topic Re: Spore counts analysis in Discussions
https://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18229#M16627
<HTML><HEAD></HEAD><BODY><P>Thanks for the comments and suggestions.</P><P>Some of my terminology was not the best. The use of the work survival and</P><P>infected is miss leading. A lab populates the medium with viable spores.</P><P>The focus is viable spores on the medium and specifically the probability</P><P>of viable spores on the medium.</P><P>Let me see clarify what we are doing. This is a sanitation study. The</P><P>two mediums are steel washers and a square piece of wool. So no spores</P><P>would live on the medium if they sprouted. The treatments are to sanitize.</P><P>The spore counts are viable spores. We want to know how effective the</P><P>treatments are at cleaning up the spores. The initial counts on the wool</P><P>averaged 2.2 million, the steel washer averaged 8500. Knowing the</P><P>probability of a viable spore on the medium after treatment is valuable.</P><P>The counts are integers. They are not continuous. I have used GLM with the</P><P>Pioson distribution as Mark suggested. I find significance but not as much</P><P>as with the binomial. The probability of a viable spore on the medium is of</P><P>interest and the treatments that significantly affect it. Is the binomial</P><P>telling me this? A low probability of a viable spore tell us there is a low</P><P>likelihood of spreading viable spores.</P><P>If the binomial is not the correct approach could the GLM Pioson treatment</P><P>estimates be divided by the initial spore estimates to produce the</P><P>probability of a viable spore on the medium. Would this be a better way to</P><P>estimate this probability?</P><P>Or do you have another approach to suggest. Thanks.</P></BODY></HTML>Fri, 29 Apr 2016 02:34:49 GMTemt_trout2016-04-29T02:34:49ZSpore counts analysis
https://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18226#M16624
<HTML><HEAD></HEAD><BODY><P><SPAN style="color: black; font-size: 12pt; font-family: 'Calibri','sans-serif';">I have been working with a scientist who is doing some experiments to sanitize various mediums form plant disease spores. The medium are infested with disease spores then the medium receive mitigating treatments. Then the surviving spores are counted. An initial count of spores is not available for each individual medium. A sample of untreated medium are tested to get the initial spore counts. A mean initial spore count is calculated for each medium type. The data is analyzed in JMP using GLM with a binomial distribution with the after treatment counts of surviving spores on individual medium, the mean initial spore counts are second variable (denominator) in the binomial analysis. With this analysis the probability of surviving spores is reported based on using the mean spore count from a sample of the medium. Are there any issues with this approach? I realize it would be preferable to have an initial count for each medium. Is using the mean initial spore medium counts an issue in this analysis? We plan to report that we are using the mean initial spore counts from a separate sample of the mediums.</SPAN></P><P></P></BODY></HTML>Thu, 28 Apr 2016 03:50:48 GMThttps://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18226#M16624emt_trout2016-04-28T03:50:48ZRe: Spore counts analysis
https://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18227#M16625
<HTML><HEAD></HEAD><BODY><P>If the method to infest each of the individual media (samples) is consistent, then you might assume that the initial spore count is the same. You could then use a GLM with Poisson distribution and log link function without an offset. Enable the option for over-dispersion for a better fit. (Check to see if it is significant.)</P><P></P><P>The binomial distribution that you chose assumes a dichotomous response but your ratio is not likely to be so.as I understand it. It is continuous, no?</P></BODY></HTML>Thu, 28 Apr 2016 14:16:27 GMThttps://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18227#M16625markbailey2016-04-28T14:16:27ZRe: Spore counts analysis
https://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18228#M16626
