Hi,   I have a Excel speadshet with date of birth (DOB) variable. It was in the normal format in the Excel as below:   02/21/1968 10/04/1936 02/05/1959 08/25/1959 05/17/1953 12/25/1946 06/13/1945 04/23/1963 03/10/1953 04/14/1948 09/22/1967 07/22/1951 09/19/1939 01/20/1955 07/24/1963 09/21/1942 09/01/1946 07/13/1947 03/30/1946 07/10/1937 07/10/1937 08/28/1961 01/13/1942 04/25/1939 03/09/1947 10/25/1963 05/07/1939 11/24/1945   However, after I import the dataset into SAS, some dates became weird numbers and the format was "char 10" as below:   19447 15055 07/24/1963 20657 12/25/1946 17825 18731 17677 17677 17447 29557 19018 06/13/1945 03/09/1947 17610 13747 30776 30776 07/13/1947 12/31/1961 02/05/1959 21692 01/20/1955 16282 22668   How do I conver "char 10" back to the normal mmddyy10. format? Thank you! ... View more

Hi,   I am working on a 1:2 matched case-control study with 20 cases and 40 controls. They were matched based on age +/- 5 years, gender, race and cancer type. We would like to compare the survival rate from the date of receiving new treatment (time 0) until the time of death or last follow up date and evaluate whether infection and smoking status would be assoicated with survival. Could anyone please verify whether my code below is correct?   proc phreg data=test; strata Matching_ID; class group infection smoking; model time*event(0) = group infection smoking /risklimits ; run;   "Matching_ID" stands for the matching group ID number. In our study, there are 3 subjects coded per matching_ID (1 case + 2 matched controls) "time" stands for the "duration from the date of receiving new treatment to endpoint death or alive on the last follow-up date" "event" stands for "death or alive", coded as (1,0) "group" stands for "cases or controls", coded as (1, 0) "infection" stands for whether they had infection or not, coded as (1,0) "smoking" stands for whether they had smoking or not, coded as (1,0)   Thanks in advance!   ... View more

Hi,   I have a matched case-control study with 1 case (lung cancer patient) matched with 2 controls on age, gender and race. I would like to conduct a conditional logistic regression to examine the association between smoking, family history of cancer, COPD and risk of lung cancer constrolling for the matching variables. I would like to run univariate conditional logistic regression model for each of the above variable with the outcome of lung cancer (yes/no).  Further, the multivariable conditional logistic regression model will be:  lung cancer (yes/no) = smoking + family history of cancer + COPD + age + gender + race   Do I need to create dummy variables for the categorical variables such as lung cancer (y/n) or smoking (y/n) in order to conduct a logistic regression? Can anyone please provide the example of SAS code?   ... View more

Hi, Does anyone know how to generate a Nomogram from Cox Regression model (Proc Phreg)? ... View more

