ADaM Supplement to the TAUG-Diabetes Version 1.0 (Draft)

Transcription

1 ADaM Supplement to the TAUG-Diabetes Version 1.0 (Draft) Prepared by the CFAST Diabetes ADaM Sub-Team Notes to Readers This is the draft version 1.0 of the CDISC ADaM Supplement to the TAUG-Diabetes. It is intended for public review only and is not a final version. This supplement is intended to be incorporated into the next version of the TAUG-Diabetes as Section 5, Statistical Analysis. This document is based on ADaM v2.1 and ADaMIG v1.0. Revision History Date Version Summary of Changes Draft Draft for Public Review See Appendix C for Representations and Warranties, Limitations of Liability, and Disclaimers.

2 CONTENTS 1 INTRODUCTION SUBJECT LEVEL ANALYSIS DATA: ADSL STRATIFICATION VARIABLES ADSL EXAMPLE ANALYSIS OF HYPOGLYCEMIC EPISODES HYPOGLYCEMIC EPISODES ANALYSIS DATASET HYPOGLYCEMIC EPISODES ANALYSIS RESULTS HYPOGLYCEMIC EPISODES SUMMARY DATASET HYPOGLYCEMIC EPISODES SUMMARY ANALYSIS RESULTS ANALYSIS OF GLYCATED HEMOGLOBIN HBA1C ANALYSIS DATASET HBA1C ANALYSIS RESULTS Longitudinal Repeated Measures Model Categorical Analysis ANALYSIS OF GLUCOSE LEVELS SELF-MONITORED GLUCOSE PROFILE ANALYSIS DATASET SELF-MONITORED GLUCOSE ANALYSIS RESULTS Longitudinal Repeated Measures Model Self-Monitored Glucose Plots MIXED MEAL TOLERANCE TEST DATASET MIXED MEAL TOLERANCE TEST ANALYSIS RESULTS APPENDICES APPENDIX A: CFAST DIABETES ADAM SUB-TEAM APPENDIX B: REFERENCES APPENDIX C: REPRESENTATIONS AND WARRANTIES, LIMITATIONS OF LIABILITY, AND DISCLAIMERS Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 1

3 Introduction This ADaM Supplement to the TAUG-Diabetes demonstrates the use of the CDISC Analysis Data Model (ADaM) to create datasets to support the analysis of statistical endpoints common to diabetes trials. Diabetes is a complex disease for which there are many clinical assessments. In turn, these clinical assessments can be used to derive a variety of statistical endpoints used to assess interventions. This document is focused on describing analysis data for 3 areas of clinical assessments, namely hypoglycemic events, HbA1c, and glucose. This is a supplement to the CDISC Therapeutic Area Standards User Guide for Diabetes (Version 1.0), and in the future these two documents are intended to be combined. It is important to note that the examples in this supplement were chosen in order to demonstrate different ADaM concepts and data structures and not necessarily because they were deemed to be of highest importance to diabetes research. The analysis of hypoglycemic events lend themselves to creation of statistical endpoints that relate to events, such as incidence or prevalence of events or event severity, and the use of the Occurrence Data Structure (OCCDS) in ADaM. The analysis of the continuous measures of HbA1c and glucose is multifaceted where statistical endpoints may range from continuous measures such as change from baseline or percent change from baseline to categorical measures such as a binary response based on achieving a pre-defined criterion. These data may be analyzed at single pre-defined point in time (eg, after 8 weeks of treatment) or longitudinally. As such these endpoints can be analyzed through the use of the ADaM Basic Data Structure (BDS). An example of a subject level analysis dataset (ADSL) is also provided and is based on ADaM. Included are examples of statistical data summaries in tabular or graphic form. These table and figure displays are for illustration purposes and not meant to imply any standard analysis presentation format or analysis method and are included to provide examples of ADaM analysis results metadata. In summary, these examples are not meant to make recommendations as to the use of these endpoints, the methods for the endpoints, nor the exact statistical methodology. It is important that each study be evaluated individually and that current ADaM documentation is referenced in order to accurately and robustly design ADaM datasets. This supplement is not intended to illustrate every possible variable that could or should be included in analysis datasets created for statistical analysis of diabetes endpoints but rather is intended to be descriptive and illustrative of the use of the ADaM model. Therefore, all examples of analysis datasets are abbreviated in nature in that they do not itemize every possible variable that would be included in an analysis dataset designed for a specific trial. The examples should not be interpreted as requirements for the statistical analysis of diabetes data. Additionally, the metadata and derivations presented are for illustrative purposes only and are not meant to imply a universally accepted definition or derivation of the variables. As such the examples should not be viewed as a statement of the standard themselves but rather an example of the application of the ADaM standard to the development of analysisready datasets. Please refer to Version 2.1 of ADaM and Version 1.0 of the Analysis Data Model Implementation Guide (ADaMIG) for required background about the ADaM and the ADaM data structures Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 2

