Longitudinal data repeated measures

For each patient, data from a clinical trial are sometimes in the form of repeated assessments over time. In such a trial observations are taken on more than one occasion for each patient over the period of the trial. An example of such data is given in the randomized clinical trial LU19 conducted by the Medical Research Council Lung Cancer Working Party [10]. In this trial the control arm of six cycles of ACE chemotherapy given 3-weekly was compared to the experimental arm of 6 cycles of ACE + G-CSF given 2-weekly. For each patient, clinicians were asked to complete a symptom assessment form before starting treatment and then after each cycle of chemotherapy, 3-weekly for the ACE arm and 2-weekly for the ACE+G-CSF group. In both groups after the completion of chemotherapy (eighteen weeks for the ACE arm, twelve weeks for the ACE + G-CSF arm) reports were to be completed each month up to six months.

Longitudinal data pose many problems for analysis which can include data missing at particular timepoints, repeated (and therefore correlated) data on each patient and the difficulty that we shall probably be performing multiple comparisons and thus we shall be increasing our chance of finding a 'positive' result by chance alone (see subgroup analysis and interim analysis sections below). Whatever the form of longitudinal data (binary, categorical or continuous) there are two principal methods for analysing them, using summary measures or model-based approaches. In cancer clinical trials longitudinal data most often occur in the form of 'quality of life data' and the presentation and analysis of such data is best done through practical example. Thus the reader is referred to Section 9.4.9 for a presentation and discussion of analysis of these data.

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