Clinical Researchers

Designing Clinical Research

2018-03-20T22:05:00.000-07:00

This book is about the science of doing clinical research in all its forms: translational research,clinical trials, patient-oriented research, epidemiologic studies, behavioral science and health services research. Codifying the nature of this broad-based science and how to do it is not straightforward, and there is no single approach that everyone agrees is best. Our first two editions drew on the terms and principles of epidemiology in a practical and reader-friendly way, emphasizing systematic and common sense approaches to the many judgments involved in designing a study.The Third Edition of Designing Clinical Research (DCR) follows the same path, adding new developments along the way. New material on observational studies includes case-crossover designs, and the use of propensity scores, instrumental variables and Mendelian randomization to control confounding. Reorganized chapters on clinical trials introduce adaptive designs, and those on studying medical tests and on utilizing existing datasets present expanded options that will be attractive to beginning investigators. The chapter on research ethics is extensively updated, the one on data management entirely new (reflecting current approaches to information technology),and a rewritten chapter on study implementation and quality control introduces practicalities of study startup and regulatory issues (‘‘Good Clinical Practices’’). An updated chapter on getting funded brings help for the challenges facing young investigators.

Clinical Kidney Transplantation...free online course from Universiteit Leiden, Leiden University Medical Center

2016-11-21T18:46:00.001-08:00

About this course:

Kidney transplantation is a major advance of modern medicine which provides high-quality of life for patients with end-stage renal disease. What used to be an experimental, risky, and very limited treatment option more than 50 years ago is now routinely performed in many countries worldwide. The number of renal transplants is expected to rise sharply in the next decade since the proportion of patients with end stage renal disease is increasing. Are you interested in clinical kidney, pancreas and islet transplantation? If you are a (bio) medical student or a health care professional who works in the (pre) clinical transplant field this might be the course for you. This course is also for anyone interested in the research and knowledge on clinical transplantation. The course will be taught by a multidisciplinary team of transplant professionals and will give you the state of the art updates. It is divided in 4 modules: 1) Before the transplant 2) The surgical procedures and the challenged patient, including the patient with diabetes 3) Early challenges 4) Late challenges after transplantation. The offered modules will include lectures, interactive patient cases, 3D movies, interviews with well-known experts and with patients and a donor, a serious game to increase knowledge of the field and of course an active forum. Become an expert and join us! The course is endorsed by The European Society of Organ Transplantation (ESOT), The International Society of Nephrology (ISN) and The Transplantation Society (TTS).

Who is this class for:

Anyone with a general interest in Clinical Kidney Transplantation, both beginners and professionals. This course is free, and will remain free for participants. You can purchase a certificate.

Taught by: Dr.Marlies Reinders

LUMC, Department of Nephrology and Transplantation
Taught by: Professor Frans Claas,

LUMC, Department of Immunohaematology and Blood Transfusion
Taught by: Professor Cees van Kooten ,

LUMC, Department of Nephrology and Transplantation
Taught by: Dr.Sebastiaan Heidt,

LUMC, Department of Immunohaematology and Blood Transfusion

Taught by: Dr.Volkert Huurman,

LUMC, Department of Transplant Surgery
Taught by: Professor Hans de Fijter,

LUMC, Department of Nephrology and Transplantation
Taught by: Professor Eelco de Koning ,

LUMC, Department of Nephrology and Transplantation and Endocrinology and Metabolism
Taught by: , Dr. Andre Baranski

LUMC, Department of Transplant Surgery
Taught by: Professor Ton Rabelink,

LUMC, Department of Nephrology and Transplantation
Taught by: Dr.Aiko de Vries,

LUMC, Department of Nephrology and Transplantation
Taught by: Dr.Jan Nico Bouwes Bavinck,

LUMC, Department of Dermatology
Taught by: Professor Leo Visser ,

LUMC, Department of Infectious Diseases
Taught by: Dr. Ingeborg Bajema,

LUMC, Department of Pathology

Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	Average User Rating 4.7See what learners said

