「Regression model」の版間の差分

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&dagger;'Multivariable' can be rephrased as 'Multiple'; Multivariable is <font color="red">'''NOT equal to 'Multivariate'!!'''</font>
 
&dagger;'Multivariable' can be rephrased as 'Multiple'; Multivariable is <font color="red">'''NOT equal to 'Multivariate'!!'''</font>
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==Penalized multivariable logistic regression model==
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*[http://www.sthda.com/english/articles/36-classification-methods-essentials/149-penalized-logistic-regression-essentials-in-r-ridge-lasso-and-elastic-net/ Penalized Logistic Regression Essentials in R: Ridge, Lasso and Elastic Net]
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*[https://jojoshin.hatenablog.com/entry/2016/07/06/180923 罰則付き・正則化回帰モデルについて(About penalized/regularized regression model)]
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==Restricted cubic spline==
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*[https://statakahiro.com/restricted-cubic-splines%E3%82%92stata%E3%81%A7%E5%AE%9F%E8%A1%8C%E3%81%97%E3%81%A6%E3%81%BF%E3%82%8B Restricted cubic splinesをStataで実行してみる]

2022年12月19日 (月) 14:14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
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
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'!!

Penalized multivariable logistic regression model

Restricted cubic spline