Course guide of Computational Statistics in Pharmacy (20411A7)

Curso 2024/2025
Approval date: 25/06/2024

Grado (bachelor's degree)

Bachelor'S Degree in Pharmacy

Branch

Ciencias de la Salud

Module

Complementos de Formación

Subject

Estadística Computacional en Farmacia

Year of study

3

Semester

1

ECTS Credits

6

Course type

Elective course

Teaching staff

Theory

  • Paula Rodríguez Bouzas. Grupo: E
  • Mariano José Valderrama Bonnet. Grupo: A

Practice

  • Paula Rodríguez Bouzas Grupos: 3 y 4
  • Mariano José Valderrama Bonnet Grupos: 1 y 2

Timetable for tutorials

Paula Rodríguez Bouzas

Email
No hay tutorías asignadas para el curso académico.

Mariano José Valderrama Bonnet

Email
No hay tutorías asignadas para el curso académico.

Prerequisites of recommendations

It is recommended to have taken and passed the following subjects: Biometrics

The recommended level of English is B2, never lower than B1 in MCERL.

Brief description of content (According to official validation report)

  1. Methods of statistical inference
  2. Statistical experiment design I. Variance analysis.
  3. Statistical experiment design II. Regression.
  4. Nonparametric statistics
  5. Sampling from finite population
  6. Treatment of qualitative data

General and specific competences

General competences

  • CG03. Saber aplicar el método científico y adquirir habilidades en el manejo de la legislación, fuentes de información, bibliografía, elaboración de protocolos y demás aspectos que se consideran necesarios para el diseño y evaluación crítica de ensayos preclínicos y clínicos. 

Specific competences

  • CE12. Aplicar los conocimientos de Física y Matemáticas a las ciencias farmacéuticas. 
  • CE13. Aplicar técnicas computacionales y de procesamiento de datos, en relación con la información referente a datos físicos, químicos y biológicos. 
  • CE14. Diseñar experimentos en base a criterios estadísticos. 
  • CE15. Evaluar datos científicos relacionados con los medicamentos y productos sanitarios. 
  • CE16. Utilizar el análisis estadístico aplicado a las ciencias farmacéuticas. 

Transversal competences

  • CT02. Capacidad de utilizar con desenvoltura las TICs 

Objectives (Expressed as expected learning outcomes)

At the end of the course it is expected that the student will be able to:

a) Outline, resolve and interpret a statistical hypothesis and analyse a variance.

b) Formulate, estimate and interpret a linear and nonlinear regression model

c) Use the adequate type of sampling, calculation the corresponding sample size.

d) Work with contingency tables for categorical data.

Detailed syllabus

Theory

  • Unit 1: Distribution function.
    • Discrete and continuous probability distributions. Expected value and variance. Distributions in sampling: t-Student, Pearson χ2 and Fisher-Snedecor distribution.
  • Unit 2 : Random variables: Statistical inference by estimation
    • Concept and properties of an estimator. Estimation methods: maximum likelihood, mean squares, Bayes, etc. Estimation with Gaussian variables: Fisher’s theorem. Estimation by means of confidence intervals. Calculation of the sample size.
  • Unit 3: Statistical inference by test of hypothesis
    • Basis concepts in statistical tests. Test with the Gaussian distribution. Test with two Gaussian variables. Interpretation of the p-value.
  • Unit 4: Statistical design of a experiment I: Analysis of variance
    • Linear decomposition of the variance. One factor designs: the ANOVA I model. Two factor designs: the ANOVA II model. Balanced designs with multiple observations: Interaction analysis. Designs by means of latin squares and greco-latin squares.
  • Unit 5: Statistical design of a experiment I: Regression
    • Introduction. Linear simple regression model. Linear multiple regression model. Non-linear regression. Logistic and Poisson regressions.
  • Unit 6: Non-parametric statistics
    • Introduction. Tests for paired variables: signs test and Rank-signs Wilcoxon test. Tests for independent variables: Mann-Withney, Kolmogorov-Smirnov and Kruskal-Wallis tests. Friedman test. Spearman’s rank-correlation. Dixon and Grubbs tests for anomalous data.
  • Unit 7: Treatment of qualitative data
    • Goodness adjust asymptotic test. Test of Independence for qualitative variables. Diagnostic agreement. Analysis of 2x2 contingency tables. Epidemiological applications. Area under ROC curve.
  • Unit 8: Sampling on finite populations
    • Probabilistic versus intentional sampling. Simple random sampling. Stratified random sampling. Sampling by means of conglomerates. Systematic sampling.