Diabetes - a Global Challenge...free online course from University of Copenhagen

2016-11-21T18:28:00.005-08:00

About this course:

Diabetes and obesity are growing health problems in rich and poor countries alike. With this course you will get updated on cutting-edge diabetes and obesity research including biological, genetic and clinical aspects as well as prevention and epidemiology of diabetes and obesity. All lectures are provided by high-profile scientists from one the world's leading universities in diabetes research. This course is part of the EIT Health Campus programme. We hope you will enjoy our course. Best Wishes

Taught by: Jens Juul Holst, Professor, MD, DMSc

Department of Biomedical Sciences & NNF Center for Basic Metabolic Research

Taught by: Signe Sørensen Torekov, Associate Professor, MSc, PhD

Department of Biomedical Sciences & NNF Center for Basic Metabolic Research

Taught by: Nicolai Jacob Wewer Albrechtsen, MD, GCSRT, PhD student

Department of Biomedical Sciences & NNF Center for Basic Metabolic Research

Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	Average User Rating 4.7See what learners said

Introduction to the Biology of Cancer...free online course from Johns Hopkins University

2016-11-21T18:19:00.003-08:00

About this course:

Over 500,000 people in the United States and over 8 million people worldwide are dying every year from cancer. As people live longer, the incidence of cancer is rising worldwide and the disease is expected to strike over 20 million people annually by 2030. This open course is designed for people who would like to develop an understanding of cancer and how it is prevented, diagnosed, and treated. The course introduces the molecular biology of cancer (oncogenes and tumor suppressor genes) as well as the biologic hallmarks of cancer. The course also describes the risk factors for the major cancers worldwide, including lung cancer, breast cancer, colon cancer, prostate cancer, liver cancer, and stomach cancer. We explain how cancer is staged, the major ways cancer is found by imaging, and how the major cancers are treated. In addition to the core materials, this course includes two Honors lessons devoted to cancers of the liver and prostate. Upon successful completion of this course, you will be able to: - Identify the major types of cancer worldwide. (Lecture 1) - Describe how genes contribute to the risk and growth of cancer. (Lecture 2) - List and describe the ten cellular hallmarks of cancer. (Lecture 3) - Define metastasis, and identify the major steps in the metastatic process. (Lecture 4) - Describe the role of imaging in the screening, diagnosis, staging, and treatments of cancer. (Lecture 5) - Explain how cancer is treated. (Lecture 6) We hope that this course gives you a basic understanding of cancer biology and treatment. The course is not designed for patients seeking treatment guidance – but it can help you understand how cancer develops and provides a framework for understanding cancer diagnosis and treatment.

Who is this class for:

This course is designed for undergraduates, graduates, nurses, physicians, other healthcare professionals, and researches who need a refresher or are considering pursuing research in the field. This course is not intended for patients seeking treatment guidance.

Taught by:

Kenneth J. Pienta, M.D., The Donald S. Coffey Professor of Urology

The James Buchanan Brady Urological Institute

Level	Beginner
Commitment	6 weeks of study, 1-4 hours/week
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	Average User Rating 4.7See what learners said

Interprofessional Healthcare Informatics..free online course from University of Minnesota

2016-11-21T18:09:00.001-08:00

About this course:

Interprofessional Healthcare Informatics is a graduate-level, hands-on interactive exploration of real informatics tools and techniques offered by the University of Minnesota and the University of Minnesota's National Center for Interprofessional Practice and Education. We will be incorporating technology-enabled educational innovations to bring the subject matter to life. Over the 10 modules, we will create a vital online learning community and a working healthcare informatics network. We will explore perspectives of clinicians like dentists, physical therapists, nurses, and physicians in all sorts of practice settings worldwide. Emerging technologies, telehealth, gaming, simulations, and eScience are just some of the topics that we will consider. Throughout the course, we’ll focus on creativity, controversy, and collaboration - as we collectively imagine and create the future within the rapidly evolving healthcare informatics milieu. All healthcare professionals and IT geeks are welcome!

