Tag Archives: reporting

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OpenClinica: Printing subject casebooks, blank casebooks and blank CRFs

Wanna print subject casebooks using OpenClinica? This article is an extract from a video demo from the OpenClinica blog website. Click the link below now.

Source: http://blog.openclinica.com/2014/10/06/video-demos-printing-subject-casebooks-blank-casebooks-and-blank-crfs/

Happy Printing!!!

 

Fair Use Notice: This video contains some copyrighted material whose use has not been authorized by the copyright owners. We believe that this not-for-profit, educational, and/or criticism or commentary use on the Web constitutes a fair use of the copyrighted material (as provided for in section 107 of the US Copyright Law. If you wish to use this copyrighted material for purposes that go beyond fair use, you must obtain permission from the copyright owner. Fair Use notwithstanding we will immediately comply with any copyright owner who wants their material removed or modified, wants us to link to their website or wants us to add their photo.

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Safety Reporting in Clinical Trials – (Sample pages)

FAIR USE-
“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

Source: ClinfoSource

How to Use SAS – Lesson 6 – SAS Arithmetic and Variable Creation

This video series is intended to help you learn how to program using SAS for your statistical needs. Lesson 6 introduces the concept of SAS arithmetic in the DATA STEP. I discuss how one can add, subtract, divide, multiply, or create their own formulas for variables in the data. I also discuss using SAS arithmetic to create new variables based on mathematical transformations of old variables, which may sometimes aid in meeting the assumptions of statistical tests. Finally, I provide basic examples of each of these methods.

Helpful Notes:

1. SAS uses many of the same arithmetic operators to add, subtract, divide and multiply as other programming languages and basic algebra.

2. Arithmetic operations on variables affect the entire list of observations. So be careful in operating with existing variables and make new variables if you can afford to.

3. The varnum ;option on the PROC CONTENTS statement can allow you to see the variables listed in the order they were created.

Today’s Code:

data main;
input x y;
cards;
1 2
3 4
5 6
7 8
;
run;

proc print data=main;
run;

data new_main; set main;
a = x + y;
b = x – y;
c = x * y;
d = x / y;
e = x ** y;
f = ((x + y) * (x – y));
run;

proc contents data=new_main varnum;
run;

proc print data=new_main;
run;

-FAIR ;USE-
“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

How to Use SAS – Lesson 5 – Data Reduction and Data Cleaning

This video series is intended to help you learn how to program using SAS for your statistical needs. Lesson 5 introduces the concept of data reduction (also known as subsetting ;data sets). I discuss how one can subset a data set (i.e. reduce a data set’s number of observations) based on some criteria using the IF statement in the DATA STEP, or using the WHERE statement in a PROC STEP. I also discuss using the KEEP, DROP, and RENAME statements for reducing data to only a handful of the original variables (i.e. reduce a data set’s number of variables). Furthermore, I show how one can label variables so that descriptive information can be presented in output and value formats so that specific values are easy to understand. Finally, I provide basic examples of each of these for three hypothetical data sets.

Helpful Notes:

1. There are two places you can reduce the data you analyze; in the DATA STEP, and in the PROC STEP.

2. To subset data in the DATA STEP, use the IF statement.

3. To subset data in the PROC STEP, use the WHERE statement.

4. Another way to reduce data is to eliminate variables using a KEEP or DROP statement. This method is useful if you are creating a second data set or analytic version of your main dataset.

5. The RENAME statement simply changes a variables name.

Today’s Code:

data main;
input x y z;
cards;
1 2 3
7 8 9
;
run;

proc contents data=main; run;
proc print data=main; run;

/* 1. Reduce data in the DATA STEP using a simple IF statement */
data reduced_main; set main;
if x = 1;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 2. Reduce data in the PROC STEP using a simple WHERE statement */
proc print data=main;
where x = 1;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 3. Reduce data in the DATA STEP by KEEPing only the variables you do want */
data reduced_main; set main;
KEEP x y;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 4. Reduce data in the DATA STEP by DROPing the variables you don’t want */
data reduced_main; set main;
DROP y;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 5. Clean up variables using the RENAME statement within a DATA STEP */
data clean_main; set main;
rename x = ID y = month z = day;
run;

proc contents data=main; run;
proc contents data=clean_main; run;

/* 6. Clean up variables using a LABEL statement within a DATA STEP */
data clean_main; set clean_main;
label ID = “Identification Number” month = “Month of the Year” day = “Day of the Year”;
run;

proc contents data=main; run;
proc contents data=clean_main; run;

/* 7. FORMAT value labels using the PROC FORMAT and FORMAT statements */
PROC FORMAT;
value months 1=”January” 2=”February” 3=”March” 4=”April” 5=”May” 6=”June” 7=”July” 8=”August” 9=”September” 10=”October” 11=”November” 12=”December”;
run;

data clean_main; set clean_main;
format month months.;
run;

proc ;freq data=clean_main;
table month;
run;

-FAIR ;USE-
“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

How to Use SAS – Lesson 4 – Merging Data Sets

This video series is intended to help you learn how to program using SAS for your statistical needs. Lesson 4 introduces the concept of merging SAS data sets using a variety of methods. I discuss how one can merge two or more data sets in the DATA STEP using the SET statement. I also describe how one can use the MERGE statement to bring two or more datasets together that may have a common index variable. Furthermore, I describe the SORT procedure (PROC ;SORT) that must be used with the MERGE statement. Finally, I provide basic methods of merging data sets using PROC SQL.

