In order to quantify the association between the exposure and the outcome of interest the incidence of disease in a group of individuals exposed to the supposed risk factor must be compared with the incidence in a group of persons not exposed. This comparison can be summarized by calculating either the ratio of the measures of disease occurrence for the two groups, which indicates the likelihood of developing the disease in the exposed individuals relative to those unexposed.In case-control studies, it is not possible to directly estimate disease incidence in those exposed and those unexposed, since people are selected on the basis of having or not having the condition of interest, not on the basis of their exposure status. It is however, possible to calculate the odds of exposure in the cases and in the controls.The odds ratio (OR) estimates the magnitude of association between the exposure and outcome. It is defined as the ratio of odds of an event occurring in one group to the odds of it occurring in another group, or to a sample-based estimate of that ratio. These groups might be an experimental group and control group.The 2x2 contingency table showing the relationship between the exposure and the disease is presented as follows:
Probability of being exposed among cases=a/(a+c)Probability of being non-exposed among cases=c/(a+c)Odds of being exposed among cases=a/cProbability of being exposed among controls=b/(b+d)Probability of being non-exposed among controls=d/(b+d)Odds of being exposed among controls=b/dOdds ratio = (odds of being exposed among cases)/ (odds of being exposed among controls)= (a/c)/ (b/d)= (a*d)/ (b*c)
In strict terms, the odds ratio obtained tells us how many more (or less, if the exposure is associated with a reduced risk) times likely the cases are to have been exposed to the factor under study compared with the controls.
If odds ratio is equal to 1 then this implies that the condition or event under study is equally likely in both groups. An odds ratio greater than 1 indicates that the condition or event is more likely in the first group. An odds ratio less than 1 indicate that the condition or event is less likely in the first group. The odds ratio can vary from zero to infinity.
In the case-control study, the risk of cervical cancer was examined in relation to a gene XXXX. The status of the genes is abnormal and normal wherein normal is considered as the referent category.
Consider an example:data gene;input cat $7. genstatus $4. count;cards;case abn 119control abn 68case nor 317control nor 319;run;
Odds of exposure among cases=119/317Odds of exposure among controls=68/319Odds ratio= odds of exposure among cases/odds of exposure among controls =1.76
The odds ratio with respect to XXXX shows that the odds of abnormal genotype occurring in case group is 1.76 times higher than it occurring in control group. The 95% confidence interval for the odds ratio is obtained as (1.26, 2.46).The cervical cancer cases were 76% more likely to have abnormal genes than controls. In short, the odds ratio indicates that women who have abnormal genes were 76% more likely to develop cervical cancer than those with normal genes.
This can be resolved as follows: ods output CommonRelRisks=cmnrelrisk; proc freq data=gene ; tables cat*genstatus/relrisk cmh ; weight count; run;
ods output close;
The output will be produced as:
data cmnrelrisk_1(keep=Value LowerCL UpperCL); set cmnrelrisk; if studytype eq " (Odds Ratio)" then if method eq "Logit **" and value ne . then output; else if studytype="Case-Control" then if method eq "Mantel-Haenszel" and value not in (.,0.0000) then output; format LowerCL UpperCL ODDSR8.3 value ODDSR8.3; run;
The cmh option will help to tackle the problem. The logit method will add 0.5 in all the cells of the 2 x 2 cross tabulation table where zero is in anyone of the cell and then odds ratio is evaluated.
Understanding the %pageXof Y macro Compiled by Rupesh R
The following macro needs to be applied for the purpose of presenting page numbers in Page X of Y format within the body of the document. When calling the macro, the proc report code should be quoted by %nrstr as explained in step 1.a below. To avoid clutter of code, here the proc report code is created as a macro. This proc report code should contain a ‘compute before_page_’ block as described in the step 5 below.
%macro pageXofY (report= /* proc report code, quoted by %nrstr */ /*1.a*/ , dummy=dummy /* name of the dummy output file */ ); %global page pages len; /* 1.b*/ /-- first run --*/ %let page = 0; %let len = 8; /* 1.c*/ filename _dummy &dummy.; /* 1.d*/ proc printto print = _dummy; run; /*1.e*/ %unquote (&report.) ;/* 1.f*/ proc printto; run;/*1.g*/ filename _dummy clear; /*1.h*/
%*-- second run --*; %let pages = &page.; /*1.i*/ %let page = 0;/*1.j*/ %let len = %eval(%length(&pages.) * 2 + 4); /* 1.k*/ %unquote(&report.);/*1.l*/ %mend pageXofY;
Explanation of Macro Step 1 (1.a): Using %nrstr function we mask the special characters and mnemonics .Here we mask & and % symbols in the proc report code.