4 Subject Level Analysis Data: ADSL The ADSL dataset structure has one record per subject and contains required variables (as specified in the ADaMIG) plus other subject-level variables that are important in describing a subject or the subject s experience in the trial. Examples of typical ADSL variables include population flags, planned and actual treatment, demographic information, randomization factors, subgrouping variables, baseline values of important measures, and important dates. ADSL variables that describe subject characteristics or disease state are often the means to creating important subpopulations. When creating new variables for these types of data, variable name fragments and variable names should be chosen to represent the content of variables as opposed to meaningless names such as VAR01, VAR02, etc. Before illustrating an ADSL specific to a diabetes trial, this supplement presents a proposal for the management of stratification variables. 2.1 Stratification Variables This supplement presents a provisional proposal for the representation of the description and the associated values for stratification factors used during randomization and treatment assignment. Before describing the proposed variables, the following brief summary of issues related to stratified randomization is provided. Stratified randomization is used to ensure balance of treatment assignments across one or more prognostic factors. A prognostic factor is an aspect of the disease or a characteristic of the subject that may influence treatment response. The prognostic factors used to stratify the randomization are specified in the protocol. As a simple example, suppose age group (<50, >=50) and gender (male, female) are considered important prognostic factors. When a subject is deemed eligible for randomization, their individual values of these factors are determined at the site and used as input to the randomization process to determine their treatment assignment. The situation may occur where the value of a factor that was used for randomization was later discovered to be in error. For example, suppose a subject was randomized according to the age group of <50 and male. Later it was discovered that the subject was actually 54 and therefore should have been randomized according to the age group of >=50 and male. If this situation happens too often, then the balance in treatment assignments across these factors is in question and this may drive the need to conduct sensitivity analyses. Therefore, there is an analysis need to have two sets of values to describe the stratification factors. In this document, these two sets of values are referred to the as randomized values and the as verified values. At present, there is not a standard method for representing the randomization strata factors and values in SDTM. Depending on the randomization process, it may be unnecessary to represent variables and values specific to stratification in SDTM since the information can be found within the appropriate domain. For example, if age and sex were used as stratification factors, then the DM variables AGE and SEX should appropriately reflect values used for randomization. However, more sophisticated randomizations or more complicated derivations of prognostic factors, such as whether a subject had ever used a particular concomitant medication for a given length of time, may be harder to identify or document in SDTM. If using an Interactive Voice Randomization System (IVRS), the values used for randomization would be captured by the system and would correspond to the values that are represented on the randomization schedule. The as verified values are typically derived by comparing the values used for randomization against the data that is in SDTM, whether it be a simple match with a single datapoint such as gender or reprogramming of more complex factors such as previous treatments. Table provides a fully itemized set of variables to allow maximum flexibility in representing the description of the prognostic factors and the values used for randomization and the values that were verified. These variables would be found in ADSL. The description of these variables is provided but the source and manner in which they are derived is a sponsor decision. In the metadata table below, the column for Codelist/Controlled terms is omitted for space reasons. In the CDISC Notes column a description of the variable is given. For illustration purposes, these descriptions are based on a hypothetical example where a trial uses 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 3

5 three stratification factors of Age group ( <50 or >=50 ), Prior treatment status ( Treatment naïve, Treatment experienced ), and Hypertension ( Y or N ). Below in Section 3.1 is another example that is specific to the full Diabetes ADSL example. Table 2.1.1: Provisional Stratification Variables Variable Name Variable Label Type Core CDISC Notes STRATA Randomized text Cond This variable represents the combination of individual stratum values used for randomization. This entire string Strata represents the combination of individual stratification values used for randomization. This variable is conditional based on whether the trial used stratified randomization. STRATAN STRATyNM STRATy STRATyN Randomized Strata (N) Description of Randomized Stratum y Value of Randomized Stratum y Value of Randomized Stratum y (N) For example, >=50, Treatment experienced, N integer Perm This is a numeric variable that corresponds to each unique value of STRATA. There must be a 1:1 correspondence between STRATA and STRATAN. For example, STRATAN=3 when STRATA = >=50, Treatment experienced, N text text Perm This is a full text description of the stratification factor y. This text description will remain constant for all subjects. These descriptive variables are included to quickly and clearly communicate critical study design information as well as to facilitate integration. This strategy is consistent with other ADaM variables such as CRIT1. For example, STRAT3NM= Hypertension Perm This is the subject level value of the y th stratification factor and the value used for randomization. For example, STRAT3= N integer Perm This is a numeric variable that corresponds to each unique value of STRATy. There must be a 1:1 correspondence between STRATy and STRATyN. For example, STRAT3N=0 when STRAT3= N STRATAV Verified Strata Text Cond This variable represents the combination of individual stratum values that were verified after randomization. This entire string represents the combination of individual stratification values that should have been used for randomization. If the values used for the randomization of a given subject were all correct, then STRATAV will equal STRATA. Otherwise, one or more components of the text string for STRATA and STRATV will be different. This variable is conditional based on whether the trial used stratified randomization and whether differences between the as randomized and as verified values are important for sensitivity analysis. For example, >=50, Treatment experienced, Y STRATyV STRATyVN Value of Verified Stratum y Value of Verified Stratum y (N) Text Perm This is the as verified subject level value of the y th stratification factor. If the value used for randomization was correct, then STRATyV will equal STRATy. For example, STRAT3V= Y integer Perm This is a numeric variable that corresponds to each unique value of STRATyV. There must be a 1:1 correspondence between STRATyV and STRATyVN. For example, STRAT3VN=1 when STRAT3V= Y 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 4

6 ADSL Example The metadata tables below provide an example of an abbreviated ADSL dataset. Variables that would commonly occur in ADSL regardless of therapeutic area, such as sex, race, age, age groups, geographic region, population flags, treatment assignments, treatment start and stop date, etc. are not shown. The variables presented below are those that may be of specific interest to the analysis of diabetes trial data. Flag variables indicating background medical history events of interest and baseline efficacy laboratory variables are considered optional variables, and only a few have been selected for reference. The example below also demonstrates all of the proposed stratification variables for a hypothetical phase III parallel group design that used two stratification factors at randomization: 1) baseline HbA1c (>7-<9%, 9%) and 2) background use of metformin in combination with insulin or metformin alone. Additional details regarding these medications such as brand, formulation, dose, etc., would be included in the medication datasets and not in ADSL unless considered critical for understanding the patient population. In this example, Subject 001 was randomized into the >7-<9% stratum, although the qualifying HbA1c value was 9.3%. The as randomized variables remain as >7-<9%, while the verified variables are updated to reflect the 9% stratum. Subject 002 was randomized correctly, and therefore has as randomized and verified strata variables that match. The following tables provide examples of an ADSL analysis dataset (Table 2.2.1), ADSL dataset metadata (Table 2.2.2), and ADSL variable metadata (Table 2.2.3). The source derivation metadata for the variables are provided for illustrative purposes and not intended to represent standard derivation logic. Within the Source/Derivation column is additional text that is meant to provide further discussion for the variable and would not be present in an actual define.xml document. Table 2.2.1: ADSL Analysis Dataset Row STUDYID USUBJID STRATA STRATAN STRAT1NM STRAT1 STRAT1N STRAT2NM STRAT2 STRAT2N 1 XYZ XYZ >7-<9%, Metformin alone 1 HbA1c >7-<9% 0 Background Diabetes Medication at Baseline Metformin alone 0 2 XYZ XYZ >=9%, Metformin + insulin 4 HbA1c >=9% 1 Background Diabetes Medication at Baseline Metformin + insulin 1 Row STRATAV STRAT1V STRAT1VN STRAT2V STRAT2V HBA1CBL HBA1CGR1 DIABCMBL DIABDURY EGFRBL HOMAIRBL 1 (cont) >=9%, Metformin alone >=9% 1 Metformin alone >=9.0 Metformin alone (cont) >=9%, Metformin + insulin >=9% 1 Metformin + insulin >=9.0 Metformin + insulin Row CPEPTBL RETINOFL NEPHROFL HEIGHTBL WEIGHTBL BMIBL BMIGR1 1 (cont) 2.3 Y <25 2 (cont) 1.2 Y Y >=30 Table 2.2.2: ADSL Dataset Metadata Dataset Name Description Class Structure Purpose Keys Location Documentation ADSL Subject Level Analysis SUBJECT LEVEL ANALYSIS DATASET One record per subject Analysis STUDYID, USUBJID ADSL.xpt ADSL.SAS Table 2.2.3: ADSL Variable Metadata Variable Length/Display Variable Label Type Name Format Codelist/Controlled Terms STUDYID Study Identifier text $15 DM.STUDYID USUBJID Unique Subject text $15 DM.USUBJID Identifier Source/Derivation/Comment 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 5

