Table 1

Functional form of BLR, POM and restricted and unrestricted PPOM

Model

Functional form

Indication for use


Binary Logistic Model (BLM)

<a onClick="popup('http://www.nutritionj.com/content/10/1/124/mathml/M1','MathML',630,470);return false;" target="_blank" href="http://www.nutritionj.com/content/10/1/124/mathml/M1">View MathML</a>

Response variable with two categories (Y = 0,1)


Proportional Odds Model (POM)

<a onClick="popup('http://www.nutritionj.com/content/10/1/124/mathml/M2','MathML',630,470);return false;" target="_blank" href="http://www.nutritionj.com/content/10/1/124/mathml/M2">View MathML</a>

Originally continuous response variable, subsequently grouped, and valid proportional odds assumption


Unrestricted Partial Proportional Odds Model (PPOM-UR)*

<a onClick="popup('http://www.nutritionj.com/content/10/1/124/mathml/M3','MathML',630,470);return false;" target="_blank" href="http://www.nutritionj.com/content/10/1/124/mathml/M3">View MathML</a>

Proportional odds assumption not valid


Restricted Partial Proportional Odds Model (PPOM-R)

<a onClick="popup('http://www.nutritionj.com/content/10/1/124/mathml/M4','MathML',630,470);return false;" target="_blank" href="http://www.nutritionj.com/content/10/1/124/mathml/M4">View MathML</a>

Proportional odds assumption not valid, and linear relationship for odds ratio (OR) between a co-variable and the response variable


Note: Y = Response variable, <a onClick="popup('http://www.nutritionj.com/content/10/1/124/mathml/M5','MathML',630,470);return false;" target="_blank" href="http://www.nutritionj.com/content/10/1/124/mathml/M5">View MathML</a> vector of explanatory variables = (x1, x2, ....., xp)

*Stata uses P > j vs. < = j for the probability comparison in case of PPOM.

Das and Rahman Nutrition Journal 2011 10:124   doi:10.1186/1475-2891-10-124

Open Data