DERIVING DICHOTOMOUS OUTCOME MEASURES FROM CONTINUOUS DATA IN RANDOMIZED CONTROLLED TRIALS OF ANALGESICS - USE OF PAIN INTENSITY AND VISUALANALOG SCALES
A. Moore et al., DERIVING DICHOTOMOUS OUTCOME MEASURES FROM CONTINUOUS DATA IN RANDOMIZED CONTROLLED TRIALS OF ANALGESICS - USE OF PAIN INTENSITY AND VISUALANALOG SCALES, Pain, 69(3), 1997, pp. 311-315
The aim of this study was to examine whether mean data from categorica
l pain intensity and visual analogue scales for both pain intensity an
d relief could be used reliably to derive dichotomous outcome measures
for meta-analysis. Individual patient data from randomised controlled
trials of single-dose analgesics in acute postoperative pain were use
d. The methods used were as follows: data from 132 treatments with ove
r 4700 patients were used to calculate mean %maxSPID (categorical pain
intensity), %maxVAS-SPID (visual analogue pain intensity) and %maxVAS
-TOTPAR (visual analogue pain relief); these were used to derive relat
ionships with the number of patients who achieved at least 50% pain re
lief (%maxTOTPAR). Good agreement was obtained between the actual numb
er of patients with >50%maxTOTPAR and the number calculated for all th
ree measures. For SPID, verification included independent data sets. F
or calculations involving each measure, summing the positive and negat
ive differences between actual and calculated numbers of patients with
>50%maxTOTPAR gave an average difference of less than 0.25 patients p
er treatment arm. Reports of randomised trials of analgesics frequentl
y describe results of studies in the form of mean derived indices, rat
her than using discontinuous events, such as number or proportion of p
atients obtaining at least 50% pain relief. Because mean data inadequa
tely describe information with a nonnormal distribution, combining suc
h mean data in systematic reviews may compromise the results. Showing
that dichotomous data can reliably be derived from mean SPID, VAS-SPID
and VAS-TOTPAR as well as TOTPAR data in previously published acute p
ain studies makes much more information accessible for meta-analysis.