Clinical Trials 36951509-SAS-Interview-QA

Clinical Trials 36951509-SAS-Interview-QA - 1.Describe the...

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Unformatted text preview: 1.Describe the phases of clinical trials? Ans:- These are the following four phases of the clinical trials: Phase 1: Test a new drug or treatment to a small group of people (20-80) to evaluate its safety. Phase 2: The experimental drug or treatment is given to a large group of people (100-300) to see that the drug is effective or not for that treatment. Phase 3: The experimental drug or treatment is given to a large group of people (1000-3000) to see its effectiveness, monitor side effects and compare it to commonly used treatments. Phase 4: The 4 phase study includes the post marketing studies including the drug's risk, benefits etc. 2. Describe the validation procedure? How would you perform the validation fo r TLG as well as analysis data set? Ans:- Validation procedure is used to check the output of the SAS program generated by the source programmer. In this process validator write the program and generate the output. If this output is same as the output generated by the SAS programmer's output then the program is considered to be valid. We can perform this validation for TLG by checking the output manually and for analysis data set it can be done using PROC COMPARE. 3. How would you perform the validation for the listing, which has 400 pages? Ans:- It is not possible to perform the validation for the listing having 400 pages manually. To do this, we convert the listing in data sets by using PROC RTF and then after that we can compare it by using PROC COMPARE. 4. Can you use PROC COMPARE to validate listings? Why? Ans:- Yes, we can use PROC COMPARE to validate the listing because if there are many entries (pages) in the listings then it is not possible to check them manually. So in this condition we use PROC COMPARE to validate the listings. 5. How would you generate tables, listings and graphs? Ans:- We can generate the listings by using the PROC REPORT. Similarly we can create the tables by using PROC FREQ, PROC MEANS, and PROC TRANSPOSE and PROC REPORT. We would generate graph, using proc Gplot etc. 6. How many tables can you create in a day? Ans:- Actually it depends on the complexity of the tables if there are same type of tables then, we can create 1-2-3 tables in a day. 7. What are all the PROCS have you used in your experience? Ans:- I have used many procedures like proc report, proc sort, proc format etc. I have used proc report to generate the list report, in this procedure I have used subjid as order variable and trt_grp, sbd, dbd as display variables. 8. Describe the data sets you have come across in your life? Ans:- I have worked with demographic, adverse event , laboratory, analysis and other data sets. 9. How would you submit the docs to FDA? Who will submit the docs? Ans:- We can submit the docs to FDA by e-submission. Docs can be submitted to FDA using Define.pdf or define.Xml formats. In this doc we have the documentation about macros and program and E-records also. Statistician or project manager will submit this doc to FDA. 10. What are the docs do you submit to FDA? Ans:- We submit ISS and ISE documents to FDA. 11. Can u share your CDISC experience? What version of CDISC SDTM have you used? Ans: I have used version 3.1.1 of the CDISC SDTM. 12. Tell me the importance of the SAP? Ans:- This document contains detailed information regarding study objectives and statistical methods to aid in the production of the Clinical Study Report (CSR) including summary tables, figures, and subject data listings for Protocol. This document also contains documentation of the program variables and algorithms that will be used to generate summary statistics and statistical analysis. 13. Tell me about your project group? To whom you would report/contact? My project group consisting of six members, a project manager, two statisticians, lead programmer and two programmers. I usually report to the lead programmer. If I have any problem regarding the programming I would contact the lead programmer. If I have any doubt in values of variables in raw dataset I would contact the statistician. For example the dataset related to the menopause symptoms in women, if the variable sex having the values like F, M. I would consider it as wrong; in that type of situations I would contact the statistician. 14. Explain SAS documentation. SAS documentation includes programmer header, comments, titles, footnotes etc. Whatever we type in the program for making the program easily readable, easily understandable are in called as SAS documentation. 15. How would you know whether the program has been modified or not? I would know the program has been modified or not by seeing the modification history in the program header. 16. Project status meeting? It is a planetary meeting of all the project managers to discuss about the present Status of the project in hand and discuss new ideas and options in improving the Way it is presently being performed. 