ICMR-NIRT INTEGRATED DEGREE COURSE

Bachelor of Science in
“Clinical Trial and Clinical Data
Management”

Department of Statistics
National Institute for Research in
Tuberculosis
Indian Council of Medical Research
CHENNAI-600031

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SHORT TITLE AND COMMENCEMENT:-

These regulations shall be called as “UNDER GRADUATE

INTEGRATED COURSE IN CLINICAL TRIAL AND CLINICAL DATA
MANAGEMENT (B.Sc. - INTEGRATED)” of the Tamil Nadu Dr.
MGR Medical University, Chennai. They shall come into force from
the academic year “B.Sc. Integrated Course in Clinical Trial and
Clinical Data Management”. The regulations and the Syllabus
framed are subject to modification by the Standing Academic board
from time to time.

OVER ALL OBJECTIVES

The B.Sc. DEGREE in CLINICAL TRIAL AND CLINICAL DATA

MANAGEMENT is aiming to provide Graduate with updated exposure
by understanding of the basic principles of clinical trial with respect
to statistics and handling of quality data management in areas of
clinical specialty in the hospital and community level.

1. ELIGIBILITY FOR ADMISSION

A pass in 10+2 with Physics, Chemistry, Mathematics & Biology

subject or an equivalent with 12 years of Schooling from a
recognized Board of University with minimum 35% marks in each
subjects separately including English for all categories.

2. AGE LIMIT FOR ADMISSION:

A candidate should have completed the age of 17 years at the time

of admission or would complete the said age on or before 31st
December of the year of admission to the B.Sc. DEGREE in
CLINICAL TRIAL AND CLINICAL DATA MANAGEMENT.

3. ELIGIBILITY CERTIFICATE:

No Eligibility Certificate is required to submit. However the

candidate who has passed any qualifying examinations other than
the Higher Secondary Course Examination conducted by the
Government of Tamil Nadu, before seeking admission shall obtain
an Eligibility Certificate from this University by remitting the
prescribed fees along with application form which shall be
downloaded from the University website (www.tnmgrmu.ac.in).

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4. REGISTRATION:
A Candidate admitted in the “B.Sc. DEGREE in CLINICAL TRIAL
AND CLINICAL DATA MANAGEMENT” in the affiliated institutions
of this University shall register his / her name with this university by
submitting the prescribed application form for registration duly
filled, along with the prescribed fee and a declaration in the format
to the University through the affiliated institution within 30 days
from the cut-off date prescribed for the course for admission. The
applications should have the date of admission of the course.

5. MIGRATION/TRANSFER OF CANDIDATE :

a) A student studying in any one of the B.Sc. DEGREE in

CLINICAL TRIAL AND CLINICAL DATA MANAGEMENT can
be allowed to migrate/transfer to another institution of Allied
Health Science under the same University.
b) Migration / Transfer can be allowed to another affiliated
institutions under extraordinary circumstances. All
migration/transfer are subject to the approval of the Vice-
chancellor.

6. COMMENCEMENT OF THE COURSE:

The course shall commence from 1st August of the academic year.

7. MEDIUM OF INSTRUCTION:
English shall be the Medium of Instruction for all the Subjects of
study and for examinations of the B.Sc. DEGREE in CLINICAL
TRIAL AND CLINICAL DATA MANAGEMENT

8. CURRICULUM :
The Curriculum and the syllabus for the course shall be as
prescribed in these regulations are subject to modifications by the
Standing Academic Board from time to time.

9. DURATION OF THE COURSE :

The duration of certified study for this B.Sc. DEGREE in CLINICAL

TRIAL AND CLINICAL DATA MANAGEMENT shall extend over a
period of four academic years including one year internship (3+1).

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10. RE-ADMISSION AFTER BREAK OF STUDY:
The regulations for re-admission are as per the University Common
Regulation for Re-admission after break of study for all courses.

