Quality of life including longitudinal data

It is clearly important that patients and clinicians are able to use and interpret the results of the analysis of quality of life (QL) data, in addition to more traditional outcome measures, to make informed decisions. However, there are considerable problems with the analysis and presentation of QL data including (i) the data are multi-dimensional (data on many symptoms and functions are collected) (ii) there are often many missing data due to patient attrition and non-compliance, and (iii) the data are longitudinal

Table 9.11 Results of chi-square tests to assess whether CAP or carboplatin was more or less effective in different subgroups of seven factors on the endpoint of overall survival (chi-square on one degree of freedom, except for histology which is a chi-square on five degrees freedom; tests for interaction were performed on the factors coordination centre and histology, while tests for trend were performed on the remaining five factors). Reprinted with permission from Elsevier Science (The Lancet, 1999, 352, 1571-6).

Table 9.11 Results of chi-square tests to assess whether CAP or carboplatin was more or less effective in different subgroups of seven factors on the endpoint of overall survival (chi-square on one degree of freedom, except for histology which is a chi-square on five degrees freedom; tests for interaction were performed on the factors coordination centre and histology, while tests for trend were performed on the remaining five factors). Reprinted with permission from Elsevier Science (The Lancet, 1999, 352, 1571-6).

Factor (subgroups)

Chi-square statistic

p-value

Coordination centre (Italy, UK)

0.98

0.32

Age in years (<55, 55-65, >65)

2.16

0.14

FIGO stage (I, II, III, IV)

0.36

0.55

Residual bulk (none, <2 cm,>2 cm)

0.76

0.39

Differentiation (poor, intermediate, good)

0.03

0.87

Histology (serous, mucinous, endometrioid,

4.73

0.45

clear cell, undifferentiated, other)

Number of patients entered by each

0.80

0.52

0 0

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