Study design and planning:

Sample Size Calculation and Power Analysis: Determining the appropriate number of participants needed to achieve statistical significance.

Randomization Design: Creating randomization schemes for different types of trials (e.g., parallel, crossover).

Adaptive Trial Design: Designing trials that allow for modifications based on interim data, often using Bayesian methods.

Endpoint Definition and Analysis Planning: Defining primary and secondary endpoints and developing statistical analysis plans (SAPs).

Protocol Development Support: Assisting in the development of study protocols, including statistical methodologies.

Data Analysis and Interpretation:

Descriptive Statistics: Summarizing data using measures like mean, median, standard deviation, and range.

Inferential Statistics: Applying hypothesis testing, regression analyses, and other statistical tests to draw conclusions from study data.

Survival Analysis: Techniques such as Kaplan-Meier estimates, Cox proportional hazards models, and time-to-event analysis, commonly used in oncology and other chronic diseases.

Longitudinal Data Analysis: Analyzing repeated measures data over time using mixed models or generalized estimating equations (GEE).

Meta-Analysis: Pooling data from multiple studies to obtain a comprehensive understanding of the effects of a treatment or intervention.

Multivariate Analysis: Performing analyses that involve multiple dependent or independent variables, such as multivariate regression or principal component analysis (PCA).