I have a dataset containing 130patients who underwent surgery. I was determining the effect of intraop blood transfusion on post-op respiratory complications and NIV use.
ROC for transfusion Vs none has AUC of 0.67.
I would like to know what should be the sample size to improve this ROC and how to figure it out on SPSS.
Dear colleague,
Did you follow all correct Steps to validate your model? https://rapidminer.com/resource/correct-model-validation/
Which model algo did you use? How many features were implemented? Perhaps take a look at Andrew Ng lessons in YouTube regarding bias and variance and how are these affect by number of samples and features resulting in over and underfitting.
Cheers
Sven
You can calculate the sample size from prevalence or probability. If you don't know the probability , take it as 50% , the sample size is around 397.. as per the formula...
Dear Kammar,
As per my little understanding, the term ROC stands for Receiver Operating Characteristic. ROC curves were first employed in the study of discriminator systems for the detection of radio signals in the presence of noise in the 1940s, following the attack on Pearl Harbor.
ROC curves are frequently used to show in a graphical way the connection/trade-off between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests. In addition, the area under the ROC curve gives an idea about the benefit of using the test(s) in question.
ROC curves are used in clinical biochemistry to choose the most appropriate cut-off for a test. The best cut-off has the highest true positive rate together with the lowest false positive rate.
As the area under an ROC curve is a measure of the usefulness of a test in general, where a greater area means a more useful test, the areas under ROC curves are used to compare the usefulness of tests.
Now ROC curves are frequently used to show the connection between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests. In addition, the area under the ROC curve gives an idea about the benefit of using the test(s) in question.
Hope the followings help you to chalk out your problems to determine or calculate the sample size in a very brilliant way.
1. https://www.who.int/ncds/surveillance/steps/resources/sampling/en/
2. Article Sample size calculation
3. https://apps.who.int/iris/handle/10665/40062
Please find the manual "Sample size determination" as an attached file.
Happy researching..
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