This Hybrid Master's Degree in Medical Research offers you the opportunity to update your knowledge through a qualification that is at the forefront of the academic world”

Today, society is more aware of the relevance of Medical Research. For example, the pandemic caused by COVID-19 marked a turning point in this field. As a result, public administrations and private entities are now advocating the promotion of projects that provide solutions to the main healthcare challenges.

In this scenario, medical professionals have seen how their work is highly valued, while collaboration between teams from different projects around the world and the techniques and methods used have improved. A reality that leads the specialist to demand an update of their knowledge. This is the reason why this academic institution has created this Hybrid Master's Degree in Medical Research.

This is a program, in which TECH has brought together a management and an expert teaching staff, which will take you to know the latest advances in the use of the R program, for statistical and graphical analysis of data or the essential information to launch a new research project. To do so, you will be provided with innovative teaching tools, which will lead you to be aware of new technologies conducive to scientific dissemination or protection of results.

In addition, thanks to the Relearning system, based on the reiteration of content, the professional will be able to reduce the long hours of study and memorization. In this way, they will advance in a much more natural and progressive way through the syllabus of this program.

An academic option that also offers a more practical vision of the reality of Medical Research. Therefore, once the 100% online theoretical phase is completed, the graduate will have access to a practical internship in a leading clinical center. A space where they will be tutored by research professionals with extensive experience in this field. An excellent opportunity to keep abreast of Medical Research through a high-level qualification.

You are before a qualification that provides you with the latest information on the new biomedical research areas”

This Hybrid Master's Degree in Medical Research contains the most complete and up-to-date scientific program on the market. The most important features include:

• Development of more than 100 clinical cases presented by experts in Health Sciences Research
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Appraisal of the feasibility of potential research projects
• Practical vision of the techniques and new technologies applied to obtain and disseminate results
• All of this will be complemented by theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection
• Furthermore, you will be able to carry out a clinical internship in one of the best centers on the international scene

In only 7 months, TECH offers you an advanced update on health research, financing and dissemination of research projects”

The program’s teaching staff includes professionals from the industry who contribute their work experience to this program, as well as renowned specialists from leading societies and prestigious universities.

 The multimedia content, developed with the latest educational technology, will provide professionals with situated and contextual learning, i.e., a simulated environment that will provide immersive specialization, designed for specializing oneself in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned and experienced experts.

This Hybrid Master's Degree will introduce you to the latest statistical data mining techniques through a specialized teaching team"

Get the most relevant information about R programming and apply its methodology in your next research"

The syllabus of this university program has been prepared by an excellent team of professionals, who seek to offer the most relevant and up-to-date information on Medical Research. In this way, the specialist will be able to delve into an advanced syllabus, which will lead them to be aware of the current scientific methods applied to health research, the generation of projects or the dissemination of the results in new informative spaces. All this, with multimedia teaching material that can be accessed at any time of the day, from an electronic device with an Internet connection. The program is completed by a practical internship in a leading research center. 

A syllabus designed to provide you with the latest scientific knowledge applied to health research”

Module 1. The Scientific Method Applied to Health Research. Bibliographic positioning of the research

1.1. Definition of the Question or Problem to be Solved
1.2. Bibliographic Positioning of the Question or Problem to be Solved

1.2.1. Information Search

1.2.1.1. Strategies and Keywords

1.2.2. Pubmed and Other Repositories of Scientific Articles

1.3. Treatment of Bibliographic Sources
1.4. Treatment of Documentary Sources
1.5. Advanced Bibliography Search
1.6. Generation of Reference Bases for Multiple Use
1.7. Bibliography Managers
1.8. Extraction of Metadata in Bibliographic Searches
1.9. Definition of the Scientific Methodology to be Followed

1.9.1. Selection of the Necessary Tools
1.9.2. Design of Positive and Negative Controls in an Investigation

1.10. Translational Projects and Clinical Trials: Similarities and Differences

Module 2. Generation of Working Groups: Collaborative Research

2.1. Definition of Working Groups
2.2. Formation of Multidisciplinary Teams
2.3. Optimal Distribution of Responsibilities
2.4. Leadership
2.5. Control of Activities Achievement
2.6. Hospital Research Teams

