SAS Programmer in Teva Pharmaceuticals

Job DescriptionThe basic responsibilities are to provide SAS

programming support in deriving analysis datasets, summary tables, and listings in the clinical trial of the new drug development.

Training Program

Computer skills and Data structure Programming procedures SAS programming skills

 

 

Programming activity flows

5

Creating programming

rules document

Creating analysis (derived) datasets

Quality control (QC) TLG

Quality control (QC) derived

datasets

Creating TLG

Request programming resource/team

Review raw data transfers

(On going)

Before test data transfer

Review TLG

Documents needed - Approved protocol and final annotated CRF- Draft (endorsed) SAP/Mocks- Raw data from DP- Programming guidelines GDL-933-01 and GDL-933-02

Biostat provides rules in a Word document (Rules_xxx, eg. Rules_1063) and saved at, ..\Binder\10_Correspondences\Rules_xxx.DOC

Notes: - Needs to be ready before programming activities start and updated on an ongoing basis;

- Includes definitions, algorithms, formulas, etc for derived variables.

Clinical Programmer(CP) follows Rules.

  

Creating Programming Rules

6

For derived datasets and summary tables, QC requires 100% and independently data verification (double programmed).

All listings must be qced according to CP SOP-0000931.

All graphs must be independently reviewed and compared to source tables if applicable.

Biostat ensures- QC processes are followed for all deliveries; - No “ERROR”, “WARNING”, and unwanted “NOTE” messages in SAS log files for all types (TLG). Note: CP has macro tool (%chklog) to check log files- QC forms are completed properly for all deliveries and saved at ..\Binder\08_QC\QC_form_xxx.xls

Biostat reviews all deliveries before sending out to team.

Quality Control (QC) and Review

7

ADVERSE EVENT (D_AE)• Keep all variables from RAW STDM domain AE except AESQE,

AESTDY, AEENDY, STAGE • Additional variables required (also see general section for other

variables):

SAS Program Example

8

Variable Label Type Derivation

STAGE Pre/Post Baseline

Character Programming guideline

AETRTEM Treatment Emergent Flag

Character Y if STAGE=’POST’

AESTDY Study Day of Start of Adverse Event

Numeric Programming guideline

AEENDY End Date/Time of Adverse Event

Numeric Programming guideline

proc sql;

create table ae1 as

select a.*, rfstdtc,rfendtc

from ae_raw a,d_dm

where a.usubjid=d_dm.usubjid

order by usubjid;

quit;

Programs for D_AE

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data ae;

set ae1;

%iso2sasdtm(dtc=rfstdtc,sasdt=rfstdt);

%iso2sasdtm(dtc=rfendtc,sasdt=rfendt);

%iso2sasdtm(dtc=aestdtc,sasdt=aeondt);

%iso2sasdtm(dtc=aeendtc,sasdt=aersdt);

aestdy = %relday(aeondt,rfstdt);

aeendy = %relday(aersdt,rfstdt);

%ae_stage;

if stage = 'POST' then

aetrtem = 'Y';

drop rfstdtc rfendtc aeondt aersdt;

run;

Programs for D_AE

10

Summary 15.3 Baseline Characteristics by Treatment Group Safety Analysis Set________________________________________________________________________________________________________________________ Variable Modafinil Placebo Total Response, n (%) (N=24) (N=24) (N=48) P-value ________________________________________________________________________________________________________________________ Clinical Global Impression - Severity Normal 0 0 0 0.8301 (a) Borderline ill 0 0 0 Slightly ill 0 0 0 Moderately ill 5 (21) 8 (33) 13 (27) Markedly ill 14 (58) 10 (42) 24 (50) Severely ill 4 (17) 6 (25) 10 (21) Among the most extremely ill patients 0 0 0 Not assessed 1 (4) 0 1 (2) Alcohol usage (per day) None 24 (100) 24 (100) 48 (100) Light usage (0-1 drink) 0 0 0 Moderate usage (2 drinks) 0 0 0 Heavy usage (>2 drinks) 0 0 0 Tobacco usage (per day) None 24 (100) 24 (100) 48 (100) Light usage (<10 cigarettes) 0 0 0 Moderate usage (10-20 cigarettes) 0 0 0 Heavy usage (>20 cigarettes) 0 0 0 Caffeine usage (per day) None 3 (13) 5 (21) 8 (17) Light usage (0-1 drink) 17 (71) 15 (63) 32 (67) Moderate usage (2-4 drinks) 3 (13) 4 (17) 7 (15) Heavy usage (>4 drinks) 1 (4) 0 1 (2)

________________________________________________________________________________________________________________________

ConclusionI believe everyone should get a internship before graduation. What I learned from my internship is not only the new skills or knowledge but also the experience to deal with real problems in a professional way. In class, we just need understand what our professor try to teach us and then finish our homework. Most of case, professor would provide us R codes and perfect dataset. We didn’t need to make any big change to the codes and datasets. But in my internship, you have to find a way to write your own codes and derive the input datasets by yourself. You have to find the textbook and try to understand them, then apply them into your job. So you should prepare to accept new staff not to depend on what you got from you previous classes.

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