Principles and Practicalities in Building ADaM Datasets

Principles and Practicalities in Building ADaM Datasets

Principles and Practicalities in Building ADaM Datasets Cathy Barrows CDISC Users Group May 25, 2012 Previously presented at: PhUSE Single Day Event North Carolina September 14, 2011 2 Goal: Present practical issues / points / considerations in building ADaM datasets In the hopes that it will be helpful information that will benefit you 3 How? Set the stage - Where ADaM has been (a bit of history) Where ADaM is now

Learnings from the development of the ADaM General Examples Document Provide an overview of the document Highlight issues encountered, questions we grappled with, and some of the thinking behind the decisions made 4 Where ADaM has been A bit of history and an analogy 5 Statistical Analysis Dataset Model: General Considerations Version 1.0 (Final 2005) Key Principles for Analysis Identified categories of analysis variables, Datasets Analysis datasets should:

defined a few specific facilitate clear and variables: unambiguous communication be useable by currently available tools be linked to machinereadable metadata be analysis-ready --DT --DTM ANLDY, which included Day 0 ANLDYT TRTP, TRTPN, TRTA, TRTAN Metadata Analysis Dataset Metadata Appendix Documents: Categorical and Change from

Baseline (for comment 2005) Illustrated structure considered by ADaM team to be most analysis-ready However the use of a particular structure in the example is not meant to imply that it is the recommended format. For example, change from baseline gave 1 recommended + 2 alternative structures 6 7 Analysis Data Model: Version 2.0 (for comment 2006) More stringent No or little change: Key Principles for Analysisrequirements for ADaM datasets:

Datasets Analysis datasets must Metadata include ADSL Added more ADaM consist of the optimum variables number of analysis datasets Defined ADSL maintain SDTM variable attributes if the identical variable also exists in an SDTM dataset. naming convention ADxxxxxx. consistently follow sponsordefined naming 8 Where we are today ADaM Model Document v2.1 Enumerates fundamental principles of ADaM Introduces and defines Traceability

Outlines the various types of ADaM metadata General considerations when creating analysis datasets including ADSL and BDS ADaM IG v1.0 Standard variable naming conventions ADSL variables BDS variables Implementation issues, standard solutions and examples Published in 9 In the spirit of continuing to develop the road that is ADaM 10 Well along in development: Compliance checks

Phase 1 available now Phase 2 under development ADAE Hopefully to be posted THIS WEEK! ADTTE Hopefully to be posted THIS WEEK! 11 In active development, but still early Metadata guidance and examples for representing metadata for ADaM General Occurrences expand ADAE model to cover similar analyses e.g. conmeds, med history, surgery Multiple Endpoints multivariate analyses

analysis variables required to be on the same record ISS/ISE Integration guidance on standards for data integration ADPK guidance for creating PK analysis datasets 12 And we have the General Examples Document 13 Analysis Data Model Examples in Commonly Used Statistical Analysis Methods Full examples of applied ADaM implementation sample data, dataset metadata, results, and results level metadata Based on ADaM Model Document V2.1 and

ADaMIG V1.0 Status: Published on the CDISC webpage in January 2012 14 Structure of the document Section 1 - Introduction Purpose Common statistical analysis methods Mapping to the examples Points to consider when building analysis datasets ADaM concepts and principles applied in example Conventions used in this document Decisions made in developing the examples 15

Structure of the document Section 2 Examples Structure of Examples: ANCOVA Introduction Categorical analysis Repeated measures Analysis Metadata (dataset and variable) Descriptive statistics Analysis dataset illustration

Logistic regression Analysis results (sample and results metadata) Multivariate ANOVA Crossover study Hys law 16 DID NOT: implement or advocate new rules or standards attempt to identify specific SDTM domains Focus is on analysis datasets not SDTM attempt to include all possible variables Did try to include those that would be included for the analysis being

described 17 Points to consider when building ADs Optimum number of analysis datasets Goal is to have the optimum number of analysis datasets needed to perform the various analyses Examples provided of a single dataset that supports multiple analyses (examples 1-4) Also note that the same analysis dataset can be used to generate descriptive statistics such as the count and percentages 18 Points to consider when building ADs Ordering of variables Authors of examples each used their own ordering - no specific ordering of variables within the illustrated datasets is applied

(ADaM makes no specific recommendation) Important to note that within an example the ordering of the variables within the illustrated analysis dataset matches the order of the variables as presented in the associated metadata. 19 Points to consider when building ADs Identification of source dataset When identifying the source dataset for a variable, the immediate predecessor is used, as described in the ADaM, for example: AGE in ADSL - source is identified as DM.AGE AGE in other analysis datasets - source is identified as ADSL.AGE 20 Points to consider when building ADs Parameter value-level metadata

