first create an id variable in each data set and merge on that data data1_; retain id 0; set data1; id=id+1; run; and then merge all 3 datasets "by id". Youll need to rename variables as @Astounding says, and then the code is easy to replace the missings
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still not clear to me what youre asking. Consider my original answer ie use a macro variable. You didn't indicate why that wouldn't work - if you did that would help me understand your question. Don't worry about explaining the bootstrap etc, i get that, i just don't know what you want (if it's not what i already assumed) edit: re "It's not that I want to avoid hard scoring" [i assume you meant hard coding], clearly you don't want to hard code, otherwise you would do it in 2 seconds, and you said yourself: "I could hard code it, but ...". So that seems to me to be the issue and that's easily solved with a macro variable
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id3 is 10 chars long and substr 3rd argument is 11. Although length is specified as 11, thus check if it ever exceeds 10 chars, if not change to substr(id3, 3, 10) i have a supplementary Q: does anyone know if you can specify a 3rd argument as missing which would indicate: from the 2nd argument to the end of the string?
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i dont understand. Is the issue that you just want to avoid hard coding? if so, why not take the median (across bootstraps) from eg univariate and make it a macro variable and then use this as a coefficient in the proc logistic code score statement?
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data test;
monthtext="January";
month=month(input("01"||substr(monthtext,1,3)||"2000",date9.));
run; from stackoverflow: https://stackoverflow.com/questions/9300020/sas-format-full-month-name-to-integer
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proc sort data=....; by herd cow_id; run; data .... retain count; set ....; by herd cow_id; if first.herd then count=1; if first.cow_id then count=count+1; run; does that work?
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this is a common problem when running data simulations and eg bootstrapping from a small sample, some of the samples don't converge. There is an ods 'convergencestatus': https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_genmod_sect050.htm i think this is how i normally do it, ie in the macro catch this variable and later discard estimates from samples that didn't converge. I've seen others do likewise
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as @Reeza says, find out where the data are missing. Maybe you can agree a method for handling the missing data with your clinical colleagues or maybe the data can be retrieved: the patient ought to know when they were diagnosed with diabetes, but perhaps they know the year but not the month and day, check these patients aren't falling out of the analysis, maybe you want to impute this as 15june etc, duration will necessarily be affected by rounding etc
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a final note regarding the significance test: i assume the null hypothesis is "Ho: everything is fine" ie "the proportional odds assumption is reasonable", thus the test is anti-conservative - all you need is a lot of missing data and you'll show every time that the proportional odds assumption cannot be rejected (using a significance test). Better to change your colleagues' default thinking on the matter
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it's more a matter of whether your audience will believe it and biological reasons or a priori reasons are more cogent than p-values, especially when simultaneously confessing missing data. A visual assessment is much more persuasive i think: http://support.sas.com/kb/37/944.html. Otherwise i think the stokes book (https://www.amazon.com/Categorical-Data-Analysis-Using-System/dp/0471224243) describes how to restructure your dataset to fit a partial proportional odds model
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it's not a good way to determine whether you believe proportional odds is a reasonable assumption in this case. That's quite a separate matter from the analysis and estimation
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