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ANALYSIS datasets are nothing but the datasets that are used for the statistical analysis of the data. Analysis datasets contains the RAW data and the variables DERIVED from the raw data. Variables, which are derived for the raw data, are used to produce the TLG’s of the clinical study.

The safety as well as efficacy endpoints (PARAMETERS) dictate the type of the datasets are required by the clinical study for GENERATING the statistical reports of the TLG’s. Sometimes the analysis datasets will have the variables not necessarily required to generate the statistical reports but sometimes they may required to generate the ad-hoc reports.

Analysis datasets are nothing but the datasets that are used for the statistical analysis of the data. Analysis datasets contains the raw data and the variables derived from the raw data. Variables, which are derived for the raw data, are used to produce the TLG’s of the clinical study.

The safety as well as efficacy endpoints (parameters) dictate the type of the datasets are required by the clinical study for generating the statistical reports of the TLG’s. Sometimes the analysis datasets will have the variables not necessarily required to generate the statistical reports but sometimes they may required to generate the ad-hoc reports.

PROC LIFEREG and PROC PHREG can also be USED to GET the survival estimates ALONG with PROC LIFETEST.

PROC LIFEREG and PROC PHREG can also be used to get the survival estimates along with PROC LIFETEST.

PROC LIFETEST is used to obtain Kaplan-Meier and life table SURVIVAL estimates (and PLOTS). Using a strata statement in Proc Lifetest, which compare survival estimates for different GROUPS.

PROC LIFETEST is used to obtain Kaplan-Meier and life table survival estimates (and plots). Using a strata statement in Proc Lifetest, which compare survival estimates for different groups.

If the p-value were greater than 0.05, you would say that the group of independent variables does not show a statistically SIGNIFICANT RELATIONSHIP with the dependent variable, or that the group of independent variables does not reliably predict the dependent variable. Note that this is an overall significance test assessing whether the group of independent variables, when USED together reliably predicts the dependent variable, and does not address the ability of any of the particular independent variables to predict the dependent variable. Using the PROC FREQ, PROC ANOVA, PROC GLM and PROC TTEST we cal CALCULATE the p-value.

If the p-value were greater than 0.05, you would say that the group of independent variables does not show a statistically significant relationship with the dependent variable, or that the group of independent variables does not reliably predict the dependent variable. Note that this is an overall significance test assessing whether the group of independent variables, when used together reliably predicts the dependent variable, and does not address the ability of any of the particular independent variables to predict the dependent variable. Using the PROC FREQ, PROC ANOVA, PROC GLM and PROC TTEST we cal calculate the p-value.

USING AMPERSAND sign…. &VAR.

Using ampersand sign…. &var.

USE a macro… with a %PUT STATEMENT.

Use a macro… with a %PUT statement.

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

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

LESS HARDWARE is REQUIRED and FEWER PERSONNEL are required.

Less hardware is required and fewer personnel are required.

We specify a BY statement with PROC LIFETEST to obtain separate ANALYSES on observations in GROUPS defined by the BY VARIABLES.

We specify a BY statement with PROC LIFETEST to obtain separate analyses on observations in groups defined by the BY variables.

PROC REPORT, proc TABULATE and DATA _null_.

Proc Report, proc Tabulate and Data _null_.

The SQL procedure supports ALMOST all the functions AVAILABLE in the DATA step for the creation of data as well as the manipulation of the data. When we compare the same result, obtained from SQL and with the Data step, PROC SQL REQUIRES LESS code and, more IMPORTANTLY it requires less time to execute the code.

The SQL procedure supports almost all the functions available in the DATA step for the creation of data as well as the manipulation of the data. When we compare the same result, obtained from SQL and with the Data step, PROC SQL requires less code and, more importantly it requires less time to execute the code.

Yes, we can generate the statistics like N, Mean, Median, Max, Min, STD & SUM using PROC SQL. But SQL procedure cannot CALCULATE all the above statistics by default, as it is the CASE with PROC MEANS.

Yes, we can generate the statistics like N, Mean, Median, Max, Min, STD & SUM using PROC SQL. But SQL procedure cannot calculate all the above statistics by default, as it is the case with PROC MEANS.

SURVIVAL ESTIMATE GRAPHS.

Survival estimate graphs.

ODS (Output Delivery System) normally used to make the output from the TABLES, Listings and GRAPHS LOOKS pretty. ODS CREATES the outputs in html, pdf and rtf FORMATS.

ODS (Output Delivery System) normally used to make the output from the Tables, Listings and graphs looks pretty. ODS creates the outputs in html, pdf and rtf formats.

Macro LIBRARIES are the libraries, which stores all the macros REQUIRED for DEVELOPING TLG’s of the clinical trial. These are very are necessary in controlling and managing the macros. With the HELP of a %INCLUDE statement; the stored macros in the macro library can be automatically called.

Macro libraries are the libraries, which stores all the macros required for developing TLG’s of the clinical trial. These are very are necessary in controlling and managing the macros. With the help of a %INCLUDE statement; the stored macros in the macro library can be automatically called.