Taught by:

Karen Monsen, Assistant Professor

School of Nursing

Commitment	7 hours of videos and quizzes
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	Average User Rating 4.3See what learners said

Case Studies in Personalized Medicine...free online course from Vanderbilt University

2016-10-24T20:05:00.002-07:00

About this course: Learn how advances in biomedicine hold the potential to revolutionize drug development, drug treatments, and disease prevention: where are we now, and what does the future hold? This course will present short primers in genetics and mechanisms underlying variability in drug responses. A series of case studies will be used to illustrate principles of how genetics are being brought to bear on refining diagnoses and on personalizing treatment in rare and common diseases. The ethical and operational issues around how to implement large scale genomic sequencing in clinical practice will be addressed.

After completing this course, learners will understand 1. The ways in which genetic variants can contribute to human disease susceptibility 2. How to choose among drug therapies based on genetic factors 3. That the functional consequences of the vast majority of genetic variants discovered by modern sequencing are unknown. This course is targeted primarily at physicians 5+ years out of training. Other healthcare providers, medical/health sciences students, and members of the public may also be interested. Course launches January 15, 2016.*
The information presented in “Case Studies in Personalized Medicine” is offered for educational and informational purposes only, and should not be construed as personal medical advice. If you have questions or concerns about a medical matter, please consult your doctor or other professional healthcare provider.

Taught by:

Dan Roden, M.D., Professor of Medicine and Pharmacology

Assistant Vice Chancellor for Personalized Medicine

Commitment	2-4 hours/week
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	Average User Rating 4.8See what learners said

Understanding Clinical Research: Behind the Statistics...free online course from coursera

2016-10-24T19:46:00.001-07:00

About this course: If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves.

If you are simply interested in properly understanding the published literature or if you are embarking on conducting your own research, this course is your first step. It offers an easy entry into interpreting common statistical concepts without getting into nitty-gritty mathematical formulae. To be able to interpret and understand these concepts is the best way to start your journey into the world of clinical literature. That’s where this course comes in - so let’s get started!

The course is free to enroll and take. You will be offered the option of purchasing a certificate of completion which you become eligible for, if you successfully complete the course requirements. This can be an excellent way of staying motivated! Financial Aid is also available.

Created by: 

  University of Cape Town

Taught by:

    Juan H Klopper, Dr

Department of Surgery

6 weeks of study, 2-3 hours/week
LanguageEnglish
How To PassPass all graded assignments to complete the course.
User Ratings

Average User Rating 4.6See what learners said

Getting and Cleaning Data...free online course from Johns Hopkins University

2016-10-24T02:06:00.000-07:00

About this course: Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

Taught by:

Roger D. Peng, PhD, Associate Professor, Biostatistics

Bloomberg School of Public Health

Taught by:

Jeff Leek, Associate Professor, Biostatistics

Bloomberg School of Public Health

Taught by:

Brian Caffo, Professor, Biostatistics

Bloomberg School of Public Health

Basic Info	Course 3 of 10 in the Data Science Specialization.
Language	English, Subtitles: Russian, Chinese (Simplified)
How To Pass	Pass all graded assignments to complete the course.
User Ratings	Average User Rating 4.4See what learners said

Exploratory Data Analysis...free online course from Johns Hopkins University

2016-10-24T01:53:00.001-07:00

About this course: This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Taught by:

Roger D. Peng, PhD, Associate Professor, Biostatistics

Bloomberg School of Public Health

Taught by:

Jeff Leek, Associate Professor, Biostatistics

Bloomberg School of Public Health

Taught by:

Brian Caffo, Professor, Biostatistics

Bloomberg School of Public Health

Basic Info	Course 4 of 10 in the Data Science Specialization.
Language	English, Subtitles: Portuguese (Brazilian), Chinese
How To Pass	Pass all graded assignments to complete the course.
User Ratings	Average User Rating 4.6See what learners said

