Hi everyone,
I have prospective study; the aim is to apply multiple logistic regression.
The sample size for the current study is determined based on the previous study. A study designed to recruited 105 patients and analyzed 207 samples. But I'm thinking to say this justification for the sample size is not correct.
The Resean is: This new study includes secondary outcomes not covered by the previous research, Am I correct to say the justification based on previous research is not appropriate? If so, could you guide me to outline the justification for the sample size?
Thanks,
If you know what analysis you are going to perform on the data, then you should perform a power analysis to tell you what sample size is needed.
There are multiple tools available to conduct a power analysis, but you need to find one that fits your planned regression. You can start by looking at the G*Power program and its capabilities (https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower ), but there are others available.
You can do a power analysis to determine if your sample size is accurate. You can use SPSS 27 (or newer versions) to do this.
The answers here indicate that using a power analysis is the best option, and they are not wrong, but the power analysis that provides the best precision is a simulation-based power analysis, which fully estimates every gritty detail from the standard errors in each coefficient to the anticipated log odds of each predictor slope. An excellent tutorial for running one for a standard linear regression can be found here:
https://youtu.be/x2hhc5KYw-c?si=1-zhAeZdramUnFtC
To adopt something similar, you can utilize the example code in this CV question to design a logistic version:
https://stats.stackexchange.com/a/46525/345611
Hello The justification of the sample size depends on the type of sampling and the sample size.
It is not good to justify your sample based on previous studies. You can use scholarship as yardstick to determine your sample. Such Taro Y. Samplying technique.