Stata 12.1 for mac

We conducted descriptive analyses of baseline clinical characteristics of older adults with depression and MCI according to subsequent progression to AD or not. We then conducted survival analyses to determine which variables were associated with increased risk of AD during follow up. Survival analysis measures the time to an event or outcome of interest and is frequently used to analyze longitudinal data of this type. An event was defined as a diagnosis of AD during follow up, and censoring at the last date of contact was used to account for participants who had not received a diagnosis of AD before loss to follow up. We used Cox proportional hazards regression models which allowed us to adjust for the effects of other covariates on risk of AD. Variables significantly associated with AD in bivariate survival analyses were selected for the multivariable Cox proportional hazards regression model. Prior to running the final model, the variables were assessed for multicollinearity using tolerance statistics (tolerance < 0.4 as a cut-point) to avoid an unstable estimate of regression coefficients. The assumption of proportionality was examined to ensure that the Cox proportional hazards assumption was met. All analyses were conducted utilizing Stata 12.1 for mac. A p value < 0.05 was considered statistically significant.