HIPAA PHI: List of 18 Identifiers and Definition of PHI

2016-10-22T07:10:00.002-07:00

List of 18 Identifiers

1. Names;
2. All geographical subdivisions smaller than a State, including street address, city, county, precinct, zip code, and their equivalent geocodes, except for the initial three digits of a zip code, if according to the current publicly available data from the Bureau of the Census: (1) The geographic unit formed by combining all zip codes with the same three initial digits contains more than 20,000 people; and (2) The initial three digits of a zip code for all such geographic units containing 20,000 or fewer people is changed to 000.
3. All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, date of death; and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older;
4. Phone numbers;
5. Fax numbers;
6. Electronic mail addresses;
7. Social Security numbers;
8. Medical record numbers;
9. Health plan beneficiary numbers;
10. Account numbers;
11. Certificate/license numbers;
12. Vehicle identifiers and serial numbers, including license plate numbers;
13. Device identifiers and serial numbers;
14. Web Universal Resource Locators (URLs);
15. Internet Protocol (IP) address numbers;
16. Biometric identifiers, including finger and voice prints;
17. Full face photographic images and any comparable images; and
18. Any other unique identifying number, characteristic, or code (note this does not mean the unique code assigned by the investigator to code the data)

There are also additional standards and criteria to protect individual's privacy from re-identification. Any code used to replace the identifiers in datasets cannot be derived from any information related to the individual and the master codes, nor can the method to derive the codes be disclosed. For example, a subject's initials cannot be used to code their data because the initials are derived from their name. Additionally, the researcher must not have actual knowledge that the research subject could be re-identified from the remaining identifiers in the PHI used in the research study. In other words, the information would still be considered identifiable is there was a way to identify the individual even though all of the 18 identifiers were removed.

Definition

What is PHI?

Protected health information (PHI) is any information in the medical record or designated record set that can be used to identify an individual and that was created, used, or disclosed in the course of providing a health care service such as diagnosis or treatment. HIPAA regulations allow researchers to access and use PHI when necessary to conduct research. However, HIPAA only affects research that uses, creates, or discloses PHI that will be entered in to the medical record or will be used for healthcare services, such as treatment, payment or operations.

For example, PHI is used in research studies involving review of existing medical records for research information, such as retrospective chart review. Also, studies that create new medical information because a health care service is being performed as part of research, such as diagnosing a health condition or a new drug or device for treating a health condition, create PHI that will be entered into the medical record. For example, sponsored clinical trails that submit data to the U.S. Food and Drug Administration involve PHI and are therefore subject to HIPAA regulations.

What is not PHI?

In contrast, some research studies use data that is person-identifiable because it includes personal identifiers such as name, address, but it is not considered to be PHI because the data are not associated with or derived from a healthcare service event (treatment, payment, operations, medical records) not entered into the medical records, nor will the subject/patient be informed of the results. Research health information that is kept only in the researcher’s records is not subject to HIPAA but is regulated by other human subjects protection regulations.

Examples of research health information not subject to HIPAA include such studies as the use of aggregate data, diagnostic tests that do not go into the medical record because they are part of a basic research study and the results will not be disclosed to the subject, and testing done without the PHI identifiers. Some genetic basic research can fall into this category such as the search for potential genetic markers, promoter control elements, and other exploratory genetic research. In contrast, genetic testing for a known disease that is considered to be part of diagnosis, treatment and health care would be considered to use PHI and therefore subject to HIPAA regulations.

Also note, health information by itself without the 18 identifiers is not considered to be PHI. For example, a dataset of vital signs by themselves do not constitute protected health information. However, if the vital signs dataset includes medical record numbers, then the entire dataset must be protected since it contains an identifier. PHI is anything that can be used to identify an individual such as private information, facial images, fingerprints, and voiceprints. These can be associated with medical records, biological specimens, biometrics, data sets, as well as direct identifiers of the research subjects in clinical trials.

Data Management for Clinical Research..free online course from Vanderbilt University

2016-10-18T07:17:00.001-07:00

About this course:

This course presents critical concepts and practical methods to support planning, collection, storage, and dissemination of data in clinical research. Understanding and implementing solid data management principles is critical for any scientific domain. Regardless of your current (or anticipated) role in the research enterprise, a strong working knowledge and skill set in data management principles and practice will increase your productivity and improve your science. Our goal is to use these modules to help you learn and practice this skill set. This course assumes very little current knowledge of technology other than how to operate a web browser. We will focus on practical lessons, short quizzes, and hands-on exercises as we explore together best practices for data management.

