How Do You Compute the MLE for p in a Treatment Effectiveness Study?

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Homework Help Overview

The problem involves a treatment effectiveness study where patients receive a risky treatment until two have died, and the number of survivors (N) is recorded. The goal is to find the maximum likelihood estimate (MLE) for the proportion (p) of patients killed by the treatment, given a specific probability distribution for N.

Discussion Character

  • Exploratory, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants discuss the formulation of the likelihood function and how to handle the terms involved, particularly the (ni+1) term in the product. There are attempts to derive a general formula for the MLE of p and to compute the likelihood for observed values of N.

Discussion Status

Participants are actively exploring the formulation of the likelihood function and its components. Some have made progress in expressing the likelihood in terms of observed values, while others are questioning how to simplify certain terms and their relevance to finding the maximum likelihood estimate. There is no explicit consensus yet, but guidance has been offered regarding the simplification of terms and the differentiation process.

Contextual Notes

Participants note the need to derive a general formula for the MLE and compute probabilities based on observed data from multiple hospitals. There are mentions of potential typos and clarifications needed in the mathematical expressions being discussed.

ianrice
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Homework Statement


1. An experiment consists of giving a sequences of patients a risky treatment until two have died, and then recording N, the number who survived. If p is the proportion killed by the treatment, then the distribution of N is:
P(N=n)=(n+1)(1-p)n p2

1)Find a general formula for the MLE for p:
2)the experiment is done in 8 hospitals and the observed values of N are 3,0,4,2,3,5,1,3. Compute the estimate for the p derived in part 1


Homework Equations


2. I know I have to start by finding a general form of ∏(ni+1)(1-p)ni p2 with ni where i goes from 1 to m.




The Attempt at a Solution


3. So I get hung up on how to handle the (ni+1) term.
 
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ianrice said:

Homework Statement


1. An experiment consists of giving a sequences of patients a risky treatment until two have died, and then recording N, the number who survived. If p is the proportion killed by the treatment, then the distribution of N is:
P(N=n)=(n+1)(1-p)n p2

1)Find a general formula for the MLE for p:
2)the experiment is done in 8 hospitals and the observed values of N are 3,0,4,2,3,5,1,3. Compute the estimate for the p derived in part 1


Homework Equations


2. I know I have to start by finding a general form of ∏(ni+1)(1-p)ni p2 with ni where i goes from 1 to m.




The Attempt at a Solution


3. So I get hung up on how to handle the (ni+1) term.


What is preventing you from computing P(N1=3,N2=0,N3=4,...,N8=3)?
 
Ray Vickson said:
What is preventing you from computing P(N1=3,N2=0,N3=4,...,N8=3)?

The question asks for the general form of the MLE of p. then to compute P(N1,N2...N8). I need help with the general form
 
ianrice said:

Homework Equations


2. I know I have to start by finding a general form of ∏(ni+1)(1-p)ni p2 with ni where i goes from 1 to m.

There's a typo in your post. Witness that hanging .

I suspect you meant ##\Pi_{i=1}^m (n_i+1)(1-p)^{n_i} p^2##

The Attempt at a Solution


3. So I get hung up on how to handle the (ni+1) term.

This isn't that hard. Let's start with m=1, i.e., one observation. Then that product is simply ##(n_1+1)(1-p)^{n_1}p^2##. With m=2, the product becomes ##(n_1+1)(1-p)^{n_1}p^2\,(n_2+1)(1-p)^{n_2}p^2##. Try collecting terms. You should get something of the form ##A (1-p)^B p^C##, where A, B, and C are related to the observed values. Now generalize to m observations.

Where does this reach a maximum?
 
ianrice said:
The question asks for the general form of the MLE of p. then to compute P(N1,N2...N8). I need help with the general form

Start with simple cases, by writing the formula for the likelihood function of P(n1,n2) and maybe P(n1,n2,n3). This will show you the pattern that applies as well to larger problems.
 
D H said:
There's a typo in your post. Witness that hanging .

