「Regression model」の版間の差分

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*'''Multiple† linear regression'''
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*'''Multivariable† linear regression'''
 
::<math>Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math>
 
::<math>Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math>
  
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*'''Multiple&dagger; binary logistic regression'''
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*'''Multivariable&dagger; 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>\log Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math><br>where <math>Y</math> is odds of outcome
  
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*'''Multiple&dagger; multinominal logistic regression'''
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*'''Multivariable&dagger; multinominal logistic regression'''
  
 
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*'''Multiple&dagger; ordinal logistic regression'''
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*'''Multivariable&dagger; ordinal logistic regression'''
  
 
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*'''Multiple proportional hazard regression'''<br>= '''Cox hazard regression'''
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*'''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>
 
::<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>
 
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&dagger;'Multiple' can be rephrased as 'Multi'''variable''''; <font color="red">'''NOT 'Multivariate'!!'''</font>
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&dagger;'Multivariable' can be rephrased as 'Multiple'; Multivariable is <font color="red">'''NOT equal to 'Multivariate'!!'''</font>

2022年12月19日 (月) 11:41時点における版

Basics & Definition
Epidemiology
Odds in statistics and Odds in a horse race
Collider bias
Data distribution
Statistical test
Regression model
Multivariate analysis
Marginal effects
Prediction and decision
Table-related commands in STATA
Missing data and imputation

Classification of Regression models

Independent variable (exposure)
Monovariable (single variable) Multivariable (multiple variables)
Dependent
variable
(outcome)
Continuous
  • Simple linear regression
[math]\displaystyle{ Y = a + bX }[/math]
  • Multivariable† linear regression
[math]\displaystyle{ Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots }[/math]
Binary
  • Simple binary logistic regression
[math]\displaystyle{ \log Y = a + bX }[/math]
where [math]\displaystyle{ Y }[/math] is odds of outcome
  • Multivariable† binary logistic regression
[math]\displaystyle{ \log Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots }[/math]
where [math]\displaystyle{ Y }[/math] is odds of outcome
Multinominal
≥ 3
  • Simple multinominal logistic regression
  • Multivariable† multinominal logistic regression
Ordinal
  • Simple ordinal logistic regression
  • Multivariable† ordinal logistic regression
Survival time
  • Multivariable proportional hazard regression
    = Cox hazard regression
[math]\displaystyle{ \log h(T) = \log h_0(T) + b_1X_1 + b_2X_2 + b_3X_3 + \cdots }[/math]
where [math]\displaystyle{ h(T) }[/math] is the hazard at time [math]\displaystyle{ T }[/math]
and [math]\displaystyle{ h_0(T) }[/math] is the baseline hazard at time [math]\displaystyle{ T }[/math]

†'Multivariable' can be rephrased as 'Multiple'; Multivariable is NOT equal to 'Multivariate'!!