7 Variable Length/Display Variable Label Type Name Format Codelist/Controlled Terms Source/Derivation/Comment STRATA Randomized Strata text $30 >7-<9%, Metformin alone; >=9%, Metformin alone >7-<9%, Metformin + insulin; >=9%, Metformin + insulin Obtained from QVAL in SUPPDM where QNAM= STRATA Note: At present there is not a standard approach for capturing stratification factors in SDTM. This variable represents the combination of individual stratum values used for randomization. The above text is an example and uses a comma as a delimiter between individual stratum values STRATAN Randomized Strata (N) integer 1 1; 2; 3; 4 =1 when ADSL.STRATA= >7-<9%, Metformin alone ; =2 when ADLS.STRATA= >=9%, Metformin alone =3 when ADSL.STRATA = >7-<9%, Metformin + insulin ; STRAT1NM Description of Randomized Stratum 1 STRAT1 Value of Randomized Stratum 1 STRAT1N Value of Randomized Stratum 1 (N) STRAT2NM Description of Randomized Stratum 2 STRAT2 Value of Randomized Stratum 2 STRAT2N Value of Randomized =4 when ADSL.STRATA= >=9%, Metformin + insulin text $20 HbA1c at Baseline Derived from ADSL.STRATA and provides a full text description of the first stratification factor text $6 >7-<9%; >=9% Derived from ADSL.STRATA and is the text string up to the first delimiter of,. integer 1 0; 1 =0 when ADSL.STRAT1= >7-<9% ; =1 when ADLS.STRAT1= >=9% text $50 Background Diabetes Medication at Baseline text $20 Metformin alone, Metformin + insulin Derived from ADSL.STRATA and provides a full text description of the second stratification factor Derived from ADSL.STRATA and is the text string after the first delimiter of,. integer 1 0; 1 =0 when ADSL.STRAT2= Metformin alone ; =1 when ADLS.STRAT2= Metformin + insulin ; Stratum 2 (N) STRATAV Verified Strata text $30 >7-<9%, Metformin alone; >=9%, Metformin alone >7-<9%, Metformin + insulin; >=9%, Metformin + insulin STRAT1V STRAT1VN STRAT2V STRAT2VN Value of Verified Stratum 1 Value of Verified Stratum 1 (N) Value of Verified Stratum 2 Value of Verified Stratum 2 (N) Obtained from QVAL in SUPPDM where QNAM= STRATAV Note that there is no standard for if and how the updated STRAT--V are captured and recorded. The above is an example of one method and it implies that the full text string of the concatenated stratum variables has been recorded. text $5 >7-<9%; >=9% Derived from ADSL.STRATAV and is the text string up to the first delimiter of,. text $5 >7-<9%; >=9% Derived from ADSL.STRATAV and is the text string up to the first delimiter of,. text $20 Metformin alone, Derived from ADSL.STRATAV and is the text string after the first delimiter of Metformin + insulin,. text $20 Metformin alone, Derived from ADSL.STRATAV and is the text string after the first delimiter of Metformin + insulin, Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 6