11. WORKING DAYS IN THE ACADEMIC YEAR:

Each academic year shall consist of not less than 240 working days

12. ATTENDANCE REQUIRED FOR ADMISSION TO EXAMINATION

a) No candidate shall be permitted to appear in any one of the
parts of this B.Sc. DEGREE in CLINICAL TRIAL AND CLINICAL
DATA MANAGEMENT Examinations unless he/she has attended
the course in the subject for the prescribed period in an
affiliated institution recognized by this University and produce
the necessary certificate of study, attendance and satisfactory
conduct from the Head of the institution.
b) A candidate is required to put in a minimum of 85% of
attendance out of 240 working days in theory in each subject
before admission to the examinations except for 1 st year
candidate where attendance will be counted for the date of
joining. The academic year should consist of not less
than 240 working days.

c) A candidate must have 100% attendance in each

of the Practical/Clinical areas before the award of
Degree.

13. CONDONATION OF LACK OF ATTENDANCE:

There shall be no condonation of lack of attendance.
14. VACATION:

There is no vacation
15. INTERNAL ASSESSMENT MARKS:

The Internal Assessment should consist of the following points for

evaluation:-
i Theory
ii Practical
iii Viva

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 iv A minimum of three written examinations shall be conducted
in each subject during a year and the average marks of the
three performances shall be taken into consideration for the
award of Internal Assessment marks.
v A minimum of one practical examination shall be conducted in
each subject
(wherever practical has been included in the curriculum) and
grades of
ongoing clinical evaluation to be considered for the award
of Internal
Assessment marks.
16. CUT-OFF DATES FOR ADMISSION TO EXAMINATIONS :

(i) 30th September of the academic year concerned

(ii)The candidates admitted up to 30 th September of the
academic year shall be registered to take up the 1 st
year examination during August of the next year.
(iii) All kinds of admission shall be completed on or
before 30th September of the academic year. There
shall not be any admission after 30th September even
if seats are vacant.

17. COMMENCEMENT OF THE EXAMINAITONS:

1st August / 1st February

If the date of commencement of examination falls on Saturdays /

Sundays or declared Public Holidays, the examination shall begin
on the next working day.

18. MARKS QUALIFYING FOR PASS:

50% of marks in the University Theory Examinations
50% of marks in the University Practical Examinations
50% of marks in the subject where internal evaluation alone is
conducted
50% of marks in aggregate in Theory, Practical I.A. & Oral taken
together

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19. CARRY OVER OF FAILED SUBJECTS:

1) A candidate has to pass in theory and practical

examinations separately in each of the paper
2) If a candidate fails either in theory or practical
examinations, he/she has to reappear for both (theory
and practical)

This will be implemented from the Academic Year 2020-21

onwards.

20. PRACTICAL EXAMINATION

Maximum number of candidates for practical examination should
not exceed 20 per day. An examiner should be a lecturer or above
in any of the affiliated institutions of Allied Health Sciences.
21. NUMBER OF EXAMINERS

One internal and one external examiner should jointly conduct

practical/ oral examination for each student
22. REVALUATION/RETOTALLING OF ANSWER

PAPERS:

Revaluation / Retotaling of answer papers is not permitted.

23. LATERAL ENTRY

Lateral entry is applicable for this U.G Degree course “B.Sc.

DEGREE in CLINICAL TRIAL AND CLINICAL DATA MANAGEMENT”
in the concerned specialty. The duration of internship is 1 year for
this degree course including Lateral Entry Admissions.

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 SCHEME OF EXAMINATIONS FOR UNDERGRADUATE INTEGRATED
COURSE
“CLINICAL TRIAL AND CLINICAL DATA MANAGEMENT
(B.Sc. - INTEGRATED)”
YEAR 1
Internal Hours
University Examinations Examinations Total per
Subject/Paper week
S.No
s Theory Practical’s Viva
Max Min Max Min Max Min Max Min
Paper 1: Basic
concepts in
1 100 50 * * * * 50 25 150 6
Clinical
Research
Paper 2:
2 Clinical Trial 100 50 * * * * 50 25 150 5
Designs
Paper 3:
Descriptive
Statistics and
3 100 50 50 25 50 25 100 50 300 5
Basics of
Clinical Data
Management
Paper 4:
Introduction to
4 software 100 50 50 25 50 25 100 50 300 5
handling and
EPI Data
Paper 5:
100(IA 50(IA
5 Communicatio * * * * * * 100 4
) )
n English
INTERNAL ASSESSMENT

Internal Assessment Total

Sl.No Subject/Papers
Max Min
Paper 5: Communication
5 100 50 100
English
6 Paper 6: Computer Science 100 50 100

