Statistics Help @ Talk Stats Forum - Biostatistics
http://www.talkstats.com/
Epidemiology and biostatistics, public health research. GLM, logistic regression, survival analysis, clinical trials.enSat, 25 Feb 2017 00:00:22 GMTvBulletin60http://www.talkstats.com/images/misc/rss.pngStatistics Help @ Talk Stats Forum - Biostatistics
http://www.talkstats.com/
Correct application of multiple testing methods in projects with several experiments
http://www.talkstats.com/showthread.php/68772-Correct-application-of-multiple-testing-methods-in-projects-with-several-experiments?goto=newpost
Thu, 23 Feb 2017 09:32:30 GMTHi!
I have a problem with evaluating how I should correct my alpha and conduct statistical texts when I have several different exeriments in the...Hi!

I have a problem with evaluating how I should correct my alpha and conduct statistical texts when I have several different exeriments in the same project, each with more than two groups to compare.

- First off, I have a series of measurements that have a control group and three seperate experimental groups that are independent.

- I have a completely different experiment, with a series of measurements on the same four groups.

The results of these two experiments are independent of each other.

So right now, I set up a Holm-Bonferroni process with alpha corrected for all the p-values that were calculated for all the experiments. But I'm not sure if this is actually correct, or if I can get away with doing a seperate correction for either experiment (less stringent corrections).

Also, right now one-way ANOVAS with Dunett's test were applied to every measurement. However, this also corrects the p-values within that measurement, so in essence I am now correcting my p-values twice, once within the seperate anovas, and once more when I correct my alpha with the H-B process. So if I do the H-B is it than okay to do simple T-tests for every measurement instead of the anovas so the results aren't corrected twice?

Thank you!
]]>BiostatisticsSzkeptikhttp://www.talkstats.com/showthread.php/68772-Correct-application-of-multiple-testing-methods-in-projects-with-several-experiments<![CDATA[Moran's I Spatial Autocorrelation. Interpreting results.]]>
http://www.talkstats.com/showthread.php/68766-Moran-s-I-Spatial-Autocorrelation.-Interpreting-results.?goto=newpost
Wed, 22 Feb 2017 19:16:47 GMTI have carried out a Moran's I test on the residuals of a GLM model in the "ape" package in R which has returned the output below:

On this basis is it correct to state that there is negative spatial autocorrelation significant at the 95% confidence?

Am I correct in that an observed value close to -1 would indicate perfect negative autocorrelation and as such the negative autocorrelation seen here is relatively low?
]]>Biostatisticsavocadorosehttp://www.talkstats.com/showthread.php/68766-Moran-s-I-Spatial-Autocorrelation.-Interpreting-results.Statistical method to compare two experiments
http://www.talkstats.com/showthread.php/68758-Statistical-method-to-compare-two-experiments?goto=newpost
Wed, 22 Feb 2017 09:23:29 GMTI have two data from biological experiment where two types of cells were measured. For each cell the data about composition of the membrane where...I have two data from biological experiment where two types of cells were measured. For each cell the data about composition of the membrane where extracted under different condition of the experiment (repeated 3 times), composition is in percentage, so sum of each column is 100%.
I would like to ask for an advice of any statistical method that could be used to compare this two data sets to check the data variability and make a conclusion that for example under the experiment condition with pH x, wild type cell have more comp1 compared to mutant cells.
The only idea I had was to calculate VMR but maybe somebody will have a better idea?