clinical sas adam adsl complete programming

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certainly! the analysis data model (adam) is a standard for clinical trial data that is defined by the clinical data interchange standards consortium (cdisc). the adam provides guidelines on how to create datasets that are suitable for statistical analysis, particularly for regulatory submissions. one of the most common datasets created in the adam is the adsl (subject-level analysis dataset).

overview of adsl

the adsl dataset contains one record per subject and includes key demographic and baseline characteristics as well as important flags and derived variables that are needed for the analysis.

key variables in adsl

typically, the adsl dataset includes the following key variables:
- **subject_id:** unique identifier for each subject.
- **study_id:** identifier for the study.
- **site_id:** identifier for the study site.
- **age:** age of the subject at the time of consent.
- **sex:** gender of the subject.
- **randomized:** indicates if the subject was randomized.
- **treatment:** treatment group assigned to the subject.
- **baseline:** various baseline measurements.
- **discontinuation:** information about subject discontinuation.

creating adsl dataset in sas

here’s a step-by-step guide on how to create an adsl dataset using sas programming:

step 1: import required libraries

make sure you have the necessary libraries set up in your sas environment.
step 2: load raw data

assuming you have raw datasets like demographics, randomization, and treatment, load them into sas.
step 3: merge datasets

combine the datasets to create the adsl dataset. use `merge` to join datasets by `subject_id`.
step 4: derive additional variables

add derived variables and flags based on your analysis requirements.
step 5: output adsl dataset

finally, save the adsl dataset to your desired location.
example of adsl dataset

the resulting adsl dataset will look something like this:

| subject_id | age | sex | randomized | treatment | treatment_group | discon ...

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