Specifications:
• This project is designed around the clinical trial case study in Chapter 8 of our textbook.
All data sets with the same context (e.g. all the Visit data sets) use the same structure and variable order, though not all variables are in all data sets. Once you know how to read one in, you know how to read them all. Critical metadata for reading these files is as follows:
– Baseline Visits variables are: Subject, Screen Failure Reason (sf reason), Screen, Sex, Date of Visit (DOV), Notification Date (notif date), Systolic Blood Pressure, Diastolic Blood Pressure, BP Units, Pulse, Pulse Units, Position (Pos), Temperature, Temperature Units, Weight, Weight Units, Pain Score
– Other visit data sets also have a variable, Starting Treatment (start trt), that is the last variable in the file. (This variable may be missing for all records if the subjects have not been randomized to their starting treatment yet.)
– The Lab variables are: Subject, DOV, Notif Date, SF Reas, Screen, Sex, ALB, Alk Phos, ALT, AST, D Bili, GGTP, C Gluc, U Gluc, T Bili, Prot, Hemoglob, Hematocr, Preg.
– The labels for the variables common to both Visit and Lab data sets are:
* Screen Failure Reason
* Failure Notification Date
* Date of Visit
* Screening Flag, 0=Failure, 1=Pass
– The labels for the lab-specific variables are as follows:
* Chem-Albumin, g/dL
* Chem-Alk. Phos., IU/L
* Chem-Alt, IU/L
* Chem-AST, IU/L
* Chem-Dir. Bilirubin, mg/dL
* Chem-GGTP, IU/L
* Chem-Glucose, mg/dL
* Uri.-Glucose, 1=high
* Chem-Tot. Bilirubin, mg/dL
* Chem-Tot. Prot., g/dL
* Hemoglobin, g/dL
* EVF/PCV, %
* Pregnancy Flag, 1=Pregnant, 0=Not
– There are a few data issues in some of the visits data. The known issues are listed below – you do not have to fix these issues. They are only listed here so you do not worry about them!
* In some cases, blood pressure values were too wide (three digits) for the raw file and ** is printed instead. In some cases, temperatures that were already in C were run through a conversion function. This caused incorrect temperature values. item In some cases, weights that were already in KG were run through a conversion function. This caused incorrect weight values.
Again – you do not need to do anything to correct the data values here.
The Adverse Event data sets follow a hierarchical structure that is quite di↵erent from the Visits or Lab data sets. As with most hierarchical data sets, each line has an indicator to tell you what kind of data appears on that line. The indicators and variables are shown below.
– S indicates a Subject Number (SUBJECT).
– D indicates dates - Start Date (STDT) and End Date (ENDT)
– V indicates Adverse Event Verbatim Text - (AETEXT)
– C indicates codes - Preferred Term Code (PTCODE), System Organ Class Code (SOCCODE), Lower Level Term Code (LLTCODE), High Level Term Code (HLTCODE), High Level Group Term Code (HLGTCODE)
– T indicates terms - Lower Level Term (LLTERM), High Level Term (HLTERM), High Level Group Term (HLGTERM), Preferred Term (PREFTERM), Body System (BODYSYS)
– A indicates adverse event e↵ects - Related (AEREL), Severity (AESEV), Serious (AESER), Action (AEACTION), and Dose (AEDOSE)
A subject may have multiple adverse events (e.g. a headache and a laceration), but the subject number is only provided before the first adverse event. Any additional adverse events continue to be listed until a new subject number is given. For example, the first subject - number 25 - has a TIA that began on 11JAN2018 and ended on 12JAN2018 and they had an issue with carpal tunnel that began on 18JAN2018 and ended on 19JAN2018.
You are not being asked to complete the entire case study; instead, I’ve selected a somewhat representative subset of the output in Chapter 8 for you to produce. Many of the output objects depend on related data sets, and just as the case study does in the book, you are only responsible for producing output for Site 1. The output objects for you to produce are as follows.