Pharmaceutical companies conduct longitudinalstudies on human subjects that often span several months. It is unrealistic to expect patients to KEEP every scheduled visit over such a long period of time.Despite every effort, patient data are not collected for some time points.

Eventually, these become missing VALUES in a SAS data set later. For reporting purposes,the most recent previously available VALUE is substituted for each missing visit. This is called the Last Observation Carried Forward (LOCF).LOCF doesn’t mean last SAS dataset observation carried forward.

It means last non-missing value carried forward. It is the values of individual measures that are the “observations” in this case. And if you have multiple variables containing these values then they will be carried forward INDEPENDENTLY.

Pharmaceutical companies conduct longitudinalstudies on human subjects that often span several months. It is unrealistic to expect patients to keep every scheduled visit over such a long period of time.Despite every effort, patient data are not collected for some time points.

Eventually, these become missing values in a SAS data set later. For reporting purposes,the most recent previously available value is substituted for each missing visit. This is called the Last Observation Carried Forward (LOCF).LOCF doesn’t mean last SAS dataset observation carried forward.

It means last non-missing value carried forward. It is the values of individual measures that are the “observations” in this case. And if you have multiple variables containing these values then they will be carried forward independently.

It is new SAS procedure that is available as a HOTFIX for SAS 8.2 version and comes as a part withSAS 9.1.3 version. PROC CDISC is a procedure that allows us to IMPORT (and export XML files that are compliant with the CDISC ODM version 1.2 schema. For more details refer SAS programming in the Pharmaceutical INDUSTRY text book.

It is new SAS procedure that is available as a hotfix for SAS 8.2 version and comes as a part withSAS 9.1.3 version. PROC CDISC is a procedure that allows us to import (and export XML files that are compliant with the CDISC ODM version 1.2 schema. For more details refer SAS programming in the Pharmaceutical Industry text book.

Although the VERIFICATION and validation are close in meaning, “verification” has more of a sense of TESTING the truth or accuracy of a statement by examining evidence or conducting experiments, while “validate” has more of a sense of DECLARING a statement to be true and MARKING it with an INDICATION of official sanction.

Although the verification and validation are close in meaning, “verification” has more of a sense of testing the truth or accuracy of a statement by examining evidence or conducting experiments, while “validate” has more of a sense of declaring a statement to be true and marking it with an indication of official sanction.

Before writing “TEST plan” you have to LOOK into on “Functional SPECIFICATIONS”. Functional specifications itself depends on “Requirements”, so one should have clear understanding of requirements and functional specifications to WRITE a test plan.

Before writing “Test plan” you have to look into on “Functional specifications”. Functional specifications itself depends on “Requirements”, so one should have clear understanding of requirements and functional specifications to write a test plan.

PROGRAMMERS sometime hardcode when they need to produce report in urgent. But it is always better to avoid hardcoding, as it overrides the database controls in clinical DATA management. Data often CHANGE in a trial over time, and the hardcode that is written today MAY not be valid in the future.Unfortunately, a hardcode may be forgotten and left in the SAS program, and that can lead to an INCORRECT database change.

Programmers sometime hardcode when they need to produce report in urgent. But it is always better to avoid hardcoding, as it overrides the database controls in clinical data management. Data often change in a trial over time, and the hardcode that is written today may not be valid in the future.Unfortunately, a hardcode may be forgotten and left in the SAS program, and that can lead to an incorrect database change.

Examples 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 ‘N’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

Examples 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 ‘N’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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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. 

Example: 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.

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. 

Example: 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.

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.

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.

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 “c:sdtm_data”;Libname dm xport “c:dm.xpt”; Proc copy; In = sdtm; Out = dm; Select dm; Run;

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 “c:sdtm_data”;Libname dm xport “c:dm.xpt”; Proc copy; In = sdtm; Out = dm; Select dm; Run;

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.

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.

I used PROC FREQ and proc UNIVARIATE to FIND the discrepancies in the data, which I REPORTED to my manager.

I used proc freq and proc univariate to find the discrepancies in the data, which I reported to my manager.

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.

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.

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.

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.

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.

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.

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 TOOL for the programmers and statisticians.

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 tool for the programmers and statisticians.

Case REPORT Tabulation, Whenever a PHARMACEUTICAL company is submitting an NDA, conpany has to SEND the CRT’s to the FDA.

Case Report Tabulation, Whenever a pharmaceutical company is submitting an NDA, conpany has to send the CRT’s to the FDA.

CDISC’s Study Data Tabulation MODEL (SDTM) has been developed to STANDARDIZE what is SUBMITTED to the FDA.

CDISC’s Study Data Tabulation Model (SDTM) has been developed to standardize what is submitted to the FDA.

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.

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.

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.

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.

I WOULD KNOW the program has been modified or not by SEEING the MODIFICATION history in the program header.

I would know the program has been modified or not by seeing the modification history in the program header.

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.

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.

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.

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.

We SUBMIT ISS and ISE DOCUMENTS to FDA.

We submit ISS and ISE documents to FDA.

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.

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.

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.

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.

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.

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.

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.

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.

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 REPORT and then after that we can compare it by using PROC COMPARE.

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 REPORT and then after that we can compare it by using PROC COMPARE.

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.

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.

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.

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.