「Regression model」の版間の差分

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==Binary logistic regression==
 
==Binary logistic regression==
===Conversion of binary logistic regression equation to outcome probability <math>p</math>===
+
===Conversion of logit of outcome odds to outcome probability <math>p</math>===
 
Equation of binary logistic regression can be converted to outcome probablity <math>p</math> as,
 
Equation of binary logistic regression can be converted to outcome probablity <math>p</math> as,
 
:<math>
 
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2023年2月6日 (月) 22:12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)
Univariable (single variable) Multivariable (multiple variables) How to derive coefficients [math]\displaystyle{ b_i }[/math]
Dependent
variable
(outcome)
Continuous
  • Single 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]

Least squares method

Binary
  • Single binary logistic regression
[math]\displaystyle{ \log Y = a + bX }[/math]
where [math]\displaystyle{ Y }[/math] is odds of outcome [math]\displaystyle{ \frac{p}{1-p} }[/math]
  • 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 [math]\displaystyle{ \frac{p}{1-p} }[/math]

Maximum likelihood estimation method

Multinominal
≥ 3
  • Single multinominal logistic regression
  • Multivariable† multinominal logistic regression

Maximum likelihood estimation method

Ordinal
  • Single ordinal logistic regression
  • Multivariable† ordinal logistic regression

Maximum likelihood estimation method

Rate ratio
  • Multivariable Poisson regression
[math]\displaystyle{ \log Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots }[/math]
where [math]\displaystyle{ Y }[/math] is rate ratio [math]\displaystyle{ \frac{events_1/person \text{-} time_1}{events_2/person \text{-} time_2} }[/math]

Maximum likelihood estimation method

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]

Maximum likelihood estimation method

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

Binary logistic regression

Conversion of logit of outcome odds to outcome probability [math]\displaystyle{ p }[/math]

Equation of binary logistic regression can be converted to outcome probablity [math]\displaystyle{ p }[/math] as,

[math]\displaystyle{ \begin{array}{lrll} \log Y = & \log \left ( \dfrac{p}{1-p} \right ) & = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots \\ \Leftrightarrow & \dfrac{p}{1-p} & = \exp (a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots) & = e^{(a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots)} \\ \Leftrightarrow & p & = \dfrac { \exp (a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots) }{ 1 + \exp (a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots) } & = \dfrac { e^{(a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots)} }{ 1 + e^{(a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots)} } \\ \end{array} }[/math]

Conversion of coefficient of binary logistic regressoin to odds ratio

When thinking about outcome probability [math]\displaystyle{ p }[/math] and the changed outcome probability [math]\displaystyle{ p\prime }[/math] by adding [math]\displaystyle{ 1 }[/math] to explanatory variable [math]\displaystyle{ X_1 }[/math], the following two equations are obtained,

[math]\displaystyle{ \begin{array}{lcl} \log \left ( \dfrac{p}{1-p} \right ) & = & a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots \\ \log \left ( \dfrac{p\prime}{1-p\prime} \right ) & = & a + b_1( {\color{red}X_1 + 1} ) + b_2X_2 + b_3X_3 + \cdots \end{array} }[/math]

Subtraction of these two equations makes,

[math]\displaystyle{ \begin{align} \log \left ( \frac{p\prime}{1-p\prime} \right ) - \log \left ( \frac{p}{1-p} \right ) & = b_1({\color{red}X_1 + 1}) - b_1X_1 \\ & = b_1 \\ \Leftrightarrow \frac {\left ( \dfrac{p\prime}{1-p\prime} \right )}{ \left ( \dfrac{p}{1-p} \right ) } & = \exp (b_1) = e^{b_1} \end{align} }[/math]


Because [math]\displaystyle{ \dfrac{p\prime}{1-p\prime} }[/math] and [math]\displaystyle{ \dfrac{p}{1-p} }[/math] are odds of [math]\displaystyle{ p\prime }[/math] and [math]\displaystyle{ p }[/math], respectively,

[math]\displaystyle{ \frac {\left ( \dfrac{p\prime}{1-p\prime} \right )}{ \left ( \dfrac{p}{1-p} \right ) } }[/math] is the odds ratio of [the probability when [math]\displaystyle{ 1 }[/math] is added to [math]\displaystyle{ X_1 }[/math]] to [the probability before adding].


Thus, converted [math]\displaystyle{ b_1 }[/math] to [math]\displaystyle{ \color{red}{e^{b_1}} }[/math] or [math]\displaystyle{ \color{red}{\exp (b_1)} }[/math] gives the odds ratio of outocome probabilities before and after variable [math]\displaystyle{ X_1 }[/math] gains [math]\displaystyle{ 1 }[/math].

Generalized linear model

Penalized multivariable logistic regression model

Restricted cubic spline