17. Describe clin-trial data base and oracle clinical Clintrial, the market's leading Clinical Data Management System (CDMS).Oracle Clinical or OC is a database management system designed by Oracle to provide data management, data entry and data validation functionalities to Clinical Trials process.18. Tell me about MEDRA and what version of MEDRA did you use in your project?Medical dictionary of regulatory activities. Version 10 19. Describe SDTM? CDISC¶s Study Data Tabulation Model (SDTM) has been developed to standardize what is submitted to the FDA. 20. What is CRT? Case Report Tabulation, Whenever a pharmaceutical company is submitting an NDA, conpany has to send the CRT's to the FDA. 21. What is annotated CRF? Annotated CRF is a CRF(Case report form) in which variable names are written next the spaces provided to the investigator. Annotated CRF serves as a link between the raw data and the questions on the CRF. It is a valuable toll for the programmers and statisticians.. 22. What do you know about 21CRF PART 11? Title 21 CFR Part 11 of the Code of Federal Regulations deals with the FDA guidelines on electronic records and electronic signatures in the United States. Part 11, as it is commonly called, defines the criteria under which electronic records and electronic signatures are considered to be trustworthy, reliable and equivalent to paper records. 23 What are the contents of AE dataset? What is its purpose? What are the variables in adverse event datasets?The adverse event data set contains the SUBJID, body system of the event, the preferred term for the event, event severity. The purpose of the AE dataset is to give a summary of the adverse event for all the patients in the treatment arms to aid in the inferential safety analysis of the drug. 24 What are the contents of lab data? What is the purpose of data set? The lab data set contains the SUBJID, week number, and category of lab test, standard units, low normal and high range of the values. The purpose of the lab data set is to obtain the difference in the values of key variables after the administration of drug. 25.How did you do data cleaning? How do you cha nge the values in the data on your own? I used proc freq and proc univariate to find the discrepancies in the data, which I reported to my manager. 26.Have you created CRT¶s, if you have, tell me what have you done in that? Yes I have created patient profile tabulations as the request of my manager and and the statistician. I have used PROC CONTENTS and PROC SQL to create simple patient listing which had all information of a particular patient including age, sex, race etc. 27. Have you created transport files? Yes, I have created SAS Xport transport files using Proc Copy and data step for the FDA submissions. These are version 5 files. we use the libname engine and the Proc Copy procedure, One dataset in each xport transport format file. For version 5: labels no longer than 40 bytes, variable names 8 bytes, character variables width to 200 bytes. If we violate these constraints your copy procedure may terminate with constraints, because SAS xport format is in compliance with SAS 5 datasets. Libname sdtm ³c:\sdtm_data´;Libname dm xport ³c:\dm.xpt´; Proc copy; In = sdtm; Out = dm; Select dm; Run; 28. How did you do data cleaning? How do you change the values in the data on your own? I used proc freq and proc univariate to find the discrepancies in the data, which I reported to my manager. 29. Definitions? CDISC- Clinical data interchange standards consortium.They have different data models, which define clinical data standards for pharmaceutical industry. SDTM ± It defines the data tabulation datasets that are to be sent to the FDA for regulatory submissions. ADaM ± (Analysis data Model)Defines data set definition guidance for creating analysis data sets. ODM ± XML ± based data model for allows transfer of XML based data . Define.xml ± for data definition file (define.pdf) which is machine readable. ICH E3: Guideline, Structure and Content of Clinical Study Reports ICH E6: Guideline, Good Clinical Practice ICH E9: Guideline, Statistical Principles for Clinical Trials Title 21 Part 312.32: Investigational New Drug Application 30. Have you ever done any Edit check programs in your project, if you have, tell me what do you know about edit check programs? Yes I have done edit check programs .Edit check programs ± Data validation. 1.Data Validation ± proc means, proc univariate, proc freq.Data Cleaning ± finding errors. 2.Checking for invalid character values.Proc freq data = patients;Tables gender dx ae / nocum nopercent;Run;Which gives frequency counts of unique character values. 3. Proc print with where statement to list invalid data values.[systolic blood pressure - 80 to 100][diastolic blood pressure ± 60 to 120] 4. Proc means, univariate and tabulate to look for outliers.Proc means ± min, max, n and mean.Proc univariate ± five highest and lowest values[ stem leaf plots and box plots] 5. PROC FORMAT ± range checking 6. Data Analysis ± set, merge, update, keep, drop in data step. 7. Create datasets ± PROC IMPORT and data step from flat files. 