Taught by:

Stephany Duda, PhD, Assistant Professor

Department of Biomedical Informatics

-----------------------------

Taught by:

Paul Harris, PhD, Professor

Biomedical Informatics and Biomedical Engineering

Level	Beginner
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	Average User Rating 4.7See what learners said

Handbook of Attachment .. Theory, Research, and Clinical Applications

2016-07-17T23:23:00.003-07:00

This comprehensive Handbook provides a landmark review and synthesis of the current state of knowledge about attachment and loss in children and adults. Bringing researchers, clinicians, and students up to date on one of one of the leading theoretical perspectives in contemporary psychology, pre-eminent authorities in the field cover the origins and development of attachment theory, biological perspectives, measurement of attachment across the lifespan, clinical applications, and emerging topics and perspectives.

Why Clinical Research Can’t Guarantee the Right Medical Answers

2016-07-17T23:16:00.002-07:00

It’s Great! Oops, No It Isn’t

Why Clinical Research Can’t Guarantee

the Right Medical Answers

The people are confused because we do not understand the process behind these conflicting results. Our health, and in fact, our very lives are dependent on clinical trials, but we know little about them.

This book explains the issues the public needs to be aware of when it comes to clinical research. It uncovers the problems in medical investigations that can not be overcome no matter how much care and diligence medical researchers bring to a research project. The basic premise that drives the writing is that it is impossible for medical researchers to guarantee that they can get all the right

answers from a single study. No matter how good the investigators are, no matter how well a study is planned, no matter how carefully the plans are executed and no matter how conscientiously the results are analyzed and interpreted – the answer may still be wrong. The deck is stacked against medical researchers and

the public – you – should be skeptical of the results no matter how impressive they seem on the surface.

Do not, however, think that a trial cannot come up with an accurate answer. Many trials have found the correct answer, but there’s never certainty that that will happen. Getting a correct result requires a combination of skill and luck. And furthermore,

the correct result from a single study is almost always a narrow finding.

The drug is proven effective, but its safety still needs to be established. Or perhaps the drug, given in a fixed regimen does not cause kidney damage, but its effect on other systems (heart, liver, lungs etc.) remains unanswered and the results when a different regimen is given may be very different. Note that I emphasize the evaluation of drugs throughout the book because drug testing is the dominant form of medical research and provides many valuable examples of the kind of problems that can be encountered

Intelligent Drug Development

2016-07-12T04:31:00.001-07:00

Clinical research is heavily regulated and involves coordination of numerous pharmaceutical-related disciplines. Each individual trial involves contractual, regulatory, and ethics approval at each site and in each country. Clinical trials have become so complex and government requirements so stringent that researchers often approach trials too cautiously, convinced that the process is bound to be insurmountably complicated and riddled with roadblocks. A step back is needed, an objective examination of the drug development process as a whole, and recommendations made for streamlining the process at all stages.

With Intelligent Drug Development, Michael Tansey systematically addresses the key elements that affect the quality, timeliness, and cost-effectiveness of the drug-development process, and identifies steps that can be adjusted and made more efficient. Tansey uses his own experiences conducting clinical trials to create a guide that provides flexible, adaptable ways of implementing the necessary processes of development. Moreover, the processes described in the book are not dependent either on a particular company structure or on any specific technology; thus, Tansey's approach can be implemented at any company, regardless of size. The book includes specific examples that illustrate some of the ways in which the principles can be applied, as well as suggestions for providing a better context in which the changes can be implemented. The protocols for drug development and clinical research have grown increasingly complex in recent years, making Intelligent Drug Development a needed examination of the pharmaceutical process.

Questionnaire Design for Social Surveys..free online course from University of Michigan

2016-07-11T15:40:00.004-07:00

About this course

This course will cover the basic elements of designing and evaluating questionnaires. We will review the process of responding to questions, challenges and options for asking questions about behavioral frequencies, practical techniques for evaluating questions, mode specific questionnaire characteristics, and review methods of standardized and conversational interviewing.

