Processes | Clinical | AE Bayesian Hierarchical Model

AE Bayesian Hierarchical Model
This process fits the multi-level Bayesian hierarchical models of Berry and Berry (2004)1 and Xia, Ma, and Carlin (2011)2. Adverse events are modeled taking into account a grouping variable, such as system organ class.
What do I need?
This process requires several demographic- and adverse event-related variables. These include:
A treatment variable (the actual treatment received by each subject (TRT01A)), the planned treatment (intent-to-treat) for each subject (TRT01P), or the description of the planned treatmen arm (ARM),
Term- (AEDECOD) and grouping-level (AEBODSYS) variables from the AE domain,
AESTDTC from the AE domain, and
RFSTDTC and RFENDTC from DM or ADSL.
Variables can be taken from the AE domain (or ADAE) and either from the subject level analysis data set (ADSL) included in the Analysis Data Model ( ADaM) folder or from the DM and AE domains in SDTM. Refer to Localization-Specific Value Specification for more information about these data sets.
Output/Results
The output generated by this process is summarized in a tabbed report. Refer to the AE Bayesian Hierarchical Model output documentation for detailed descriptions and guides to interpreting your results.

1
Berry, S.M., and Berry, D.A. (2004). Accounting for Multiplicities in Assessing Drug Safety:
A Three-Level Hierarchical Mixture Model. Biometrics 60, 418-426.

2
Xia, H.A., Ma, H., and Carlin, B.P. (2011). Bayesian Hierarchical Modeling for Detecting Safety Signals in Clinical Trials. Journal of Biopharmaceutical Statistics 21, 1006-1029.