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 YEAR 2
University Examinations Internal Hour
Examinatio s per
Total
ns wee
Sl.N Subject/Pape k
o rs Practical’
Theory Viva
s
Ma Min Max Min Max Mi Max Min
x n
Paper 1: 100 50 * * 50 25 50 25 200 5
Importance of
1 Ethics in
Clinical
Research
Paper 2: 100 50 * * 50 25 50 25 200 5
Pharmacology
and drug
2
Development
in Clinical
Research
Paper 3: 100 50 50 25 50 25 100 50 300 7
3 Inferential
Statistics
Paper 4: 100 50 50 25 50 25 100 50 300 8
Software
Handling part
4 I: SPSS, EPI
INFO, RED Cap
and Open
Clinica

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 YEAR- 3
Internal Hour
Examinatio s per
University Examinations Total
ns wee
Sl.N Subject/Pape k
o rs Practical’
Theory Viva
s
Ma Min Max Min Max Mi Max Min
x n
Paper 1: 100 50 * * 50 25 50 25 200 5
Regulations In
1
Clinical
Research
Paper 2: Data 100 50 * * 50 25 50 25 200 5
Safety
Monitoring
2
Board and
Clinical Trial
Management
Paper 100 50 50 25 50 25 100 50 300 7
3:Advanced
3
Statistical
Methods
Paper 4: 100 50 50 25 50 25 100 50 300 8
Software
4
Handling Part
II: SAS, R

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Year 1
Paper 1: Basic Concepts in Clinical Research
UNIT I
Basics of epidemiology, Definition, scope, and uses of epidemiology Measures of
disease and death frequency, Mortality and morbidity, epidemiological study
designs, Observational studies, descriptive studies, experimental studies,
Ecological studies , cross sectional studies, cohort studies, case control studies,
incidence, prevalence, odds ratio, relative risk.
UNIT II
Basics of Clinical Trials: Who can be in clinical trials? need clinical trials, Brief History of Clinical Trials,
Glossary of Common Terms in clinical Trials: Clinical Research, Healthy Volunteer, Inclusion/Exclusion
Criteria, Informed Consent, Patient Volunteer, Phases of Clinical Trials, Placebo, Protocol, Principal
Investigator, Randomization, Single- or Double-Blind, Studies, Types of Clinical Trials. - Diagnostic
trials, Natural history studies, Prevention trials, Quality of life trials, Screening trials, Treatment trials.
Clinical Trial Protocol and its components. Type of analyses: ITT, mITT and PP.
UNIT II
Randomized Controlled Trial (RCT): what is a randomized controlled trial? Reasons for randomization,
Features of RCT. Who sponsors and runs clinical trials? How should an RCT be designed?, How should
an RCT be conducted?: Random allocation, Allocation concealment, Blinding, Conduct, Outcome
ascertainment, Sample size, Power of a study. How should an RCT be reported? Randomization and
Masking, Overview of Clinical Study Design
UNIT III
Clinical Trials Metrics Collection, Clinical Data Management, Data Processing – Database -Definition of
Data Management and its benefits -Types of data:, data collection methods, raw, physical collection,
models, images etc. –Data entry - File naming – Data assurance : quality control and assurance of data,
Medical coding, dictionary management and maintenance of quality documents

UNIT IV
Missing data , Submitting data , Metadata: Metadata standards , submitting Data , File formats ,Preserve:
Backup of data , Migration: Transformation of data , Discovering data ,Integrate: Merging of multiple
data sets , Data Citation , Data retrieval, Archiving , Double data entry and checking , Quality control and
Data Cleaning
References
Leon Gordis (2014). EPIDEMILOGY. Elsevier
Lawrence MF, Curt DF, David LD (2010) Fundamentals of clinical trials

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Paper2: Clinical Trial Designs

Unit 1: Comparison Structure: Parallel, Crossover, and Group Allocation Designs

Unit 2: Extensions of the Parallel Design: Factorial and Large Simple Designs

Unit 3: Superiority, Equivalency and Non-Inferiority Designs

Unit 4: Adaptive Design

Reference
Lawrence MF, Curt DF, David LD (2010) Fundamentals of clinical trials
Tom Brody (2016). Clinical trials. Elsevier

