Welcome to clinical data management program using SAS. In this video we will be discussing about the concept of creating tabulated reports and for this we are going to use the procedure called proc tabulate. So we'll be doing proc tabulate data equals CDM dot disease class disease. Here we are mentioning the categorical variable and table disease. Here we are mentioning the tabulated variable and then run. Let's run this code.

So see we got the disease was tabulation in our report, and proc tabulate with this filter by default use the count values. So there are three little n patients suffering from Alzheimer's disease at least Suffering from HIV or AIDS quantities a patient suffering from breast cancer hundred 19 patients suffering from diabetes 66 patients suffering from endometriosis hundred patients suffering from gastritis and so on. I know we are remember what was the disease data is all about that this is the same dataset which we were using before also consisting of ID gender, date of birth zip code employment status education marital status children ancestry average commute daily duties available vehicles, military service and disease is the diseases that they're suffering from now let's let's do the further modifications are proctored. Suppose we want the count group again how do we do it? Or how do we go with it?

Specifically, we have to use proc tabulate data equals CDM dot disease, class, disease, even disease On and then Dra. So see we got the tabulated record with respect to the disease account versus respect to the disease and we got all gender, it's a total so we got the total number of patients that is 2000. So that is equal to the total number of observations that we have. Now let's do the further modification that is we want the tablet reports with respect to disease and gender, diseases gender or gender is disease. How do we do that? Again using the same procedure called proc tabulate, data equals CDM.

Doc disease, class, disease gender These are categorical variables again, we'll have to mention the tabulated variables such as disease, karma, gender, and then run before I run this code, let me explain your few concepts. When we give comma in the table state Vickery TABLE statement operators control the format of the table. Now, when we give comma on the table statement, it goes to the new table dimension, if we give blank in the table statement, it concatenates table information and it will give asterisk that is stored in the table statement, then it crosses that is it crossness subgroup information. Now let's run this code. So here we got disease by gender report that is, there are 162 female patients suffering from Alzheimer's disease. 177 million patients suffering from Alzheimer's disease severe 36 female patients suffering from HIV and 44 male patients suffering from HIV then again see 55 fewer patients are suffering from diabetes 64 million patients suffering from diabetes.

For breast cancer there are newer female patients or normal patients and in this way it goes on let's move to the further modification of proc tabulate suppose we want to we want the disease and gender wise report or disease and generalized tabulated report but we want to filter the data that is we want only for the married patients not for the single we don't want for all patients we want to filter the data we want only for the medications. So how do we do it? We'll be using the same procedure called proc tabulate data equals CDM dot disease. We'll be using where statement for filtering data where marital status this movie Bernie Madoff marital status equals to merit that is I want or neither we hope to reform with respect to the money patience, pose or merit. Next thing we'd be using the categorical variables as class disease and gender turns evil, disease or my gender then huh?

So let's run this code. See, so making a previous report we got the disease vijender tabulated report with respect to all patients. If we compare both the reports here, the data Already filtered. We only are getting the report with respect to the patients who are married. That is as I was just wondering if a female patients wanted on 39 nations 23 female patients are suffering from HIV 31 male patients are suffering from HIV severely hundred 15 female patients are suffering from breast cancer many patients that that our patients are suffering from breast cancer. Similarly for hypertension hundred 70 million patients and there are 130 million patients out there and it goes on in this way.

Now let's do the further modification of our tabulated reports that is v one the row total as well as the current total that is the count of all the rows as well as the column How do we do that? We will be working we will be doing these to the filter to kotomi that is we are again going to generate the tabulated report with respect to those patients who are married. If you want you can remove the filter here we are keeping the filter that is cropped tablet data equals CDM Dark disease this man I just teaches equals madad. loss, disease, coma plus disease and gender the book he's on Chroma gender on vendre let's run this code See we got on that is we got the row total, we have given all with respect to diseases we go the role and we gave all this respect to gender that's where we got the column total also.

So, there are total 264 patients which is suffering from Alzheimer's disease there is already a plus happening. Similarly, if you see how hypertension there are total 230 patients suffering from hypertension that is hundred 1700 30 total number of female patients are 718 total number of new patients are 770. So that's what we have going on with respect to gender. So we got the row total and the column total and we also got the report with respect to the filter data that is only the patients who are married the report is displayed only with respect to the tabulated report is displayed only with respect to that patients. So in this video, we are going to do it here in my coming video we'll be discussing about the further modifications of creating tabulated reports. Perform now let me end this video over here Thank you Goodbye See word for the next reading