Practice

The practices will be developed in the computer room and will consist in studying the solution of case studies by means of statistics using a statistical program. The statistical knowledge to solve the cases is the theoretical content of the subject.

Bibliography

Basic reading list

  • E. Cobo, P. Muñoz y J.A. González: Bioestadística para no Estadísticos. Elsevier, Barcelona (2007).
  • C.M. Cuadras: Problemas de Probabilidades y Estadística (2 vols.). EUB, Barcelona (1999).
  • V. Quesada, A. Isidoro y L.A. López: Curso y Ejercicios de Estadística. Alhambra, Madrid (2000).
  • F. Rius y F.J. Barón: Bioestadística. Thomson-Paraninfo, Madrid (2008).
  • S.M. Ross: Introducción a la Estadística. Reverté, Barcelona (2007).
  • M.L. Samuels, J.A. Witmer y A. Schaffner: Fundamentos de Estadística para las Ciencias de la Vida. Pearson, Madrid (2012).

Complementary reading

  • D. S. Shafer, Beginning Statistics, Zhiyi Zhang Publisher: lardbucket.org (2014). Electronic version at https://2012books.lardbucket.org/pdfs/beginning-statistics.pdf
  • J.S. Milton: Estadística para Biología y Ciencias de la Salud. McGraw-Hill, Madrid (2001).
  • A. Martín-Andrés y J.D. Luna del Castillo: Bioestadística para Ciencias de la Salud. Norma, Madrid (2005).
  • C. Pérez: Estadística Práctica con Statgraphics®. Prencite Hall, Madrid (2002).

Recommended links

Teaching methods

  • MD01. Lección magistral/expositiva 
  • MD02. Sesiones de discusión y debate 
  • MD03. Resolución de problemas y estudio de casos prácticos 
  • MD06. Prácticas en sala de informática 
  • MD10. Realización de trabajos individuales 
  • MD12. Tutorías 
  • MD13. Participación en plataformas docentes 

Assessment methods (Instruments, criteria and percentages)

Ordinary assessment session

The final assessment of the subject consists of three different parts:

  1. Partial and final exam: 5 points
  2. Attendance and exam of the practical classes: 3 points
  3. Class work: 2 points.

Extraordinary assessment session

This is the resit exam and its assessment consists of three different parts:

  1. Exam of problems: 5 points
  2. Exam of the practical classes: 3 points
  3. Theory exam (short or multiple choice questions on concepts): 2 points.

Single final assessment

Students who cannot comply with the requirements of the continuous assessment system may qualify for the single assessment system, in accordance with article 8 of the “Regulation on the Evaluation and Grading of Students of the University of Granada". Those who qualify for this type of assessment in accordance with the said regulation will have to request it to the head of the Department in the first two weeks from the date of enrollment, alleging and proving the reasons that justify not being able to follow the continuous assessment system.

The content of the subject is the one described in the syllabus aforementioned. The assessment consists of the same three parts of the resit exam and will take place on the same date as the ordinary or extraordinary exam as it corresponds.

Additional information

  • Students with Specific Educational Support Needs (NEAE). In accordance with the recommendations of the CRUE and the Secretariat for Inclusion and Diversity of the University of Granada, the systems for the acquisition and evaluation of competences included in this teaching guide will be applied in this teaching guide will be applied in accordance with the principle of design for all people, facilitating learning and the demonstration of knowledge according to the needs and functional diversity of the students.The teaching methodology and assessment will be adapted to the needs and functional diversity of the students with specific educational support needs (SEN), in accordance with Article 11 of the Regulations for the Evaluation and Grading of students at the University of Granada, published in the Official Bulletin of the University of Granada no. 112 of 9 November 2016.
  • UGR Inclusion and Diversity. In the case of students with disabilities or other specific educational support needs, the tutoring system must be adapted to their needs, in accordance with the recommendations of the tutoring system must be adapted to their needs, in accordance with the recommendations of the University's Inclusion Unit of the University, and the Departments and Centres will proceed to establish the appropriate measures to ensure that tutorials are held in accessible places. Likewise, at the request of the lecturer, support may be requested from the competent University unit in the case of special methodological adaptations.

Información de interés para estudiantado con discapacidad y/o Necesidades Específicas de Apoyo Educativo (NEAE): Gestión de servicios y apoyos (https://ve.ugr.es/servicios/atencion-social/estudiantes-con-discapacidad).