Part 1: In reporting and interpreting the findings of logistic regression, researchers use the so-called "odds ratios" instead of the conventional regression coefficients (the "b"). Why? Use a...

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Part 1: In reporting and interpreting the findings of logistic regression, researchers use the so-called "odds ratios" instead of the conventional regression coefficients (the "b"). Why? Use a specific example from the literature to make a point.
Please use Chapter 23 of the book forreferenceand to use as the specific example. The link to the book is herehttps://drive.google.com/file/d/1WoRx7k8Q45L6jyPPC9xCSjoGfXzDAgI7/view?usp=sharing
Part 2:Examine the data in Table 23.5 (pp. 991 and 992), which report the outcomes for the Physicians' Health Study that assessed the effect of a low-dose aspirin regimen on risk of heart attack. The book is the same as above.

a. For the group of patients that had the low-dose aspirin regimen, calculate the odds of death.


b. For the group of patients that received placebo, calculate the odds of death.


c. Compute the odds ratio that tells us how much more likely a heart attack was for patients in the placebo group (compared with this aspirin group).


d. Compute the odds ratio that tells us how much less likely a heart attack was for patients in the aspirin group (compared wit hthe placebo group).


e. In simple language, how did the aspirin regimen change the odds of heart attack?


f. Now look at the data in a different way. What percentage of the aspirin regimen group had a heart attack? What percentage of the placebo group had a heart attack? Does looking at these two percentages give you a different impression about the impact of aspirin?



Answered 1 days AfterJul 12, 2021

Answer To: Part 1: In reporting and interpreting the findings of logistic regression, researchers use the...

Atreye answered on Jul 14 2021
147 Votes
Part 1:
Odds ratios are generally used to represent the strength of relationship between risk facto
rs and outcomes in any clinical experiment. Odds and odds ratios are connected to the probability of a binary outcome (an outcome which may either present or absent, such as (Heart attack). The odds are the ratio of the probability that an event occurs to the probability that the event does not occur.
For example, suppose that the probability of death is 0.282 in a group of nonowners. This can be expressed as the odds of dying: 0.282/(1 − 0.282) = 0.393. When the probability is small, odds are virtually identical to the probability. For example, for a probability of 0.05, the odds are 0.05/(1 − 0.05) = 0.052. This similarity does not exist when the value of a probability is large.
Therefore, when we have a binary outcome, the odds ratio is used to interpret to the coefficients of the model...
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