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Paper 3: Descriptive Statistics and Clinical Data Management
UNIT I
Statistical Methods: Definition and scope of Statistics, concepts of statistical population and sample.
Data: quantitative and qualitative, attributes, variables, scales of measurement nominal, ordinal, interval
and ratio. Presentation of data: tabular and graphical, including histogram and ogives.
UNIT II
Measures of Central Tendency: arithmetic mean, geometric mean, harmonic mean, median, mode,
weighted mean. Measures of Dispersion: range, quartile deviation, mean deviation, standard deviation,
coefficient of variation, Moments, absolute moments, factorial moments, skewness and kurtosis,
Sheppard’s corrections.
UNIT III
Probability: Introduction, random experiments, sample space, events and algebra of events. Definitions
of Probability – classical, statistical and axiomatic. Conditional Probability, laws of addition and
multiplication, independent events, theorem of total probability, Bayes’ theorem and its applications.
Bernoulli distribution, Uniform distribution, Binomial distribution, Poisson distribution, Normal
distribution.
UNIT IV

SAMPLING DISTRIBUTIONS: Limit Theorems: Chebychev’s inequality, Weak Law of Large Numbers,
Central Limit Theorems. De Moivre-Laplace and Levy-Lindberg theorems. Proofs and applications-
Concepts of statistic, parameter, pivotal quantity, sampling distribution and standard error. Chi-square, t
and F distributions, their properties and interrelationships. Independence of sample mean and variance in
random sampling from Normal distribution. Sampling distribution of the standard statistics-sample mean,
sample variance, student’s t and F statistics

UNIT V
Excel for data management, Basic data analysis and visualization in Excel
References
Goon AM, Gupta MK, Dasgupta B (2008). Fundamentals of Statistics, Published by Prentice Hall, 2nd
edition. 2.
Gupta SC, Kapoor VK (2000). Fundamentals of Mathematical Statistics, Sultan Chand Sons. 10th edition.
Pagano M, Gauvreau K (2007). Principles of Biostatistics. Duxbury Press.
Rohatgi VK, Saleh AK(2001). An Introduction to Probability and Statistics, John Wiley & Sons.
Bernard R (2010). Fundamentals of Biostatistics. Cengage Learning
William GC (1997). Sampling Techniques. John Wiley & sons 3rd edition.

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Paper 4: Introduction to Software Handling and EPI Data
UNIT I:
Introduction to Computers, Concepts of computing, data and information, Data entry, Transcribing data,
Clinical Data coding, Database creation, Logical checks, Importing and exporting files, Merging
database, Data Review, Data Validation, Discrepancy Management, Data privacy, Database Quality
Control, Cleaning data, Missing list, Electronic data capture, CRF form design, Database design, Edit
Check and Edit Check Testing, Publishing and sharing data
UNIT II:

EpiData software: Overview of the software, data documentation sheet and creation of documentation
sheet, QES, REC, CHK triplet, CHK commands unrelated to a specific field, creating derived fields and
concept of temporary variables, Double Data Entry and Validation, use an external file for creating a label
block and use it in EpiData, capture data entry time, Exporting Data to other Analysis Software, Data
Safety and Security

References

Steve B, Mark M, Damien J, Andrzej R (2001). Data Management for Surveys and Trials . A practical
premier using epidata. The Epidata Association
INTERNAL PAPER
Paper 5: Communication English
UNIT I:
Communication: Role of communication, Defining Communication, Classification of communication,
Purpose of communication, Major difficulties in communication, Barriers to communication,
Characteristics of successful communication, The seven Cs, Communication at the work place, Human
needs and communication “Mind mapping”, Information communication
UNIT II:
Comprehension passage: Reading purposefully, Understanding what is read, Drawing conclusion,
Finding and analysis
UNIT III:
Explaining: How to explain clearly, Defining and giving reasons, Explaining differences, Explaining
procedures, Giving directions
UNIT IV:
Writing business letters: How to construct correctly, Formal language, Address, Salutation, Body,
Conclusion