Parameter value-level metadata are included for BDS analysis datasets required in variable-level metadata for a BDS analysis dataset (currently stated that way in the ADaM v2.1 document) ADSL no parameter value-level metadata Note that parameter value-level metadata is NOT a separately defined set of metadata parameter identifier is simply an additional metadata element 21 Points to consider when building ADs Analysis-ready Contain all of the variables needed for the specific analysis No need for first manipulating data Only simple manipulations (i.e., minimal programming), if any, to prepare for analysis

22 Analysis-ready What is meant by minimal programming? Select? yes Sort? yes Transpose? no because of the variations in terms of the variable to be transposed, how to define the new variable names, what other fields should be included in the transposed dataset, etc. Merge or Join? sponsor decision difficult to draw the line as to which merges are minimal and which are no longer minimal, so no distinction made by ADaM 23 Options chosen / Decisions made in the development of the examples

Not intended to imply a requirement or standard! 24 Parameter Identifier Only one PARAM/PARAMCD in the dataset 3 options considered Parameter Identifier = *ALL* for all variables Parameter Identifier = the PARAMCD for all variables Combination: Parameter Identifier = the PARAMCD for variables that have metadata dependent on the analysis parameter Parameter Identifier = *ALL* for variables expected to be consistent across analysis parameters 25 Illustration of Parameter Identifier:

Parameter Identifier PARAMCD Variable Variable Name Label PARAMCD Parameter Code Variable Display Codelist / Type Format Controlled Terms text $8 BMDLS *ALL* AVISIT

Analysis Visit text $11 BMDLS AVAL Analysis Value float 8.1 BASELINE, MONTH 6, MONTH 12, MONTH 18,

MONTH 24, MONTH 30, MONTH 36, Source / Derivation Populated with BMDLS for records corresponding to Lumbar Spine Bone Mineral Density (based on XX.XXTESTCD) Refer to Section X.X of the SAP for a detailed description of the windowing and imputation algorithms used to determine the analysis visit based on ADBMD.ADY AVAL = XX.XXSTRESN or an imputed value if XX.XXSTRESN is missing, apply the LOCF algorithm, i.e. set AVAL equal to the value for the previous post-baseline time point (AVISIT). If the previous timepoint is baseline, leave AVAL missing

26 Parameter value-level metadata: use of *ALL*, *DEFAULT* ? Many decisions about the metadata revolve around its usefulness in the future machine readable and executable Two camps regarding parameter value-level metadata 1) fully itemize so that every variable has metadata for every value of PARAMCD 2) use *ALL* and *DEFAULT* to simplify entry for metadata that does not change across PARAMCDs Important to understand that *ALL* and *DEFAULT* are intended as short cuts how you implement them and/or display them in 27 Illustrating two approaches to

parameter value-level metadata Parameter Variable Variable Display Codelist / Identifier Name Type Format Controlled Terms BIL CRIT1FL text $1 Y, N ALT CRIT1FL text $1 Y, N AST CRIT1FL text $1 Y, N HYS1FL CRIT1FL text $1 HYS2FL

CRIT1FL text $1 BIL CRIT1FN integer 1.0 1=Y, 0=N ALT CRIT1FN integer 1.0 1=Y, 0=N AST CRIT1FN integer 1.0 1=Y, 0=N HYS1FL CRIT1FN integer 1.0 1=Y, 0=N HYS2FL CRIT1FN integer 1.0 1=Y, 0=N Y if ADLBHY.AVAL>1.5*ADLBHY.ANRHIN, N otherwise Y if ADLBHY.AVAL>1.5*ADLBHY.ANRHIN, N otherwise Y if ADLBHY.AVAL>1.5*ADLBHY.ANRHIN, N otherwise Blank if ADLBHY.PARAMTYP=DERIVED

Blank if ADLBHY.PARAMTYP=DERIVED From ADLBHY.CRIT1FL From ADLBHY.CRIT1FL From ADLBHY.CRIT1FL From ADLBHY.CRIT1FL From ADLBHY.CRIT1FL *DEFAULT* BIL ALT AST *ALL* Blank if ADLBHY.PARAMTYP=DERIVED Y if ADLBHY.AVAL>1.5*ADLBHY.ANRHIN, N otherwise Y if ADLBHY.AVAL>1.5*ADLBHY.ANRHIN, N otherwise Y if ADLBHY.AVAL>1.5*ADLBHY.ANRHIN, N otherwise From ADLBHY.CRIT1FL CRIT1FL CRIT1FL CRIT1FL