Step 2 (1.b): Initialize three global macro variables § The variable ‘page’ returns the current page number § The variable ‘pages’ returns the total number of pages § The variable ‘len’ returns the expected length of the string ‘_XofY’ (2.c) .
Step 3 (1.c): The initial values of macro variables ‘page’ and ‘len’ are assigned as 0 and 8 respectively.
Step 4 (1.d): A dummy file path is defined for the output of proc printto
Step 5 (1.e): PRINTTO procedure is used for printing the output in the specified dummy file (_dummy).
Step 6 (1.f): The proc report code created for generating the output is executed using %unquote function in this step.
%UNQUOTE is to restore normal tokenization of a value whose tokenization was altered by a previous macro quoting function. %UNQUOTE takes effect during macro execution. If the value is not unmasked before it reaches the SAS compiler, the DATA step does not compile correctly and it produces error messages.
1) Execute the entire report procedure and generate an output to the specified file path “&dummy.”
2) In report procedure we should include the following set of statements.
3) Before/After each page of reporting the variable ‘page’ is calculated and so when we exit from this procedure the macro variable ‘page’ has the value of total number of pages. (i.e. The initial value of ‘page’ is ‘0’ when it comes to the first page of file then it becomes ‘1’ and so on, see step (2.a))
4) We get the total pages of report in variable ‘page’.Here the variable “pages” is not initialized. So symget (‘pages’) will return nothing
Step 7 (1.g): Exit the printto procedure and SAS reset the file path to the output window (by default).
Step 8 (1.h): Clear the dummy file.
Step 9 (1.i): Assign the current value of variable ‘page’ to the macro variable ‘pages’. i.e. we assign the total number of pages to the variable ‘pages’
Step 10 (1.j): Reset the variable ‘page’ to ‘0’.
Step 11 (1.k): Calculate the approximate length of the variable ‘_XofY’ based on current value of ‘pages’.
Step 12 (1.l): Re-execute the report procedure for getting the output as per the requirement.
1) Execute the entire report procedure and generate output in the specified path given in the ods rtf file statement.
2) This time the compute block call the ‘page’ value as the current page and ‘pages’ as the total number of pages. So we will get the page number as Page X of Y form.
The following example illustrates the above macro.Here we get page X of Y on the top of each page.
If suppose we have ten observations and we are required to present the output such that the observation are distributed on different pages depending on the value of the variable “order”. Also the different pages need to have different footers , footers being presented in the body of the document.As a first step, a variable "page" is created which represents the page number corresponding to the observation in the dataset as described below.
data catval1; set catval end=eof; if order le 4 then page=1; else if 5 le order le 7 then page=2; else if order ge 8 then page=3; run;
The footers can be presented in the body of the document using compute before /after statements in proc report as below.
proc report data= catval1 ; column order var1 var2 ; ……………………………… ……………………………… Other SAS statements; ………………………………… ……………………………….. compute before page; If page=1 then footer= "\li75 @ Subjects who select more than one race” ; If page=2 then footer=" "; If page=3 then footer="\li75 + BMI = Weight (kg) / [Height (m)]^2"; endcomp;
/*here _page_ is the SAS generated variable */
compute after _page_ / style=[just=l protectspecialchars=off]; footer11= footer; footer12="\li75 # Overall p-value for continuous variables “ footer13="\li for Categorical variables from CMH general association test"; line @1 footer11 $300.; line @1 footer12 $300.; line @1 footer13 $300.; endcomp;
/* this will give different footers in each page*/ by page; run;
The first page will contain the following footers
@ Subjects who select more than one race # Overall p-value for continuous variables for Categorical variables from CMH general association test
The second page will contain the following footers
# Overall p-value for continuous variables for Categorical variables from CMH general association test
The third page will contain the following footers + BMI = Weight (kg) / [Height (m)]^2 # Overall p-value for continuous variables for Categorical variables from CMH general association test
NOTE: Groups are not created because the usage of parameter is DISPLAY.
