Contingency Table Interpretation

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SUMMARY

The discussion focuses on interpreting the relationship between treatment types and responses in a contingency table analysis. A chi-square test confirmed that treatment and response are not independent. The user is exploring methods to describe this relationship, considering the Cochran-Armitage Trend Test and analyzing conditional versus marginal probabilities. Specific probabilities were calculated for different treatments, highlighting significant differences in outcomes based on treatment type.

PREREQUISITES
  • Understanding of chi-square tests and their application in statistical analysis.
  • Familiarity with contingency tables and how to interpret them.
  • Knowledge of conditional and marginal probabilities.
  • Experience with Cochran-Armitage Trend Test for analyzing ordered categorical data.
NEXT STEPS
  • Research the application of the Cochran-Armitage Trend Test in contingency tables.
  • Learn how to calculate and interpret conditional probabilities in statistical contexts.
  • Explore advanced techniques for combining treatment categories in statistical analysis.
  • Study the implications of dependent versus independent variables in experimental design.
USEFUL FOR

Statisticians, data analysts, and researchers involved in clinical trials or treatment efficacy studies will benefit from this discussion, particularly those focused on interpreting contingency tables and statistical relationships.

Mogarrr
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Homework Statement


10.24 Is there any relationship between the type of treatment and the response? What form does the relationship take?

Here's that data (column variables are responses):
<br /> \newcommand\T{\Rule{0pt}{1em}{.3em}}<br /> \begin{array}{|c|c|c|c|c|}<br /> \hline Treatment &amp; +Smear &amp; +Smear,-Culture &amp; -Smear,-Culture &amp; Total\T \\\hline<br /> Peniclillin \T &amp; 40 &amp; 30 &amp; 130 &amp; 200 \\\hline<br /> Spectinomycin(low dose)\T &amp; 10 &amp; 20 &amp; 70 &amp; 100 \\\hline<br /> Spectinomycin(high dose)\T &amp; 15 &amp; 40 &amp; 45 &amp; 100 \\\hline<br /> Total\T &amp; 65 &amp; 90 &amp; 245 &amp; 400 \\\hline<br /> \end{array}<br />

Homework Equations

The Attempt at a Solution


I've already done a chi-square test and found what was hinted in the question, the treatment and response are not independent.

What's a good way to describe the relationship?

I've thought of combining 2 treatment so that I could do Cochran Armitage Trend Test, but given the treatments, I see no clear way of combining categories.

I'm also thinking of commenting on the conditional probabilities and how they differ from the marginal probabilities. (I think the probabilities in the cells can be thought of as conditional probabilities). For example: the probability of a negative smear, negative culture given the treatment was a high dose of spectinomycin is \frac {45}{100} compared to the probability of a negative smear, negative culture, which is \frac {245}{400}.
 
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Thanks for the post! Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post?
 

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