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UNIT V:
Report writing: Reporting an accident, Reporting what happened at a session, Reporting what happened
at a meeting
References
Amit G (2018). English communication. SBPD publications
Elizabeth TR, Deborah B, Lori LH, Jill M, Sarah VH (2011), Communications Handbook for Clinical
Trials. Family Health International
YEAR 2
Paper 1: Importance of Ethics in Clinical Research
UNIT I
General ethical issues in clinical trials, General principles, Historical guidelines in Clinical Research:
Nuremberg code-Declaration of Helsink-Belmont report
UNIT II:
International Conference on Harmonization (ICH)-Brief history of ICH-Structure of ICH- ICH
Harmonization Process, Responsible conduct of research, Ethical review procedures, Informed consent
process, Vulnerability
UNIT III:
Guidelines for Good Clinical Practice-Glossary-The Principles of ICH GCP-Institutional Review Board/
Independent Ethics Committee-Investigator-Sponsor-Clinical Trial Protocol and Protocol Amendment(S)-
Investigator’s Brochure-Essential Documents for the conduct of a Clinical Trial, Biological materials,
biobanking and datasets, Research during humanitarian emergencies and disasters
References
Roli M (2017). National ethical guidelines for biomedical and health research involving human
participants. ICMR
Declaration of Helsinki: ethical principles for medical research involving human subjects. Fortaleza:
World Medical Association. 2013
Federal Policy for the Protection of Human Subjects (‘Common Rule’). U.S. Department of Health and
Human Services; (1991); 2001, 2017
International ethical guidelines for health-related research involving humans. Geneva: Council for
International Organizations of Medical Sciences; 2016.

WHO (2011) Standards and Operational Guidance for Ethics Review of Health-Related Research with
Human Participants

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Paper 2: Pharmacology and drug development in clinical Research
UNIT I
Introduction to Pharmacology - Introduction to Drug Discovery and Development - Sources of Drugs
-Approaches to Drug Discovery - Evolutionary Classification of the strategies for Drug Discovery -
UNIT II
Concept of Essential Drugs - Routes of Drug Administration Pharmacokinetics, Pharmacodynamics
UNIT III
Investigational New Drug Application and Approval - Preclinical Testing - Phases of Clinical trials - -
Pharmacokinetics – Pharmacodynamics - Drug assay - Pharmacogenomics and Protein-based therapies, -
Recent advances
References
Gupta SK (2011). Drug discovery and clinical research. Jaypee Brothers Medical Publishers
Turner, JR (2010). New Drug Development: An Introduction to Clinical Trials. Springer
Paper 3: Inferential statistics
UNIT I:
Point Estimation: Concepts of parameter, random sample and its likelihood. Properties of estimators-
Unbiasedness, Efficiency, Consistency and sufficient condition for consistency. Sufficiency, Factorization
theorem, Minimum variance unbiased estimator, Rao Cramer lower bound of variance and related results.
Methods of estimation-maximum likelihood and method of moments
UNIT II:
Test of significance: Statistical hypotheses-Simple and composite, Statistical tests, Critical region, Type I
and Type II errors, power of a test Interval estimation: Concepts of confidence interval and confidence
coefficient, the confidence interval for mean, difference between means, variance and ratio of variances
under normality. The large sample confidence interval for proportions and correlation coefficients
UNIT III:
Testing of Hypothesis: Definition of Most Powerful (MP), Uniformly Most Powerful(UMP), Neyman
Pearson Lemma, Monotone Likelihood Ratio Property, Statement of the theorem which gives UMP tests
for testing one-sided hypothesis for distribution with MLR property, Likelihood Ratio test, LRT for single
mean for normal case (large and small samples), for equality of two means for unknown but equal
variances. LRT for single variance and equality of two variances