Helpful Notes:
1. Use one SET statement when you have the same variables, but different observations.

2. Use two SET statements when you have different variables, but the same observations.

3. Use the MERGE statement when you have a common index variable, and any new variables or observations.

4. The MERGE statement first requires that you use the SORT procedure (PROC SORT) to sort on the index variable before merging.

5. Make sure that you add the BY statement after the MERGE statement in your DATA step or you will have a new dataset that is merged incorrectly.

6. PROC SQL is an advanced method of merging data that can be very powerful for large datasets. It uses different kinds of “JOINS” that I will provide more information on in a later video.

Today’s Code:
data main;
input x y z;
cards;
1 2 3
7 8 9
;
run;

/* 1. Use one SET statement when you have the same variables, but different observations */
data more_people;
input x y z;
cards;
4 5 6
3 6 9
;
run;

data final;
set main more_people;
run;

proc print data=final; run;

/* 2. Use two SET statements when you have different variables, but the same observations */
data more_vars;
input a b c;
cards;
20 40 60
10 20 30
;
run;
data new_final;
set main;
set more_vars;
run;

proc print data=new_final; run;

/* 3. Use the MERGE statement when you have a common index variable, and any new variables or observations */
data more_vars_and_people;
input x a b c;
cards;
1 20 40 60
7 10 20 30
2 11 12 13
3 14 15 16
;
run;

* The MERGE statement requires that you use an index variable to merge on (e.g. an ID variable).;
* Thus, you must SORT your data BY that index variable.;
proc sort data=main;
by x;
proc sort data=more_vars_and_people;
by x;
run;
data merged_final;
merge main more_vars_and_people;
by x;
run;

proc print data=merged_final; run;

/* 4. SQL is an advanced programming language for databases. Here, I provide a basic example to merge the two datasets using a LEFT JOIN. I will include more information about JOIN types in a follow up video. For now, think of a LEFT JOIN as one that only includes the data from the second dataset (more_vars_and_people) that corresponds to data from the original dataset (main).
*/
proc ;sql;
create table sql_final as
select L.*, R.*
from main as L
LEFT JOIN more_vars_and_people as R
on L.x = R.x;
quit;

proc print data=sql_final; run;

-FAIR ;USE-
“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

How to Use SAS – Lesson 2 – Creating Datasets on the Fly

This video series is intended to help you learn how to program using SAS for your statistical needs. Lesson 2 introduces some basic data step programming to define variables and specify their values for data sets containing one or more observations.

I also introduce two procedures: the PRINT procedure (PROC ;PRINT) to display the data contents in the OUTPUT window, and the CONTENTS procedure (PROC CONTENTS) to summarize the data set. Finally, I introduce the concept of libraries to show another method of inspecting the data set by physically opening it from the temporary WORK library.

Helpful Notes:

1. PROC PRINT – displays the entire data set by observation in the OUTPUT window
2. PROC CONTENTS – summarizes the properties of a data set, including an alphabetic listing of the variables and a count of the number of observations.
3. The assignment operator (“=”) directly specifies the value of a variable in the data step.
4. The INPUT statement defines one or more variables of our data set.
5. The CARDS statement specifies the values for each of the INPUT variables (in order).
6. It is a good rule of thumb to always pair the INPUT and CARDS statements together.
7. DON’T FORGET SEMI;COLONS! They end statements and without them, you will most certainly have errors arise.
8. If you have any errors, always, ALWAYS, ALWAYS check the LOG first!
9. Creating datasets “on-the-fly” just means you’re making a new dataset without bringing in the data from any other source.

Today’s Code:

data main;
input x y z;
cards;
1 2 3
7 8 9
;
run;

proc print data=main;
run;

proc contents data=main;
run;

-FAIR ;USE-
“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

How to Use SAS – Lesson 1 – The SAS Interface

This video series is intended to help you learn how to program using SAS for your statistical needs. Lesson 1 introduces the SAS window, it’s various environments ;and what each environment does. I also provide some basic code to create a data set, make a new variable & assign it a value, and then run the print procedure (PROC PRINT) to see what the values are of each variable in the specified data set.

Helpful Notes:

Here are the five primary “environments” that SAS uses:

1. RESULTS: where output is shown in tree structure
2. EXPLORER: the interfacing environment between SAS and your computer
3. OUTPUT: the output of your code
4. LOG: the “middle-man” between you and the SAS system
5. EDITOR: where you type your code

Today’s Code:

data main;
x = 1;
y = 2;
run;

proc print data=main;
run;


-FAIR USE-
“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

SAS Programming Tip: Formatted Input

This material is related to the type of content covered in the following courses:

SAS Programming Introduction: Basic Concepts
https://support.sas.com/edu/schedules.html?ctry=us&id=106

SAS Programming 1: Essentials
https://support.sas.com/edu/schedules.html?ctry=us&id=277

SAS Programming 2: Data Manipulation Techniques course
https://support.sas.com/edu/schedules.html?ctry=us&id=278

For more information about SAS programming, visit: https://support.sas.com/edu/courses.html?ctry=us

-FAIR USE-
“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

SAS Enterprise Guide Tip: Exporting Results and Preserving Historical Versions

SAS Instructor David Ghan shows you how to keep historical versions of your exported results using SAS Enterprise Guide.

This material is covered in the SAS Training course “SAS Enterprise Guide 1: Querying and Reporting”. To learn more about this course, visit https://support.sas.com/edu/schedules.html?ctry=us&id=492

-FAIR USE-
“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”