Model X-Ray Image Data into ADaM BDS Structure
Vincent Guo
NJ CDISC Users Group meeting, Sep 17, 2014
Introduction
 X-ray image data is important and special efficacy data
• To demonstrate long time efficacy on joint/bone structural preservation
• Score system developed to quantify the assessment
• Complex
 This presentation will cover:
• SDTM data for X-ray image
• Analysis requirements
• Challenges, options considered, and solutions as to bridge the gap from source data
to analysis
• Demo of the dataset
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SDTM Data
 Data is collected in a custom domain.
 Assessments (X-ray images) are performed by
•
•
•
•
•
test
location (joint)
body side
visit
two different readers and possible a third consensus read.
 Joint score is the result recorded in the source data.
USUBJID VISIT
1 W24
1 W24
1 W24
1 W24
2 W24
2 W24
2 W24
2 W24
2 W24
2 W24
OMTEST
EROSION
EROSION
EROSION
EROSION
EROSION
EROSION
EROSION
EROSION
EROSION
EROSION
OMLOC
DIP4
DIP4
DIP4
DIP4
DIP4
DIP4
DIP4
DIP4
DIP4
DIP4
OMLT
RIGHT
RIGHT
LEFT
LEFT
RIGHT
RIGHT
RIGHT
LEFT
LEFT
LEFT
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OMEVAL
OMSTRESC
READER 1
2
READER 2
3
READER 1
2
READER 2
3
READER 1
2
READER 2
4
CONSENSUS
3
READER 1
2
READER 2
4
CONSENSUS
3
Analysis Requirements
 Evaluation of Joint structural damage by visit
• Parameter: Modified total Sharp score (mTSS) change from baseline
• Covariate: Modified total Sharp score (mTSS) baseline
• Consensus read to be used
 Evaluation of the proportion of subjects without disease
progression at each visit
 Comparison of proportion of subjects with no disease
progression between the two periods: from baseline to W24
versus from W24 to W52.
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Definition and Derivation
 Modified total Sharp score (mTSS) change from baseline
for post-baseline assessments
• Defined as sum of joint scores change from baseline
• Imputation needed in case of missing joint score change from baseline:
- Joints grouped into segments; segment score calculated as subtotal of joint score
change from baseline within the segment:
• Missing imputed with average of change from baseline of non-missing joints if
>50% of joints non-missing;
• otherwise, segment score is missing.
- Total score (mTSS): sum of segment scores
• Missing imputed with average of non-missing segments if >50% of segments nonmissing;
• otherwise, total score is missing.
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Definition and Derivation
 Demo of imputation of missing joint change from baseline
Baseline
Post-baseline
change
Segment 1
Joint 1
4
6
2
Segment 1
Joint 2
5
2
-3
Segment 1
Joint 3
6
4
-2
Segment 1
Joint 4
2
3
1
Segment 1
Joint 5
3
4
1
Segment 1
Joint 6
Missing not imputed
Missing not imputed
-0.2 (imputed)
Segment 1
Joint 7
Missing not imputed
Missing not imputed
-0.2 (imputed)
Segment 1
Joint 8
Missing not imputed
Missing not imputed
-0.2 (imputed)
Segment 1
Segment score
N/A
N/A
-1.6
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Definition and Derivation
 Modified total Sharp score (mTSS) baseline
• Defined as sum of joint score at baseline
• No imputation in case of missing joint scores at baseline
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Definition and Derivation
 No disease progression
• At each visit, defined as mTSS change from baseline <= 0
• Comparison between two periods, defined as change of mTSS
change from baseline <= 0
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Challenges and Solutions
 Challenge #1: How to create PARAM for mTSS change from
baseline?
Solution
Alternative
•
PARAM created for mTSS change
from baseline
(PARAMCD=TSSCBSI)
•
•
AVAL stores change from baseline
•
Only for post-baseline visits
•
Different PARAMs for Reader 1,
Reader 2 and consensus read.
•
No creation of PARAM for individual
joints or individual segments
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Because of the definition of mTSS
change from baseline,
conventional method that
calculates absolute total score for
each visit and change from
baseline at total score level is not
applicable
Challenges and Solutions
 Challenge #2: Need baseline score to be covariate
Solution
Alternative
•
PARAM created for mTSS baseline
(PARAMCD=TSSBS)
•
•
AVAL stores baseline
•
Only for baseline visit
•
Different PARAMs for Reader 1,
Reader 2 and consensus read.
