Expertise in Analytical Methods

Statistical Model Building Services

At Clievi, we offer comprehensive Statistical Model Building services tailored for clinical and evidence-based research. Our expertise spans a range of analytical techniques, including Linear Regression, Logistic Regression, and Survival Analysis, ensuring precise and reproducible results for clinical studies, epidemiological research, and healthcare analytics.

Linear Regression Analysis

Linear Regression is a foundational statistical technique for modeling the relationship between a dependent variable and one or more independent variables. Our services include:

• Simple Linear Regression – Evaluating the association between a single predictor and outcome variable.

• Multiple Linear Regression – Assessing multiple independent variables simultaneously to determine their impact on a continuous outcome.

• Assumption Checks & Diagnostics – We conduct tests for multicollinearity (Variance Inflation Factor), homoscedasticity, normality of residuals, and influential data points.

• Model Optimization – Employing stepwise regression, ridge regression, and LASSO (Least Absolute Shrinkage and Selection Operator) to improve predictive accuracy and mitigate overfitting.

• Clinical Applications – Used in pharmacokinetics modeling, dose-response relationships, and health economics evaluations.

Logistic Regression Analysis

Logistic Regression is essential for predicting binary or categorical outcomes in clinical research. We provide:

• Binary Logistic Regression – Used for two-class classification problems, such as disease presence vs. absence.

• Multinomial & Ordinal Logistic Regression – Extending logistic models to handle multiple outcome categories.

• Model Calibration & Performance Metrics – Evaluating predictive performance using ROC Curves, AUC (Area Under Curve), Sensitivity, Specificity, and Precision-Recall curves.

• Feature Selection & Regularization – Implementing L1 (LASSO) and L2 (Ridge) regularization techniques to prevent model overfitting.

• Clinical Applications – Widely used for risk factor analysis, diagnostic test evaluation, and treatment response prediction.

Survival Analysis

Survival Analysis is critical for time-to-event data in medical research. Our services include:

• Kaplan-Meier Estimation – Non-parametric survival curve estimation to compare patient groups.

• Cox Proportional Hazards Model – Evaluating the effect of multiple covariates on survival while adjusting for confounders.

• Accelerated Failure Time (AFT) Models – Alternative parametric models when proportional hazards assumption is violated.

• Competing Risks & Time-Dependent Covariates – Advanced modeling techniques to handle multiple risk factors and time-varying predictors.

• Clinical Applications – Used in oncology research, patient survival prognosis, and drug efficacy trials.

Why Choose Clievi for Statistical Model Building?

• Expert Consultation – Our team includes seasoned biostatisticians and epidemiologists who ensure robust study designs.

• Customized Solutions – We tailor statistical models to meet specific research objectives and regulatory requirements.

• Advanced Data Handling – Expertise in handling large-scale clinical datasets, EHR integration, and real-world evidence (RWE) analytics.

• Regulatory Compliance – Adherence to ICH-GCP, FDA, and EMA guidelines for clinical data analysis.

• Transparent & Reproducible Methods – Full documentation and code scripts for replicability in peer-reviewed publications.

Get in Touch

Enhance the accuracy and reliability of your research with our Statistical Model Building services. Contact us today for a consultation and discover how our expertise can drive meaningful insights in clinical and evidence-based research.