Hi,   I used the SAS code from this article (https://www.frontiersin.org/articles/10.3389/fdata.2019.00004/full) to do 1:2 matching for 1 case with 2 controls.    Does anyone know how to generate a column with case ID numbers that the 2 controls matched with in the "matches" file step?   /*Example Code*/ DATA population; /*Example of population dataset*/ input uniqueid gender $ age ethnic $ casecontrol; /*gender and ethnic are defined as categorical variables by the following $*/ cards;               1 f 26 1 1               2 m 27 1 1               3 m 30 2 0               4 f 25 2 0               5 m 29 1 0               6 f 26 1 1               7 f 28 2 0               8 f 28 1 0               9 m 25 1 1               10 m 26 2 0               11 f 30 3 0               12 m 29 2 0               13 f 27 1 0               14 m 27 3 0               15 f 26 1 1               16 f 25 2 0               17 f 29 1 0               18 f 30 3 0               19 m 26 1 0               20 m 26 2 1               21 f 30 2 0               22 m 25 3 0               23 m 27 1 0               24 m 28 1 1 ; run;   %LET agerange = 5; /*For this example, we have chosen the age range to be 5. Controls can therefore be up to 5 years younger or older than the case*/ %LET ratio = 3; /*We have chosen to match 3 controls for each case*/   DATA cases controls;    SET population;    IF casecontrol = 1 THEN OUTPUT cases;    ELSE OUTPUT controls; RUN;   PROC FREQ NOPRINT DATA=cases;    TABLES age*gender*ethnic/OUT=caseout; /*Since we want ‘ethnic’ to be a matching variable, we need to add it here*/ RUN;   %MACRO sample(v_age, v_gender, v_count,v_ethnic); /*Here, ethnic is also added*/      DATA qualify1;       SET controls;     WHERE (&v_age-&agerange <= age <= &v_age+&agerange)       AND       (gender = "&v_gender")       AND       (ethnic = "&v_ethnic"); /*You will also need to add your extra variable to this step.*/      case_age = &v_age;    case_gender = "&v_gender";    case_ethnic = "&v_ethnic"; /* You will also need to add your extra variable to this step.*/      SEED = RANUNI(0);    PROC SORT;       BY SEED;      DATA qualify2;       SET qualify1 NOBS=totobs;       IF _N_ <= &v_count*&ratio;       IF &v_count*&ratio <= totobs THEN tag = 'yes';          ELSE tag = 'no';      PROC APPEND BASE=matches DATA=qualify2 force; /*new data set matches will contain the matched controls*/      PROC SORT DATA=qualify2 OUT=temp1 (KEEP=uniqueid);       BY uniqueid;      PROC SORT DATA=controls OUT=temp2;       BY uniqueid;      DATA controls; /*the dataset controls is updated so that the controls already matched are removed and can not be matched again*/       MERGE temp1(IN=in1) temp2(IN=in2);       BY uniqueid;       IF in2 AND NOT in1;   %MEND sample;   DATA _NULL_;    SET caseout;    CALL EXECUTE ('%sample('||age||','||gender||','||count||','||ethnic||')'); /* You will also need to add your extra variable to this step.*/ RUN;   /*The next part is for testing if any of the cases have not received the wanted amount of controls*/   PROC FREQ NOPRINT DATA=matches;    TABLES case_age*case_gender*case_ethnic/OUT=con_out; /* You will also need to add your extra variable to this step.*/   PROC SORT DATA = caseout(RENAME=                 (age=case_age gender=case_gender count=case_cnt ethnic=case_ethnic)); /* You will also need to add your extra variable to this step.*/    BY case_age case_gender case_ethnic; /* You will also need to add your extra variable to this step.*/   PROC SORT DATA = con_out (RENAME= (count=con_cnt));    BY case_age case_gender case_ethnic; /* You will also need to add your extra variable to this step.*/   DATA final (DROP=percent);    MERGE caseout con_out;    BY case_age case_gender case_ethnic; /* You will also need to add your extra variable to this step.*/   con_need = case_cnt*&ratio; IF con_cnt = . THEN con_cnt = 0; diff = con_cnt-con_need;   PROC PRINT DATA = final; /*creates a table showing what characterizes the cases who have not received enough matches and how many matches they are missing*/    WHERE diff < 0;    TITLE 'Insufficient Matches'; RUN;     Thank you all in advance.  ... View more

Dear All,   I am designing a matched case-control study. I have 25 patients diagnosed with COVID and cancer as the case group. I would like to select 50 subjects from a dataset containing 4000 cancer patients as a comparison/control group based on the following matching variables: age (+/-5 years), cance type (8 cancer types: lung, melanoma, sarcoma...etc), gender (male vs. female), year of chemotherapy, race (White vs. Black vs. Others) and survival > 6 months (Yes/No). The priority of the matching variables are listed as the order above.   Can you please advise me on the code? Please let me know if you need further clarifications.Thank you!     ... View more