CRIT1FL CRIT1FN text text text text integer $1 $1 $1 $1 1.0 Y, N Y, N Y, N 1=Y, 0=N Source / Derivation

Fully itemized Used shortcuts 28 Parameter value-level metadata: use of *ALL*, *DEFAULT* ? Metadata for PARAMCDs for which the variable is null? In this example, PARAMTYP=DERIVED for the HYS1FL and HYS2FL parameters Parameter Variable Identifier Name *ALL* Variable Label Variable Display Codelist / Source / Derivation Type Format Controlled

Terms float 7.3 ADLB.ANRHIN if ANRHIN Analysis Normal ADLBHY.PARAMTYP= , Range Upper Limit (N) 1 blank otherwise *DEFAULT* ANRHIN Analysis Normal Range Upper Limit (N) 2 HYS1FL ANRHIN Analysis Normal Range Upper Limit (N) HYS2FL ANRHIN Analysis Normal Range Upper Limit (N) float float

float 7.3 ADLB.ANRHIN Not populated for records with PARAMCD=HYS1FL Not populated for records with PARAMCD=HYS2FL 29 Codelist / Controlled Terminology Repeat the codelist metadata (whether it is a list or a link to a list) every time variable is included in a dataset, as in Dataset Variable 1Variable Display Codelist / Controlled Terms Source / Derivation option below?

Name Name Type Format ADSL AGEGR1 text $6 <25y, 25-50y, >50y ADEFF AGEGR1

text $6 <25y, 25-50y, >50y ADEFF AGEGR1 text $6 ADSL RACE text $50

RACE ADEFF RACE text $50 RACE ADEFF RACE text $50 Derived from ADSL.AGE

1 ADSL.AGEGR1 ADSL.AGEGR1 2 DM.RACE 1 2 ADSL.RACE ADSL.RACE 30 Codelist include values that do not appear in the dataset? Example: Males and females both eligible for study Only males enrolled

Should SEX have codelist of M,F or M? Decision is to include all possible values because it could be important to know that value was an option and not used (Example: severity levels of AEs) 31 But what about the codelist for PARAMCD? For PARAMCD, only the values actually used in the specified analysis dataset should be included in the codelist within the variable metadata for PARAMCD Similarly, there should be no value used as a parameter identifier for that analysis dataset that is not a PARAMCD within the dataset 32

Intentional blanks Result identifier: Can be left blank the results being described are not just one specific portion of the display. Programming statements can be omitted : Can be left blank the information provided in the other metadata elements is sufficient to describe the analysis performed. How to indicate in metadata? leave the metadata element empty Illustration of Metadata Field 33 Metadata

DISPLAY IDENTIFIER Summary E.2 DISPLAY NAME Subjects with >3% Change from Baseline in Lumbar Spine Bone Mineral Density at Month 36 (ITT Population, OC Data) RESULT IDENTIFIER PARAM DXA BMD at Lumbar Spine (g/cm^2) PARAMCD BMDLS ANALYSIS VARIABLE

CRIT1FL REASON Pre-specified in SAP DATASET ADBMD SELECTION CRITERIA ITTFL=Y and PARAMCD=BMDLS and AVISIT=MONTH 36 and ANL01FL=Y and DTYPE= and PCHG not missing DOCUMENTATION See SAP Section XX for details. Percentage in each treatment group of the number of subjects with non-missing percent change data at Visit 8 (i.e., AVISIT=MONTH 36) who had >3% change in BMD from Baseline. Subjects with missing change from baseline BMD data at Visit 8 are excluded from the analysis.

Number of subjects at MONTH 36 with CRIT1FL=Y divided by the number of subjects at MONTH 36 with non-missing PCHG. Fishers exact test used for treatment comparison. PROGRAMMING STATEMENTS 34 Where is imputation defined AVAL or DTYPE? AVAL include details of the imputation, since is part of how to derive AVAL DTYPE indicates whether or not the imputation was performed for the record 35 Example of AVAL and DTYPE when imputation is involved Codelist /

Parameter Variable Variable Variable Display Controlled Identifier Name Label Type Format Terms BMDLS BMDLS AVAL Analysis Value DTYPE Derivation Type float 8.1

text $4 Source / Derivation AVAL = XX.XXSTRESN or an imputed value if XX.XXSTRESN is missing, apply the LOCF algorithm, i.e. set AVAL equal to the value for the previous post-baseline time point (AVISIT). If the previous timepoint is baseline, leave AVAL missing LOCF Populated with LOCF if XX.XXSTRESN is missing, to indicate that on that record ADBMD.AVAL is populated using Last Observation Carried Forward method