Data Management for Clinical Research...free online course from Vanderbilt University

2016-07-11T15:35:00.000-07:00

About this course:

This course presents critical concepts and practical methods to support planning, collection, storage, and dissemination of data in clinical research.

Understanding and implementing solid data management principles is critical for any scientific domain. Regardless of your current (or anticipated) role in the research enterprise, a strong working knowledge and skill set in data management principles and practice will increase your productivity and improve your science. Our goal is to use these modules to help you learn and practice this skill set.

This course assumes very little current knowledge of technology other than how to operate a web browser. We will focus on practical lessons, short quizzes, and hands-on exercises as we explore together best practices for data management.

Key Topics in Clinical Research and Statistics

2016-07-10T22:33:00.003-07:00

Key Topics in Clinical Research aims to provide a short, clear, highlighted reference to guide trainees and trainers through research and audit projects, from first idea, through to data collection and statistical analysis, to presentation and publication. This book is also designed to assist trainees in preparing for their specialty examinations by providing comprehensive, concise, easily accessible and easily understandable information on all aspects of clinical research and audit.

Implementing Research in the Clinical Setting

2016-07-10T22:21:00.002-07:00

This practical handbook aims to give the practising nurse and health-care professional the tools to implement research in the clinical setting. There is no intention here of revisiting the deep theoretical issues related to research; rather the book seeks to offer a clear toolkit on how the research might be implemented. Chris Bassett has drawn together the work of six acknowledged experts in this field.

Epidemiology and Biostatistics An Introduction to Clinical Research

2016-07-09T06:04:00.002-07:00

This text provides students and other health professionals with the knowledge necessary to interpret clinical research articles, design clinical studies, and learn essential epidemiological concepts in an expedient and concise manner. Fundamental concepts are presented in a highly succinct format suitable for students with no previous background in epidemiology or statistics.

Drawing on teaching experience and student feedback, the author has created a text that attempts to recreate the perspective of learning epidemiology and biostatistics for the first time. The text serves as a rapid, intensive course in clinical research methods that can be used by students taking the required epidemiology course, residency and fellowship programs for physicians entering the clinical research portion of their training, and clinical research programs in fields such as nursing and nutrition that need an intensive course in clinical research design.

The Ethical Conduct of Clinical Research Involving Children

2016-07-09T05:59:00.000-07:00

In recent decades, advances in biomedical research have helped save or lengthen the lives of children around the world. With improved therapies, child and adolescent mortality rates have decreased significantly in the last half century. Despite these advances, pediatricians and others argue that children have not shared equally with adults in biomedical advances. Even though we want children to benefit from the dramatic and accelerating rate of progress in medical care that has been fueled by scientific research, we do not want to place children at risk of being harmed by participating in clinical studies.

Ethical Conduct of Clinical Research Involving Children considers the necessities and challenges of this type of research and reviews the ethical and legal standards for conducting it. It also considers problems with the interpretation and application of these standards and conduct, concluding that while children should not be excluded from potentially beneficial clinical studies, some research that is ethically permissible for adults is not acceptable for children, who usually do not have the legal capacity or maturity to make informed decisions about research participation. The book looks at the need for appropriate pediatric expertise at all stages of the design, review, and conduct of a research project to effectively implement policies to protect children. It argues persuasively that a robust system for protecting human research participants in general is a necessary foundation for protecting child research participants in particular.

Design and Analysis of Clinical Trials....Correlation and Agreement.. Lesson 18

2016-07-08T22:26:00.000-07:00

Lesson 18

Correlation and Agreement

Introduction

Many biostatistical analyses are conducted to study the relationship between two continuous or ordinal scale variables within a group of patients.

Purposes of these analyses include:

assessing correlation between the two variables, i.e., identifying whether values of one variable tend to be higher (or possibly lower) for higher values of the other variable;
assessing the amount of agreement between the values of the two variables, i.e., comparing alternative ways of measuring or assessing the same response;
assessing the ability of one variable to predict values of the other variable, i.e., formulating predictive models via regression analyses.

