Introduction to Biostatistics

• Students will understand basic biostatistics concepts, terminology, variables, measurements, and measurement scales.
• Students will be able to select a simple random sample and other scientific samples from a population of subjects.
• Students will understand the processes involved in the scientific method and the design of experiments.

Introduction to Biostatistics

• Biostatistics is a key component in health sciences.
• Biostatistics allows for the analysis of data to inform clinical decisions, public health policies, and medical research outcomes.
• Biostatistics provides tools for organizing, analyzing, and interpreting data effectively.
• Biostatistics helps with designing experiments, conducting clinical trials, and improving public health outcomes.

What is Statistics

• Statistics include the collection, organization, summarization, and analysis of data.
• Data includes numbers resulting from measurements or counts.
• The goal of statistics is to draw meaningful inferences from sample data.

Sources of Data

• Primary sources of data include routinely kept records like hospital records.
• Surveys are used when routine data is unavailable.
• Experiments are controlled environments for specific hypotheses.
• Examples of external sources are published reports or research data banks.

Concept of Biostatistics

• Biostatistics applies statistical methods to biological, medical, and health-related studies.
• Biostatistics aids in designing experiments, analyzing data, and interpreting results in medical research.
• Examples of biostatistics include assessing the effectiveness of a new drug and analyzing the correlation between smoking and lung cancer.

Role of a Biostatistician

• A biostatistician applies statistical methods to medical data in research settings.
• The role includes designing research studies, analyzing data to uncover trends and relationships, and interpreting results to ensure medical conclusions are based on solid statistical evidence.
• Biostatisticians ensure that data-driven decisions in healthcare are accurate and reliable.

Examples of Biostatistics

• A study found that 50% of children who drink milk regularly have stronger bones.
• Doctors found that 70% of patients with a specific illness recover within three weeks with treatment.
• A clinical trial found that a new medicine helped 80% of children feel better.
• A survey found that 60% of people get a flu shot every year.
• Statistics help us understand patterns and make decisions based on data from real-life situations.
• Biostatistics is used to describe things like preferences and health.

Uses of Biostatistics

• Biostatistics can be a vital tool for making evidence-based decisions in health and medicine.
• Statistical methods are used to evaluate treatment effectiveness, monitor disease outbreaks, and improve patient care.
• Knowing statistics is crucial for students in health sciences
• Understanding statistics enables collecting, analyzing, and interpreting clinical data, which aids in research and improves patient outcomes.
• In health and medicine, biostatistics helps professionals make accurate diagnoses, develop effective treatments, and contribute to public health initiatives.

Why Study Biostatistics

• Numerical data is essential in healthcare for making evidence-based decisions and improving patient outcomes.
• Biostatistics is widely applied in clinical trials, epidemiology, patient care, public health, and medical research.
• Analyzing genetic data to understand diseases is a component of medical research.
• Statistical methods help healthcare professionals make critical decisions that directly impact lives.
• Biostatistics is the foundation of evidence-based medicine, enabling medical professionals to provide the best possible care.

Who Uses Statistics and Biostatistics

• Statistical methods are essential for professionals in various fields, especially in healthcare.
• Physicians use statistics to evaluate treatment effectiveness, monitor patient outcomes, and make evidence-based decisions.
• Epidemiologists use statistics to study disease patterns, outbreaks, and public health trends.
• Healthcare administrators use statistics to allocate resources efficiently and improve operational performance.
• Pharmacologists use statistics to assess drug efficacy and safety through biostatistical analysis.
• Biostatistics plays a critical role in modern medicine, ensuring that decisions and policies are based on reliable, scientific evidence.

Types of Statistics

• Two types of statistics are Descriptive and Inferential
• Descriptive statistics focuses on organizing, summarizing, and presenting data in a clear and understandable way.
• Descriptive statistics organizes data into frequency distributions, presenting the data with various types of graphs, and calculating important measures like central tendency and dispersion.
• A clinical trial collected data on the blood pressure levels of 200 patients before and after treatment This data is summarized using measures like the mean and standard deviation to compare treatment effectiveness.
• A study on the prevalence of diabetes in a city surveyed 1,000 individuals, finding that 15% are diabetic
• This proportion can be visualized using a pie chart to show the distribution of diabetic vs non-diabetic individuals.
• Researches found that 90% of participants showed immunity after receiving the vaccine
• Descriptive statistics such as percentages and rates are used to summarize the effectiveness of treatments in clinical studies.
• Inferential statistics has to do with using data from a sample to make conclusions or decisions about a larger population.
• Inferential statistics makes predictions, estimates, and generalizations about a population based on sample data, and evaluates how confident results are.
• In clinical trials, researchers use a sample of patients to make conclusions about the effectiveness of a new drug; based on the response of this sample, inferring whether the drug will work for the larger population of patients.