Another example of AVAL and DTYPE when imputation is involved 36 Codelist / Variable Controlled Source / Derivation Name Terms AVAL numeric version of XX.XXSTRESN or an imputed value Imputation methods: If there are non-missing data before and after the missing data, the missing data will be imputed using linear interpolation taking time of the measurement into account (INTERPOL: Linear interpolation) If there are no observed data after the missing data and it is the first visit of a period the missing data will be imputed using last observation carried forward. (LOCF: Last observation carried forward) DTYPE INTERPOL, Populated with imputation method used when the value of AVAL is imputed

LOCF 37 The examples illustrate various concepts, as well as providing an example of a dataset to support a specific analysis 38 Analysis of Covariance and more Analysis dataset that supports multiple analyses: Analysis of covariance Categorical analysis Repeated measures Descriptive statistics Included are identification of baseline

values, change from baseline analysis, and handling of missing dataExamples 1 4 39 Logistic regression analysis Analysis dataset that supports a logistic regression including covariates Included is one way to use CRITy and CRITyFL in supporting a categorical analysis. Example 5 40 Multivariate Analysis of Variance Analysis dataset that supports estimation of treatment effect for multiple variables (subscale scores) in the dataset an assessment of overall treatment effect (i.e., a test of the main effect of study drug

on the combined subscales) Included are analysis results metadata for specific items on a summary table Example 6 41 Multivariate Analysis of Variance Illustrated analysis dataset is not analysisready for the analysis of overall treatment effect a transpose of the dataset is needed Included are metadata to support the transpose Alternative: provide the transposed dataset as an ADaM dataset that is not compliant with BDS but fulfills the other requirements of an ADaM dataset 42 Metadata Field

DISPLAY NAME RESULT IDENTIFIER PARAMCD ANALYSIS VARIABLE DOCUMENTATION PROGRAMMING STATEMENTS Metadata Multivariate Analysis of Variance Testing the Hypothesis of No Overall Treatment Effect at Week 6 (ITT Population) Test for Overall Treatment Effect Considering All Subscales ANXIETY, DPRESS, ANGER, VIGOR, FATIGUE, CONFUS AVAL Wilks Lambda multivariate test of treatment effect. See SAP Section XX for details. Program: t-mood-effect.sas The MANOVA statement in PROC GLM is used to generate the result after first transposing ADMOOD. The six mood subscale scores are the dependent variables in the model, with treatment being the only independent variable. PROC TRANSPOSE DATA=ADMOOD OUT=ADMOODHZ; VAR AVAL;

ID PARAMCD; BY USUBJID TRTPN; RUN; PROC GLM DATA=ADMOODHZ; CLASS TRTPN; MODEL ANXIETY DPRESS ANGER VIGOR FATIGUE CONFUS = TRTPN / NOUNI; MANOVA H=TRTPN; RUN; Also note the multiple PARAMCDs 43 Repeated Measures Analysis of a Crossover Study Analysis datasets to support a crossover design study using a mixed effect model Included are multiple baseline types, multiple imputation methods, an analysis dataset created from another analysis dataset

Example 7 44 Illustrates 3 analysis datasets ADSL the required subject-level analysis dataset illustrates how the treatment and period variables are used for this study design ADFEV includes the individual responses that are collected during the study and imputed records ADFEVAUC includes derived response data based on the ADFEV dataset 45

Categorical Analysis of Subjects Meeting Hys Law Criteria Analysis dataset that supports an analysis of lab data based on Hy's Law criteria (liver function) Included are creation of new rows to contain new analysis parameters, the use of PARAMTYP, the use of the CRITy and SHIFTy variables Example 8 46 Other interesting points to observe in the document: Difference between DTYPE and PARAMTYP is illustrated AVAL and AVALC do not both need to be populated on each row - illustrated in Example 8 Use of different contents in the same CRIT variables as long as there is consistency

within a parameter illustrated in Example 8 47 Other interesting points to observe in the document: Variable types used in the document are those from CRT-DDS No Core column The column is in the ADaMIG as part of defining variables - it is not a metadata element Multiple hyperlinks are indicated in the examples The ability to include hyperlinks will be driven by the software that the sponsor uses for submissions The presentation formats used in this document for metadata are for the purposes of illustration of 48 Questions?

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