A SAS dataset named subject is created. The SAS code for the creation of the dataset subject is as follows: data subject; input patid name$ sex$ visit $ parameter$ result $; cards; 0101 lisha f Screening weight 65 0101 lisha f Screening height 150 0101 lisha f Visit1 weight 66 0101 lisha f Visit1 height 150 0102 manu m Screening weight 80 0102 manu m Screening height 165 0102 manu m Visit1 weight 80 0102 manu m Visit2 height 165 ; run;
The aim is to summarize the values across a single observation. The template is as follows:
When using the following code, the output is generated but a Note is also generated in the log as mentioned below:
The SAS log will look like as follows:: 1 2 3 data subject; 4 input patid name$ sex$ visit $ parameter$ result $; 5 cards; NOTE: The data set WORK.SUBJECT has 8 observations and 6 variables. NOTE: DATA statement used: real time 0.01 seconds cpu time 0.00 seconds 14 ; 15 run; 16 17 proc report data=subject nowindows; 18 column patid name sex visit parameter result; 19 define patid/"Patient Number" group; 20 define name/"Subject Number" group; 21 define sex/"Sex" group; 22 define visit/"Visit" group; 23 define parameter/"Test" display; 24 define result/"Values" display; 25 run; NOTE: Groups are not created because the usage of parameter is DISPLAY. NOTE: There were 8 observations read from the data set WORK.SUBJECT. NOTE: PROCEDURE REPORT used: real time 0.01 seconds cpu time 0.01 seconds
A note is produced in the SAS log “NOTE: Groups are not created because the usage of parameter is DISPLAY.” To remove this note from the log the following code should be used.
The log is generated as 1 2 3 data subject; 4 input patid name$ sex$ visit $ parameter$ result $; 5 cards; NOTE: The data set WORK.SUBJECT has 8 observations and 6 variables. NOTE: DATA statement used: real time 0.00 seconds cpu time 0.00 seconds 14 ; 15 run; 16 17 proc report data=subject nowindows; 18 column patid name sex visit parameter result; 19 define patid/"Patient Number" order; 20 define name/"Subject Number" order; 21 define sex/"Sex" order; 22 define visit/"Visit" order; 23 define parameter/"Test" display; 24 define result/"Values" display; 25 run; NOTE: There were 8 observations read from the data set WORK.SUBJECT. NOTE: PROCEDURE REPORT used: real time 0.00 seconds cpu time 0.00 seconds
The SAS log is now free of the notes, errors and warnings. Instead of using the group variable, use order with display in the define statement.
Applying Cochran-Mantel–Haenzel (CMH) general association test
Here the objective is to determine the general association between categorical variables subject and ranking for each emotion.
The CMH general association test is applied for this purpose. Proc freq is as described below to carry out the test. Proc freq generates p-values corresponding to each emotion and the results are outputted to the dataset hypnosis1.
The dataset is created as follows
data hypnosis; input subject emotion $ ranking @@; cards; 1 fear 4 1 joy 3 1 sadness 1 1 calmness 2 2 fear 4 2 joy 2 2 sadness 3 2 calmness 1 3 fear 3 3 joy 2 3 sadness 4 3 calmness 1 4 fear 4 4 joy 1 4 sadness 2 4 calmness 3 5 fear 1 5 joy 4 5 sadness 3 5 calmness 2 6 fear 4 6 joy 3 6 sadness 2 6 calmness 1 7 joy 4 7 joy 1 7 sadness 2 7 calmness 3 8 joy 3 8 joy 4 8 sadness 2 8 calmness 1 ; run;
The following code is used to carry out the analysis. The option ‘cmh’ in the ‘tables’ statement carries out the test. The option ‘cmhga’ presents the P-value for CMH General Association without a warning in the log.
In the dataset p_cmhga will be labeled as ’ P-value for CMH General Association’; df_cmhga as “DF for CMH General Association” and _cmhga_ as “CMH General Association” .
We have a column in proc report with either 1 or 2 digits right of the decimal and an uncertain number of digits to the left. For enhanced output, the values have to be presented such that they are aligned with respect to the decimal point.
When the display value contains a decimal point, we can use just=dec to align the numbers directly. The JUST=DEC option can be used to align the decimal points in the values in a column
proc sql noprint; select xpath into :fullPath from dictionary.extfiles where index(upcase(xpath),"%upcase(&progName) " ) gt 0 ; select setting into :engine from sashelp.voption where optname="ENGINE"; quit;
Let the SAS editor containing the code be KR-PH-XXX-SAS-Init.sas and it is stored in a folder named SAS Programs_Final.
The following %let statements assigns the folder and editor names to macro variables pgmfld and pgm respectively.
%let pgmfld = SAS Programs_Final; /*Folder in which SAS code is stored*/ %let pgm= KR-PH-XXX-SAS-Init.sas; /*Name of the program editor*/
The following macro is then used to extract the path of the editor from sashelp.vextfl and determine the path of the folder as “dir1” and of the editor as “dir2”.
