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The analysis dataset includes treatment and demographic information drawn from the ADSL. In this example, we only show some of the required variables plus important stratification variables from ADSL. One variable mentioned body surface area (BSA), can be computed and added to ADSL. However, since the subjects are children, their body surface area changes over time. Therefore it would probably be better to have a separate dataset to capture this over time. In this example, we will show one variable for BSA with the variable "Baseline Body Surface Area (m2)" which can be added to ADSL.

Questions. 

Is screening considered visit 1, or is it to include the subject into the study? SDTM team agreed to change to VISIT 1 instead of SCREENING.

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Should i use Male and 6 years? please advise.

Analysis Datasets

Below is the table of The table below presents the analysis datasets and unique parameters created generated for the this example. The Subject Level Analysis Dataset (ADSL is required as part of the process of creating the )  is a necessary component in the creation process of subsequent analysis datasets for human trials.

The ADSL includes incorporates demographics, treatment groups, study dates, and stratification variables. For In this example, the ADSL is not shown other than displayed directly but serves as a source for standard ADaM variables. See Additional information on this can be found in Section 2.3.1 of the ADaM Implementation Guide (ADaMIG) v1.3 for additional information.

The ADCVEF is a dataset capturing only the tests required for dataset selectively captures tests pertinent to this analysis, selecting for specifically those with CVTESTCD equal to "LVEF_C" or "RVEF_C". The relationship dataset RELREC shows the datset relationshipsillustrates the connections between datasets. There is exists a many-to-one relationship between the cardiovascular tests from (CV) and the laboratory data collected at during the same visits. Therefore by subsetting the LB with LBLNKID not missing, this selects the laboratory data that can be merged with CV by USUBJID and VISIT (or USUBJID and AVISIT in ADaM) In ADaM analysis datasets we can also use a windowing strategy to select the laboratory draw closest to that visit to merge with CV which is computed in AVISIT in which case all LB records would be kept for the tests of interest. 

ADCVNTRP ADLBNTRP is a subset of LB selecting for LBTESTCD equal to "BNPPRONT", and LBLINKID not missing. In this case, many lab draws may occur throughout the year, but we are only interested in the ones linked to a specific visit. By subsetting the Laboratory Results (LB) dataset with LBLNKID not missing, we can select laboratory data that aligns with CV data and sort using USUBJID and VISIT. If LBLNKID were not available, a windowing strategy could be employed 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 a Basic Structure dataset with additional variables added from ADCVEF.

Analysis Datasets

The following are subsequent section outlines the data structures for the example ADaM datasets. Note that the In this example, columns for derived variables, BASE, and CHG, PCHG and CHGCAT1 have been added for the efficacy analysis of fto facilitate analyses of efficacy endpoints. The Specifically, example demographics demographic data for ADSL were created for an 8 year old male child. Also, BSA at baseline (BSABASE), an example of a custom variable, was added from ADSL. The ADaM standard only suggests variable order, so the demographics and treatment variables have been put at the end, so that the variables of interest are shown at the beginning right of the dataset. Since we are combining two SDTM domains into one analysis dataset, some variables unique to LB will be blank for records from CV.

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Example 2 

The dataset above can also be set up to provide multivariate analyses by taking the lab tests of interest, transposing and merging with the ADCVNTPR dataset. In that case, the records from LB are not needed as a row.

The variables transposed are:

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