2.6.1. Clinical Research
2.6.2. Basic Research
2.6.3. Translational Research

2.7. Creation of Collaborative Networks for Health Research
2.8. New Spaces for Health Research

2.8.1. Thematic Networks

2.9. Networked Biomedical Research Centers
2.10. Biobanks of Samples: International Collaborative Research

Module 3. Generation of Research Projects

3.1. General Structure of a Project
3.2. Presentation of Background and Preliminary Data
3.3. Definition of the Hypothesis
3.4. Definition of General and Specific Objectives
3.5. Definition of the Type of Sample, Number and Variables to be Measured
3.6. Establishment of the Scientific Methodology
3.7. Exclusion/Inclusion Criteria in Projects with Human Samples
3.8. Establishment of the Specific Team: Balance and Expertise
3.9. Expectations: an Important Element that we Forget
3.10. Budget Generation: a fine Tuning Between the Needs and the Reality of the Call
3.11. Ethical Aspects

Module 4. The Clinical Trial in Health Research

4.1. Types of Clinical Trials (CT)

4.1.1. Clinical Trials Promoted by the Pharmaceutical Industry
4.1.2. Independent Clinical Trials
4.1.3. Drug Replacement

4.2. Phases of CE
4.3. Main Figures Involved in CE5
4.4. Generation of Protocols

4.4.1. Randomization and Masking
4.4.2. Non-Inferiority Studies

4.5. Ethical Aspects
4.6. Patient Information Sheet
4.7. Informed Consent
4.8. Good Clinical Practice Criteria
4.9. Drug Research Ethics Committee
4.10. Search for Funding for Clinical Trials

4.10.1. Public. Main Spanish, European, Latin American and U.S. Agencies.
4.10.2. Private. Main Pharmaceutical Companies

Module 5. Project Financing

5.1. Search for Financing Opportunities
5.2. How to Adjust a Project to the Format of a Call for Proposals?

5.2.1. Keys to Success
5.2.2. Positioning, Preparation and Writing

5.3. Public Calls for Proposals. Main European and American Agencies
5.4. Specific European Calls for Proposals

5.4.1. Horizon 2020 Projects
5.4.2. Human Resources Mobility
5.4.3. Madame Curie Program

5.5. Intercontinental collaboration Calls: Opportunities for International Interaction
5.6. Calls for Collaboration with the United States
5.7. Strategy for Participation in International Projects

5.7.1. How to Define a Strategy for Participation in International Consortia
5.7.2. Support and Assistance Structures

5.8. International Scientific Lobbying

5.8.1. Access and Networking

5.9. Private Calls for Proposals

5.9.1. Foundations and Funding Organizations for Health Research in Europe and the Americas
5.9.2.  Private Funding Calls for Proposals from U.S. Organizations

5.10. Securing the Loyalty of a Funding Source: Keys to Lasting Financial Support

Module 6. Statistics and R in Health Research

6.1. Biostatistics

6.1.1. Introduction to The Scientific Method
6.1.2. Population and Sample. Sampling Measures of Centralization
6.1.3. Discrete Distributions and Continuous Distributions
6.1.4. General Outline of Statistical Inference. Inference about a Normal Population Mean. Inference about a General Population Mean
6.1.5. Introduction to Nonparametric Inference

6.2. Introduction to R

6.2.1. Basic Features of the Program
6.2.2. Main Object Types
6.2.3. Simple Examples of Simulation and Statistical Inference
6.2.4. Graphs
6.2.5. Introduction to R Programming

6.3. Regression Methods with R

6.3.1. Regression Models
6.3.2. Variable Selection
6.3.3. Model Diagnosis
6.3.4. Treatment of Outliers
6.3.5. Regression Analysis

6.4. Multivariate Analysis with R

6.4.1. Description of Multivariate Data
6.4.2. Multivariate Distributions
6.4.3. Dimension Reduction
6.4.4. Unsupervised Classification: Cluster Analysis
6.4.5. Supervised Classification: Discriminant Analysis

6.5. Regression Methods for Research with R

6.5.1. Generalized Linear Models (GLM): Poisson Regression and Negative Binomial Regression
6.5.2. Generalized Linear Models (GLM): Logistic and Binomial Regressions
6.5.3. Poisson and Negative Binomial Regression Inflated by Zeros
6.5.4. Local Fits and Generalized Additive Models (GAMs)
6.5.5. Generalized Mixed Models (GLMM) and Generalized Additive Mixed Models (GAMM)

6.6. Statistics Applied to Biomedical Research with R I

6.6.1. Basic Notions of R. Variables and Objects in R. Data handling. Files. Graphs
6.6.2. Descriptive Statistics and Probability Functions
6.6.3. Programming and Functions in R
6.6.4. Contingency Table Analysis
6.6.5. Basic Inference with Continuous Variables

