Categorical data survival

Measures such as the difference in event rates, OR, RR, RRR and NNT do not easily translate into the categorical data context. If we want to construct such measures in these cases we would collapse the outcome categories to two, the binary case, and proceed as before. In the categorical example covered earlier this could involve collapsing categories A, B and C to produce a binary outcome death/survival. 

The simplest form of qualitative data is binary data in which there are only two possible values, for example, death/survival or success/failure each of which needs to be defined within a specified time interval has pain relief been achieved within two hours of treatment, success - or not, failure. This form of data is extremely common in medical research and yet it ignores the possibility of gradation, success may not be total but only partial and yet not be total failure. These considerations lead naturally to the concept of ordered categorical or ordinal data. 

The non-linear mixed effects model is the most widely used method and has proven to be very useful for continuous measures of drug effect, categorical response data, and survival-type data. The nonlinear mixed-effects modeling software (NONMEM) introduced by Sheiner and Beal is one of the most commonly used programs for population analysis. A detailed review of software for performing population PK/PD analysis is available. ... 

In the sections that follow, terminologies and functions used to characterize survival data are first explained, followed by the application of nonlinear mixed effects modeling to the analysis of nonrandomly censored ordered categorical longitudinal data with application to analgesic trials. 

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