Hi all,   I am preparing a report about the interactions of a bunch of users with an online reporting tool. I have broken the users down into 3 groups (G1, G2, and G3). I am comparing them over about 20 metrics. The data collection period covers from 2019 to 2022 but not every participant has data in all the data collection intervals in this period. It seems like each subject has reporting periods that are unique to them and were not the same as other subjects! I have used Tableau and a date scaffold to create the following plots on three of the metrics that I have to report on. The date scaffold is very similar to the idea presented here: https://tarsolutions.co.uk/blog/tableau-scaffolding-dates-calculating-deferred-revenue/ Some plots are clearly different between the 3 groups (like the green plots). Some are not really that different between the 3 groups (like the yellow line plots). I have attached a sample of my data containing a few users to this post. The original data file included Reporting_Period_Start_Date and Reporting_Period_End_Date and the "Value" was being compared which was "Numerator"/"Denominator". I have created the Date_scaf and corresponding Daily_Numerator and Daily_Denominator and AVGs based on the original data.  So my question is; I can see that the green line plots are much different between the 3 groups but is there a statistical test for this difference?  I tried using ANOVA:   proc ANOVA data=SAS_Stat_Q; class Group; model AVGs = Group; means Groups /hovtest welch; run; But this is incorrect. What the test interprets as means is that AVGs for each group are added together and then divided by the number of records in that group (a classic mean). What I need is a test that considers each date in the date_scaf for example 11/26/2020 which makes for the following table:   Date_scaf Reporting_Period_End_Date Reporting_Period_Start_Date AVGs Daily_Denominator Daily_Numerator Denominator Numerator Value personID Group 11/26/2020 11/28/2020 11/1/2020 10.54166 0.285714 3.011903 8 84.3333 10.54166 person009 G3 11/26/2020 11/28/2020 11/1/2020 12.73263 0.857143 10.91369 24 305.5832 12.73263 person004 G3 11/26/2020 11/28/2020 11/1/2020 20.76333 0.714286 14.83095 20 415.2666 20.76333 person007 G3 11/26/2020 11/28/2020 11/1/2020 1.356139 0.678571 0.920237 19 25.76664 1.356139 person011 G2 11/26/2020 11/28/2020 11/1/2020 2.133332 0.071429 0.152381 2 4.266664 2.133332 person021 G2 11/26/2020 11/28/2020 11/1/2020 11.18214 0.5 5.59107 14 156.5499 11.18214 person010 G1 11/26/2020 11/28/2020 11/1/2020 7.892497 0.714286 5.637498 20 157.8499 7.892497 person055 G1 11/26/2020 11/28/2020 11/1/2020 6.466663 0.321429 2.07857 9 58.19996 6.466663 person047 G1  so now for G1 the average that I want for this date is sum(Daily_Numerator for persons 010, 055, 047)/sum(Daily_Denominator for persons 010, 055, 047). This would be (13.30713/1.5357)=8.6652   Similar averages should be calculated for groups G2 and G3 and the results of all three to be compared.    Does anyone know what statistical test should be used here? ... View more

Hi all, I have a dataset of patients using a drug. Each person has used the drug for Total_days and the dosage of each day is shown in dose_day[i]. Most patients have interruptions (days with dose_day[i]=0) when they received the drug. A simplified data set looks like this: ID Total_days dose_day1 dose_day2 dose_day3 dose_day4 dose_day5 dose_day6 dose_day7 dose_day8 dose_day9 dose_day10 dose_day11 dose_day12 dose_day13 dose_day14 dose_day15 dose_day16 dose_day17 dose_day18 dose_day19 dose_day20 dose_day21 dose_day22 dose_day23 dose_day24 dose_day25 1 18 30 30 30 15 0 0 45 45 45 60 60 0 0 0 45 45 60 60 0 0 0 0 0 0 0 2 25 15 15 15 25 25 25 25 25 40 0 0 0 0 40 40 40 40 40 40 40 40 25 25 25 25 3 21 70 70 70 70 70 50 50 50 50 50 50 50 30 30 0 0 0 20 20 20 40 0 0 0 0 4 19 40 40 40 40 60 60 60 0 0 0 50 50 50 50 50 50 65 65 65 0 0 0 0 0 0 5 15 15 15 15 15 15 25 25 25 25 25 25 10 10 10 10 0 0 0 0 0 0 0 0 0 0 6 25 80 80 80 80 80 80 80 50 50 50 50 0 0 0 0 0 50 50 50 90 90 90 90 90 90 7 22 25 25 25 40 40 40 40 40 65 65 65 65 65 65 65 0 0 0 0 40 40 40 0 0 0   I want to know what their dose trend was. Basically, if I was doing this task manually I would make a plot for each person with x being 1 to i where i=Total_days and y being the amount of drug received on that day (Total_dose[i]) and then I would fit a trendline on that plot. My goal is to have a variable "slope" that is "Pos" if the slope of the trend line is positive "Neg" if the slope is negative and "Zero" in the unlikely case that the slope is actually zero.   Does anyone know how can this be done in SAS 9.4 TS?   Many thanks,             ... View more