– Section 8.2: 8.2.3, 8.2.4, 8.2.5, 8.2.6
– Section 8.3: 8.3.2, 8.3.3
– Section 8.4: 8.4.3, 8.4.4
– Section 8.5: 8.5.6, 8.5.8
– Section 8.6: 8.6.1, 8.6.2, 8.6.3
– Section 8.7: 8.7.1, 8.7.3
You should read the introduction in Chapter 8 to get a feeling for the data sets and pay attention to the information that accompanies the output objects as it is there to give you important information about the output objects. Even some of the tables, e.g. 8.4.1, and both 8.5.7A and 8.5.7B are specifically intended to provide information helpful for producing other tables.
If the text around an output object asks you to do further work (e.g., also produce the results for another site), you
do not need to do that. You only need to produce the output objects mentioned above.
Unless otherwise specified, your output should be identical to those in the book. Color choices do not need to match exactly.
While you should be able to look at the outputs in Chapter 8 and determine most of what needs to be done, I’m including the following list of details to either provide additional clarity on producing an output object or change the way an output object is created. I’ve also included any instructions that you cannot determine from Chapter 8. These items are in no particular order, but are numbered to make it easier for us to communicate with each other about the specifics.
– For this section, your budget is at most 17 DATA steps and 7 PROC steps.
1. Read in all Visit and Lab data for Site 1
2. The visit data should be combined into a single data set
– Ensure the position variable (Pos) uses the correct controlled terminology: RECUMBENT or SEATED. (We are only doing this for Site 1, but other sites used other terms like Reclined or Sitting. Make sure if you encountered values like those, that your code would correct the issue.)
– Derive a new variable VisitNum that contains the values 0, 1 , 2, 3, 4 depending on which visit it is. (Baseline is 0, then number sequentially.)
– Derive a new variable VisitMonth that contains the values 0, 3, 6, 9, 12 depending how long after the baseline visit the current visit is. (Baseline is 0, 3 month visit is 3, and so on.)
– Derive a new variable, VisitC, that contains a character description of the visit based on the value of VisitNum.
* 0 maps to Baseline Visit, 1 maps to 3 Month Visit, 2 maps to 6 Month Visit, and so on.
* Do not use conditional logic to create this variable.
3. The lab data sets should also be appropriately combined into a single data set.
– The same three variables must be derived.
– No data cleaning is needed
4. To produce Output 8.5.6 you’ll need to manipulate the combined visits data.
5. To produce Output 8.5.8, you’ll need to manipulate the combined lab data and use it in conjunction with the Lab Info data set from the textbook.
6. To produce Outputs 8.6.1, 8.6.2, and 8.7.3, you’ll need to manipulate the combined visits data again. You must use arrays to reshape the data for these outputs.
7. You will need at least one format for the data generation, but if you want to use more than one that is allowed.
8. While you do not need to complete the code for the outputs as part of this section, I strongly encourage you to review the outputs to help with your programming – especially for those output objects that cannot be created from the simply concatenated visit and lab data sets.
– For this section, your budget is at most 1 DATA steps and 17 PROC steps.
1. You’ll need three custom formats and you may want to use some SAS-provided formats as well. The list of SAS formats can be found here: here.
2. The reports you create should have no dates.
3. You need to create three resulting reports - Final Report Site 1.pdf, Final Tables Site 1.rtf, and Final Presentation Site 1.pptx. As always, please use your name when creating the files, e.g. Stark Final Report Site 1.pdf These files will not have the same set of output objects in each.
(a) PDF: All output objects are sent to the pdf. It uses the Meadow style.
(b) RTF: All tables are sent to the rtf, but no figures. It uses the Sapphire style.
(c) PowerPoint: Only 8.2.4, 8.2.5, 8.6.3, and 8.7.1. It uses the PowerPointDark style.
(d) Images should always be created as PNG files in addition to destinations listed above.
(e) Name the PNG files the same as the output name. For example, save Output 8.4.4 as Output 8_4_4.png to make it easy for you to find it.
(f) No output should go to any unintended destination.
4. All images use 300 dpi for resolution and are 6 inches wide.
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