a guide to the adam basic data structure for dataset designers

A Guide to the ADaM Basic Data Structure

for Dataset Designers

Michelle Barrick and John Troxell

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Introduction

• The motivation for this paper was our need to provide guidance to new developers of ADaM BDS datasets

• We will be describing some new concepts useful in describing how the BDS works

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Outline

• ADaM Dataset Classes

• The BDS Dataset Design Challenge

• Observed vs. Derived

• Row Categories and Related Variables

• Examples

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ADaM Dataset Classes

• ADSL

– provides subject-level facts

• ODS

– supports counts of occurrences like AEs, conmeds

• BDS

– supports almost all other analyses

– flexible and powerful

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A BDS Dataset is Like a Clock

• ADaM documents give us

– philosophy of a good clock

– descriptions of clock parts

– snapshots of some assembled clocks

• But we need to design a custom timepiece!

– often a study has unique needs

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BDS Datasets are Flexible

• BDS is open on the right

– one parameter per row

– other variables can be added

• BDS has a rich row structure

– unique in CDISC

– makes BDS powerful

• Understand how variables and rows interact!

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Observed vs. Derived

• Not well defined in CDISC

• We define Observed and Derived for the purposes of this presentation about the BDS

• Our definitions are consistent with ADaM documents but go beyond

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Observed vs. Derived: SDTM

• In explaining the BDS, we ignore what is collected vs. derived vs. assigned in SDTM

• To simplify, we just consider everything in SDTM to be Observed

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Entity Observed Derived

SDTM Everything in SDTM Nothing in SDTM

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Observed vs. Derived Parameters: PARAMTYP (Parameter Type)

• PARAMTYP=DERIVED if the parameter is derived from other BDS parameters

• PARAMTYP=null otherwise

• A parameter that is highly derived from SDTM is not “Derived”; it is “Observed”

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BDS Parameter as a whole Parameter not derived from other BDS Parameters (PARAMTYP=null)

Parameter derived from other BDS Parameters (PARAMTYP=DERIVED)

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Observed vs. Derived Rows: Initial set of rows

• All parameters have an initial set of rows

• Other rows may be derived from the initial set

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Observed vs. Derived Rows: Row Categories

• Observed row category

– The initial set of rows created per definition of the parameter

– for both Observed and Derived Parameters

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Observed vs. Derived Rows: Row Categories (cont’d)

• Derived Timepoint row category

– Derived from initial set of rows

– Rows created to impute or derive AVAL at a “timepoint” (e.g. AVISIT)

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Observed vs. Derived Rows: Row Categories (cont’d)

• Alternative Baseline row category

– Derived from initial set of rows

– Rows created to make use of an alternative baseline definition

• Note: A row may be in both the Derived Timepoint and the Alternative Baseline categories

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BDS Parameter as a whole Parameter not derived from other BDS Parameters (PARAMTYP=null)

Parameter derived from other BDS Parameters (PARAMTYP=DERIVED)

Rows within *any* BDS Parameter, whether observed or derived

Initial set of rows Rows derived from the initial set of rows of the parameter

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Variables and Row Category

Row Category

DTYPE

(Derivation Type)

BASETYPE

(Baseline Type)

PARAMTYP

(Parameter Type)

Observed Row null Populated if more than one

baseline type is defined for the

parameter

Populated with

"DERIVED" if

parameter is derived

Derived

Timepoint Row

Populated Populated if more than one

baseline type is defined for the

parameter

Populated with

"DERIVED" if


Alternative

Baseline Row

Populated if record

is a derived

timepoint record

Populated Populated with

"DERIVED" if


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Table 1: Observed Rows for PARAM= “PLASMA LDL Cholesterol Friedewald (mmol/L)”

• This is the initial set of rows for this parameter

• Row Category is Observed for the initial rows

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Row ADT VISIT AVISIT ABLFL ANL01FL AVAL BASE CHG DTYPE BASETYPE Row

Categories

1 01AUG V 1 SCREENING Y 2.50 4.10 Observed

2 17AUG V 2 BASELINE 3.04 4.10 Observed

3 17AUG V 2 BASELINE Y Y 4.10 4.10 Observed

4 29AUG V 3 WEEK 2 Y 3.47 4.10 -0.63 Observed

5 14SEP V 4 WEEK 4 Y 4.25 4.10 0.15 Observed


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Table 1: Observed Rows for PARAM= “PLASMA LDL Cholesterol Friedewald (mmol/L)”

• Note no data for visit 5 (week 6)

• PARAMTYP (not shown) – DERIVED if parameter derived from BDS parameters

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Table 2: Imputation of a Missing Timepoint

*Note: DrvTmpt is abbreviated to represent Derived Timepoint

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Categories






7 14SEP V 4 WEEK 6 Y 4.25 4.10 0.15 LOCF DrvTmpt*


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Table 3: Creation of a Derived Summary Timepoint Row