•
No creation of PARAM for individual
joints or individual segments
•
Custom variable BASESCO
(baseline mTSS score) created as a
column using AVAL of this PARAM
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•
Leave it to reporting/analysis level
without adding baseline score as a
variable in the dataset, which is not
analysis ready.
Conventional BASE is not
applicable for this purpose.
Challenges and Solutions
 Demo of ADaM Dataset for Challenge #1 and #2:
USUBJID PARAMCD AVISITN AVAL
1 TSSBS1
BASESCO
0
10
10
1 TSSCBSI1
16
2
10
1 TSSCBSI1
24
3
10
1 TSSCBSI1
24
2
10
1 TSSCBSI1
52
-1
10
0
11
11
1 TSSCBSI2
16
4
11
1 TSSCBSI2
24
6
11
1 TSSCBSI2
24
4
11
1 TSSCBSI2
52
0
11
0
10
10
1 TSSCBSI
16
3
10
1 TSSCBSI
24
4.5
10
1 TSSCBSI
24
3
10
1 TSSCBSI
52
-0.5
10
1 TSSBS2
1 TSSBS
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Challenges and Solutions
 Challenge #3: How to handle various imputations?
Challenge
Solution
(a) Imputing missing data
 Linear extrapolation
 LOCF
Apply ADaM methodology
(insert new rows and use
DTYPE)
(b) Imputing missing consensus
read by taking the average of
Reader 1 and Reader 2

New rows for the imputed
consensus reads

Custom variable to indicate
consensus type: original
CONSENSUS (collected) or
AVERAGE (imputed)
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Alternative
It is not appropriate to use
DTYPE as ADaM rule
specifies that DTYPE should
be used to indicate rows that
are derived within a given
value of PARAM but this
imputation is done between
parameters
Challenges and Solutions
 Demo of ADaM Dataset for Challenge #3:
USUBJID PARAMCD AVISITN AVAL
1 TSSBS1
DTYPE
CONSTYPE
BASESCO
0
10
10
1 TSSCBSI1
16
2
10
1 TSSCBSI1
24
3 ENDPOINT
10
1 TSSCBSI1
24
2 LOCF
10
1 TSSCBSI1
52
-1
10
0
11
11
1 TSSCBSI2
16
4
11
1 TSSCBSI2
24
6 ENDPOINT
11
1 TSSCBSI2
24
4 LOCF
11
1 TSSCBSI2
52
0
11
0
10
CONSENSUS
10
1 TSSCBSI
16
3
CONSENSUS
10
1 TSSCBSI
24
4.5 ENDPOINT CONSENSUS
10
1 TSSCBSI
24
1 TSSCBSI
52
1 TSSBS2
1 TSSBS
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3 LOCF
-0.5
CONSENSUS
10
AVERAGE
10
Challenges and Solutions
 Challenge #4: How to handle no disease progression?
Challenge
Solution
Alternative
(a) Evaluation of the
proportion of subjects
without disease progression
at each visit
 AVAL is change from baseline
(PARAMCD=TSSCBSI)
Create new PARAM
 CRIT1 (AVAL<=0)
 no disease progression at each visit
Pros:
• No need to create new PARAM (new rows)
• Easily preserve DTYPE information (linear
extrapolation, LOCF) for imputation as
everything is at the same row.
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Cons:
• Dataset actually becomes more
complex due to imputation.
Challenges and Solutions
 Demo of ADaM Dataset for Challenge #4a:
CRIT1FL
(AVAL<=0) DTYPE
USUBJID PARAMCD AVISITN AVAL
1 TSSBS1
0
CONSTYPE
10
BASESCO
10
1 TSSCBSI1
16
2N
1 TSSCBSI1
24
3N
ENDPOINT
10
1 TSSCBSI1
24
2N
LOCF
10
1 TSSCBSI1
52
-1 Y
10
11
11
1 TSSBS2
0
10
1 TSSCBSI2
16
4N
1 TSSCBSI2
24
6N
ENDPOINT
11
1 TSSCBSI2
24
4N
LOCF
11
1 TSSCBSI2
52
0Y
1 TSSBS
0
11
11
10
CONSENSUS
10
1 TSSCBSI
16
3N
CONSENSUS
10
1 TSSCBSI
24
4.5 N
ENDPOINT CONSENSUS
10
1 TSSCBSI
24
3N
LOCF
CONSENSUS
10
1 TSSCBSI
52
-0.5 Y
AVERAGE
10
15 | Presentation Title | Presenter Name | Date | Subject | Business Use Only
Challenges and Solutions
 Challenge #4: How to handle disease progression?
