2,992
回編集
差分
ナビゲーションに移動
検索に移動
→Classification of Regression models
|-
!colspan="2" rowspan="2" style="width:150px"|
!colspan="23"|Independent variable (exposure)
|-
!style="width:300px"|Univariable (single variable)
!style="width:300px"|Multivariable (multiple variables)
!How to derive coefficients <math>b_i</math>
|-
!rowspan="6"|Dependent<br>variable<br>(outcome)
!Continuous
|
*'''Simple Single linear regression'''
::<math>Y = a + bX</math>
|
*'''Multivariable† linear regression'''
::<math>Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math>
|Least squares method
|-
!Binary
|
*'''Simple Single binary logistic regression'''
::<math>\log Y = a + bX</math><br>where <math>Y</math> is '''odds''' of outcome <math>\frac{p}{1-p}</math>
|
*'''Multivariable† binary logistic regression'''
::<math>\log Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math><br>where <math>Y</math> is '''odds''' of outcome <math>\frac{p}{1-p}</math>
|Maximum likelihood estimation method
|-
!Multinominal<br>≥ 3
|
*'''Simple Single multinominal logistic regression'''
|
*'''Multivariable† multinominal logistic regression'''
|
|-
!Ordinal
|
*'''Simple Single ordinal logistic regression'''
|
*'''Multivariable† ordinal logistic regression'''
|
|-
!Rate ratio
*'''Multivariable Poisson regression'''
::<math>\log Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math><br>where <math>Y</math> is '''rate ratio''' <math>\frac{events_1/person \cdot time}{events_2/person \cdot time}</math>
|
|-
!Survival time
*'''Multivariable proportional hazard regression'''<br>= '''Cox hazard regression'''
::<math>\log h(T) = \log h_0(T) + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math><br>where <math>h(T)</math> is the hazard at time <math>T</math><br>and <math>h_0(T)</math> is the baseline hazard at time <math>T</math>
|
|}
