We have a series of experiments where we measure virus transmission to plants when exposed to virus-infected insects for different time periods, so all of the experiments have similar types of independent and dependent variables. In one experiment, there are 6 time periods (1 to 24 hours) and 25 plants were tested (individually) for each time period. The response for each plant is yes or no (Plants are scored as virus infected). For 2 of the time intervals, all of the plants were negative for virus infection (0/25 for each time interval).
I am using PROC GLIMMIX in SAS for the analyses. For all of the other experiments, using a binary distribution in the model statement gives reasonable results. For the experiment where two of the time intervals had 0 positive plants, if I use a binary distribution in the model statement, the standard errors for the two groups with 0 transmissions are huge, thus distorting the results.
If I use a negative binomial distribution (based on counts of virus positive plants) the results seem reasonable. Since the same number of plants were tested for each time interval, using this approach works, but it differs from the other experiments.
Is there a method to adjust/account for the zeros in treatment groups that would allow the binary distribution return reasonable results?