Challenge
Solution
Alternative
(b) Comparison of
proportion of
subjects with no
disease progression
between the two
periods: from
baseline to W24
versus from W24 to
W52.
For PARAMCD=TSSCBSI,
Create new PARAM (e.g.
one for disease
progression from baseline
visit to W24, another one
for disease progression
from W24 to W52)
 Populate:
 BASETYPE (W24 AVAL as baseline)
 BASE (W24 AVAL)
 CHG (change of change from baseline  change from
W24 to W52 = W52 AVAL – W24 AVAL[BASE])
 CRIT2 (BASE<=0)
 no disease progression from baseline to W24
 CRIT3 (CHG<=0)
 no disease progression from W24 to W52
where AVISIT=W52
Pros:
• Analysis ready “one proc away”.
• Easily keep DTYPE information for imputation
• Data flow can be traced within the dataset.
Cons:
• Dataset looks complex at the first sight
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Pros:
 Dataset looks simpler
Cons:
 Not analysis ready “one
proc away”.
 Data flow is not easily
traced within the
dataset.
Challenges and Solutions
 Demo of ADaM Dataset for Challenge #4b:
CRIT1FL
(AVAL<=0) ABLFL BASE
USUBJID PARAMCD AVISITN AVAL
1 TSSBS1
0
16
2N
1 TSSCBSI1
24
3N
1 TSSCBSI1
24
1 TSSCBSI1
52
0
CONSTYPE
BASESCO
10
WEEK 24 AVAL AS BASELINE
10
3
N
WEEK 24 AVAL AS BASELINE ENDPOINT
10
2N
3
N
WEEK 24 AVAL AS BASELINE LOCF
10
-1 Y
3
-4 N
WEEK 24 AVAL AS BASELINE
10
Y
Y
11
4N
1 TSSCBSI2
24
6N
1 TSSCBSI2
24
1 TSSCBSI2
52
11
4.5
N
WEEK 24 AVAL AS BASELINE
11
4.5
N
WEEK 24 AVAL AS BASELINE ENDPOINT
11
4N
4.5
N
WEEK 24 AVAL AS BASELINE LOCF
11
0Y
4.5
-4.5 N
WEEK 24 AVAL AS BASELINE
11
Y
Y
10
1 TSSCBSI
16
3N
1 TSSCBSI
24
4.5 N
1 TSSCBSI
24
1 TSSCBSI
DTYPE
N
16
0
BASETYPE
3
1 TSSCBSI2
1 TSSBS
CRIT3FL
(CHG<=0)
10
1 TSSCBSI1
1 TSSBS2
CRIT2FL
(BASE<=0)
CHG
3N
-0.5 Y
52 (CHG 0-52) (CHG 0-52)
Y
CONSENSUS
10
3
N
WEEK 24 AVAL AS BASELINE
CONSENSUS
10
3
N
WEEK 24 AVAL AS BASELINE ENDPOINT CONSENSUS
10
3
N
WEEK 24 AVAL AS BASELINE LOCF
3
-3.5 N
Y
(CHG 0-24) (CHG 24-52) (CHG 0-24) (CHG 24-52) WEEK 24 AVAL AS BASELINE
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CONSENSUS
10
AVERAGE
10
Conclusion
 Data is collected in custom domain which contains special elements
that are not in standard findings domains such as LB, VS, EG.
 Complicated definitions and derivations lead to complexity in design
and implementation of ADaM dataset.
 ADaM principles and methodology have been followed and adapted.
 It has demonstrated that sufficient tools are available for us to create a
compliant and “analysis ready” ADaM dataset for this custom domain
although some special situations require us to go beyond what’s
specified in ADaM IG.
 The ADaM dataset created allows us to perform analyses easily.
18 | Presentation Title | Presenter Name | Date | Subject | Business Use Only