Delve into the research study so that you master the new tools and apply them to your own projects and those in which you collaborate"

It has taken years of research to achieve the medical advances that are currently applied in the health service. However, this discipline is increasingly demanding and its speed is highly valued. Therefore, the mastery of clinical information management techniques is key to the management of the health and social care field, research, publication of articles, theses and applied reports. In this way, experts could bring a wide prestige to their studies and focus them in a scientific line with greater guarantees.

For this reason, TECH Global University offers a Postgraduate diploma in Health Research Tools that delves into the interpretation of the information involved in the use of basic statistical tools and scientific methodology integrated by companies specialized in field work. In addition, thanks to TECH, students will delve into applied medical information on the preparation of reports, studies and documents aimed at decision-making on socio-health issues.

This is a 100% online program that is ideal for balancing study with the students' professional and personal lives. TECH applies the innovative Relearning methodology to facilitate professionals the gradual assimilation of the syllabus not requiring long hours of memorization, typical of orthodox teaching. In addition, the student will have the support of a teaching team specialized in the area that has participated in numerous studies in the health field.

Expand your knowledge in the definition of general and specific objectives of research projects, in order to master their implementation"

This Postgraduate diploma in Health Research Tools contains the most complete and up-to-date scientific program on the market. The most important features include: 

• Case studies presented by experts in Health Sciences Research
• The graphic, schematic, and practical contents with which they are created, provide medical information on the disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection work
• Content that is accessible from any fixed or portable device with an Internet connection

Graphical representations of data are key in healthcare research and other advanced analysis. Delve into this field with guaranteed success through a 100% online program"

The program includes, in its teaching staff, professionals from the sector who bring to this program the experience of their work, in addition to recognized specialists from prestigious reference societies and universities.  

Its multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide an immersive education programmed to learn in real situations.  

The design of this program focuses on Problem-Based Learning, by means of which the professional must try to solve the different professional practice situations that are presented throughout the academic course. For this purpose, the student will be assisted by an innovative interactive video system created by renowned experts.   

Be part of the evolution of clinical research projects thanks to the simple examples of simulation and statistical inference offered by TECH"

Master the ROC Curves and the types of multiple regression analysis to apply to your scientific trials and offer a with greater precision service"

The contents of this Postgraduate diploma in Health Research Tools has been developed by experts in health sciences Thus, TECH Global University offers a program that explores the generation of research projects, statistics and R in health research and graphical representations of data health research and other advanced analysis. All this, through the innovative Relearning methodology, which will exempt students from long hours of study, turning it into a constant learning based on theoretical and practical exercises. 

The scientific context is constantly changing at a fast pace. Don't get left behind in the statistical update and use the innovative tools that TECH offers you"

Module 1. Generation of Research Projects 

1.1. General Structure of a Project 
1.2. Presentation of Background and Preliminary Data 
1.3. Definition of the Hypothesis 
1.4. Definition of General and Specific Objectives 
1.5. Definition of the Type of Sample, Number and Variables to be Measured 
1.6. Establishment of the Scientific Methodology 
1.7. Exclusion/Inclusion Criteria in Projects with Human Samples 
1.8. Establishment of the Specific Team: Balance and Expertise 
1.9. Ethical aspects and Expectations: an Important Element that we Forget 
1.10. Budget Generation: a fine Tuning Between the Needs and the Reality of the Call

Module 2. Statistics and R in Health Research   

2.1. Biostatistics 

2.1.1. Introduction to The Scientific Method 
2.1.2. Population and Sample. Sampling Measures of Centralization 
2.1.3. Discrete Distributions and Continuous Distributions 
2.1.4. General Outline of Statistical Inference. Inference about a Normal Population Mean. Inference about a General Population Mean 
2.1.5. Introduction to Nonparametric Inference 

2.2. Introduction to R 

2.2. 1 Basic Features of the Program 
2.2. 2 Main Object Types 
2.2. 3 Simple Examples of Simulation and Statistical Inference 
2.2. 4 Graphs 
2.2. 5 Introduction to R Programming 
2.3. Regression Methods with R  

2.3.1. Regression Models 
2.3.2. Variable selection 
2.3.3. Model diagnosis 
2.3.4. Outlier treatment 
2.3.5. Regression analysis 

2.4. Multivariate Analysis with R 

2.4.1. Description of Multivariate Data 
2.4.2. Multivariate Distributions 
2.4.3. Dimension Reduction 
2.4.4. Unsupervised Classification: Cluster Analysis 
2.4.5. Supervised Classification: Discriminant Analysis 

2.5. Regression Methods for Research with R 

2.5.1. Generalized Linear Models (GLM): Poisson Regression and Negative Binomial Regression 
2.5.2. Generalized Linear Models (GLM): Logistic and Binomial Regressions 
2.5.3. Poisson and Negative Binomial Regression Inflated by Zeros 
2.5.4. Local Fits and Generalized Additive Models (GAMs) 
2.5.5. Generalized Mixed Models (GLMM) and Generalized Additive Mixed Models (GAMM) 

2.6. Statistics Applied to Biomedical Research with R I 

2.6.1. Basic Notions of R. Variables and Objects in R. Data handling. Files Graphs 
2.6.2. Descriptive Statistics and Probability Functions 
2.6.3. Programming and Functions in R 
2.6.4. Contingency Table Analysis 
2.6.5. Basic Inference with Continuous Variables 

2.7. Statistics Applied to Biomedical Research with R II 

2.7.1. Analysis of Variance 
2.7.2. Correlation Analysis 
2.7.3. Simple Linear Regression 
6.7.4. Multiple Linear Regression 
2.7.5. Logistic Regression 

2.8. Statistics Applied to Biomedical Research with R III 

2.8.1. Confounding Variables and Interactions 
2.8.2. Construction of a Logistic Regression Model 
2.8.3. Survival Analysis 
2.8.4. Cox Regression 
2.8.5. Predictive Models. ROC Curve Analysis 

2.9. Statistical Data Mining Techniques with R I 

2.9.1. Introduction. Data Mining. Supervised and Unsupervised Learning. Predictive Models Classification and Regression
2.9.2. Descriptive Analysis Data Pre-Processing
2.9.3. Principal Component Analysis (PCA) 
2.9.4. Cluster Analysis. Hierarchical Methods. K-Means 

2.10. Statistical Data Mining Techniques with R II 

2.10.1. Model Evaluation Measures. Predictive Ability Measures. ROC Curves 
2.10.2. Models Assessment Techniques. Cross-Validation. Bootstrap Samples 
2.10.3. Tree-Based Methods (CART) 
2.10.4. Support Vector Machines (SVM) 
2.10.5. Random Forest (RF) and Neural Networks (NN) 

Module 3. Graphical Representations of Data in Health Research and Other Advanced Analysis

3.1. Types of Graphs 
3.2. Survival Analysis 
3.3. ROC Curves 
3.4. Multivariate Analysis (Types of Multiple Regression) 
3.5. Binary Regression Models 
3.6. Massive Data Analysis  
3.7. Dimensionality Reduction Methods 
3.8. Comparison of Methods: PCA, PPCA and KPCA 
3.9. T-SNE (t-Distributed Stochastic Stochastic Neighbor Embedding) 
3.10. UMAP (Uniform Manifold Approximation and Projection) 

Make the most of this opportunity to learn about the latest advances in this subject to apply it to your daily practice"