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UNIT IV:
Test for the mean, equality of two means, ANOVA, variance and equality of two variances (large and
small samples), large sample tests for proportions, test for correlation coefficients-simple, multiple and
partial. Test for regression coefficients. Fisher’s Z transformation and its applications.
UNIT V:
Non-Parametric Tests: Need for non-parametric tests, Sign test for one sample and two samples,
Wilcoxon signed-rank test, Median test, Wald Wolfowitz run test, Mann Whitney U test, Run test for
randomness, test for independence based on Spearman’s rank correlation coefficient(small and large
samples),. Chi-square test, goodness of fit, independence of attributes in the contingency table, and
equality of many proportions. Kruskal Wallis Test.-Sequential Probability Ratio Test: Need for sequential
test, Wald’s SPRT, Sequential-test for the mean of Normal population when variance is known and for the
proportion.-Derivation of expressions for OC and ASN functions in Bernoulli and Normal distributions.
UNIT VI
Bivariate data: Definition, scatter diagram, simple, partial and multiple correlations (3 variables only),
rank correlation. Simple linear regression
Principle of least squares and fitting of polynomials and exponential curves. Theory of attributes:
Independence and association of attributes, consistency of data, measures of association and contingency,
Yule’s coefficient of colligation.
References
Hogg RV, Tanis EA (2001). Probability and Statistical Inference, Prentice Hall International Inc.
Kale BK (1999). A first Course on Parametric Inference, Narosa Publishing House.
Goon AM, Gupta M. K., Dasgupta B (2008): Fundamentals of Statistics, Published by Prentice Hall, 2nd
edition. 2.
Gupta SC, Kapoor VK (2000): Fundamentals of Mathematical Statistics, Sultan Chand Sons 10th
edition.
Pagano M Gauvreau K (2007). Principles of Biostatistics. Duxbury Press
Rohatgi VK, Saleh AK (2001). An Introduction to Probability and Statistics, John Wiley & Sons.
Beth D, Robert GT (2004). Basic & Clinical Biostatistics. Lange Medical Books
Douglas C M, Elizabeth A P, Vining GG (2006). Introduction to Linear Regression Analysis. Wiley India
Pvt Ltd

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Paper 4: Software handling part I: SPSS, EPI INFO, Red cap and Open Clinica
UNIT I.
Getting acquainted with the SPSS program

Review of terminology: Basic categories of research, What is a variable, Categorical versus continuous,
Independent versus dependent variables, Non repeated versus repeated measures variables, Measurement
scale, Common statistical programs, Orientation to SPSS program, What is under each menu, Getting data
into SPSS: Creating a new data set, Valid variable names, Variable view, Adding value labels, Reading in
an existing data set (Excel)

Data management and descriptive statistics

Data entry in SPSS

SPSS techniques for cleaning data- Univariate statistics, creating, modifying, and copying charts/graphs
for categorical variables-Creating histograms and boxplots for continuous variables.

Merging and restructuring datasets: Why not to use cut-and-paste-Add variables, Consistency of
subject identifiers, Add cases, Consistency of variable types-Restructuring datasets, Long/thin/vertical-
Short/wide/horizontal
Data analysis using SPSS: Summarization of data, bivariate correlations, Interpreting correlation
coefficients, Pearson, spearman, and point biserial correlations, Scatterplots, Adding a linear regression
line, Caution: outliers and non, linear relationships, Scatterplots to demonstrate time trends, Phi
coefficient, Crosstabs, Bivariate associations with continuous variables: Correlations using values, _Time
Corrected Scatterplots, Adding a line of best fit, scatterplots using for continuous values ,Parametric and
non, parametric methods, multivariable regression methods

UNIT II: EPI INFO

Form Designer – Create the questionnaire, form, or form to collect and view data.
Enter – Enter data and show existing records in the form.
Classic Analysis – Run statistical analyses, lists, tables, graphs, charts, etc.
Map – Create maps from Map-Server or Shape Files.
Options – User custom configuration of Epi Info.

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UNIT III: RED CAP

Building a Project: Introduction to Project Development- Online Designer- Data Dictionary

- Project Field Types

Basic Features & Modules: Applications Overview- The Calendar- Scheduling Module-
Data Access Groups for multi-site projects

Types of REDCap Projects: Types of Projects- Traditional Project (classic model with data entry forms)
Single Survey Project- Longitudinal Project (multi-use data entry forms,abstract time-points),
Longitudinal Project+ Scheduling (multi-use data entry forms,defined time points), Operations( use case
for non-study/non-trial)

Special Features within REDCap Projects: Defining Events in Longitudinal Projects,

Designating Instruments for Events in Longitudinal Projects, Repeatable instruments
and events, RED Cap Mobile App, Locking Records, Data Resolution, Workflow

References

Beth MS, Janie HW, Dennis MG (2018). An Easy Guide to Research Design & SPSS. Sage publication
IBM (2016). Programming and Data Management for IBM SPSS Statistics 24: A Guide for IBM SPSS
Statistics and SAS Users Andrew GD, Kevin MS, Minn MS (2010). Epi Info and Open Epi in
Epidemiology and Clinical Medicine: Health Applications of Free Software. Creates pace Independent
Pub.
Vanderbilt University (2015). REDCap Beginner’s Guide

