When analyzing the primary outcome of time to a major adverse cardiovascular event, what statistical test is most appropriate?

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The choice of Cox proportional hazards regression for analyzing the primary outcome of time to a major adverse cardiovascular event is appropriate due to the nature of the data and the type of analysis required. This statistical method is specifically designed for survival analysis, where the focus is on the time until an event occurs, which in this case is a major adverse cardiovascular event.

Cox proportional hazards regression allows researchers to examine the relationship between several predictor variables and the time to an event, accounting for censored data (instances when a participant does not experience the event during the study period). This model assesses the hazard ratio, estimating how the risk of the event changes with specific variables while considering the timing of the event, making it particularly effective in clinical trials or cohort studies examining cardiovascular outcomes.

In contrast, other statistical methods listed are not suitable for this type of analysis. Multivariable linear regression is used for continuous outcomes rather than time-to-event outcomes. Multivariable logistic regression is used for binary outcomes, assessing the probability of an event occurring without accounting for the timing of that event. Spearman correlation, being a non-parametric test, measures the strength and direction of the association between two ranked variables but does not analyze time-to-event data. Thus, selecting Cox proportional hazards

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