How Do You Convert Between HT(t), FT(t), and fT(t) in Probability Calculations?

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The discussion focuses on converting between the hazard function (HT(t)), cumulative distribution function (FT(t)), and density function (fT(t)) in probability calculations. It explains that the hazard function can be derived from the cumulative distribution function using the relationship λ(t) = f(t)/(1 - F(t)). Additionally, it highlights that the cumulative hazard function can be expressed as Λ(t) = -log(1 - F(t)), with the hazard function being its derivative. The conversation also touches on the need for the hazard function, clarifying that while the probability density function is used for specific moments, the cumulative distribution function is applicable over time intervals. Understanding these relationships is essential for effectively applying survival analysis in probability.
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Homework Statement



Looking for a step by step online guide/tutorial/worked example showing equations for getting to the hazard function from the density function, the cumulative distribution function from the hazard function, and vice versa

Homework Equations



HT(t) = hazard function
FT(t) = Cumulative distribution function
fT(t) = Density function

The Attempt at a Solution

 
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Distribution function and density function I know but I had to look up "Hazard function".
According to Wikipedia,
http://en.wikipedia.org/wiki/Survival_analysis
If we have the cumulative distribution function, [/itex]F(t)= Pr(T\le t)[/itex], we define S(t)= 1- T(t). Then the Hazard function, \lambda(t), is given by
\lambda = -\frac{S'(t)}{S(t)}
Directly in terms of F, then, since S'(t)= (1- F(t))'= -F'(t),
\lambda = \frac{F'(t)}{1- F(t)}

If f(t) is the density function, f(t)= F'(t), then
\lambda = \frac{f(t)}{1- F(t)}

Alternatively, we can define the "cumulative hazard function", \Lambda(t)= -log(1- F(t)) and then the hazard function is the derivative: \lambda(t)= d \Lambda(t)/dt
In any case, finding \lambda involves solving a first order differential equation.

To take a simple example, the uniform distribution from 0 to 1, the density function is a constant, f(x)= 1, so F(x)= \int_0^t 1 dx= t and the hazard function is given by \lambda(t)= f(t)/(1- F(t))= 1/(1- t). Alternatively, the cumulative hazard function is \Lamba(t)= -log(F(t))= -log(1-t) and the hazard function is the derivative of that: \lambda(t)= d(-log(1-t))/dt= 1/(1-t).

Going the other way, if we were given \lambda(t)= 1/(1- t), then \lambda(t)= F'/(1- F)= 1/(1- t) so finding \lambda(t) requires solving a differential equation: F'= \lambda(t)(1- F)= (1- F)/(1- t). That's a "separable" differential equation: dF/(1- F)= dt/(1-t) . Integrating, log(1- F(t))= log(1- t)+ C1 so 1- F(t)= C2(1- t). In order that F(0)= 0, we must have C2= 1 so 1- F(t)= 1- t and F(t)= t as before.
 
thanks for the link, I am wondering at what stage we use the hazard function, I understand the p.d.f is used for a moment in time, while the c.d.f is used for a time period i.e 0< = T, at what stage do we need the hazard function?
 
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