%macro filePath; %global fpath maxRef;
proc sql noprint; select xpath into :fPath from sashelp.vextfl where xpath ? "&pgm"; quit;
This macro helps in determining the path of the code and enables the code to be executed in any computer provided the SAS code is saved within a folder and the naming conventions are followed as above.
14.3.08
Hiring
15.2.08
New Abode Finally after three years of exemplary hard work of all the present & past employees of KREARA, our dream of a beautiful new office came to frution on January 27th 2008. Thanks to all our clients and employees who have helped us make this dream come true
23.11.07
Randomisation - Data sets
Compiled by Prajitha Nair Here is a piece of code that will generate mock randomisation list that the programmers could use to generate the outputs before unblinding the data
/*Creating the dummy dataset for randomisation*/
Proc format; value treat 1='Test' 2='Reference' 3='Placebo'; run;
/*Randomly assigning medical kit number to treatments*/ proc plan seed=111605; factors medkit_no=220 random block=1 random/noprint; output out=first; treatments treat=3 cyclic (1 2 3 ); run;
proc sort data=first out=rand_trt; by medkit_no; run;
data rand_trt(keep=medkitno treat); set rand_trt; medkitno=put(medkit_no, z3.); format treat treat.; run;
/*merge to get the randomised patient included in the study*/ proc sort data=rand_trt; by medkitno; run;
proc sort data=medkit_alloc;/*sas dataset imported from mysql*/ by medkitno; run;
data formo.randl; merge rand_trt(in=a) medkit_alloc(in=b); by medkitno; run;
11.10.07
Read only access to SAS Data sets Compiled by Soumya Gopinath
Lock the entire SAS data library by using the option Access=Readonly.
An annoying period (.) appeared in the p-value column when the break statement was applied in proc REPORT. How can we eliminate this period (.) from the report (rtf file)?
Solution: Firstly, the data type of p-value variable is numeric then the break option will replace the missing values with a period. To avoid this situation change the data type of p-value variable to character. Then the missing values will be replaced by blank space only. Eg: /* Create dataset with 5 variables and 8 observations*/ data test; input usubjid $ parameter $ visit $ trtgrp result; datalines; 001 FEV1 VISIT1 1 2.34 002 FEV1 VISIT1 3 0.98 001 FEV1 VISIT4 1 2.04 002 FEV1 VISIT4 3 1.98 001 FVC VISIT1 1 2.34 002 FVC VISIT1 3 2.98 001 FVC VISIT4 1 2.44 002 FVC VISIT4 3 1.99 ; run; /* Carrying out ANOVA*/ ods output ModelAnova= test1; proc glm data=test; by parameter visit; class trtgrp; model result=trtgrp/ss3; lsmeans trtgrp/adjust=t pdiff; run;
data test2; set test1; test=' '; /* dummy variable*/ keep parameter visit probf Test; run;
proc report data = test2 nowd spacing = 2 headline headskip split = '*' missing; column parameter visit test,(probf); define parameter / group order=data left 'Parameter' ; define visit / display left 'Timepoint' ; define Test / across center "Test" ; define probf/ display left "p-value"; break after parameter/summarize suppress; run;
ods _all_ close; /* Closing all ODS outputs statements*/
In the above example probf is a numeric variable within the across variable ‘test’. So in the output a period occurred when the break statement active. Eliminate this we add one more statement in the dataset Test2. probf1=put (probf, pvalue6.); /* for converting numeric type to character*/
Generally we can say that the numeric variables within the across variable in REPORT procedure should make a period in the blank space and to avoid this convert numeric variables to character by using PUT function.
SAS with mySQL Compiled by Soumya Gopinath How to transfer the MySQL Databases named dbformo and dbformo_add to SAS datasets library names formoSQL and AddSQL respectively?
Solution: libname libnameODBC datasrc=sourceuser=user name password=passwordschema=database;
By using the procedure COPY we can copy the library formoSQL and AddSQL to our permanent SAS libraries FormoDM and FormoAdd respectively. Before executing the connection string add an ODBC Data source name in the current system, which should be the same as given in datasrc= option. The process,
1) Settings -> Control Panel ->Administrative Tools ->Data Source (ODBC) 2) Click on the Add button. 3) Select the driver for which you want to set up a data source (MySQL ODBC 3.51 Driver) and click Finish button. 4) Add required information in Connector/ODBC window. Click OK button. Then the process is completed.
Odds of exposure among cases=119/317Odds of exposure among controls=68/319Odds ratio= odds of exposure among cases/odds of exposure among controls =1.76
