Conditional logistic regression: Difference between revisions

Revision as of 17:10, 24 July 2017

Revision as of 19:10, 4 April 2018

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\begin{align}

& \mathbb{P}(Y_{i1}=1,Y_{i2}=0|X_{i1},X_{i2},Y_{i1}+Y_{i2}=1) \\
& =\frac{\mathbb{P}(Y_{i1}=1|X_{i1}) \mathbb{P}(Y_{i2}=0|X_{i2})}{\mathbb{P}(Y_{i1}=1|X_{i1}) \mathbb{P}(Y_{i2}=0|X_{i2})+\mathbb{P}(Y_{i1}=0|X_{i1}) \mathbb{P}(Y_{i2}=1|X_{i2})}\\[6pt]

\ & =\frac{\frac{\exp(\alpha_i+\boldsymbol{\beta}^\top X_{i1})}{1+\exp(\alpha_i+\boldsymbol{\beta}^\top X_{i1})}\times\frac{1}{1+\exp(\alpha_i+\boldsymbol{\beta}^\top X_{i2})}}{\frac{\exp(\alpha_i+\boldsymbol{\beta}^\top X_{i1})}{1+\exp(\alpha_i+\boldsymbol{\beta}^\top X_{i1})}\times\frac{1}{1+\exp(\alpha_i+\boldsymbol{\beta}^\top X_{i2})}+\frac{1}{1+\exp(\alpha_i+\boldsymbol{\beta}^\top X_{i1})}\times\frac{\exp(\alpha_i+\boldsymbol{\beta}^\top X_{i2})}{1+\exp(\alpha_i+\boldsymbol{\beta}^\top X_{i2})}}\\[6pt]

\ & =\frac{\exp(\boldsymbol{\beta}^\top X_{i1})}{\exp(\boldsymbol{\beta}^\top X_{i1})+\exp(\boldsymbol{\beta}^\top X_{i2})}. \\[6pt]