Advanced Statistics for Epidemiology
Expert-defined terms from the Advanced Skill Certificate in AI in Public Health and Epidemiology course at HealthCareStudies (An LSPM brand). Free to read, free to share, paired with a globally recognised certification pathway.
Advanced Statistics for Epidemiology #
Advanced statistical methods used in epidemiology to analyze and interpret data related to disease patterns, risk factors, and health outcomes.
Bayesian Statistics #
A statistical approach that combines prior knowledge with current data to make inferences about population parameters.
Confounding Variable #
A variable that distorts the relationship between the independent and dependent variables in a study, leading to incorrect conclusions.
Cox Proportional Hazards Model #
A statistical model used to analyze the relationship between survival time and one or more predictor variables.
Descriptive Statistics #
Statistical techniques used to summarize and describe the main features of a dataset.
Hazard Ratio #
A measure of how much more likely an event is to occur in one group compared to another group over time.
Hypothesis Testing #
A statistical method used to determine whether there is enough evidence to reject a null hypothesis.
Incidence Rate #
The number of new cases of a disease that occur in a population at risk during a specific time period.
Logistic Regression #
A statistical model used to examine the relationship between a categorical dependent variable and one or more independent variables.
Multiple Imputation #
A technique used to handle missing data by creating multiple sets of imputed values based on the observed data.
Negative Binomial Regression #
A regression model used to analyze count data with overdispersion, where the variance exceeds the mean.
Odds Ratio #
A measure of association between an exposure and an outcome in a case-control study.
Parametric Survival Analysis #
A statistical method used to model survival data assuming a specific distribution for the survival times.
Poisson Regression #
A regression model used to analyze count data when the outcome variable follows a Poisson distribution.
Random Effects Model #
A statistical model that accounts for clustering or repeated measures within the data by including random effects in the model.
Receiver Operating Characteristic (ROC) Curve #
A graphical representation of the trade-off between sensitivity and specificity for a binary classifier.
Relative Risk #
A measure of the strength of association between an exposure and an outcome in a cohort study.
Sample Size Calculation #
The process of determining the number of subjects needed for a study to detect a specified effect size with a given level of confidence.
Survival Analysis #
A statistical method used to analyze time-to-event data, such as time until death or disease recurrence.
Time Series Analysis #
A statistical technique used to analyze data collected at regular intervals over time to identify patterns and trends.
Zero #
Inflated Poisson Regression: A regression model used to analyze count data with excess zeros, where the data may come from a mixture of two processes.