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Daniel_He

DIAN HE

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Introduction to Deep learning
Introduction to machine learning
Chapter 1 Trial Design and Statistical Strategy
Statistical Results Interpretation Summary Table
Clinical Trial Designs and Sample Size
Go No-Go Decision
Oncology_Simulated_Figures
Example for Each Core Listing
Oncology_Comprehensive_Table_Shells
ADAM_METADATA
ADaM Reviewer guide
Statistical Reviewer Guide
SDTM_METADATA
Typical workflow for ADaM database generation and validation
ALPHA Allocation Tables in seamless, adaptive, group sequential design
A Seamless adaptive sequential design example
Adaptive Design vs sequential design
Responsibilities of a Project Biostatistician
Biostatistician Role in QC
Decision based on statistical support
Role of the Project Statistician in an FDA Type C Meeting
Statistical Inspection Readiness Checklist
IND VS NDA
Role of the Project Statistician in a Phase II III seamless
Role of the Project Statistician in the Estimand Framework
Biostatistician's Role in Blinded Review
Statistical modeling
Adaptive randomization probabilities and Interim Analysis
ISE/ISS pooling/ meta analysis
R shiny practice
BE Simulation Report
Real-World Study
Diagnostic test
Joint model with longitudinal and survival data
Competing Risk Cox Model
Time-Dependent Cox Regression Model
Survival Analysis
Save And Finalize Your trained Model
Linear mixed model MCMC
Linear regression MCMC
Mixed model and linear model, Join model and Two stages model, MCMC
Expectation maximum
Nested mixed model
Common issues in Statistics
Central Limit Theorem
Fixed or Random Factors
Quantile Regression
Poisson regression
Logistic distribution and logistic regression
Common statistical distribution
Bayesian Inference in R
Bias analysis and control
Statistics in clinical trial
Imputing Missing Data with MICE
Mixed effect model- Introduction
Epidemiology
Causal inference and associated regression
Simulations and Bootstrapping
Sequence Analysis week 5
Principle components analysis
House price prediction part 2
Fit regression model for a fan-shaped relation
Comparison of mixed models in SAS and R- covariance structure
Matching and Weighting analyses
Doubly robust estimators_TMLE_Superlearner
Variable selection
1 Causal inference introduction
Mediation analysis
Instrumental variables
Double Robust Estimators
3 Standardization and G formula
2 IP Weighting
Which covariate should be adjusted
How to use subscripts/superscripts for a figure
Read and write excel file
Least-squares means with interaction
Spline regression
How to calculate the prediction interval for LMM
Simulating data for linear mix model
Combine estimates by pooling rules
MCMC sampling
Causal inference intro
SuperLearner_sl3
SuperLearner
Longitudinal targeted maximum likelihood estimation
Confounding and effect measure modification
Common sas commands 2
How to use sas format
How to create advanced plots
Common SAS statements
Calculus
ggplot2 parameters
Linear algebra basics
Linear mixed model variance decomposition (nlme)
Linear mixed model covariance decomposition with random slopes (lme4)
Linear mixed model covariance decomposition with random intercept (lme4)
Multiple linear regression practice
Linear mixed model practice
Linear mixed model theory
Linear regression theory (simple and multiple)
Maximum likelihood estimation
Probability basics
Data visualization 2
Data visualization
Deep neural networks- regression
Machine learning- KNN
Simple machine learning
Deep neural network
Convolutional neural network
Data wrangling
Data summarization
R basics