<HTML><HEAD></HEAD><BODY><P>I am not sure what you are trying to analyze as your dependent variable given your description above. It sounds like you are trying for a binary "contaminated/not contaminated" response, but then you are using a simulated initial spore count and an actual post-treatment spore count as your only input variables? I am a little confused as to your goal here because if there are <EM>any</EM> spores surviving post-treatment, then your medium <EM>is</EM> contaminated. Your probability is automatically 100%. Now, if some variables related to the treatment (e.g., time of treatment or temperature or...) were included in the model such that you were trying to determine the effectiveness of your treatment, and the final number of spores post-treatment were your response, then I could see the point of this exercise. And to Mark's point, if your response is the post-treatment spore counts, then you'd want the Poisson distribution and log link function for the GLM. Or am I missing something here?</P></BODY></HTML>Thu, 28 Apr 2016 14:43:51 GMThttps://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18228#M16626sfigard2016-04-28T14:43:51ZRe: Spore counts analysis
https://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18229#M16627
<HTML><HEAD></HEAD><BODY><P>Thanks for the comments and suggestions.</P><P>Some of my terminology was not the best. The use of the work survival and</P><P>infected is miss leading. A lab populates the medium with viable spores.</P><P>The focus is viable spores on the medium and specifically the probability</P><P>of viable spores on the medium.</P><P>Let me see clarify what we are doing. This is a sanitation study. The</P><P>two mediums are steel washers and a square piece of wool. So no spores</P><P>would live on the medium if they sprouted. The treatments are to sanitize.</P><P>The spore counts are viable spores. We want to know how effective the</P><P>treatments are at cleaning up the spores. The initial counts on the wool</P><P>averaged 2.2 million, the steel washer averaged 8500. Knowing the</P><P>probability of a viable spore on the medium after treatment is valuable.</P><P>The counts are integers. They are not continuous. I have used GLM with the</P><P>Pioson distribution as Mark suggested. I find significance but not as much</P><P>as with the binomial. The probability of a viable spore on the medium is of</P><P>interest and the treatments that significantly affect it. Is the binomial</P><P>telling me this? A low probability of a viable spore tell us there is a low</P><P>likelihood of spreading viable spores.</P><P>If the binomial is not the correct approach could the GLM Pioson treatment</P><P>estimates be divided by the initial spore estimates to produce the</P><P>probability of a viable spore on the medium. Would this be a better way to</P><P>estimate this probability?</P><P>Or do you have another approach to suggest. Thanks.</P></BODY></HTML>Fri, 29 Apr 2016 02:34:49 GMThttps://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18229#M16627emt_trout2016-04-29T02:34:49ZRe: Spore counts analysis
https://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18230#M16628
<HTML><HEAD></HEAD><BODY><P>When you see Pioson think Poisson, My bad.</P></BODY></HTML>Fri, 29 Apr 2016 02:41:25 GMThttps://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18230#M16628emt_trout2016-04-29T02:41:25ZRe: Spore counts analysis
https://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18231#M16629
<HTML><HEAD></HEAD><BODY><P>I'm not exactly sure what you're trying to get out of the experiment or how the experiment is set up, so a few more details might be useful.</P><P>I suspect that you are comparing the effect of different treatment durations with different disinfectants (chemical, or physical (heat,steam)).</P><P>Often one is interested in the log decrease in spore or bacteria counts. For example, I spike my apple sauce with 10^8 cfu/ml of my favorite organism, and then run jars through several sterilization cycles (temp and time). Then I open the jars and do counts on a couple samples from each jar and use the data to find a minimum time temperature combination that gives me a 3 log (or 6) log decrease in counts. <SPAN style="font-size: 10pt; line-height: 1.5em;">The same kind of experiment might be run on a surface, dose the surface, recover with and without treatment, and compare the results. </SPAN></P><P><SPAN style="font-size: 10pt; line-height: 1.5em;">I</SPAN><SPAN style="font-size: 10pt; line-height: 1.5em;">n either case the response is not binomial (0 or 1) <STRONG><EM>unless</EM></STRONG> I'm <SPAN style="text-decoration: underline;">only</SPAN> looking at whether I got greater than or less than a 6 log decrease or maybe complete sterilization vs. not complete. In most cases scientists (and regulators) are more interested the estimate of the log reduction and the confidence intervals around the estimate (and whether you can operate in a condition where the CI is below the required threshold.)</SPAN></P><P><SPAN style="font-size: 10pt; line-height: 1.5em;"><BR /></SPAN></P><P><SPAN style="font-size: 10pt; line-height: 1.5em;"><BR /></SPAN></P></BODY></HTML>Thu, 05 May 2016 15:41:19 GMThttps://community.jmp.com/t5/Discussions/Spore-counts-analysis/m-p/18231#M16629Byron_JMP2016-05-05T15:41:19Z