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

Vincent Guo

NJ CDISC Users Group meeting, Sep 17, 2014

Model X-Ray Image Data into ADaM BDS Structure

Introduction

| Presentation Title | Presenter Name | Date | Subject | Business Use Only2

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

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 OMTEST OMLOC OMLT OMEVAL OMSTRESC

1 W24 EROSION DIP4 RIGHT READER 1 21 W24 EROSION DIP4 RIGHT READER 2 31 W24 EROSION DIP4 LEFT READER 1 21 W24 EROSION DIP4 LEFT READER 2 32 W24 EROSION DIP4 RIGHT READER 1 22 W24 EROSION DIP4 RIGHT READER 2 42 W24 EROSION DIP4 RIGHT CONSENSUS 32 W24 EROSION DIP4 LEFT READER 1 22 W24 EROSION DIP4 LEFT READER 2 42 W24 EROSION DIP4 LEFT 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.

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.



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 6 Missing not imputed Missing not imputed -0.2 (imputed)



Segment 1 Segment score N/A N/A -1.6



Modified total Sharp score (mTSS) baseline

• Defined as sum of joint score at baseline

• No imputation in case of missing joint scores at baseline



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

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



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.



Demo of ADaM Dataset for Challenge #1 and #2:

USUBJID PARAMCD AVISITN AVAL BASESCO

1 TSSBS1 0 10 10

1 TSSCBSI1 16 2 10

1 TSSCBSI1 24 3 10

1 TSSCBSI1 24 2 10

1 TSSCBSI1 52 -1 10

1 TSSBS2 0 11 11

1 TSSCBSI2 16 4 11

1 TSSCBSI2 24 6 11

1 TSSCBSI2 24 4 11

1 TSSCBSI2 52 0 11

1 TSSBS 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



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



Demo of ADaM Dataset for Challenge #3:

USUBJID PARAMCD AVISITN AVAL DTYPE CONSTYPE BASESCO

1 TSSBS1 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

1 TSSBS2 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

1 TSSBS 0 10 CONSENSUS 10

1 TSSCBSI 16 3 CONSENSUS 10

1 TSSCBSI 24 4.5 ENDPOINT CONSENSUS 10

1 TSSCBSI 24 3 LOCF CONSENSUS 10

1 TSSCBSI 52 -0.5 AVERAGE 10



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.



Demo of ADaM Dataset for Challenge #4a:

USUBJID PARAMCD AVISITN AVALCRIT1FL (AVAL<=0) DTYPE CONSTYPE BASESCO

1 TSSBS1 0 10 10

1 TSSCBSI1 16 2 N 10

1 TSSCBSI1 24 3 N ENDPOINT 10

1 TSSCBSI1 24 2 N LOCF 10

1 TSSCBSI1 52 -1 Y 10

1 TSSBS2 0 11 11

1 TSSCBSI2 16 4 N 11

1 TSSCBSI2 24 6 N ENDPOINT 11

1 TSSCBSI2 24 4 N LOCF 11

1 TSSCBSI2 52 0 Y 11


1 TSSCBSI 16 3 N CONSENSUS 10

1 TSSCBSI 24 4.5 N ENDPOINT CONSENSUS 10

1 TSSCBSI 24 3 N LOCF CONSENSUS 10

1 TSSCBSI 52 -0.5 Y AVERAGE 10



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)

Pros: Dataset looks simpler

Cons: Not analysis ready “one

proc away”. Data flow is not easily

traced within the dataset.



Demo of ADaM Dataset for Challenge #4b:

USUBJID PARAMCD AVISITN AVALCRIT1FL (AVAL<=0) ABLFL BASE CHG

CRIT2FL (BASE<=0)

CRIT3FL (CHG<=0) BASETYPE DTYPE CONSTYPE BASESCO

1 TSSBS1 0 10 10

1 TSSCBSI1 16 2 N 3 N WEEK 24 AVAL AS BASELINE 10

1 TSSCBSI1 24 3 N Y 3 N WEEK 24 AVAL AS BASELINE ENDPOINT 10

1 TSSCBSI1 24 2 N 3 N WEEK 24 AVAL AS BASELINE LOCF 10

1 TSSCBSI1 52 -1 Y 3 -4 N Y WEEK 24 AVAL AS BASELINE 10

1 TSSBS2 0 11 11

1 TSSCBSI2 16 4 N 4.5 N WEEK 24 AVAL AS BASELINE 11

1 TSSCBSI2 24 6 N Y 4.5 N WEEK 24 AVAL AS BASELINE ENDPOINT 11

1 TSSCBSI2 24 4 N 4.5 N WEEK 24 AVAL AS BASELINE LOCF 11

1 TSSCBSI2 52 0 Y 4.5 -4.5 N Y WEEK 24 AVAL AS BASELINE 11


1 TSSCBSI 16 3 N 3 N WEEK 24 AVAL AS BASELINE CONSENSUS 10

1 TSSCBSI 24 4.5 N Y 3 N WEEK 24 AVAL AS BASELINE ENDPOINT CONSENSUS 10

1 TSSCBSI 24 3 N 3 N WEEK 24 AVAL AS BASELINE LOCF CONSENSUS 10

1 TSSCBSI 52-0.5

(CHG 0-52)Y(CHG 0-52)

3(CHG 0-24)

-3.5(CHG 24-52)

N(CHG 0-24)

Y(CHG 24-52) WEEK 24 AVAL AS BASELINE 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.

Documents

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