**Threshold analysis**

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exercises

This
module runs two-

Threshold
effect means when the risk factor (X) reaches to certain point, its
relationship with outcome (Y) starts to change, could be from no relation to
positive or negative relation, or from positive or negative relation to no
relation, or from positive relation to negative relation, etc.

If
you already have the turning point (the point where the relationship starts to
change) is, you can input it in the “Enter turning points” box.

To
let this module find the turning point for you, choose “Auto determine
2-pieces-wise regression”. This module
uses *maximized log likelihood *method. It first runs multiple two-piece-wise
regression models. Each model uses a
percentile (between 10% and 90%) of risk factor (X) as turning point, and then
picks the model with the turning point that provides maximum log likelihood.

The
output table includes one-line linear regression (model I), two-piece-wise
regression (model II) that gives maximum log likelihood, and p-value from log
likelihood ratio test comparing model I versus model II.

The
module automatically detects whether the outcome variable is binary or
continuous and selects logistic or linear regression as appropriate. You
also can specify the distribution and link function for each outcome manually,
right click the variable and then select “Change functions”. In the popup
window, select the distribution and link function.

**What it’s used for**

This module is used to determine whether the risk factor(s)
has any threshold effect or saturate effect on the outcome(s). It also
describes the relationship between risk factor(s) and outcome(s) if such effect
exists.

**A
screen shot of sample design:**

**A
sample output table:**