Reference

• About MGPS runs
  The main statistical scores computed by an MGPS run are EBGM, EB05, EB95, representing the Empirical Bayes Geometric Mean and the 90% confidence interval from 5% to 95%.
• MGPS computations
  The analytical core of the Oracle Empirica Signal application is a high-performance implementation of the MGPS (Multi-Item Gamma Poisson Shrinker) algorithm.
• MGPS independence model
  Prior to Version 5.0 of WebVDME (which was the previous name of the Oracle Empirica Signal application), signal scores for 3D data mining results were computed using the MGPS independence model described here. Starting in WebVDME 5.0, the primary way of computing signal scores for 3D results changed to the MGPS interaction model.
• Information Component computations
  When you specify data mining parameters for an MGPS run, you can include Information Component (IC) computations.
• RGPS computations
  When you specify data mining parameters for a two-dimensional MGPS run without subset variables, you can include Regression-Adjusted Gamma Poisson Shrinker Algorithm (RGPS) computations.
• Use the PRR calculator
  When creating a data mining run, you can specify that Proportional Reporting Ratio (PRR) and Chi-square should be computed and included in the results for each drug-event combination against a specified background database.
• PRR computations
  When specifying data mining parameters for a run, you can perform PRR and ROR computations.
• Select a case series for PRR computation
  PRR computations are made on a case series.
• ROR computations
  When specifying data mining parameters for a run, you can specify that PRR and ROR computations be performed.
• About logistic regression runs
  Logistic regression is a statistical tool for modeling how the probability of a response depends on the presence of multiple predictors, or risk factors.
• Logistic regression computations
  There are two types of logistic regression: standard logistic regression and extended logistic regression.
• Logistic regression log files
  Logistic regression data mining runs produce log files to provide information on run processing. These text files are available for download on the Job Detail page for the LR job in the run.
• Guidelines for specifying drugs for logistic regression
  For a logistic regression run, you define the number of predictor values (that is, how many drugs) to include in computations for each of the events you select for the run.
• Drug and event hierarchies
  A hierarchy determines how adverse event terms and drug terms are organized in the source data. For example, the FOI FDA Adverse Event Reporting System (AERS) database uses the Medical Dictionary for Regulatory Activities (MedDRA) classification system.
• Timestamped data
  Timestamping is a technique for representing multiple versions of the same source data within one database.
• Specify an As Of date
  During some activities, if you select a data configuration that supports timestamped data, the Select 'As Of' Date page appears and you must specify an As Of date and time.
• Stratification variables
  Item values can be associated in an MGPS data mining run because they are associated jointly with other variables, such as gender or age.
• Dimensions and patterns
  On the Select Criteria page, you can specify the dimension and pattern for an MGPS run as criteria for viewing data mining results.
• View interactions
  Each row in the table presents two predictor values, which appear in the columns labeled Item1 and Item2, and a response, which appears in the column labeled for the selected event variable, such as PT.
• Case scoring algorithm
  A case score indicates how well populated a report is for a given product-event combination.
• Increased frequency algorithm
  Reporting proportion (RP) is used as a proxy for exposure adjusted reporting rates and is defined as the number of cases for a product-event combination divided by the total number of cases for the product within the time period.