Life sciences
10 topics
Life sciences
Biostatistics
Biostatistics develops and applies statistical methods to biology and medicine. It drives the design and analysis of clinical trials, epidemiological studies, and genomics pipelines — sharing its toolkit with econometrics, ML, and signal processing.
Topics in this field
Bayesian Adaptive Trials
Clinical trials that use Bayesian inference to update beliefs about treatment effects and modify trial conduct based on accumulating evidence.
Clinical Trial Design
Principled design of randomized controlled trials, covering sample size, error control, adaptive methods, and regulatory frameworks.
Cox Proportional Hazards
Semi-parametric regression model for survival data that estimates covariate effects without specifying the baseline hazard.
Generalized Linear Models
A unified regression framework extending linear models to non-normal outcomes via exponential family distributions and link functions.
Kaplan-Meier Estimator
The product-limit non-parametric estimator of the survival function, the universal starting point for time-to-event analysis.
Linear Mixed Models
Regression with random effects for correlated and hierarchical data. Used throughout clinical research.
Logistic Regression
Regression model for binary and polytomous outcomes using the logit link, yielding odds ratio estimates directly interpretable in clinical and epidemiological research.
Meta-Analysis
Quantitative synthesis of results across multiple studies, combining effect estimates to increase precision and assess heterogeneity.
Multiple Testing Corrections
Methods for controlling error rates when simultaneously testing many hypotheses, from conservative Bonferroni corrections to FDR control in genomics.
Survival Analysis
Statistical methods for time-to-event data, accounting for censoring and the hazard of an event occurring over time.