Bootstrap stata how to#

This ordinal scaling of HRQoL measures means they generate data with discrete, bounded and non-standard distributions, which may lead to several problems in determining sample size and analysing the data. We will also assume that there exists an underlying continuous latent variable, Z, that measures HRQoL (although not necessarily Normally distributed), and that the actual measured outcomes, X, are ordered categories that reflect contiguous intervals along this continuum. These 'summated scores' are usually treated as if they were from a continuous distribution and were Normally distributed. 0 = 'poor', 1 = 'moderate' and 2 = 'good') and the scores across similar questions are then summed to generate a HRQoL score. The responses are often analysed by assigning equally spaced numerical scores to the ordinal categories (e.g.

This means that responses to individual questions are usually classified into a small number of ordered response categories, e.g. HRQoL measures such as the Short Form (SF)-36, Nottingham Health Profile (NHP) and European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 are described in Fayers and Machin and are usually measured on an ordered categorical (ordinal) scale.

Bootstrap stata how to#

Investigators are now asking statisticians for advice on how to plan and analyse studies that have used HRQoL measures. Health Related Quality of Life (HRQoL) measures are now frequently used in clinical trials and health services research, both as primary and secondary endpoints. Future research with other HRQoL outcome measures, interventions and populations, is required to confirm this conclusion. While particular to our datasets, these findings are likely to generalise to other HRQoL outcomes, which have discrete, bounded and skewed distributions. This is likely because the t-test and linear regression are robust to the violations of assumptions that HRQoL data are likely to cause (i.e. Overall, in the datasets we studied, using the SF-36 outcome, bootstrap methods produce results similar to conventional statistical methods. The standard methods included t-tests, linear regression, summary measures and General Linear Models. We then compared and contrasted the bootstrap with standard methods of analysing HRQoL outcomes. We 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).

The bootstrap is one such computer intensive non-parametric method for analysing data. For this reason, conventional statistical advice would suggest that non-parametric methods be used to analyse HRQoL data. Thus, standard methods of analysis such as the t-test and linear regression that assume Normality and constant variance may not be appropriate. The ordinal scaling of HRQoL measures means they tend to generate data that have discrete, bounded and skewed distributions. 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. HRQoL outcomes, like the SF-36, are usually measured on an ordinal scale. Investigators are now asking statisticians for advice on how to analyse studies that have used HRQoL outcomes. Health-Related Quality of Life (HRQoL) measures are becoming increasingly used in clinical trials as primary outcome measures.