Trial documentation including protocols, SOPs and CRFs will be shared electronically with participating study centres. Protocol amendments will be submitted to the Royal Brisbane and Women’s Hospital HREC and local site research governance offices and disseminated to the relevant parties at each study site. Data recorded on printed CRFs will be scanned to the study centre in Townsville where it will be entered centrally and examined for data quality. This will allow confirmation of entry criteria and collection of set entry and outcome data. Examples of important baseline data which will be collected include age, gender, presence of diabetes and/or dyslipidaemia, concurrent medications and maximum aortic diameter. At completion of the trial the database will be checked for errors and data confirmed with source documentation where required. Analysis of primary and secondary endpoints will be based on intention-to-treat at the time of randomisation. All participants who meet the eligibility criteria, provide written informed consent and are enrolled in the study will be included in the primary analysis, regardless of adherence to medication allocation.
To identify potential confounders, collected clinical and demographic data will be compared between groups via univariate statistics. The distribution of all continuous data variables will be assessed for normality using the Kolgorov-Smirnov test. Normally distributed continuous variables will be compared between test groups via t test; non-normally continuous distributed variables will be compared between groups using the Mann-Whitney U test. Nominal data will be compared using the chi-squared test. Characteristics showing a p value < 0.100 on univariate tests will be considered as potential confounders and will be entered as covariates in subsequent binary logistic regression models assessing the association of each of the outcome measures with treatment allocation. Following binary logistic regression, the association of all covariates with treatment allocation will be reported as odds ratios and 95% confidence intervals. For all analyses, p values <0.05 will be considered to be significant. Data will be published in a peer-reviewed journal with copies of the paper available to participants if required.
To identify potential confounders, collected clinical and demographic data will be compared between groups via univariate statistics. The distribution of all continuous data variables will be assessed for normality using the Kolgorov-Smirnov test. Normally distributed continuous variables will be compared between test groups via t test; non-normally continuous distributed variables will be compared between groups using the Mann-Whitney U test. Nominal data will be compared using the chi-squared test. Characteristics showing a p value < 0.100 on univariate tests will be considered as potential confounders and will be entered as covariates in subsequent binary logistic regression models assessing the association of each of the outcome measures with treatment allocation. Following binary logistic regression, the association of all covariates with treatment allocation will be reported as odds ratios and 95% confidence intervals. For all analyses, p values <0.05 will be considered to be significant. Data will be published in a peer-reviewed journal with copies of the paper available to participants if required.
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