8 Variable Name HBA1CBL HBA1CGR1 Variable Label HbA1c at Baseline (%) HbA1c at Baseline (%) Group 1 DIABCMBL Background Diabetes Medication DIABDURY Duration of Diabetes (years) EGFRBL egfr MDRD (ML/MIN/1.73 M**2) Type Length/Display Format Codelist/Controlled Terms Source/Derivation/Comment float 8.1 Value of LB.LBSTRESN where LB.LBTESTCD= HBA1C and LB.LBDTC is the closest prior date to DM.RFSTDTC. This represents the value collected just prior to randomization and the value that should have been used to determine the stratification used for randomization. text $10 >7-<9%, >=9% text $20 Metformin alone, Metformin + insulin Categorization of ADSL.HBA1CBL Note: When the accurate value of HbA1c group was used for randomization, this variable will duplicate the information found in STRAT1U and in STRAT1 and the subject was randomized correctly. However in this example, it is considered helpful to have as a separate variable with an explicit variable label. = Metformin + insulin if CM.CMCAT= DIABETES and CM.CMTRT= INSULIN and CMSTDTC is before or on the first dose date (ADSL.TRTSDT) = Metformin alone otherwise This represents the updated stratification value. Note: See note above for HBA1CBL. This variable is similar, yet captures the information pertaining to background medication in a separate variable. float 8.1 Difference between ADSL.SCRSDT (screening date) and MH.MHSTDTC where MH.MHTERM= DIABETES. See SAP for details regarding imputation of partial dates. float 8.1 Value of LB.LBSTRESN where LB.LBTESTCD= EGRFL and LB.LBDTC is the closest prior date to DM.RFSTDTC. This represents the value collected just prior to dosing. HOMAIRBL Baseline HOMA-IR float 6.2 Value of LB.LBSTRESN where LB.LBTESTCD= HOMAIR and LB.LBDTC is the closest prior date to DM.RFSTDTC. This represents the value collected just prior to dosing. CPEPTBL RETINOFL NEPHROFL HEIGHTBL WEIGHTBL BMIBL Baseline C-peptide (ng/ml) Medical Hx of Diabetic Retinopathy Flag Medical Hx of Diabetic Nephropathy Flag Height at Baseline (cm) Weight at Baseline (kg) Body Mass Index at Baseline (kg/m 2 ) float 6.2 Value of LB.LBSTRESN where LB.LBTESTCD= CPEPTIDE and LB.LBDTC is the closest prior date to DM.RFSTDTC. This represents the value collected just prior to dosing. text $1 Y = Y where MH.MHTERM = 'DIABETIC RETINOPATHY' and MHOCCUR='Y' text $1 Y = Y where MH.MHTERM = 'NEPHROPATHY' and MHOCCUR='Y' integer 8 The last available of VS.VSSTRESN for VS.VSTESTCD= HEIGHT before or on the first dose date (ADSL.TRTSDT) float 8.1 The last available of VS.VSSTRESN for VS.VSTESTCD= WEIGHT before or on the first dose date (ADSL.TRTSDT) float 8.1 ADSL.WEIGHTBL /ADSL.HEIGHTBL**2 BMIGR1 BMI Group 1 text $10 <25, >=25 - <30, >=30 Categorization of ADSL.BMIBL 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 7

9 Analysis of Hypoglycemic Episodes The examples of hypoglycemic data provided in the following sections are based on methodologies widely used throughout clinical research within diabetes. Hypoglycemic episodes are mainly self-reported events where the information is gathered in patient diaries. From there, the data are entered into a hypo form in the ecrfs. Hypoglycemic events are often summarized and analyzed by American Diabetes Association (ADA) classification (see Seaquist et al. [Diabetes Care. 2013;36: ] 1 for details) and each event is classified based on different kinds of information collected from the hypo form. Plasma glucose may be measured to support classification according to the ADA severity classes. These glucose concentrations will be in LB SDTM domain together with all planned glucose measurements eg, planned samples evaluated at a central laboratory. It should be noted that the measurements obtained from hypo forms will usually only be included for the purpose of classifyinghypoglycemia (eg, according to the ADA criteria) whereas all other analyses of plasma glucose will be done without these observations. There are two abbreviated analysis datasets presented below. The first dataset gathers all information related to hypoglycemic events from the relevant SDTM domains and includes the derived ADA-classification for each event. The second dataset is built from the first dataset and allows for an analysis ready approach to the summarization of hypoglycemic episodes by classification for each subject. Note that hypoglycemic episodes are important adverse events for diabetic patients and presentations of analyses of hypoglycemic episodes are often based on the safety analysis set and actual treatment. However, a reduction in hypoglycemic episodes can also be considered a positive property of an investigational drug and hence hypoglycemic episodes can also be considered efficacy endpoints and summarized by planned treatment for various efficacy populations. 3.1 Hypoglycemic Episodes Analysis Dataset In this analysis dataset example, ADHYPO, shown below, each row represents one hypoglycemic episode. This analysis dataset collates onto one record the pertinent data for each episode that is represented in multiple SDTM domains. Data for two subjects are provided below illustrating multiple hypoglycemic events for each subject. The MIDS variable from the CE SDTM domain identifies the individual hypoglycemic episode. Details on a given event are mapped from the SDTM domain CE, in line with the SDTM Diabetes TAUG. A number of variables are ADaM variables that can be transferred directly from ADSL and hence are easily traced back to their respective domains. Further, a number of analysis variables are derived such as ADY, the relative analysis day and the traceability for these variables are ensured by the metadata shown in Table Finally, a number of sponsor defined variables are present, such as SELFTRFL ( was the subject able to self-treat her or himself? (yes/no) and LMLRELTM ( last meal relative time ). The first variable mentioned is needed to find the ADA class for each event and the second variable is needed in the statistical analysis of the hypoglycemic episodes, since events within 2 hours from last main meal will be analyzed separately in the example protocol. The source/derivation metadata provided below serve as an example of typical metadata and should not be interpreted as precise methods for how these variables should be derived. The first four rows present the four hypoglycemic episodes experienced by the subject Based on ADHYPO a summary table of hypoglycemic episodes, presenting number, and percentage of subjects experiencing at least one event together with number of events, by treatment arm, can directly be produced, see example in Table The summary can also be presented by time in study, see Table Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 8