Variables, Quantitative and Qualitative

• A variable is a characteristic that can take on different values.
• Types of variables include quantitative (measurable) and qualititative (categorical).
• Quantitative variables are measurable, such as height, weight, and age.
• Qualitative variables are categorical, such as medical diagnoses and ethnic groups.
• Key concept: Random variables are influenced by chance.
• Discrete Random Variables take distinct, separate values, such as hospital admissions or number of patients.
• Continuous Random Variables take any value within a range, such as weight or height.

Population and Sample

• Population is the entire set of entities of interest in a study.
• An example of population would be all children in a county school system.
• Sample is a subset of a population, used for practical measurement.
• An example of sample use would be a selected group of children from the hospital system.
• Statistical terminology can have different meanings in every-day language versus statistical contexts.
• Understanding the basic vocabulary is crucial for effective communication and analysis.

Introduction to Measurement

• Measurement refers to the assignment of numbers to objects or events according to a set of rules.
• Measurement scales categorize the results of measurements.
• The scales include nominal, ordinal, interval, and ratio.
• Different types of data require different statistical methods for analysis.

Nominal Scale

• Nominal Scale is The lowest measurement scale, used for classifying observations into mutually exclusive categories.
• Examples of what can be used include: Male/Female, Married/Not Married, Medical Diagnoses
• Numbers are used to "name" or classify the data.
• Medical diagnoses example: 1 = Flu, 2 = Cold, 3 = COVID-19

Ordinal Scale

• Ordinal scale is when Observations are ranked according to some criterion, but the differences between categories are not necessarily equal.
• Examples of what can be used include: Socioeconomic status (low, medium, high), Improvement status (unimproved, improved, much improved)
• Improvement: 1 = Unimproved, 2 = Improved, 3 = Much Improved

Interval Scale

• Interval scale lets measuring the distance between two measurements; this is known and consistent. The zero point can be either positive or negative.
• Example: Temperature (Celsius or Fahrenheit)
• A temperature of zero does not imply the absence of the measured quantity.
• Temperature: 0°C is not the absence of heat.

Ratio Scale

• Ratio Scale is The highest level of measurement, where both equality of intervals and ratios can be determined, and there is a true zero point.
• Examples of what can be used include: Height, Weight, Length
• Zero represents a complete absence of the quantity being measured.
• A height of 0 cm means no height.

Summary of Measurement Scales

• Nominal Scale: Classification, no order or distance.
• Ordinal Scale: Ordered categories, unequal distances.
• Interval Scale: Ordered categories, equal distances, arbitrary zero.
• Ratio Scale: Ordered categories, equal distances, true zero.
• Measurement scales are crucial in selecting appropriate statistical methods.
• Understanding the scale helps in interpreting data and determining the level of analysis.

Sampling and Statistical Inference

• Statistical inference is the procedure by which a conclusion about a population based on a sample is reached.
• The validity of the inference depends on the sampling method.
• A scientific sample, such as a simple random sample, is needed to make valid inferences.

Definition: Simple Random Sample

• A sample of size n drawn from a population of size N is a simple random sample if every possible sample of size n has the same chance of being selected.
• Sampling can occur with or without replacement
• Sampling with replacement: After each draw, the selected item is returned to the population.
• Sampling without replacement: Once an item is drawn, it is not returned, ensuring that no item appears more than once in the sample.

Systematic Sampling

• In systematic sampling, a starting point is selected, and then every k-th subject is chosen.
• If the starting point is subject 4 and the interval k = 18, subjects 4, 22, 40, ..., are selected.
• When systematically sampling, the first subject is selected randomly and the sample interval dictates every following selected subject

Summary of Sampling Methods

• Simple Random Sampling: Every subject has an equal chance of being selected.
• Systematic Sampling: Select a random starting point and pick every k-th subject.
• Sampling with Replacement: Reintroduce the subject into the population after selection.
• Sampling without Replacement: Do not reintroduce selected subjects.
• Both simple random sampling and systematic sampling are widely used in biostatistics and healthcare research.
• Careful attention to the sampling method is crucial for drawing valid inferences.
• Randomization ensures the reliability of conclusions drawn from a sample.

The Scientific Method

• The scientific method is a systematic process used to gather, analyze, and report scientific data.
• It is based on empirical data, ensuring results are unbiased, replicable, and testable.
• The key steps of the scientific method include observation, formulation of hypotheses, and designing an experiment.
• Phenomena are observed to generate questions for further exploration.
• A hypothesis offers a testable statement about the observed phenomena.
• Random assignment to experimental and control groups ensures a valid test.
• Measurements must be accurate and precise to ensure valid results.
• Measurements cannot be not accurate, the results of the experiment may be invalid.
• Accuracy refers to how close a measurement is to the true value.
• Precision refers to the consistency of measurements.
• Even precise measurements can be invalid if they are not accurate.
• The scientific method provides a structured framework for conducting research.
• Proper experimental design and statistical analysis are crucial for producing valid, reliable, and replicable results.
• In biostatistics, accuracy, precision, and appropriate hypothesis testing are essential.
• "Research findings must be replicated before they are considered scientifically credible."