*Note: PB Max = Post Baseline Maximum; DrvTmpt = Derived Timepoint

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Categories






7 14SEP V 4 WEEK 6 Y 4.25 4.10 0.15 LOCF DrvTmpt


8 PB MAX* Y 5.10 4.10 1.00 MAXIMUM DrvTmpt*

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Table 4: Creation of an Alternative Baseline

*Note: PB Max = Post Baseline Maximum; DrvTmpt = Derived Timepoint

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Categories

1 01AUG V 1 SCREENING Y 2.50 4.10 LAST Observed

2 17AUG V 2 BASELINE 3.04 4.10 LAST Observed

3 17AUG V 2 BASELINE Y Y 4.10 4.10 LAST Observed

9 17AUG V 2 AVG BASELINE Y Y 3.57 3.57 AVERAGE AVERAGE DrvTmpt*

4 29AUG V 3 WEEK 2 Y 3.47 4.10 -0.63 LAST Observed

5 14SEP V 4 WEEK 4 Y 4.25 4.10 0.15 LAST Observed

7 14SEP V 4 WEEK 6 Y 4.25 4.10 0.15 LOCF LAST DrvTmpt


8 PB MAX* Y 5.10 4.10 1.00 MAXIMUM LAST DrvTmpt

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Table 5: Creation of Records to Make Use of an Alternative Baseline

Row ADT VISIT AVISIT ABLFL ANL01FL AVAL BASE CHG DTYPE BASETYPE Row Cat

1 01AUG V 1 SCREENING Y 2.50 4.10 LAST Observed

2 17AUG V 2 BASELINE 3.04 4.10 LAST Observed

3 17AUG V 2 BASELINE Y Y 4.10 4.10 LAST Observed

9 17AUG V 2 AVG BASELINE Y Y 3.57 3.57 AVERAGE AVERAGE DrvTmpt*

4 29AUG V 3 WEEK 2 Y 3.47 4.10 -0.63 LAST Observed


7 14SEP V 4 WEEK 6 Y 4.25 4.10 0.15 LOCF LAST DrvTmpt


8 PB MAX* Y 5.10 4.10 1.00 MAXIMUM LAST DrvTmpt

10 29AUG V 3 WEEK 2 Y 3.47 3.57 -0.10 AVERAGE AltBase*

11 14SEP V 4 WEEK 4 Y 4.25 3.57 0.68 AVERAGE AltBase

12 14SEP V 4 WEEK 6 Y 4.25 3.57 0.68 LOCF AVERAGE DrvTmpt

AltBase

13 26SEP V 6 WEEK 8 Y 5.10 3.57 1.53 AVERAGE AltBase

14 PB MAX Y 5.10 3.57 1.53 MAXIMUM AVERAGE DrvTmpt

AltBase

*Note: PB Max = Post Baseline Maximum; DrvTmpt = Derived Timepoint; AltBase = Alternative Baseline

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Table 6: Record Selection Criteria

• Data as Observed analysis of change from baseline at Week 6, where baseline is defined as the last value observed prior to treatment.

– AVISIT='WEEK 6' and DTYPE='' and BASETYPE='LAST' and ANL01FL='Y' • Note: PARAMCD and population ignored here for brevity

• Data as Observed analysis of change from baseline at Week 6, where baseline is defined as the average of the values collected at the baseline visit.

– AVISIT='WEEK 6' and DTYPE='' and BASETYPE='AVERAGE' and ANL01FL='Y'

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Table 6: Record Selection Criteria (cont’d)

• Last Observation Carried Forward analysis of change from baseline at Week 6, where baseline is defined as the last value observed prior to treatment.

– AVISIT='WEEK 6' and (DTYPE='' or DTYPE='LOCF') and BASETYPE='LAST' and ANL01FL='Y'

• Last Observation Carried Forward analysis of change from baseline at Week 6, where baseline is defined as the average of the values collected at the baseline visit.

– AVISIT='WEEK 6' and (DTYPE='' or DTYPE='LOCF') and BASETYPE='AVERAGE' and ANL01FL='Y'

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Table 6: Record Selection Criteria (cont’d)

• Analysis of change from baseline to maximum post-baseline value, where baseline is defined as the last value observed prior to treatment.

– AVISIT='POST BASELINE MAX' and DTYPE='MAXIMUM' and BASETYPE='LAST'

• Analysis of change from baseline to maximum post-baseline value, where baseline is defined as the average of the values collected at the baseline visit.

– AVISIT='POST BASELINE MAX' and DTYPE='MAXIMUM' and BASETYPE='AVERAGE'

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Questions?

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Michelle A. Barrick

Eli Lilly and Company

[email protected]

John Troxell

John Troxell Consulting LLC

[email protected]

mailto:[email protected]

mailto:[email protected]

a guide to the adam basic data structure for dataset designers

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