10 Table 3.1.1: ADHYPO Analysis Dataset Row STUDYID USUBJID MIDS CEDECOD WASAEYN ASTDTM TRTEMFL SELFTRFL SYMPFL NOCTFL GLUCSTD GLUCCONV ADY LMLDTM 1 XYZ HYPO 1 Hypoglycemia Y 07Sep :29:00 Y N Y N Sep :33:00 2 XYZ HYPO 2 Hypoglycemia N 10Sep2012 9:12:00 Y Y N N Sep2012 8:15:00 3 XYZ HYPO 3 Hypoglycemia N 10Sep :05:00 Y Y Y Y Sep :06:00 4 XYZ HYPO 4 Hypoglycemia N 11Sep :40:00 Y Y Y N Sep :24:00 5 XYZ HYPO 5 Hypoglycemia N 18Sep :39:00 Y Y N N Sep :27:00 6 XYZ HYPO 1 Hypoglycemia N 22Oct :28:00 Y Y N N Sep2012 9:58:00 7 XYZ HYPO 2 Hypoglycemia N 25Oct :59:00 Y Y Y N Oct :50:00 8 XYZ HYPO 3 Hypoglycemia N 17Nov2012 3:30:00 Y N N Y Nov :01:00 Row LMLRELTM LMLRELTU LEXDTM LEXRELTM LEXRELTU ASEV ASEVGR1 TRTA 1 (cont) 146 Minutes 07Sep :29: Minutes Severe Hypoglycemia Documented symptomatic or severe hypoglycemia Drug A 2 (cont) 57 Minutes 10Sep2012 8:12:00 60 Minutes Severe Hypoglycemia Documented symptomatic or severe hypoglycemia Drug A 3 (cont) 119 Minutes 10Sep :05: Minutes Severe Hypoglycemia Documented symptomatic or severe hypoglycemia Drug A 4 (cont) 44 Minutes 11Sep :10:00 58 Minutes Severe Hypoglycemia Documented symptomatic or severe hypoglycemia Drug A 5 (cont) 199 Minutes 18Sep :29: Minutes Severe Hypoglycemia Documented symptomatic or severe hypoglycemia Drug B 6 (cont) 210 Minutes 20Sep :31: Minutes Pseudo symptomatic hypoglycemia Pseudo symptomatic hypoglycemia Drug B 7 (cont) 189 Minutes 25Oct :29: Minutes Severe Hypoglycemia Documented symptomatic or severe hypoglycemia Drug B 8 (cont) 91 Minutes 18Nov :30: Minutes Severe Hypoglycemia Documented symptomatic or severe hypoglycemia Drug B Table 3.1.2: ADHYPO Dataset Metadata Dataset Name Dataset Description Dataset Location Dataset Structure Keys Class Documentation ADHYPO Hypoglycemic Episodes Analysis Dataset adhypo.xpt One record per subject per event STUDYID, USUBJID, MIDS OCCDS ADHYPO.SAS/SAP Table 3.1.3: ADHYPO Variable Metadata Variable Length/Display Variable Label Type Name Format Codelist/Controlled Terms Source/Derivation/Comment STUDYID Study Identifier text $12 ADSL.STUDYID USUBJID Unique Subject Identifier text $20 ADSL.USUBJID MIDS Disease Milestone ID text CE.MIDS CEDECOD Dictionary-Derived Term text CE.CEDECOD WASAEYN Was this an adverse event text FAORRES where FA.MIDS=CE.MIDS and FATESTCD="WASAEYN" ASTDTM Analysis Start Datetime integer Datetime. Onset of the hypoglycemic episode. Derived based on CE.CESTDTC. ADY Analysis Relative Day integer The number of days from date of first dose until onset of hypoglycemic episode, derived from ASTDTM and ADSL.TRTSDT TRTEMFL Treatment Emergent text $1 Y,N If ADSL.TRTSDT <= ASTDT<=(ADSL.TRTEDT +1) then Analysis Flag TRTEMFL= Y 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 9

11 Variable Name SELFTRFL Variable Label Subject able to treat self flag Type Length/Display Format Codelist/Controlled Terms Source/Derivation/Comment text $1 Y,N FAORRES where FA.MIDS=CE.MIDS and FAOBJ="HYPOGLYCEMIC EVENT" and FACAT="TREATMENT ADMINISTRATION" and FATESTCD="TXASSIST" and FATEST^="TREATMENT ASSISTANCE" SYMPFL Symptomatic Event flag text $1 Y,N CEYN where CECAT= HYPO SYMPTOMS NOCTFL Nocturnal Event flag text $1 Y,N Based on sponsor definition of nocturnal event, eg, from midnight until 6:00 in the morning. GLUCSTD Glucose (mmol/l) at the time of the event float 8.1 LBSTRESN where LB.MIDS=CE.MIDS AND LBTESTCD="GLUC" where LBSTRESN is converted to standard SI units of mmol/l if necessary. GLUCCONV Glucose (mg/dl) at the time of the event float 8.1 LBSTRESN where LB.MIDS=CE.MIDS AND LBTESTCD="GLUC" where LBSTRESN is converted to conventional units of mg/dl if necessary. LMLDTM Last Meal Datetime integer Datetime. MLSTDTC where ML.MIDS=CE.MIDS AND RELMIDS="LAST MEAL LMLRELTM Time between last meal and hypo onset LMLRELTU Time between last meal and hypo onset Unit LEXDTM Last Exposure to Study Drug Datetime LEXRELTM Time between hypo onset last exposure and hypo onset LEXRELTU Time between last exp and hypo onset unit ASEV Analysis Severity/Intensity integer 4. PRIOR TO HYPO" AND MLTRT="MEAL" Time from last meal to onset of hypo (ASTDTM-LMLDTM) text $7 Minutes Unit of time from last meal to onset of hypo integer Datetime. integer 4. EXSTDTC where EX.MIDS=CE.MIDS AND RELMIDS="LAST DOSE PRIOR TO HYPO" AND EXCAT="HIGHLIGHTED DOSE" Time from last exposure to drug to onset of hypo. (ASTDTM-LEXDTM) text $7 Minutes Unit of time from last exposure to drug to onset of hypo unit text $22 Severe hypoglycemia/ Documented symptomatic hypoglycemia/ Asymptomatic hypoglycemia/ Probable symptomatic hypoglycemia/ Pseudo-hypoglycemia/ Unclassifiable hypoglycemia ASEVGR1 Pooled Severity Group 1 text $45 Severe hypoglycemia/ Documented symptomatic hypoglycemia/ Asymptomatic hypoglycemia/ Probable symptomatic hypoglycemia/ Pseudo-hypoglycemia/ Unclassifiable hypoglycemia Based on ADA Classification: TRTA Actual Treatment text $32 ADSL.TRT01A Severe hypoglycemia/ Documented symptomatic hypoglycemia/ Asymptomatic hypoglycemia/ Probable symptomatic hypoglycemia/ Pseudo-hypoglycemia/ Unclassifiable hypoglycemia Categorization based on ADA classes, eg, Documented and Severe Hypoglycemia 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 10

