STATISTICAL ANALYSIS

Data analysis was performed using the Statistical Package for the Social Sciences (SPSS) Version 14.0. Results are described as ‗significant‘ if two-sided p-values were less than or equal to 0.05, but this is purely a conventional value. Estimates of means are shown with 95% two-sided confidence intervals.

2.3.1 Patients’ Data Analysis

Chi-Square test is a statistical procedure for comparing proportions in two groups. In this study the Chi-Square test was used to compare the extra questions that were asked to the patients in two or more groups. It was also used to examine if there was a difference between countries for the two measures (gender and smoking status).

ANOVA Post Hoc test was used to compare each centre to every other one to see if they are significantly different in terms of their total SGRQ scores.

The effects of various study variables upon the QoL scores components were assessed using linear regression (As the QoL scores were measured on a continuous scale). Initially the separate effect of each variable upon the outcome was examined separately in a series of univariate analyses. For the continuous explanatory variables, the relationship with the QoL outcome was examined to see if a non-linear (i.e. curved) relationship was more appropriate than a linear (straight line) one.

The joint effect of the variables upon QoL in a multivariate analysis was assessed. This analysis has the advantage that the effect of each variable upon the QoL is

Page 54 adjusted for the other factors in the analysis. A backwards selection procedure was used to retain only the statistically significant variables. This involves removing the non-significant variables, one at a time, until all remaining variables were significant.

The differences between the three countries were examined in terms of a number of parameters. All continuous variables were approximately normally distributed, and so analysis of variance (ANOVA) was used to compare between the three countries. The Chi-square test was used to compare the categorical variables (Gender and smoking status) between countries.

The differences in QoL between countries were examined next. Firstly the differences between countries were examined without considering any other factors in the analysis. Subsequently, the same difference was examined, this time adjusting for factors found to be significant in the first part of the analysis. Smoking status was included in this analysis, as this factor was almost significant in the multivariate analysis. All the analysis was performed using linear regression.

Additionally, the interaction between each of the explanatory variables and country was examined. A significant interaction would imply that the difference between country varied by that factor.

The final set of analysis examined the effect of the three dyspnoea variables upon QoL. This analysis was performed in three stages. Firstly the effect of each variable upon QoL was examined separately. Then the variables were examined together in a multivariate analysis. Finally the multivariate analysis was extended to also include factors previously found to significantly influence QoL and centre.

Page 55 Before the multivariate analysis, the collinearity between the three dyspnoea variables was examined. Collinearity between variables occurs when two or more variables are very similar to each other. In such situations this can influence the results of regression analysis and produce unusual results. Collinearity was assessed using variance inflation factors (VIFs), with values of over 10 used to indicate collinearity. If this was present, then the solution is to only include one of the collinear variables in the regression analysis.

2.3.2 Control Group Data Analysis

The first set of analysis examined the differences between the countries in terms of a number of parameters. Data on some parameters was available for all three countries, whilst other information was only available for the UAE and Morocco. Where information was available for 3 countries, the comparisons were made between the 3 countries, and where it was available for only two, the comparisons were restricted to these two countries. All continuous variables were approximately normally distributed. Therefore, analysis of variance (ANOVA) was used to compare between the continuous measures between three countries, whilst the unpaired t- test was used where data was only present for two countries. The Chi-square test was used to compare the categorical variables between countries.

The effects of various study variables upon the QoL scores were examined. Due to missing data for some variables, this analysis was restricted to subjects from the UAE and Morocco only. As the QoL scores were measured on a continuous scale, the analysis was performed using linear regression. Initially the separate effect of each variable upon the outcome was examined separately in a series of univariate analyses. For the continuous explanatory variables, the relationship with the QoL

Page 56 outcome was examined to see if a non-linear (i.e. curved) relationship was more appropriate than a linear (straight line) one.

The second stage in the analysis process was to examine the joint effect of the variables upon QoL in a multivariate analysis. This analysis has the advantage that the effect of each variable upon the outcome is adjusted for the other factors in the analysis. A backwards selection procedure was used to retain only the statistically significant variables. This involves removing the non-significant variables, one at a time, until all remaining variables were significant.

The differences in QoL between countries were examined next. Firstly the differences between countries were examined without considering any other factors in the analysis. Subsequently, the same difference was examined, this time adjusting for factors that could potentially confound the country differences. The factors adjusted for were restricted to those where there was data for all three countries, namely age, BMI, gender and smoking status.

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