This lesson will focus only on correlation and agreement, (issues numbered 1 and 2 listed above).

Learning objectives & outcomes

Upon completion of this lesson, you should be able to do the following:

Recognize appropriate use of Pearson correlation, Spearman correlation, Kendall’s tau-b and Cohen’s Kappa statistics.
Use a SAS program to produce confidence intervals for correlation coefficients and interpret the results.
Adapt a SAS program to produce the correlation coefficients, their confidence intervals and Kendall’s tau-b.
Recognize situations that call for the use of a statistic measuring concordance.
Distinguish between a concordance correlation coefficient and a Kappa statistic based on the type of data used for each.
Interpret a concordance correlation coefficient and a Kappa statistic.

Common Statistical Methods for Clinical Research with SAS® Examples

2016-07-08T22:11:00.004-07:00

Glenn Walker and Jack Shostak's Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, is a thoroughly updated edition of the popular introductory statistics book for clinical researchers. This new edition has been extensively updated to include the use of ODS graphics in numerous examples as well as a new emphasis on PROC MIXED. Straightforward and easy to use as either a text or a reference, the book is full of practical examples from clinical research to illustrate both statistical and SAS methodology. Each example is worked out completely, step by step, from the raw data.

Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, is an applications book with minimal theory. Each section begins with an overview helpful to nonstatisticians and then drills down into details that will be valuable to statistical analysts and programmers. Further details, as well as bonus information and a guide to further reading, are presented in the extensive appendices. This text is a one-source guide for statisticians that documents the use of the tests used most often in clinical research, with assumptions, details, and some tricks--all in one place.

Design and Analysis of Clinical Trials.....Medical Diagnostic Testing.. Lesson 17

2016-07-05T07:39:00.001-07:00

Lesson 17

Medical Diagnostic Testing

Introduction

A diagnostic test is any approach used to gather clinical information for the purpose of making a clinical decision (i.e., diagnosis). Some examples of diagnostic tests include X-rays, biopsies, pregnancy tests, medical histories, and results from physical examinations.

From a statistical point of view there are two points to keep in mind:

the clinical decision-making process is based on probability;
the goal of a diagnostic test is to move the estimated probability of disease toward either end of the probability scale (i.e., 0 rules out disease, 1 confirms the disease).

Here is an example taken from Greenberg et al (2000, Medical Epidemiology, Third Edition ). A 54-year-old woman visits her family physician for an annual check-up. The physician observes that:

she had no illnesses during the preceding year and there is no family history of breast cancer,
her physical exam is unremarkable, (nothing unusual is apparent),
her breast exam is normal (no signs of a palpable mass), and
her pelvic and rectal exams are unremarkable.

Based on the woman's age and medical history, the initial (prior) probability estimate of breast cancer is 0.003. The physician recommends that the woman have a mammogram, due to her age. Unfortunately, the results of the mammogram are abnormal. This yields a modification of the women's prior probability of breast cancer from 0.003 to 0.13 (notice the Bayesian flavor of this approach - prior probability modified via existing data). Next, the woman is referred to a surgeon who agrees that the physical breast exam is normal. The surgeon consults with a radiologist and they decide that the woman should undergo fine needle aspiration (FNA) of the abnormal breast detected by the mammogram. (diagnostic test #2) The FNA specimen reveals abnormal cells, which again revises the probability of breast cancer, from 0.13 to 0.64. Finally, the woman is scheduled for a breast biopsy the following week to get a definitive diagnosis.

Ideally, diagnostic tests always would be correct, non-invasive, and inflict no side effects. If this were the case, a positive test result would unequivocally indicate the presence of disease and a negative result would indicate the absence of disease. Realistically, however, every diagnostic test is fallible.

