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Jul 11, 2017 11:31 AM
(1417 views)

Hello,

Firstly I would like to thank the community for being here to help with newbe questions. Ever since moving to this institution I have loved JMP!

I am trying to do survival statistics for a study where I tracked cage mortality for 18 cages containing 50 flies each across 20 days. The cages each have variable number of dead per day. After reading and watching the "Advanced Mastering JMP: Analyzing Survival Data " I am still at a loss and wondering if this survival analysis is not suited for my type of stats. Maybe I would be better suited with a regression or something.

Any suggestions or tips on better sources for troubleshooting would be greatly apreciated.

Joe

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Jul 11, 2017 5:12 PM
(2644 views)

Solution

I think that there are at least three ways to analyze the mortality in JMP: Life Distribution, Survival, and Probit (using GLM in Fit Model). Let's start with the first two. You data is fine but it should be organized like this:

You have 20 cages running for 20 days. Enter the **number of new dead flies** in the Count column for a given day and cage. Enter the number of surviving flies at the end of the table, in the rows with Censor = 1:

Then use the two saved table scripts to start the analyses.

I attached this template data table for you so you have to do is enter the Count data.

Learn it once, use it forever!

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Jul 11, 2017 12:33 PM
(1410 views)

Survival analysis is suitable here (but it does not exclude other methods - it depends on your aims). So, in the data table you have the colums:

1. number of fly (1, 2, 3 etc)

2. died (1), alive (0, this is censor cod). (Note, in JMP by default censor code is 1, so you need to change it)

3. time to death (if the fly died) or 20 days (if the fly is alive)

4. cage code (if it matters to you)

P.S. If you turn curve ie. that the curve goes from bottom to top (the survival platform gives you this possibility), you will get a cumulative probability of death

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Jul 11, 2017 2:10 PM
(1392 views)

Right now I have the data recorded as mortality per day per cage. The cages are the replicates and I am not concerned with individual flies, but rather the overall survival of each cage population. The method you mention above would have me catalog the survival of 900 individual flies. This would take a tremendous amount of time! Not to mention I have data from a repeat study with the same amount of replicates.

My data is merely the tracking of loss over 20 days for each cage. I was hoping there would be an easier way to do this in JMP.

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Jul 11, 2017 2:15 PM
(1389 views)

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Jul 11, 2017 2:56 PM
(1382 views)

OK. 1. On the 20th day, open the cage and see how many flies are alive (a), and how many died (d). Such for each cell.

2. You will get a series of numbers

a1, d1 (for first cage)

a2, d2 (for second)

a3, d3 (for third)

etc.

3) Then do a pair comparison with contingency tables 2x2 ("Fit Y by X" platform)

Compare:

a1, d1

a2, d2

then сompare:

a1, d1

a3, d3

then сompare

a2, d2

a3, d3

etc.

4) As a result, you can select the cage pairs that are statistically significantly different (exact Fisher test). I don't think there will be many of them.

P.S. If the cage are independent, then the effect of multiple comparisons will not be.

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Jul 11, 2017 5:12 PM
(2645 views)

I think that there are at least three ways to analyze the mortality in JMP: Life Distribution, Survival, and Probit (using GLM in Fit Model). Let's start with the first two. You data is fine but it should be organized like this:

You have 20 cages running for 20 days. Enter the **number of new dead flies** in the Count column for a given day and cage. Enter the number of surviving flies at the end of the table, in the rows with Censor = 1:

Then use the two saved table scripts to start the analyses.

I attached this template data table for you so you have to do is enter the Count data.

Learn it once, use it forever!

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Jul 12, 2017 2:46 PM
(1277 views)

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Jul 12, 2017 11:30 AM
(1338 views)

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Jul 12, 2017 1:15 PM
(1324 views)

I wanted to introduce a so-called “Complementary Log-Log model” for binomial response data as you have (i.e., # of dead files per cage population). This model is often applied to discrete survival time data.

The model is available in Fit Model’s **Generalized Linear Model** personality by choosing **Distribution: Binomial** and **Link Function: Comp LogLog**.

Suppose you have recorded mortality (# of failures) for each cage at the end of Day 20. Let N be the cage population (i.e., trials). You will add both the mortality count and N columns as Y, and add Cage and any other covariates as model effects. The results allow you to test if the overall mortality rates differ significantly by cage and to predict the mortality.

If you want to estimate the baseline survival function then you would need to create a series of indicator variables that identify # of dead flies for each day and add them as model effects.

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Jul 12, 2017 1:42 PM
(1317 views)

Please, specify for JMP 10.

Analyze=>Fit model=>?

I don't see "Generalized Linear Model" here. Thanks!