12 Hypoglycemic Episodes Analysis Results A first presentation of the hypoglycemic episodes will often be a summary table, where the number of total events is presented together with the number and percentage of subjects with events. An example of the simple summary table is shown in Table 3.2.1, which presents hypoglycemic episodes that occur within two hours since the last main meal split by diurnal and nocturnal and by severity. The events can also be summarized by time (using the ADY variable) in the trial, as shown in Table For these two table examples, analysis results metadata are not presented. Table 3.2.1: Summary of Post-Meal Hypoglycemic Episodes by Severity Hypoglycemic episodes within 2 hours since last meal by severity Treatment Emergent Summary Safety Analysis Set Drug A Drug B N (%) E N (%) E Number of subjects Diurnal 30 (69.8) (79.1) 81 Documented Symptomatic 17 (39.5) (55.8) 52 Pseudo Symptomatic 14 (32.6) (37.2) 25 Unclassifiable 6 (14.0) 10 4 ( 9.3) 4 Nocturnal 1 ( 2.3) 1 1 ( 2.3) 1 Documented Symptomatic 1 ( 2.3) 1 0 Unclassifiable 0 1 ( 2.3) 1 N: Number of subjects; %: Percentage of subjects; E: Number of events Table 3.2.2: Summary of Hypoglycemic Episodes by Classification and Time Hypoglycemic Episodes by Classification and Time Treatment Emergent Summary Safety Analysis Set Drug A Drug B Total N (%) E N (%) E N (%) E Number of Subjects Pseudo Symptomatic 1 ( 0.8) 5 1 ( 1.0) 1 2 ( 0.9) 6 Week 1 1 ( 0.8) 3 0 ( 0.0) 0 1 ( 0.5) 2 Week 2 0 ( 0.0) 0 0 ( 0.0) 0 1 ( 0.5) 1 End of treatment 1 ( 0.8) 2 0 ( 0.0) 0 0 ( 0.0) 0 Documented Symptomatic 16 (12.2) (15.4) (13.6) 101 Week 1 4 ( 3.0) 10 3 ( 4.2) ( 6.7) 35 Week 2 2 ( 1.6) 3 7 ( 8.8) 25 9 ( 4.0) 16 End of treatment 10 ( 9.1) 41 6 ( 7.9) ( 4.9) 50 N: Number of subjects; %: Percentage of subjects; E: Number of events 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 11

13 Hypoglycemic Episodes Summary Dataset The analysis dataset ADHYSUM is built from ADHYPO and supports the statistical analysis of the hypoglycemic events and the illustration of summary of frequencies of hypoglycemic episodes, see Table The dataset includes one observation per combination of subject, analysis parameter, time window and indicator (eg, treatment emergent flag). Each record is a summary of the type of hypoglycemic episode described by the parameter, per subject. For each combination of parameter and the timing variable, AVISIT, records are created even if no hypoglycemic episodes occurred. The statistical model presented below is based on the actual treatment received (TRTA) and adjusted for subject level values of country and sex and therefore these variables are included in ADHYSUM from ADSL to support analysis readiness. The duration of exposure (TRTDURD) is added to the dataset in order to facilitate exposure adjusted incidence rates. For overall summaries the records which have cumulative frequency count within the text of PARAM and AVISIT= End of treatment can be selected. Table 3.3.1: ADHYSUM Analysis Dataset Row STUDYID USUBJID PARAMCD PARAM AVISIT AVAL TRTEMFL TRTDURD SEX AGE COUNTRY TRTA 1 XYZ ASSYMP Asymptomatic Hypoglycemia (frequency count) Week 1 3 N 72 F 35 DZA Drug B 2 XYZ ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) Week 1 3 N 72 F 35 DZA Drug B 3 XYZ ASSYMP Asymptomatic Hypoglycemia (frequency count) Week 2 1 Y 72 F 35 DZA Drug B 4 XYZ ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) Week 2 4 Y 72 F 35 DZA Drug B 5 XYZ ASSYMP Asymptomatic Hypoglycemia (frequency count) Week 3 0 Y 72 F 35 DZA Drug B 6 XYZ ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) Week 3 4 Y 72 F 35 DZA Drug B 7 XYZ ASSYMP Asymptomatic Hypoglycemia (frequency count) Week 4 1 Y 72 F 35 DZA Drug B 8 XYZ ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) Week 4 5 Y 72 F 35 DZA Drug B 9 XYZ ASSYMP Asymptomatic Hypoglycemia (frequency count) Endpoint 2 Y 72 F 35 DZA Drug B 10 XYZ ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) End of Treatment 7 Y 72 F 35 DZA Drug B 19 XYZ DOCSEV Documented or Severe Hypoglycemia (frequency count) Endpoint 4 Y 72 F 35 DZA Drug B 20 XYZ DOCSEVC Documented or Severe Hypoglycemia (cumulative frequency count) End of Treatment 17 Y 72 F 35 DZA Drug B Table 3.3.2: ADHYSUM Dataset Metadata Dataset Name Dataset Description Dataset Location Dataset Structure Keys Class Documentation ADHYSUM Hypoglycemic Episodes ADHYSUM.xpt One record per subject per analysis visit STUDYID, USUBJID, AVISIT, BDS ADHYSUM.SAS/SAP Summary Data per parameter PARAMCD Table 3.3.3: ADHYSUM Variable Metadata Variable Length/Display Variable Label Type Name Format Codelist/Controlled Terms STUDYID Study Identifier text $12 ADSL.STUDYID USUBJID Unique Subject Identifier text $20 ADSL.USUBJID AVISIT Analysis Visit text $13 Week -1, Week 0, Week 1, Week N, End of Treatment, Endpoint PARAM Parameter text $80 See parameter value metadata PARAMCD Parameter Code text $8 See parameter value metadata Source/Derivation/Comment Refer to Section X.X of the SAP for windowing and imputation algorithms based on ADHYPO.ADY. End-of-treatment will be defined as the last week, where the subject is on treatment Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 12

