In identifying predictors of myocardial infarction (MI), which statistical test is most useful when dealing with a discrete outcome?

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When assessing predictors of a discrete outcome, such as the occurrence of a myocardial infarction (MI), multivariable logistic regression is the most suitable statistical test. This is because logistic regression is specifically designed for binary or dichotomous outcomes—situations where the outcome can take only two possible values, such as "event occurs" (in this case, an MI) or "event does not occur."

Logistic regression provides the ability to model the relationship between multiple independent variables and the probability of the occurrence of the event. This method calculates odds ratios, allowing researchers to understand how changes in predictor variables affect the likelihood of MI. It also accommodates various types of predictor variables, including continuous, categorical, and ordinal types, which adds to its utility in clinical settings.

On the other hand, the other statistical tests mentioned have different applications. For example, Spearman correlation is useful for assessing the strength and direction of a monotonic relationship between two continuous or ordinal variables but does not handle binary outcomes effectively. Multivariable linear regression is appropriate when the outcome variable is continuous rather than binary, making it unsuitable for this scenario. Cox proportional hazards regression is primarily employed for time-to-event data, such as survival analysis, rather than for direct measurement of binary outcomes

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