To examine the impact of MHT use on cardiac structure and function, multivariable linear regression models were fitted for each cardiac (dependent) variable. With our sample size, the study has 80% power at the 5% significance level to detect a 0.15 standard deviation difference in any of the continuous variables; this would be considered a small effect size . Co-variates included in the model (Model 1) were age, age at menopause, ethnicity, height, weight, systolic blood pressure, diastolic blood pressure, smoking status, regular alcohol use, presence of raised cholesterol, presence of diabetes, Townsend deprivation index and income. Height and weight were included as covariates in the model rather than indexing the dependent variables, as the use of ratios in regression analysis can lead to spurious results and misinterpretation . The adjustment made ensured all variables in the model were appropriately adjusted for body composition. The variance inflation factor was calculated to test for multicollinearity. Where cardiac variables had been log-transformed, the beta coefficients were anti-logged and expressed as a percentage difference.