Adverse events classifying data from

This scale is best defined as one in which an ordering of values can be assigned. Examples of data from clinical studies measured on an ordinal scale include severity of an adverse event classified as mild, moderate, or severe age categorized as < 65, 65-70, 71-75, and > 75 years. The ordinal nature of the measurement scales means that we can say that a mild headache is less severe than a moderate headache, which is less severe than a severe headache. However, we cannot say that the difference between mild and moderate is the same as the difference between moderate and severe. 

Pharmacovigilance systems capture, store, process, maintain, classify, and report adverse event data. Any snch systems generating reports for regnlatory authorities (e.g., expedited reports, periodic safety updates) and the interfaces into them from a variety of sources, should be validated. Specific considerations when validating these systems are ... 

Semiquantitative methods are used to describe the relative risk scale. For example, risk can be classified into categories such as low, medium, high, or very high. The number of levels of risk can vary from (say) a to b. In a semiquantitative approach, different scales are used to characterize the likelihood of adverse events and their consequences. Analyzed probabilities and their consequences do not require accurate mathematical data. Semiquantitative assessment is useful especially if quantification of risk is difficult. At the same time, qualitative interpretation is too subjective. As already discussed, the risk graph (highly project dependent) discussed in Chapter 1 in conjunction with available guided risk nomogram or LOPA approach, is commonly used for this purpose. Here discussions wiU be mainly on the LOPA approach (LOPA wiU be dealt with separately later). 

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