Introduction to Biostatistics

Introduction to Biostatistics - Lecture 1

• The lecture is titled "Introduction to Biostatistics, Lecture 1."
• The presenter is Fahad Alkenani, BPharm, RPh, MSc, DIPBA, PhD, C-KPI, C-DA, CSPP
• The lecture is in the Department of Pharmacy Practice, College of Pharmacy, Taibah University, 2024-1446.

Outlines

• The lecture covers introduction to biostatistics, key differences between statistics and biostatistics, data in biostatistics (types and sources), descriptive statistics, inferential statistics, sampling methods, and common statistical programs.

Basics of Biostatistics

• The lecture highlights historical figures in biostatistics, showcasing important contributors to the field.

The Statistical Analysis Journey

• The lecture outlines the steps in a statistical analysis. This includes transforming research ideas into a research question, choosing the appropriate study design and sample size, collecting data, analyzing data using appropriate statistical methods, finding and interpreting the p-value, and reaching a conclusion or drawing a conclusion regarding the research question.

Statistics vs. Biostatistics

• Statistics involves the development and application of methods for data collection, presentation, analysis, and decision-making.
• Biostatistics applies these methods to biological problems, including public health, medicine, and biology.

What is Studying Biostatistics Useful For?

• Biostatistics is useful for the design and analysis of research studies
• Describing and summarizing data
• Formulating scientific evidence regarding a specific idea
• Concluding if an observation is significant or due to chance
• Understanding and evaluating published research, especially related to clinical trials and epidemiological studies.

Biostatistician Roles

• Biostatisticians play essential roles in drug discovery, identifying risk factors for diseases, designing and analyzing clinical studies, and developing statistical methods from medical and public health data.

Terminology

• Population: The entire group of individuals of interest.
• Sample: A portion of the population selected for analysis.
• Sample Unit: A single element or data point in a sample.
• Variable: A characteristic of an individual or item (e.g., age, weight).
• Data: Values that a variable can assume.
• Data Set: A collection of data values.
• Datum: A single value in a data set, also known as a data value.

Why Biostatistics is Crucial for Pharmacists

• Biostatistics is fundamental to evidence-based practice.
• It's essential for drug development, clinical trials, public health, and ensuring the quality and safety of pharmacy practice.

Introduction to Statistics

• A student taking an introductory statistics course will learn how to calculate and visualize descriptive statistics, construct confidence intervals, perform hypothesis tests, and fit regression and ANOVA models.

Parameter vs. Statistics

• Parameter: A numerical characteristic of a population.
• Statistic: A numerical characteristic of a sample (calculated from the sample data).
• Parameters are represented using Greek letters (e.g., μ)
• Statistics are represented using Roman letters (e.g., x).

The Basic Paradigm

• The diagram illustrates the relationship between a population, its parameters, a sample, and its accompanying descriptive statistics.
• Inferential analysis allows conclusions to be drawn about populations from sample analysis.

Data

• Data is the raw material of statistics.
• Data sources include records, surveys, external sources, and experiments. Examples are counting patients or measuring patient weight.

Types of Data

• The lecture introduced different types of data.

Data Variables

• A data variable is something that varies or differs between individuals or groups. Examples are sex, age, weight, marital status, and satisfaction rates.
• Variable types affect how data is summarized, presented graphically and analyzed.

Qualitative Data

• Qualitative (categorical) data is non-numerical. It can be:
• Nominal: Categories with no inherent order.
• Ordinal: Categories with a natural order. Examples of Ordinal Data Include: Education, disease severity

Quantitative Data

• Quantitative data is numerical and can be:
• Discrete: Whole numbers only (counts)
• Continuous: Can take on any value within a range.

Levels of Data Measurement

• Data can be categorized according to different levels of measurement, including 1. Numerical/Continuous 2. Numerical/Discrete 3. Ordinal and 4. Nominal.

Role of Variables (Independent and Dependent)

• Independent variables are the potential causes or factors being investigated.
• Dependent variables are the effects or outcomes.

Inferential Analysis

• This crucial component of biostatistics allows researchers to draw conclusions about populations from sample data.
• Key concepts and methods include hypothesis testing which involves formulating null and alternative hypotheses.
• Common methods for inferential analysis includes t-tests, analysis of variance (ANOVA), regression and non-parametric tests.

Descriptive Statistics

• Descriptive analysis provides a summary of the characteristics of a dataset.
• It is typically the first step in any statistical analysis, offering insights into the structure of the data and guiding further analytical approaches.
• Examples of concepts and methods include: measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation), measures of shape, and graphical methods (histograms, pie charts, etc.).

Common Statistical Programs

• The lecture lists commonly-used statistical software programs including SPSS, R, SAS, Stata, Excel, Python, JMP, Minitab, and MATLAB.