14 Variable Length/Display Variable Label Type Name Format Codelist/Controlled Terms Source/Derivation/Comment AVAL Analysis Value integer 8 See parameter value metadata ADY Analysis Relative Day integer 8 The relative day of AVAL and/or AVALC. The number of days from DM.RFSTDTC to ADT. TRTEMFL Treatment Emergent Analysis Flag Text $1 Y,N If ADSL.TRTSDT <= ASTDT<=(ADSL.TRTEDT +1) then TRTEMFL= Y TRTDURD Total Treatment Duration integer 8 ADSL.TRTDURD (Days) SEX Sex text $1 ADSL.SEX AGE Age integer 8 ADSL.AGE TRTA Actual Treatment text $32 ADSL.TRT01A Table 3.3.4: ADHYSUM Parameter [CL.PARAM. ADHYSUM] Permitted Value (code) Asymptomatic Hypoglycemia (frequency count) Asymptomatic Hypoglycemia (cumulative frequency count) Documented symptomatic or Severe Hypoglycemia (frequency count) Documented symptomatic or Severe Hypoglycemia (cumulative frequency count) Table 3.3.5: ADHYSUM Parameter Code [CL.PARAMCD. ADHYSUM] Permitted Value (Code) Display Value (Decode) ASSYMP Asymptomatic Hypoglycemia (frequency count) ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) DOCSEV Documented symptomatic or Severe Hypoglycemia (frequency count) DOCSEVC Documented symptomatic or Severe Hypoglycemia (cumulative frequency count) Table 3.3.6: Parameter Value Level List ADHYSUM [AVAL] Length/ Codelist/ Variable Where Type Display Controlled Source/Derivation/Comment Format Terms AVAL PARAMCD= ASSYMP Integer 3. Derived: AVAL equals the number of events in ADHYPO that occur during the period defined by AVISIT and have a value of ASEV of Asymptomatic hypoglycemia AVAL PARAMCD= ASSYMPC Integer 3. Derived: AVAL equals the number of asymptomatic hypoglycemic events that have occurred from the beginning of the trial up to AVISIT. It is equal to the sum of all values of AVAL from all records in ADHYSUM for a given value of AVISIT where PARAMCD= ASSYMP. AVAL PARAMCD= DOCSEV Integer 3. Derived: AVAL equals the number of records in ADHYPO that occur during the period defined by AVISIT and have a value of ASEV of Documented Symptomatic AVAL PARAMCD= DOCSEVC Integer 3. Derived: This is a running total of the number of document symptomatic events that have occurred from the beginning of the trial thru the period of time indicated by AVISIT. It is equal to the sum of all values of AVAL from all records in ADHYSUM for a given value of AVISIT where PARAMCD= DOCSEV Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 13

15 Hypoglycemic Episodes Summary Analysis Results The summary statistics in Table are presented for all hypoglycemic episodes as well as by ADA classification group. The statistics presented in the current example are number of subjects experiencing an event, the number of events and the raw event rate. To estimate and present the event rate information, exposure time is needed and Table is based on the ADHYSUM data set. Table 3.4.1: Summary of Hypoglycemic Episodes by Classification Hypoglycemic Episodes by Classification Treatment Emergent Summary Safety Analysis Set Drug A Drug B Total N (%) E R N (%) E R N (%) E R Number of subjects Total events 18 ( 13.7) ( 18.3) ( 15.7) ADA Severe hypoglycemia 1 ( 0.8) ( 1.0) ( 0.9) Documented symptomatic hypoglycemia 16 ( 12.2) ( 15.4) ( 13.6) Asymptomatic hypoglycemia 9 ( 6.9) ( 2.9) ( 5.1) Probable symptomatic hypoglycemia 3 ( 2.3) ( 1.9) ( 2.1) Pseudo-hypoglycemia Unclassifiable N: Number of subjects; %: Percentage of subjects; E: Number of events; R: Event rate per 100 exposure years; Severe: Subject unable to treat himself/herself and/or have a recorded PG < 3.1 mmol/l (56 mg/dl) Treatment emergent episodes occur after trial product administration after randomization and no later than 1 day after last trial product administration. The hypoglycemic episodes can also be summarized by concomitant medication group (eg, with or without metformin), time since last meal (eg, within 1 hour of last meal) or other relevant categorical variables. The event rate over time since randomization for hypoglycemic episodes can be presented graphically by a mean cumulative function plot. In Figure the severe and documented symptomatic events are compared between the treatment arms. The figure is made based on the cumulative episodes by subjects over time, found in the ADLHYSUM dataset Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 14

16 Figure 3.4.1: Mean Cumulative Function Plot of Documented Symptomatic Hypoglycemic Episodes Documented Symptomatic Hypoglycemic Episodes Treatment Emergent - Mean Cumulative Function - Safety Analysis Set Number of Episodes per Subject Different approaches can be made to statistical analysis of hypoglycemic episodes. The negative binomial regression, Poisson regression and several zero inflated models, are evaluated in Bulsara et al (Diabet Med. 2004;21:914-9) 2 and Aschner et al (Lancet. 2012;379:2262-9) 3. In Table the documented symptomatic or severe hypoglycemic episodes are modelled by a negative binomial distribution and compared between the treatment arms. The predicted population mean rates (LSMeans) and the estimated rate ratios between treatment arms are presented. The analysis is based on ADHYSUM and the result metadata are presented in Table Table 3.4.2: Hypoglycemic Episodes Full Analysis Set Hypoglycemic Episodes Treatment Emergent Statistical Analysis Full Analysis Set FAS N Estimate 95% CI p Documented Symptomatic or Severe Hypoglycemic Episodes Time since Randomisation (Weeks) LSMeans, Events per 100 PYE Drug A Drug B Treatment Ratio Drug A / Drug B 0.82 [ 0.64 ; 1.04] 0.15 N: Number of subjects contributing to analysis; CI: Confidence Interval; PYE: Patient Years Exposure The number of events is analyzed using a Negative Binomial Regression model using a log link and the logarithm of the exposure time (100 years) as offset. The model includes treatment and sex as fixed effects, and age as covariate Number of Episodes per Subject Drug A Drug B 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 15