Paper 5: Computer Science

UNIT I: INTRODUCTION TO COMPUTERS AND OPERATION SYSTEMS
Evolution of Computers, Generation of Computers, Cassification of computes Analog Digital
and Hybrid Computers Classification of Computers according to size, types of OS
UNIT II: INTRODUCTION TO PROGRAMMING CONCEPTS
Types of Programming Languages, software, Classification of software
UNIT III: INTRODUCTION TO DATA BASE MANAGEMENT SYSTEMS
Need of DBMS, Storage of data and retrieval of data, file system and DBMS Architecture

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UNIT IV: DISCRETE MATHEMATICS
Logic and Boolen algebra, set theory, relations and functions. Mathematical reasoning and
Graphs
UNIT V: INTRODUCTION TO ARTIFICIAL INTELLIGENE AND MACHINE
LEARNING
Background on Artificial intelligence and Machine Learning Types of Machine Learning, deep
learning, Supervised Learning, Unsupervised Learning and Reinforcement Learning.
UNIT IV: DISCRETE MATHEMATICS
Logic and Boolen algebra, set theory, relations and functions. Mathematical reasoning and
Graphs
YEAR 3:
Paper 1: Regulations in Clinical Research
UNIT I

Introduction of Clinical Trial Regulation, Aims and Key benefits of the regulations, regulatory and data
strategy, Evolution of regulatory changes in India, Regulatory requirements for the conduct of clinical
trials in India, Regulatory Bodies, Framework and Procedures (INDIA), Regulatory Bodies, Framework
and Procedures (Foreign), Central Drugs Standard Control Organization (CDSCO), Initiatives and
Priorities of CDSCO
UNIT II

Food and Drug Administration (US FDA), advances the FDA’s mission, Drug and cosmetic act- Schedule
Y- ICMR Guideline- data Safety monitoring board Regulations: Roles and Responsibilities,
Membership, Meetings, Study Reports for DSMB Meetings, Reports from the DSMB, Relationship
Between DSMBs and IRBs, Reimbursement,
References
WHO (2002). Handbook for good clinical research practice: Guidance for implementation. WHO Library
Cataloguing-in-Publication Data
Josef K, Paul M, Graeme S (2000). Good Clinical Practice: Standard Operating Procedures for Clinical
Researchers. Wiley

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Paper 2: Data Safety Monitoring Board and Clinical Trial Management
UNIT I
Project Management, Protocol development in Clinical Research, Informed Consent, Case Report Form,
Investigator’s Brochure (IB), Selection of an Investigator and Site
UNIT II
Clinical Trial Stakeholders ,Contract Research Organization (CRO) ,Site management organizations
(SMO), Ethical and Regulatory Submissions ,Recruitment Techniques ,Retention of Clinical Trial
Subjects
UNIT III
Monitoring Visits, Investigator Meeting, Documentation in Clinical Trials, Regulatory Binder, Record
Retention – Pharmacovigilance, Training in clinical Research, Project Auditing, Inspection, Fraud and
Misconduct, Roles and Responsibilities of Clinical Research Professionals
References
JoAnn P, Cris W (2017). A Practical Guide to Managing Clinical Trials. CRC Press
Alexey L (2017). Project Management in Clinical Trials
Paper 3: Advanced Statistical Methods
Logistic regression, life table construction, log rank test, survival analysis, Kaplan Meir curve, parametric
and non-parametric methods, Weibull regression, cox regression, exponential regression, nonlinear
regression methods, Poisson regression, Negative binomial regression, Ridge regression
References
David C(2015). Modelling Survival Data in Medical Research. Chapman and Hall
Long JS (1997). Regression Models for Categorical and Limited Dependent Variables. Sage publications

