol | The missing EMR characteristic total cholesterol defined as value in 6 months prior to and including index date. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic total cholesterol was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Mean | Standard Deviation | mg/dl | Up to 20 months |
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| Primary | Missing EMR Characteristic: Systolic BP (Blood Pressure) | The missing EMR characteristic systolic BP defined as value in 6 months prior to and including index date. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic systolic BP was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Mean | Standard Deviation | mmHg | Up to 20 months |
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| Primary | Missing EMR Characteristic: Diastolic BP | The missing EMR characteristic diastolic BP defined as value in 6 months prior to and including index date. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic diastolic BP was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Mean | Standard Deviation | mmHg | Up to 20 months |
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| Primary | Binary EMR Characteristic: Neuropathy | The missing EMR characteristic neuropathy defined as participants with any note of diabetic neuropathy. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic neuropathy was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Number | Percentage of participants | Up to 20 months |
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| Primary | Binary EMR Characteristic: Nephropathy | The missing EMR characteristic nephropathy defined as participants with any note of diabetic nephropathy. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic nephropathy was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Number | Percentage of participants | Upto 20 months |
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| Primary | Binary EMR Characteristic: Retinopathy | The missing EMR characteristic retinopathy defined as participants with any note of diabetic retinopathy. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic retinopathy was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Number | Percentage of participants | Up to 20 months |
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| Primary | Binary EMR Characteristic: Pancreatitis | The missing EMR characteristic pancreatitis defined as participants with any note of prior pancreatitis. The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic pancreatitis was the dependent variable and all claims-based covariates were included as independent variables. The estimated value represented is actually prediction accuracy defined by C-statistics. | All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available. | Posted | Number | Percentage of participants | Up to 20 months |
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| 0 |
| 0 |
| 0 |
| 0 |
Boehringer Ingelheim (BI) acknowledges that investigators have the right to publish the study results. Investigators shall provide BI with a copy of any publication or presentation for review prior to any submission. Such review will be done with regard to proprietary information, information related to patentable inventions, medical, scientific, and statistical accuracy within 60 days. BI may request a delay of the publication in order to protect BI's intellectual property rights.
| D004700 | Endocrine System Diseases |
| D011799 | Quinazolines |
| 1.00-2.99 years |
|
| 3.00-4.99 years |
|
| 5.00-6.99 years |
|
| 7+ years |
|
| Normal |
|
| Overweight |
|
| Obese |
|
| Severe Obesity |
|