17 Table 3.4.3: Hypoglycemic Events Analysis Results Metadata Metadata Field Metadata DISPLAY IDENTIFIER Table DISPLAY NAME Statistical Analysis by negative binomial model of severe and documented symptomatic Hypoglycemic Episodes, by ADA classification RESULT IDENTIFIER Sum of severe and documented symptomatic hypoglycemic events PARAM Documented symptomatic or Severe Hypoglycemia (cumulative frequency count) PARAMCD DOCSEVC ANALYSIS VARIABLE AVAL ANALYSIS REASON confirmatory secondary endpoint, as pre-specified in the protocol ANALYSIS DATASET ADHYSUM SELECTION CRITERIA FASFL="Y" and AVISIT= End of Treatment DOCUMENTATION Protocol section x.x: The number of documented symptomatic or severe hypoglycemic episodes will be analyzed based on the Full Analysis Set using a negative binomial regression model with a log-link function, and the logarithm of the time period in which a hypoglycemic episode is considered treatment emergent as offset. The model will include treatment and sex as factors and age as covariate. PROGRAMMING proc genmod data=adhysum; model AVAL = trtp sex age / dist=nb link=log offset=log(trtdurd); run; STATEMENTS 4 Analysis of Glycated Hemoglobin Most phase III diabetes studies will use the continuous clinical endpoint of HbA1c to derive the primary efficacy endpoint of the trial. However there are a number of derived statistical endpoints and analysis methods that are used. The examples below serve to demonstrate the use of the ADaM standard to create an analysis dataset to support two typical endpoints. This example is based on a phase III, parallel group study designed to determine efficacy of Drug A for patients with Type II diabetes. The primary endpoint is defined as the change in HbA1c from baseline. This will be analyzed using observed data with a longitudinal repeated measures analysis including the fixed categorical effects of treatment, week and treatment-by-week interaction as well as the continuous fixed covariate baseline HbA1c. A secondary endpoint is defined as the proportion of subjects who experienced one or more values of HbA1c < 7%. This categorical data will be analyzed using chi-square tests with the use of exact tests as appropriate. The ADaM dataset below demonstrates the use of the Basic Data Structure (BDS) for both variables as described above using one analysis parameter for the continuous HbA1c measure. This example additionally includes the use of the variable DTYPE to illustrate how data for missed visits could be imputed. These are added for demonstration purposes only and are not used in the specified analysis examples. 4.1 HbA1c Analysis Dataset The following tables provide examples of a BDS structured dataset (Table 4.1.1), analysis dataset metadata (Table 4.1.2) and analysis variable metadata (Table 4.1.3) for HbA1c analyzed as a continuous variable and separately as a categorical variable. Note that some variables that could be important to individual trials may not be presented within this document, as only selected variables were chosen to focus on the most important concepts. Many of the critical variables will be the same across studies within a program, however each individual trial needs to be evaluated independently. Some trials may require additional variables such as age of onset of diabetes (years) or baseline fasting glucose Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 16

18 Table 4.1.1: ADHBA1C Analysis Dataset Row STUDYID USUBJID PARAM PARAMCD VISIT AVISIT AWTARGET ADY TRTP ITTFL ABLFL BASE AVAL CHG ANL01FL CRIT1 CRIT1FL DTYPE LBSEQ 1 XYZ XYZ HbA1c (%) HBA1C Visit 2 Baseline 1 1 Drug A Y Y Y <7% N XYZ XYZ HbA1c (%) HBA1C Visit 3 Week Drug A Y Y <7% N XYZ XYZ HbA1c (%) HBA1C Visit 4 Week Drug A Y Y <7% N XYZ XYZ HbA1c (%) HBA1C Visit 5 Week Drug A Y Y <7% Y XYZ XYZ HbA1c (%) HBA1C Visit 6 Week Drug A Y Y <7% Y XYZ XYZ HbA1c (%) HBA1C Visit 2 Baseline 1 1 Drug B Y Y Y <7% N XYZ XYZ HbA1c (%) HBA1C Visit 3 Week Drug B Y Y <7% N XYZ XYZ HbA1c (%) HBA1C Visit 4 Week Drug B Y Y <7% N XYZ XYZ HbA1c (%) HBA1C Visit 5 Week Drug B Y <7% N XYZ XYZ HbA1c (%) HBA1C Visit 5.1 Week Drug B Y Y <7% N LOCF XYZ XYZ HbA1c (%) HBA1C Visit 6 Week Drug B Y Y <7% N LOCF Table 4.1.2: ADHBA1C Analysis Dataset Metadata Dataset Description Class Structure Purpose Keys Location Documentation ADHBA1C HbA1c Analysis Basic Data One record per subject per parameter Analysis STUDYID, USUBJID, ADHBA1C.xpt ADHBA1C.SAS/SAP Data Structure per analysis visit and day PARAMCD, AVISIT, ADY Table 4.1.3: ADHBA1C Analysis Variable Metadata Variable Length/Display Variable Label Type Codelist/Controlled Terms Name Format Source/Derivation/Comment STUDYID Study Identifier text 3 ADSL.STUDYID USUBJID Unique Subject text 20 ADSL.USUBJID Identifier PARAM Parameter text 32 HbA1c (%) Populated with HbA1c (%) for records corresponding to HbA1c (LB.LBTESTCD= HBA1C ) PARAMCD Parameter Code text 8 HBA1C Populated with HBA1C (based on LB.LBTESTCD= HBA1C ) VISIT Visit Name text 20 Visit 2, Visit 3, Visit 4, Visit LB.VISIT 5, Visit 5.1, Visit 6 AVISIT Analysis Visit text 11 Baseline, Week 4, Week 8, Refer to Section X.X of the SAP for windowing algorithm based on Week 12, Week 24 ADHBA1C.ADY. Baseline visit is defined as the last available value prior to randomization. AWTARGET Analysis Window integer 3 Refer to Section X.X of the SAP for windowing algorithm Target ADY Analysis Relative integer 3 Refer to Section X.X of the SAP for windowing algorithm based on Day ADHBA1C.ADY TRTP Planned text 15 Drug A, Drug B ADSL.TRT01P Treatment ITTFL Intent-To-Treat text 1 Y, N ADSL.ITTFL Population Flag ABLFL Baseline Record text 1 Y Set to Y when HBA1C.AVISIT= Baseline. See SAP for visit windowing. Flag 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 17