1. Model X-Ray Image Data into ADaM BDS Structure
Vincent Guo
NJ CDISC Users Group meeting, Sep 17, 2014

2. 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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3. 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.
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4. 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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5. 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 non- missing;
otherwise, total score is missing.
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6. Definition and Derivation
Demo of imputation of missing joint change from baseline
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7. 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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8. 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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9. 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
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
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10. 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
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.
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11. Challenges and Solutions
Demo of ADaM Dataset for Challenge #1 and #2:
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12. Challenges and Solutions
Challenge #3: How to handle various imputations?
Challenge
Solution
Alternative
(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)
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
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13. Challenges and Solutions
Demo of ADaM Dataset for Challenge #3:
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14. 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)
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.
Create new PARAM
Cons:
Dataset actually becomes more complex due to imputation.
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15. Challenges and Solutions
Demo of ADaM Dataset for Challenge #4a:
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16. 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,
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
Create new PARAM (e.g. one for disease progression from baseline visit to W24, another one for disease progression from W24 to W52)
Dataset looks simpler
Not analysis ready “one proc away”.
Data flow is not easily traced within the dataset.
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17. Challenges and Solutions
Demo of ADaM Dataset for Challenge #4b:
USUBJID
PARAMCD
AVISITN
AVAL
CRIT1FL (AVAL<=0)
ABLFL
BASE
CHG
CRIT2FL (BASE<=0)
CRIT3FL (CHG<=0)
BASETYPE
DTYPE
CONSTYPE
BASESCO 1
TSSBS1 10
TSSCBSI1 16 2 N 3
WEEK 24 AVAL AS BASELINE 24 Y
ENDPOINT
LOCF 52 -1 -4
TSSBS2 11
TSSCBSI2 4
4.5 6
-4.5
TSSBS
CONSENSUS
TSSCBSI
-0.5
(CHG 0-52)
(CHG 0-24)
-3.5
(CHG 24-52)
AVERAGE
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18. 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.
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