Biostatistics Clinic
-
-
Purpose
This clinic will provide personalized one-to-one consultations, offering tailored statistical support to medical researchers, healthcare professionals, public health officials, and students. Emphasizing both frequentist and Bayesian statistical methods, including advanced clinical trial designs such as adaptive trials, the clinic aims to enhance the quality and impact of medical research.
Date
Every Friday, starting from the first of week August 2024
TIME
2.30 pm – 5.00 pm
VENUE
- Level 8 Common Room, UMBI
- Stats Unit, Level 7, UMBI
-
Package Rate Description Individual Consultation Sessions RM150 per hour – Package Deals 5-hour package: RM675 (10% discount) – 10-hour package: RM1200 (20% discount) Specialized Consultation Packages Clinical Trial Design Package includes detailed guidance on study design, power analysis (sample size calculation for both frequentist and Bayesian design (decision-theoretic utility function, assurance calculation) and frequentist adaptive design techniques (adaptive randomization, interim analyses (O’Brien Fleming, Pocock, Haybittle-Peto methods, alpha spending methods etc) Bayesian Statistical Methods and Computational Training includes personalized computational training using R programming and application of Bayesian methods in research -
Dr. Muhammad Irfan Abdul Jalal
Dr Muhammad Irfan Abdul Jalal is a medical-qualified, Royal Statistical Society (RSS)-accredited Bayesian statistician who has been working in the medical research field since 13 years ago. His research interests include Bayesian statistical programming, survival analysis and adaptive clinical trial design. He is an avid user of R and Python programming languages, with extensive experiences in SPSS, Minitab and Stata software. He gained his medical degree from Queen’s University of Belfast in 2006, MSc in Medical Statistics (USM) in 2011 and PhD (Mathematics) from Newcastle University, UK in 2020. His thesis on Bayesian Survival Analysis using Integrated Nested Laplace Approximation (INLA) contributed towards improved computational efficiency in computing Bayesian posterior distributions through efficient R programming.