1st Lecture Introduction to Biostatistics PDF

Introduction to Biostatistics “Lecture 1” Fahad Alkenani, BPharm, RPh, MSc, DIPBA, PhD, C-KPI, C-DA, CSPP Department of Pharmacy Practice, College of Pharmacy, Taibah University 2024-1446 1 Outlines Int...

Introduction to Biostatistics “Lecture 1” Fahad Alkenani, BPharm, RPh, MSc, DIPBA, PhD, C-KPI, C-DA, CSPP Department of Pharmacy Practice, College of Pharmacy, Taibah University 2024-1446 1 Outlines Introduction to Biostatistics Statistics vs. Biostatistics ❖Key Differences and Applications Data in Biostatistics ❖Types and Sources of Data Descriptive Statistics Inferential Statistics Sampling Methods Common Statistical Programs 2 Basics of Biostatistics 3 The statistical analysis journey 06 01 02 03 04 05 Transforming Choosing the Performing the Analyzing data Getting and Reaching a the research idea proper study study and (using the interpreting the conclusion into a research design and collecting data appropriate p-value (answer) question selecting a statistical regarding the suitable sample method). research question 4 Statistics vs. Biostatistics: What’s the Difference? Statistics: it is the science which deals with Biostatistics development and application of the most appropriate methods for the: Statistics applied to biological (life) Collection of data problems, including: Presentation of the collected data Public health Analysis and interpretation of the Medicine results Making decisions on the basis of such Biology and environmental analysis Transform numbers into useful information that can help you in reaching decision. 5 What is studying biostatistics useful for? The design and analysis of research studies. Describing and summarizing the data we have. Analyzing data to formulate scientific evidence regarding a specific idea. To conclude if an observation is of real significance or just due to chance. To understand and evaluate published scientific research papers. It is a basic part of some fields as clinical trials and epidemiological studies. 6 Biostatistician Roles Identify and develop Identify risk factors for treatments for disease and diseases estimate their effects Develop statistical Design, monitor, analyze, methodologies to address interpret, and report questions arising from results of clinical studies medical/public health data 7 Terminology Population: The entire group of people that you want to understand Sample: A portion of the population that is selected for analysis Sample Unit: An element or one case or data point Variable: Characteristic of an individual or item (weight or Age) Data: Values that the variable can assume Data Set: a collection of data values Datum: Each value in the data set; also known as data value 8 Why Biostatistics is Crucial for Pharmacists Foundation of evidence-based practice. Essential for drug development and clinical trials. Critical for public health and epidemiology. Ensures quality and safety in pharmacy practice. Example: Role of biostatistics in COVID-19 vaccine development. Understanding biostatistics equips pharmacists with the skills to critically evaluate research, make evidence-based decisions, and ultimately improve patient care outcomes 9  For example, a student who takes an introductory statistics course may learn about the following topics: How to calculate descriptive statistics How to visualize data How to construct confidence intervals How to perform hypothesis tests How to fit regression models How to fit ANOVA models A student who then takes a biostatistics course would learn how to apply each of these statistical methods to answer research questions in biology, public health, and medicine. If a student wants to become a biostatistician, they must first learn about the concepts taught in an introductory statistics course. They can then take a biostatistics course to learn how to apply statistical methods to specific research questions in the field of biology 10 Parameter Vs Statistics Parameter: Is a number that describes a Population characteristics; indicated by Greek Letters: population mean (μ); population standard deviation (σ) Statistics: Is a number that describes a Sample characteristics; indicated by Roman letters: sample mean (x̄ ); sample standard deviation (SD) 11 12 Data The raw material of Statistics is data For example: When a hospital administrator counts the number of patients (counting) When a nurse weighs a patient (measurement) Sources of Data: 1. Routinely kept records (hospital medical record) 2. Surveys 3. External sources (published reports, research literature 4. Experiments 13 14 Types of Data 15 Data variables A data variable is "something that varies" or differs from person to person or group to group. Variables are the items that we collect data about. Examples of data variables are sex, age, weight, marital status, satisfaction rate, etc. When dealing with data, it is important to recognize the type of each data variable for the following reasons: Summarizing data: describing a variable correctly as using mean with standard deviation or using frequency with percentage depends on the type of data variable. Graphical presentation: choosing the proper graph to present the data depends on the type of data variable. Analyzing data: choosing suitable statistical tests depends on the type of data variables. 16 Data variables They are also known as qualitative or nominal data; they have NO unit of measurement. A variable of this type consists of different categories. Individuals are described as belonging to any of the categories of this variable. Categorical variables are either nominal or ordinal. 17 Type of Variables Quantitative Data Qualitative Data ▸ Discrete Variable ▸ Nominal Variable ▸ Continuous Variable ▸ Ordinal Variable ▸ Dichotomous (binary) variable 20 Categorical (Qualitative) Variables They represent data that can be sorted into categories but cannot be measured numerically. 1. Nominal; These are variables with no inherent order or ranking. Examples: Blood types (A, B, AB, O) or gender (male, female, non-binary) 2. Ordinal: These variables have a natural order or ranking, but the intervals between categories may not be equal Examples: Education level (primary, secondary, tertiary) or disease severity (mild, moderate, severe). 21  Quantitative Variables 1. Continuous data: Interval: The zero definition is arbitrary; e.g., temperature: 0 degree C doesn’t mean no temperature. An interval or a difference of 1 degree C means the same thing all the way along the scale; No sense to compute a ratio here: is 100 degrees C twice as hot as 50 degrees C? Ratio data: Has a true absolute zero; e.g., height: zero height is no height Can compute ratio or differences: 4 g weight is twice the wt of 2 grams 22  Continuous data examples: Temperature Time Weight Cholesterol level Concentration of fluoride in drinking water 23  2. Discrete data: Numbers represent measurable quantities Occur when the variable can only take certain whole numerical values. These are often counts of numbers of events. Can take on only specified values that differ by fixed amounts Often count data (e.g., number of hospitalizations) Example: Number of risk factors Number of patients 0 -- no risk factors Number of visits to a GP in a 1 -- one risk factor particular year. 2 -- two risk factors Number of episodes of illness in an 3 -- three risk factors individual over the last five years. 4 -- four risk factors 24 Levels of data measurement Numerical Exact age Continuous Numerical Age in Discrete years Age Ordinal group Nominal Young/old Role of Variables Independent Variables Proposed Cause or Predictor Variables Proposed Outcome or Outcome Variables Inferential Analysis Definition: is a crucial component of biostatistics that allows researchers to draw conclusions about populations based on sample data. This approach is fundamental in medical research, epidemiology, and public health studies. Here's an overview of key concepts and methods in inferential analysis: Hypothesis Testing: It involves formulating null and alternative hypotheses and using statistical tests to make decisions about these hypotheses based on sample data 27 Inferential analysis Common Inferential Statistical Methods: 1. T-test 2. Analysis of Variance (ANOVA) 3. Regression Analysis 4. Non-parametric tests 28 Descriptive Analysis Definition: It provides a summary of the main 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 29 Concepts And Methods In Descriptive Analysis Measures of Central Tendency Measures of Dispersion Measures of Shape Graphical Methods 30 Common Statistical Programs 1. SPSS (Statistical Package for the Social Sciences) 2. R 3. SAS (Statistical Analysis System) 4. Stata 5. Excel 6. Python 7. JMP (Jump) 8. Minitab 9. MATLAB 31