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

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. | Presentation Title | Presenter Name | Date | Subject | Business Use Only

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. | Presentation Title | Presenter Name | Date | Subject | Business Use Only

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. | Presentation Title | Presenter Name | Date | Subject | Business Use Only

Definition and Derivation Demo of imputation of missing joint change from baseline | Presentation Title | Presenter Name | Date | Subject | Business Use Only

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

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

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

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. | Presentation Title | Presenter Name | Date | Subject | Business Use Only

Challenges and Solutions Demo of ADaM Dataset for Challenge #1 and #2: | Presentation Title | Presenter Name | Date | Subject | Business Use Only

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

Challenges and Solutions Demo of ADaM Dataset for Challenge #3: | Presentation Title | Presenter Name | Date | Subject | Business Use Only

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. | Presentation Title | Presenter Name | Date | Subject | Business Use Only

Challenges and Solutions Demo of ADaM Dataset for Challenge #4a: | 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, 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. | Presentation Title | Presenter Name | Date | Subject | Business Use Only

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

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. | Presentation Title | Presenter Name | Date | Subject | Business Use Only