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Analysis methods by data type

Modelling quantitative outcome variables

A statistical model has, at its root, a mathematical representation of the relationship between one variable, called the outcome or y variable, and one or more explanatory or x variable(s). Many models have the simple form: y=systematic component + random error, where,  the systematic component, but not the random error, is a mathematical function of the explanatory variables. Analysis can be used

  • Linear regression
  • Nonlinear regression
  • General linear models

Modelling binary data

  • Logistic regression
  • Multiple logistic regression
  • Conditional logistic regression
  • Proportional odds model
  • Generalised estimating equations (GEE)

Modelling follow-up data

  • Basic functions of survival time
  • Estimating the hazard function
  • Probability models
  • Proportional hazards regression models
  • The Cox proportional hazards model
  • The Weibull proportional hazards model
  • Poisson regression
  • Pooled logistic regression