I suspect you meant ##\Pi_{i=1}^m (n_i+1)(1-p)^{n_i} p^2##
This isn't that hard. Let's start with m=1, i.e., one observation. Then that product is simply ##(n_1+1)(1-p)^{n_1}p^2##. With m=2, the product becomes ##(n_1+1)(1-p)^{n_1}p^2\,(n_2+1)(1-p)^{n_2}p^2##. Try collecting terms. You should get something of the form ##A (1-p)^B p^C##, where A, B, and C are related to the observed values. Now generalize to m observations.

Where does this reach a maximum?



So I've collected terms for m=3 and get the following:

(n1+1)(n2+1)(n3+1)(1-p)n1(1-p)n2(1-p)n3p2p2p2

So by using your A(1-p)BpC:

I think B=∑ni
C=2n
A is where I'm still stuck. Am I missing something completely I can't seem to figure out how to reduced (n1+1)(n2+1)(n3+1)

And to find the maximum I need to set the derivative to 0 and find p the parameter the question ask for.
 
What are ##(1-p)^{n_1}(1-p)^{n_2}(1-p)^{n_3}## and ##p^2p^2p^2##? Surely you can reduce these to a simpler form!
 
D H said:
What are ##(1-p)^{n_1}(1-p)^{n_2}(1-p)^{n_3}## and ##p^2p^2p^2##? Surely you can reduce these to a simpler form!

I got those:

##(1-p)^{n_1}(1-p)^{n_2}(1-p)^{n_3}## = (1-p)∑ni

##p^2p^2p^2## = p2n
 
ianrice said:
So I've collected terms for m=3 and get the following:

(n1+1)(n2+1)(n3+1)(1-p)n1(1-p)n2(1-p)n3p2p2p2

So by using your A(1-p)BpC:

I think B=∑ni
C=2n
A is where I'm still stuck. Am I missing something completely I can't seem to figure out how to reduced (n1+1)(n2+1)(n3+1)

And to find the maximum I need to set the derivative to 0 and find p the parameter the question ask for.

Why would you care about reducing ##(n_1+1) (n_2+1) \cdots (n_m+1)##? It has no effect whatsoever on the optimal value of p. In other words, the p that maximizes f(p) is the same p that maximizes 1000*f(p) or (1/100)*f(p) or any K*f(p) for constant K.
 
  • #10
Ray Vickson said:
Why would you care about reducing ##(n_1+1) (n_2+1) \cdots (n_m+1)##? It has no effect whatsoever on the optimal value of p. In other words, the p that maximizes f(p) is the same p that maximizes 1000*f(p) or (1/100)*f(p) or any K*f(p) for constant K.

Ok right I understand. I was confused.

So am I correct with the following next steps:


L(p)= ∏(ni+1)(1-p)∑nip2n

Then in the next take is to find l(p) which is l(p)=Ln(L(p))

So I end up with:

l(p)= Ln(∏(ni+1))+∑niLn(1-p)+2n*Ln(p)


Then taking the derivative of dl(p)/dp and setting that to zer
dl(p)/dp= 0+ [itex]\frac{∑n}{1-p}[/itex] +[itex]\frac{2n}{p}[/itex]
 
  • #11
ianrice said:
Ok right I understand. I was confused.

So am I correct with the following next steps:


L(p)= ∏(ni+1)(1-p)∑nip2n

Then in the next take is to find l(p) which is l(p)=Ln(L(p))

So I end up with:

l(p)= Ln(∏(ni+1))+∑niLn(1-p)+2n*Ln(p)


Then taking the derivative of dl(p)/dp and setting that to zer
dl(p)/dp= 0+ [itex]\frac{∑n}{1-p}[/itex] +[itex]\frac{2n}{p}[/itex]

Almost OK, but ##d \ln(1-p) /dp = -1/(1-p)##, not ##+1/(1-p)##. The thing you wrote could never = 0 for any p between 0 and 1.
 

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