Mj. Ijzerman et al., Comparative trials on hybrid walking systems for people with paraplegia: an analysis of study methodology, PROS ORTHOT, 23(3), 1999, pp. 260-273
A new orthosis(SEPRIX) which combines user friendliness with low energy cos
t of walking has been developed and will be subject to a clinical compariso
n with conventional hip-knee-ankle-foot orthoses. In designing such compara
tive trials it was considered it may be worthwhile to use previous clinical
studies as practical examples. A literature search was conducted in order
to select all comparative trials which have studied two walking systems (hi
p-knee-ankle-foot orthoses) for patients with a complete thoracic lesion. S
tudy population, intervention, study design, outcome measurement and statis
tical analyses were examined. Statistical power was calculated where possib
le.
Of 12 selected studies, 7 were simple A-B comparisons, 2 A-E comparisons wi
th a replication, 2 cross-over trials and I non-randomised parallel group d
esign, the last of which was considered internally invalid due to severe co
nfounding by indication. All A-B comparisons were considered internally inv
alid as well, since they have not taken into account that a comparison of t
wo orthoses requires a control for aspecific effects (like test effects) wh
ich may cause a difference. Statistical power could only be examined in 4 s
tudies and die highest statistical power achieved in one study was 47 %. It
is concluded that statistical power was too low to be able to detect diffe
rences. Even analysis through interval estimation showed that the estimatio
n of the difference was too imprecise to be useful. Since the majority of t
he surveyed papers have reported small studies (of only 4-6 patients), it i
s assumed that lack of statistical power is a more general problem. Three p
ossibilities are discussed in order to enhance statistical power in compara
tive trials, i.e. multicentre studies, statistical pooling of results and i
mproving the efficiency of study design by means of interrupted time series
designs.