Statistical Analysis and Programming

Statistical Genetics: We provide support for genetic studies, including gene association studies, genome-wide association studies (GWAS), and other genetic analyses, ensuring proper handling of large-scale genetic data.

Biomarker Development and Validation: Our team helps identify and validate biomarkers, using statistical approaches to assess their diagnostic, prognostic, or therapeutic potential.

Survival Analysis: We offer expertise in survival analysis, including Cox proportional hazards models, Kaplan-Meier curves, and time-to-event analysis, to assess and interpret time-dependent outcomes.

Joint Modeling of Longitudinal and Survival Data: We assist in modeling both longitudinal data and survival outcomes simultaneously, such as the relationship between repeated measures of biomarkers and time-to-event outcomes.

Structural Equation Modeling (SEM): Our team is experienced in using SEM to model complex relationships between observed and latent variables, assessing both direct and indirect effects.

Bayesian Methods: For complex data and models, we apply Bayesian methods to incorporate prior information, estimate uncertainty, and make predictions.

Monte Carlo Simulations: We apply Monte Carlo simulations to model complex systems, estimate uncertainties, and explore various possible scenarios under different assumptions.

Prediction Modeling and Machine Learning: We implement various prediction modeling techniques, including regression models, random forests, and other machine learning algorithms, to forecast future outcomes based on your data.

Longitudinal Analysis: Our statisticians assist in analyzing repeated measures and data collected over time, using techniques such as mixed-effects models and growth curve analysis. 

Multilevel/Hierarchical Models: We support the analysis of data with hierarchical or nested structures (e.g., patients within hospitals, students within schools), using multilevel models to account for the dependence between observations.

Quasi-Experimental Designs: We provide statistical expertise in evaluating quasi-experimental research designs, including propensity score matching and instrumental variable analysis, to draw causal inferences from non-randomized studies.

Complex Survey Samples: We support the analysis of complex survey data, using sampling weights, stratification, and clustering to ensure accurate estimation of population parameters.

Psychometric Reliability: We offer support in evaluating the reliability and validity of measurement instruments, using techniques such as factor analysis and Cronbach's alpha.

Imputation and Missing Data Strategies: We provide strategies for handling missing data, including multiple imputation and other advanced methods to minimize bias and maximize the accuracy of your results.

 Nonparametric Methods: When assumptions of normality are not met, we implement nonparametric techniques for comparing groups or assessing relationships between variables.

Meta-Analysis: Our team can conduct meta-analyses, synthesizing results from multiple studies to provide an overall estimate of effect and assess study heterogeneity.