We lead in biostatistical and data management services for pharmaceutical, biotech, medical device industries and research institutes. We provide biostatistical Input to study protocols and writing of Statistical Analysis Plan (SAP) Development for Clinical Research, including Randomization and Masking, Sample Size Estimation, Systematic review and meta-analysis, Survival time Analysis, etc.

Randomized clinical trials

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

  • 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

Observational studies

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