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Paper 4: Software Handling Part II: SAS, R and Python
UNIT-I : SAS
Introduction to SAS program , SAS Data types and Libraries , Data Steps and Proc Steps , Format & In
format , Creating Output Proc Print, Proc Contents , Output Delivery System (ODS) ,Reading Raw data ,
Column input , Understanding Data step processing , Formatted Input and List input , Reading date and
Time format , Reading Instream data , Creation of raw data file from dataset ,Managing Variables in
dataset , Assignment and Cumulative statement , Sub setting data, drop and keep option , If - else, if- else
with do statement, Select When, Do, loop Statement , Managing SAS Dataset using set statement , SAS
functions Overview , String Functions , Conversion Functions , Date Functions , Mathematical Functions,
Descriptive statistics, Proc means and proc freq , Proc report, column, define, headline, head skip,
compute, order and group , Proc tabulate Proc transpose, Combining data set, one to one reading,
concatenation and merge , Array, single and multi, dimensional array , Proc print, proc import and proc
export
UNIT II : R
Installation and Initialization ,Basic Linux Commands ,Package Management and process Monitoring.,
Important Files, Directories and Utilities ,Advance Shell Programming ,System Services ,User
Administration ,File System Security & Advanced File System Management ,Server Configuration &
Virtualization ,Samba and Mail Services Virtualization ,Advance Security & Networking Concepts.
,Concept of Data Analytics & ,Data Manipulation in R ,Data Import Techniques, Exploratory Data
Analysis, Data Visualization, Data Mining: Clustering Technique, Data Mining: Association Rule Mining
and Sentiment Analysis, ANOVA, Predictive Analysis & Simulation, Implementation of Decision tree,
Introductory Concepts, Database Design, Relational Model and SQL, Database design using the relational
model, Storage and Indexing Structures, Transaction Processing and Concurrency Control (OLTP &
OLAP),Database recovery techniques, Query Processing and Optimization, Database Security and
Authorization, Enhanced Data Models for specific applications, Enhanced Data Models for specific
applications, Distributed databases and issues

UNIT III:
Basic Java ,Arrays, Objects and Classes, Control Flow Statements, Inheritance and Interfaces, Exception
Handling & Serialization, Collections, Reading and Writing files, Python Basics, OOPs concept in
Python, Exception Handling in Python, Python for Data Science an Introduction, Pre Processing of Data,
Visualizing the Data, Exploratory Data Analysis, Clustering and identification of Outliers using Python,
DS Performing Cross, Validation, Selection, and Optimization using Python, Learning from Data using
Python

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References
Lorda DD, Susan JS (2012). The Little SAS Book: A Primer. SAS Institute
Ron C (2015). An Introduction to SAS University Edition. SAS Institute
Venables WN, Smith DM, The R Core Team (2019). An Introduction to R. R Core Team
Norman M (2011).The Art of R Programming – A Tour of Statistical Software Design. No Starch Press
Winston C (2012). R Graphics Cookbook. O'Reilly Media
Jack V (2016). Python Data Science Handbook: Essential Tools for Working with Data. O′Reilly
Eric M(2015). Python Crash Course. No Starch Press
POSTINGS FOR INTERNSHIP:- (12 Months)
v) Clinical Trial Data Monitoring Unit-I (Department of STATISTICS, ICMR-NIRT) : 3 Months
i Monitoring Screening Activities with respect to Patient enrolment
ii Checking eligibility criteria
iii Randomization / Treatment Allocation
iv Monthly monitoring / investigation or schedule of protocol checking
v Identifying Discrepancies and resolve queries
vi Periodical manual data posting and data extraction in a structured format
vii Treatment Adherence Checking
2. Clinical Data Management Unit (e-source Wing, Department of STATISTICS, ICMR-NIRT): 3 Months
- Form Design, Database Creation and platform checking, identifying deficiency, resolve queries
and related modification
- Database CRF checking, Reviewing CRFs, Documentation Analysis and Deficiency Checking,
Export, File conversion and Data transfer mode for analysis Stage)
- Quality Checking and Quality Assurance
- Data cleaning, Decoding and creating data definition
3. Clinical Trial Data Monitoring Unit-II (Department of STATISTICS, ICMR-NIRT): 3 Months
- Clinical Trial Data CONSORT preparation Stage 1 Analysis
- ITT and mITT analysis, Number Needed to Treat Analysis
- per Protocol Analysis, sample size re-estimation, power analysis
4. Industrial Training and Report Submission: 3 Months
- Hospitals and Research Centres
- Pharmaceutical Companies and Healthcare Industries

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