8. Extract data ± LIBNAME.9. SAS/STAT ± PROC ANOVA, PROC REG. 10. Duplicate Data ± PROC SORT Nodupkey or NoduplicateNodupkey ± only checks for duplicates in BYNoduplicate ± checks entire observation (matches all variables)For getting duplicate observations first sort BY nodupkey and merge it back to the original dataset and keep only records in original and sorted. 11.For creating analysis datasets from the raw data sets I used the PROC FORMAT, and rename and length statements to make changes and finally make a analysis data set. 31. What is Verification? The purpose of the verification is to ensure the accuracy of the final tables and the quality of SAS programs that generated the final tables. According to the instructions SOP and the SAP I selected the subset of the final summary tables for verification. E.g Adverse event table, baseline and demographic characteristics table.The verification results were verified against with the original final tables and all discrepancies if existed were documented. 32. What is Program Validation? Its same as macro validation except here we have to validate the programs i.e according to the SOP I had to first determine what the program is supposed to do, see if they work as they are supposed to work and create a validation document mentioning if the program works properly and set the status as pass or fail.Pass the input parameters to the program and check the log for errors. 33. What do you lknow about ISS and ISE, have you ever produced these reports? ISS (Integrated summary of safety):Integrates safety information from all sources (animal, clinical pharmacology, controlled and uncontrolled studies, epidemiologic data). "ISS is, in part, simply a summation of data from individual studies and, in part, a new analysis that goes beyond what can be done with individual studies."ISE (Integrated Summary of efficacy)ISS & ISE are critical components of the safety and effectiveness submission and expected to be submitted in the application in accordance with regulation. FDA¶s guidance Format and Content of Clinical and Statistical Sections of Application gives advice on how to construct these summaries. Note that, despite the name, these are integrated analyses of all relevant data, not summaries. 34. Explain the process and how to do Data Validation? I have done data validation and data cleaning to check if the data values are correct or if they conform to the standard set of rules.A very simple approach to identifying invalid character values in this file is to use PROC FREQ to list all the unique values of these variables. This gives us the total number of invalid observations. After identifying the invalid data «we have to locate the observation so that we can report to the manager the particular patient number.Invalid data can be located using the data _null_ programming. Following is e.g DATA _NULL_; INFILE "C:PATIENTS,TXT" PAD;FILE PRINT; ***SEND OUTPUT TO THE OUTPUT WINDOW; TITLE "LISTING OF INVALID DATA"; ***NOTE: WE WILL ONLY INPUT THOSEVARIABLES OF INTEREST;INPUT @1 PATNO [email protected] GENDER [email protected] DX [email protected] AE $1.; ***CHECK GENDER;IF GENDER NOT IN ('F','M',' ') THEN PUT PATNO= GENDER=; ***CHECK DX; IF VERIFY(DX,' 0123456789') NE 0 THEN PUT PATNO= DX=; ***CHECK AE; IF AE NOT IN ('0','1',' ') THEN PUT PATNO= AE=; RUN; For data validation of numeric values like out of range or missing values I used proc print with a where statement. PROC PRINT DATA=CLEAN.PATIENTS; WHERE HR NOT BETWEEN 40 AND 100 AND HR IS NOT MISSING OR SBP NOT BETWEEN 80 AND 200 AND SBP IS NOT MISSING OR DBP NOT BETWEEN 60 AND 120 AND DBP IS NOT MISSING;TITLE "OUT-OF-RANGE VALUES FOR NUMERICVARIABLES"; ID PATNO; VAR HR SBP DBP; RUN; If we have a range of numeric values µ001¶ ± µ999¶ then we can first use user defined format and then use proc freq to determine the invalid values. PROC FORMAT; VALUE $GENDER 'F','M' = 'VALID'' ' = 'MISSING'OTHER = 'MISCODED'; VALUE $DX '001' - '999'= 'VALID'' ' = 'MISSING'OTHER = 'MISCODED'; VALUE $AE '0','1' = 'VALID'' ' = 'MISSING'OTHER = 'MISCODED'; RUN; One of the simplest ways to check for invalid numeric values is to run either PROC MEANS or PROC UNIVARIATE.We can use the N and NMISS options in the Proc Means to check for missing and invalid data. Default (n nmiss mean min max stddev).The main advantage of using PROC UNIVARIATE (default n mean std skewness kurtosis) is that we get the extreme values i.e lowest and highest 5 values which we can see for data errors. If u want to see the patid for these particular observations «..state and ID patno statement in the univariate procedure. 35. Roles and responsibilities? Programmer: Develop programming for report formats (ISS & ISE shell) required by the regulatory authorities.Update ISS/ISE shell, when required. Clinical Study Team: Provide information on safety and efficacy findings, when required.Provide updates on safety and efficacy findings for periodic reporting. Study Statistician Draft ISS and ISE shell.Update shell, when appropriate.Analyze and report data in approved format, to meet periodic reporting requirements. 