Page History

This section illustrates example analysis datasets for the following endpoints:

• Percent change in ejection fraction over time
• Decline in ejection fraction over a time period (1 year) grouped by a decline greater than 10.0%.
• Percent change in NTproBNP over a period of time (1 year)

Source Data

The SDTM examples used as the source data are from Section 2.1, Basic CMR Tests for Systolic Function. The CV dataset contains 16 rows, 1-8 for visit 1 and 9-16 for visit 6. Of these rows, the CVTESTCD = "LVEF_C" and CVTESTCD = "RVEF_C" representing the Ventricular Ejection Fraction, Calculated (%) for left and right were selected for the analysis. The LB dataset test where LBTESTCD = "BNPPRONT" was used to add the value of BNPPRONT to compute the percent change over time, and then it was added as a potential covariate in each row for the last analysis. As in all ADaM datasets, the Subject Level Analysis Dataset (ADSL) was merged in to the SDTM datasets to capture all the necessary demographics, treatments and other required variables for analysis of the data.

Example Analysis Datasets

The analysis datasets for this example include treatment and demographic information drawn from the Subject Level Analysis Dataset (ADSL), defined in the Metadata Tables below. Only some of the required variables from ADSL are shown for illustrative purposes. The ADSL incorporates demographics, treatment groups, study dates, and stratification variables.  Additional information on the ADSL can be found in Section 2.3.1 of the ADaM Implementation Guide (ADaMIG) v1.3.

The ADSL is combined with other SDTM dataset to create the analysis datasets. More than one SDTM dataset can be combined in this way. Also, stratification variables can be created in the ADSL to subset by, or to add variables needed in the analyses. For instance,  stratification variable might be type of concomitant medications, such as an ACE inhibitors. for this we added a flag ACEMEDFL, which is coded Y or N. Another variable added; body surface area (BSABL), is derived from the LB dataset at the baseline visit and added to ADSL. However, when

This section illustrates example analysis datasets for the following endpoints:

• Percent change in ejection fraction over time
• Decline in ejection fraction over a time period (1 year) grouped by a decline greater than 10.0%.
• Percent change in NTproBNP over a period of time (1 year)

Source Data

The SDTM examples used as the source data are from Section 2.1, Basic CMR Tests for Systolic Function. The CV dataset contains 16 rows, 1-8 for visit 1 and 9-16 for visit 6. Of these rows, the CVTESTCD = "LVEF_C" and CVTESTCD = "RVEF_C" representing the Ventricular Ejection Fraction, Calculated (%) for left and right were selected for the analysis. The LB dataset test where LBTESTCD = "BNPPRONT" was used to add the value of BNPPRONT to compute the percent change over time, and then it was added as a potential covariate in each row for the last analysis. As in all ADaM datasets, the Subject Level Analysis Dataset (ADSL) was merged in to the SDTM datasets to capture all the necessary demographics, treatments and other required variables for analysis of the data.

Analysis Datasets

The example analysis dataset includes treatment and demographic information drawn from the ADSL, defined in the Metadata Table below. In this example, only some of the required variables, plus important stratification variables from ADSL, are shown for illustrative purposes. For example, one variable added; body surface area at baseline (BSABL)  was computed for baseline records in ADSL. However, since the subjects are children, the body surface area changes over time. Therefore, it would probably be necessary to have a separate dataset to represent this over time.

The table below shows the example analysis datasets and unique parameters for this example. The Subject Level Analysis Dataset (ADSL)  is a necessary component in the creation process of subsequent analysis datasets for human trials. The ADSL incorporates demographics, treatment groups, study dates, and stratification variables. In this example, the ADSL is not illustrated, but serves as a source for standard ADaM variables. Additional information can be found in Section 2.3.1 of the ADaM Implementation Guide (ADaMIG) v1.3.

The ADCVEF dataset includes tests pertinent to this analysis, those for which CVTESTCD is equal to "LVEF_C" or "RVEF_C". The relationship dataset RELREC illustrates the relationship between datasets. 

The ADCVNTRP dataset is a subset of the Laboratory Results (LB) SDTM dataset, for which LBTESTCD is equal to "BNPPRONT". In this case, many lab draws may occur throughout the year, but only those linked to a specific visit are included. By subsetting the LB dataset with LBLNKID not missing, laboratory data that aligns with CV data can be filtered out and sorted using USUBJID and VISIT. If LBLNKID is not available, a windowing strategy could be used to select the laboratory draw closest to that visit for merging with CV, computed in AVISIT.

ADCMRI is an efficacy analysis dataset with only a few records for more complex modeling of changes in ejection fraction, plus the ProB-type test results. It is an ADaM Basic Data Structure (BDS) dataset, with additional variables added from ADCVEF.

an additional non-standard variable is added to the analysis dataset with the subject's current BSA by visit. 

The ADCVNTP dataset includes tests pertinent to these analyses. The record LB are selected where CVTESTCD is equal to "LVEF_C" or "RVEF_C".  It also includes a subset of the Laboratory Results (LB) SDTM dataset, for which LBTESTCD is equal to "BNPPRONT". The relationship dataset RELREC illustrates the relationship between datasets. In this case, many lab draws may occur throughout the year, but only those linked to a specific visit are included. By subsetting the LB dataset with LBLNKID not missing, laboratory data that aligns with CV data can be filtered out and sorted using USUBJID and VISIT. If LBLNKID is not available, a windowing strategy could be used to select the laboratory draw closest to that visit for merging with CV, computed in AVISIT. In the example, only two LB records are shown which are linked to CV where CVTESTCD = "LVEF_C" or CVTESTCD = "R.VEF_C"

ADCVMR is an efficacy analysis dataset with only a few records for more complex modeling of changes in ejection fraction, plus the ProB-type test results. It is an ADaM Basic Data Structure (BDS) dataset, with additional variables added from ADCVNTP.

The tableS below showsthe example analysis dataset metadata and parameters used for the analysis. 

Analysis Datasets

The subsequent section provides the data structures for the example ADaM datasets. In this example, columns for derived variables BASE, CHG, PCHG and CHGCAT1 have been added to facilitate analyses of the efficacy endpoints. Specifically, example demographic data for ADSL were created for an 8 year old male. Also, a custom variable for BSA at baseline (BSABASE), and an example of a stratification variable for subjects with was added from ADSL.

...

• Left Ventricular Ejection Fraction, Calculated (%)
• Right Ventricular Ejection Fraction, Calculated (%)
• N-Terminal ProB-type Natriuretic Peptide (pg/mL)