6.7. Statistics Applied to Biomedical Research with R II

6.7.1. Analysis of Variance
6.7.2. Correlation Analysis
6.7.3. Simple Linear Regression
6.7.4. Multiple Linear Regression
6.7.5. Logistic Regression

6.8. Statistics Applied to Biomedical Research with R III

6.8.1. Confounding Variables and Interactions
6.8.2. Construction of a Logistic Regression Model
6.8.3. Survival Analysis
6.8.4. Cox Regression
6.8.5. Predictive Models. ROC Curve Analysis

6.9. Statistical Data Mining Techniques with R I

6.9.1. Introduction. Data Mining. Supervised and Unsupervised Learning. Predictive Models. Classification and Regression
6.9.2. Descriptive Analysis. Data Pre-Processing
6.9.3. Principal Component Analysis (PCA)
6.9.4. Principal Component Analysis (PCA)
6.9.5. Cluster Analysis. Hierarchical Methods. K-Means

6.10. Statistical Data Mining Techniques with R II

6.10.1. Model Evaluation Measures. Predictive Ability Measures. ROC Curves
6.10.2. Models Assessment Techniques. Cross-Validation. Bootstrap Samples
6.10.3. Tree-Based Methods (CART)
6.10.4. Support Vector Machines (SVM)
6.10.5. Random Forest (RF) and Neural Networks (NN)

Module 7. Graphical Representations of Data in Health Research and Other Advanced Analysis

7.1. Types of Graphs
7.2. Survival Analysis
7.3. ROC Curves
7.4. Multivariate Analysis (Types of Multiple Regression)
7.5. Binary Regression Models
7.6. Massive Data Analysis
7.7. Dimensionality Reduction Methods
7.8. Comparison of Methods: PCA, PPCA and KPCA
7.9. T-SNE (t-Distributed Stochastic Stochastic Neighbor Embedding)
7.10. UMAP (Uniform Manifold Approximation and Projection)

Module 8. Dissemination of Results I: Reports, Memoirs and Scientific Articles

8.1. Generating a Scientific Report or Memory of a Project

8.1.1. Optimal Approach to the Discussion
8.1.2. Presentation of the Limitations

8.2. Generation of a Scientific Article: How to Write a Paper on Based on the Data Obtained?

8.2.1. General Structure
8.2.2. Where Does the Paper Go?

8.3. Where to Start?

8.3.1. Adequate Representation of the Results

8.4. The Introduction: The Mistake of Starting with this Section
8.5. The Discussion: The Cusp Moment
8.6. The Description of Materials and Methods: The Guaranteed Reproducibility
8.7. Selection of the Journal where the Paper will be Submitted

8.7.1. Choice Strategy
8.7.2. Priority List

8.8. Adaptation of the Manuscript to the Different Formats
8.9. The Cover Letter: Concise Presentation of the Study to the Editor
8.10. How to Respond to Reviewers' Doubts? The Rebuttal Letter

Module 9. Dissemination of Results II: Symposia, Congresses, Dissemination to Society

9.1. Presentation of Results at Congresses and Symposia

9.1.1. How is a Poster Generated?
9.1.2. Data Representation
9.1.3. Focusing the Message

9.2. Short Communications

9.2.1. Data Representation for Short Communications
9.2.2. Focusing the Message

9.3. The Plenary Lecture: Notes on How to Keep the Attention of the Specialized Audience for More than 20 Minutes
9.4. Dissemination to the General Public

9.4.1. Need Vs. Opportunity
9.4.2. Use of References

9.5. Use of Social Networks for the Dissemination of Results
9.6. How to Adapt Scientific Data to the Popular Language?
9.7. Hints for Summarizing a Scientific Paper in a Few Characters

9.7.1. Instant Dissemination via Twitter

9.8. How to turn a Scientific Paper into a Popularization Material

9.8.1. Podcast
9.8.2. YouTube Videos
9.8.3. Tik Tok
9.8.4. Comic Book

9.9. Popular Literature

9.9.1. Columns
9.9.2. Books

Module 10. Protection and Transfer of Results

10.1. Protection of Results: Overview
10.2. Valorization of the Results of a Research Project
10.3. Patents: Pros and Cons
10.4. Other Forms of Protection of Results
10.5. Transfer of Results to Clinical Practice
10.6. Transfer of Results to Industry
10.7. The Technology Transfer Contract
10.8. Trade Secrets
10.9. Generation of Spin-Off Companies from a Research Project
10.10. Search for Investment Opportunities in Spin-Off Companies

With this qualification you will be up to date on the latest procedural advances for the conduct of clinical trials in health research”