36. Explain Types of Clinical trials study you come across? Single Blind Study When the patients are not aware of which treatment they receive. Double Blind Study When the patients and the investigator are unaware of the treatment group assigned. Triple Blind Study Triple blind study is when patients, investigator, and the project team are unaware of the treatments administered. 37. What are the domains/datasets you have used in your studies? Demog Adverse Events Vitals ECG Labs Medical History PhysicalExam etc 38. Can you list the variables in all the domains? Demog: Usubjid, Patient Id, Age, Sex, Race, Screening Weight, Screening Height, BMI etc Adverse Events: Protocol no, Investigator no, Patient Id, Preferred Term, Investigator Term, (Abdominal dis, Freq urination, headache, dizziness, hand-food syndrome, rash, Leukopenia, Neutropenia) Severity, Seriousness (y/n), Seriousness Type (death, life threatening, permanently disabling), Visit number, Start time, Stop time, Related to study drug? Vitals: Subject number, Study date, Procedure time, Sitting blood pressure, Sitting Cardiac Rate, Visit number, Change from baseline, Dose of treatment at time of vital sign, Abnormal (yes/no), BMI, Systolic blood pressure, Diastolic blood pressure. ECG: Subject no, Study Date, Study Time, Visit no, PR interval (msec), QRS duration (msec), QT interval (msec), QTc interval (msec), Ventricular Rate (bpm), Change from baseline, Abnormal. Labs: Subject no, Study day, Lab parameter (Lparm), lab units, ULN (upper limit of normal), LLN (lower limit of normal), visit number, change from baseline, Greater than ULN (yes/no), lab related serious adverse event (yes/no).Medical History: Medical Condition, Date of Diagnosis (yes/no), Years of onset or occurrence, Past condition (yes/no), Current condition (yes/no). PhysicalExam: Subject no, Exam date, Exam time, Visit number, Reason for exam, Body system, Abnormal (yes/no), Findings, Change from baseline (improvement, worsening, no change), Comments 39. Give me the example of edit ckecks you made in your programs?Exa mples of Edit Checks Demog:Weight is outside expected rangeBody mass index is below expected ( check weight and height) Age is not within expected range. DOB is greater than the Visit date or not.. Gender value is a valid one or invalid. etc Adverse Event Stop is before the start or visit Start is before birthdate Study medicine discontinued due to adverse event but completion indicated (COMPLETE =1) Labs Result is within the normal range but abnormal is not blank or µN¶Result is outside the normal range but abnormal is blank Vitals Diastolic BP > Systolic BP Medical History Visit date prior to Screen datePhysicalPhysical exam is normal but comment included 40. What are the advantages of using SAS in clinical data management? Why should not we use other software products in managing clinical data? ADVANTAGES OF USING A SAS®-BASED SYSTEM Less hardware is required. A Typical SAS®-based system can utilize a standard file server to store its databases and does not require one or more dedicated servers to handle the application load. PC SAS® can easily be used to handle processing, while data access is left to the file server. Additionally, as presented later in this paper, it is possible to use the SAS® product SAS®/Share to provide a dedicated server to handle data transactions. Fewer personnel are required. Systems that use complicated database software often require the hiring of one ore more DBA¶s (Database Administrators) who make sure the database software is running, make changes to the structure of the database, etc. These individuals often require special training or background experience in the particular database application being used, typically Oracle. Additionally, consultants are often required to set up the system and/or studies since dedicated servers and specific expertise requirements often complicate the process.Users with even casual SAS® experience can set up studies. Novice programmers can build the structure of the database and design screens. Organizations that are involved in data management almost always have at least one SAS® programmer already on staff. SAS® programmers will have an understanding of how the system actually works which would allow them to extend the functionality of the system by directly accessing SAS® data from outside of the system.Speed of setup is dramatically reduced. By keeping studies on a local file server and making the database and screen design processes extremely simple and intuitive, setup time is reduced from weeks to days.All phases of the data management process become homogeneous. From entry to analysis, data reside in SAS® data sets, often the end goal of every data management group. Ad...
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  • Spring '10
  • Raman
  • Data Management, Clinical trial, Database management system

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