The use of bootstrap methods for estimating sample size and analysing health-related quality of life outcomes (particularly the SF-36)

Health-Related Quality of Life (HRQoL) measures are becoming increasingly used in clinical trials and health services research, both as primary and secondary outcome measures. Investigators are now asking statisticians for advice on how to plan (e. g. sample size) and analyse studies using HRQoI_ outcomes. HRQoL outcomes like the SF-36 are usually measured on an ordinal scale. However, most investigators assume that there exists an underlying continuous latent variable that measures HRQoL, and that the actual measured outcomes (the ordered categories), reflect contiguous intervals along this continuum. The ordinal scaling of HRQoL measures means they tend to generate data that have discrete, bounded and skewed distributions. Thus, standard methods of analysis such as the t-test and linear regression that assume Normality and constant variance may not be appropriate. For this reason, non-parametric methods are often used to analyse HRQoL data. The bootstrap is one such computer intensive non-parametric method for estimating sample sizes and analysing data. From a review of the literature, I found five methods of estimating sample sizes for two-group cross-sectional comparisons of HRQoL outcomes. All five methods (amongst other factors) require the specification of an effect size, which varies according to the method of sample size estimation. The empirical effect sizes calculated from the various datasets suggested that large differences in HRQoL (as measured by the SF-36) between groups are unlikely, particularly from the RCT comparisons. Most of the observed effect sizes were mainly in the 'small' to 'moderate' range (0.2 to 0.5) using Cohen's (1988) criteria. I compared the power of various methods of sample size estimation for two-group cross-sectional study designs via bootstrap simulation. The results showed that under the location shift alternative hypothesis, conventional methods of sample size estimation performed well, particularly Whitehead's (1993) method. Whitehead's method is recommended if the HRQoL outcome has a limited number of discrete values (< 7) and/or the expected proportion of cases at either of the bounds is high. If a pilot dataset is readily available (to estimate the shape of the distribution) then bootstrap simulation may provide a more accurate and reliable estimate, than conventional methods. Finally, I used the bootstrap for hypothesis testing and the estimation of standard errors and confidence intervals for parameters, in four datasets (which illustrate the different aspects of study design). I then compared and contrasted the bootstrap with standard methods of analysing HRQoL outcomes as described in Fayers and Machin (2000). Overall, in the datasets studied with the SF-36 outcome the use of the bootstrap for estimating sample sizes and analysing HRQoL data appears to produce results similar to conventional statistical methods. Therefore, the results of this thesis suggest that bootstrap methods are not more appropriate for analysing HRQoL outcome data than standard methods. This result requires confirmation with other HRQoL outcome measures, interventions and populations.