Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.
Showing results for Search instead for Did you mean:Can somone guide me
Include the information used for sample size calculations (e.g., expected P/AI in each treatment, expected time of ovulation and SD for each treatment, power). Part of this information was provided in the response to the reviewers (related to P/AI) and should be summarized in the manuscript so readers have access to it. Some of the information on sample size calculations was provided in lines 228-233, which seems to be a post-hoc power analyses using the proportions observed in the study. This should be removed as it is clear that the experiment was sufficiently powered for group comparisons that were statistically significant and underpowered for comparisons that were not statisticaly significant. Sample size calculations for continuous and time-dependent variables are still pending. Adjustment for multiple comparisons should also be implemented considering that the experiment had three treatments and individual comparisons were made.
1 ACCEPTED SOLUTIONIt sounds to me that you need to perform a perspective power analysis (not a retrospective (post hoc) power analysis). PROC POWER and PROC GLMPOWER perform perspective power analysis. You need to start with an exemplary data set that represents the conjectured values for your data (for PROC GLMPOWER and CUSTOM analysis in PROC POWER), or use other appropriate statement in PROC POWER for your analysis with conjectured parameter values.
6 REPLIES 6 Calcite | Level 5Can you guide about the first three lines.
Reviewer comments: Include the information used for sample size calculations (e.g., expected P/AI in each treatment, expected time of ovulation and SD for each treatment, power). Part of this information was provided in the response to the reviewers (related to P/AI) and should be summarized in the manuscript so readers have access to it. Some of the information on sample size calculations was provided in lines 228-233, which seems to be a post-hoc power analyses using the proportions observed in the study. This should be removed as it is clear that the experiment was sufficiently powered for group comparisons that were statistically significant and underpowered for comparisons that were not statisticaly.
Already i had submiited the power and sample size calculation applied on my data set. But he is asking something else.
I have moved the second post into this thread. It is virtually identical and will allow all participants to know they are contributing to the same thread.
--------------------------
The hash OUTPUT method will overwrite a SAS data set, but not append. That can be costly. Consider voting for Add a HASH object method which would append a hash object to an existing SAS data set
Would enabling PROC SORT to simultaneously output multiple datasets be useful? Then vote for
Allow PROC SORT to output multiple datasets
It sounds to me that you need to perform a perspective power analysis (not a retrospective (post hoc) power analysis). PROC POWER and PROC GLMPOWER perform perspective power analysis. You need to start with an exemplary data set that represents the conjectured values for your data (for PROC GLMPOWER and CUSTOM analysis in PROC POWER), or use other appropriate statement in PROC POWER for your analysis with conjectured parameter values.
Calcite | Level 5It would be helpful to provide a little more context for this question, but my reading of this is that you have submitted a manuscript for publication and you are posting a portion of the reviewer comments. If that is correct, I think the reviewer is asking for information about how you arrived upon the number of participants that you enrolled in the study. They are assuming that, to come up with a target sample size, you conducted a power analysis *prior* to the study. The reviewer is asking for the parameters of your power calculation (i.e., the effect sizes assumed in each group, variance, etc.). If you didn't do a power calculation *prior* to the study, you won't be able to fix that, you will just need to state that as a limitation. It sounds like what you reported were calculations based on the study data *after* the study had been completed, and they are asking you to remove that information.
As to the comment about multiple comparisons, many of the SAS stat procedures have options to adjustment for multiple tests. A starting point might be the MULTTEST procedure.