Clinical Statistics
Randomized clinical trials
This includes medical device trials
Design and Interpretation of Clinical Trials (phase I-IV)
- Statistical input to study protocols
- Randomization and Masking
Writing of Statistical Analysis Plans
- Study Overview
- Outcomes (Primary, secondary, Exploratory), for safety and Efficacy
- Analysis Population
- Statistical Analysis Methods: General principles, sample size, primary and secondary outcome analysis, subgroup analyses, sensitivity analyses, Missing data, multiplicity
- Trial Monitoring: Interim analysis, Safety monitoring, data quality monitoring,
Sample Size Estimation
- Choose Statistical Significance Level (α)
- Select Statistical Power (1-β)
- Estimate Effect Size
- Decide on Variability
- Choose One or Two-Tailed Test
- Choose an appropriate formula for sample size calculation based on the study design
Randomized Clinical Trial Analysis Using SAS, R, and Python
- CDISC standards with ADaM datasets are used to ensure traceability, reproducibility, and compatibility across studies
- Outcomes Analysis: Primary as well as secondary outcomes
- Analysis by Assigned Treatment (Intention to Treat)
- Subgroup Analysis
- Independent review,
- Code review or double programming
- Reporting results from RCTs
Systematic review and meta-analysis
- Comprehensive Search Strategy
- Methodological Rigor
- Primary and secondary outcomes
- Relevance of primary and secondary outcomes
- Meta-analysis: PICO, Forest Plot
- Transparent Reporting
Survival time Analysis
- Censoring
- Kaplan Meier Curve
- Log Rank Test
- Cox Regression
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Observational studies
We Ensure Quality Work
Basic Analysis Methods
- Estimation of Epidemiological Measures
- Confidence Intervals for the Odds Ratio
- Hypothesis Testing for Epidemiological Measures
Stratified Analysis Methods
- Estimation in Stratified 2×2 Tables
- Confidence Intervals for the Combined Odds Ratio
- Hypothesis Testing for the Combined Odds Ratio
- Tests for Homogeneity and Trend
Evaluation Methods for Matching
- General Evaluation Strategy for Individual Pairing
- Propensity Score Matching
- Estimation of the Odds Ratio
- Confidence Intervals for the Odds Ratio
- Hypothesis Testing for the Odds Ratio
- Evaluation Principles of Frequency Matching
Logistic Regression for Binary Health Outcomes
- Approximate Confidence Intervals for the Model Parameters
- Statistical Tests on the Model Parameters
- Proportional Odds Models
- Interpretation
Statistical Models for Count Data
- Poisson Regression: Statistical Tests on the Parameters, Incorporation of Time at Risk, Overdispersion
- Binomial Model
- Negative Binomial Model
Advanced Statistical Models for Epidemiological Data
- Generalized mixed models
- Bayesian Models
- Simulation studies
- Generalized Additive Models
- Jump Regression
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Make the right decision with your data
Are you seeking professional input for your data analysis, whether in clinical statistics or other fields? Let us leverage our expertise to assist you effectively