Learning objectives & outcomes

Upon completion of this lesson, you should be able to do the following:

calculate and provide confidence intervals for the sensitivity and specificity of a diagnostic test,
calculate accuracy and predictive values of a diagnostic test,
state the relationship of prevalence of disease to the sensitivity, specificity and predictive values of a diagnostic test,
test whether sensitivity or specificity of 2 tests are significantly different, whether the results come from a study in two groups of patients or one group of patients tested with both tests, and
select an appropriate cut-off for a positive test result, given an ROC curve, for different cost ratios of false positive/false negative results.

Design and Analysis of Clinical Trials.....Overviews and Meta-analysis.. Lesson 16

2016-07-03T22:33:00.004-07:00

Lesson 16

Overviews and Meta-analysis

Introduction

An overview (also called a systematic review) attempts to summarize the scientific evidence related to treatment, causation, diagnosis, or prognosis of a specific disease. An overview does not generate any new data - it reviews and summarizes already-existing studies.

Overviews, which are relied upon by many physicians, are important because there usually exist multiple studies that have addressed a specific research question. Yet these types of studies may differ with respect to:

Design
Patient population
Quality
Results

Although it appears that conducting an overview is easy, it requires a good deal of effort and care to do it well. For example, determining inclusion and exclusion criteria for studies is a major challenge for researchers when putting together a useful overview.

What does this process involve? There are six basic steps to an overview:

Define a focused clinical question
Conduct a thorough literature search
Apply inclusion/exclusion criteria to the identified studies
Abstract/summarize the data from the eligible studies
Perform statistical analysis (meta-analysis), if appropriate
Disseminate the results

Learning objectives & outcomes

Upon completion of this lesson, you should be able to do the following:

Describe the processes for conducting a systematic overview.
Describe how publication bias can affect the results of a systematic review.
Recognize patterns in a ‘funnel plot’ that would indicate potential publication bias.
Evaluate the quality of a clinical report with the Jaded scale.
Recognize the appropriate use of a fixed effects model vs. a random effects model for a meta-analysis. State how the weights differ between the fixed and random approaches.
Describe the rationale for a test of heterogeneity among the studies used in a meta-analysis.
Describe methods for performing a sensitivity analysis of the meta-analysis.

Conducting GCP- Compliant Clinical Research

2016-07-03T22:18:00.000-07:00

Conducting GCP-Compliant Clinical Research Wendy Bohaychuk and Graham Ball Good Clinical Research Practices, UK and Canada The overall aim of this work is to provide a reference book which describes the general framework for conducting GCP-compliant clinical research, particularly pharmaceutical industry clinical research. Wendy Bohaychuk and Graham Ball run a consultancy, GCRP Ltd., which has conducted over 820 GCP audits involving more than 200 companies in the last 10 years. More than 5,000 individuals have been involved in their training courses to help people perform GCP-compliant clinical research. They have authored several books and articles including:

* Standard operating procedures for investigators

* Standard operating procedures for sponsors and CROs

* GCP - an indexed reference

Drawing on their wealth of experience, they have produced this enlightening and practical reference work which fills an educational gap in the understanding of GCP at all levels. Written in concise language simple enough to be accessible to those new in the field, the dozens of real-life stories and detailed case studies at the end of each chapter make the book an invaluable resource for the more experienced, highlighting what can go wrong in a clinical study: A study of prostate cancer in the UK - An investigator brochure was not provided. The company argued that a brochure was unnecessary because the drug was already marketed. Indeed it was - for hypertension! A study of cardiovascular surgery in the UK - The consent dates were changed (by overwriting) to indicate that the patients had provided consent before the study started. The original dates post-dated the start of the study. A study of hypertension in Germany - The investigator brochure predated the study by nine years! Checklists are provided throughout the book to help monitors, auditors and investigators ensure that nothing important is overlooked. The authors present the topic of GCP with remarkable clarity, insight and enthusiasm emphasizing that this code of practice was not designed to make studies more difficult for investigators or more expensive for sponsors and CROs but, in the final analysis, to ensure the safety and well-being of study participants and future patients who will benefit from well-conducted, GCP-

compliant studies.

6 weeks of study, 2-3 hours/week
Language	English
How To Pass	Pass all graded assignments to complete the course.
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