Pinned Repositories
causal
Causal Inference using R. Examples are from the book Causal Inference (Chapman & Hall/CRC, 2012) by Miguel Hernán and Jamie Robins. Complete contributor/reviewer list is updated in http://www.hsph.harvard.edu/faculty/miguel-hernan/causal-inference-book/
EpiMethods
A unique open textbook to teach the nuances of applying advanced epidemiological methods using real data.
into2ML
A very basic introduction to machine learning.
intro2R
This is a R online textbook for those who are not familiar with data wrangling. For providing some practical introduction to data wrangling, NHANES datasets will be used as examples in this tutorial. Target audience is those interested in health data analysis, but these data wrangling skills are easily transferable to other fields. General understanding of a syntax based program is required as pre-requisite. For any comments regarding this document, reach out to Ehsan Karim http://ehsank.com/
iptw
This R package calculates various measures of association and helps understand and visualize the link between causal models such as Marginal Structural Models and Standerdized measures.
Scientific-Writing-for-Health-Research
Adaptation and Deployment of OER for Communicating Scientific Research Findings in Health Sciences Education
simMSM
R package that simulates data suitable for fitting Marginal Structural Model.
spph504-007
SPPH 504 (section 007): Application of Epidemiological Methods
SPPH504007SurveyData
Survey Data: Design and Examples
TMLEworkshop
Targeted maximum likelihood estimation (TMLE) enables the integration of machine learning approaches in comparative effectiveness studies. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified.
ehsanx's Repositories
ehsanx/intro2R
This is a R online textbook for those who are not familiar with data wrangling. For providing some practical introduction to data wrangling, NHANES datasets will be used as examples in this tutorial. Target audience is those interested in health data analysis, but these data wrangling skills are easily transferable to other fields. General understanding of a syntax based program is required as pre-requisite. For any comments regarding this document, reach out to Ehsan Karim http://ehsank.com/
ehsanx/Causal-Inference-Methods-for-Pragmatic-Trials
Developing and Evaluating Causal Inference Methods for Pragmatic Trials
ehsanx/MSMsim
marginal structural model simulation
ehsanx/ARCH
Repo for the ARCH project
ehsanx/BDA_course_Aalto
Bayesian Data Analysis course at Aalto
ehsanx/CausalInferenceIntro
Guest lecture for SPPH 500
ehsanx/CI_CovSel
ehsanx/classimb_calibration
ehsanx/course-content
NMA Computational Neuroscience course
ehsanx/course-content-dl
ehsanx/ds-tools-ai4ph
Data Science Tools: AI4PH Lecture
ehsanx/DynamicPrediction
R code for implementation of methods referred to in the manuscript entitled "Dynamic Survival Prediction Combining Landmarking with a Machine Learning Ensemble: Methodology and Empirical Comparison"
ehsanx/ebal-py
Python implementation of Entropy Balancing for binary and continuous treatment
ehsanx/exppreg
Simulation study code for "A potential outcomes approach to defining and estimating gestational age-specific exposure effects during pregnancy"
ehsanx/imputeSparse
ehsanx/Intro-to-Python
Introduction to Python. Uses the basic 3.x python libraries to tackle a few projects
ehsanx/iTMLE
ehsanx/maars
ehsanx/manuscript-colab-tools-for-new-users
A template for basic manuscript using colab tools
ehsanx/ML_ShortCourse
ehsanx/Modified_Dragonnet
ehsanx/nhanes_mortality_associations
ehsanx/OAL-rejoinder-2022
ehsanx/OW_SGA
ehsanx/OW_SGA_survival
ehsanx/rsmatch
Risk set matching in R
ehsanx/sequential_trials
R code for implementation of the simulation study described in the paper: "Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models"
ehsanx/setoguchi
setoguchi simulation algorithm from 2008 paper
ehsanx/Simulations-SNNs-vs-Cox
This repository stores the R-code of a simulation study to compare survival neural networks (SNNs) with Cox models for clinical trial data. The predictive performance of ML techniques is compared with statistical models in a simple clinical setting (small/moderate sample size, small number of predictors) with Monte Carlo simulations. Synthetic data (250 or 1000 patients) are generated that closely resemble 5 prognostic factors pre-selected based on a European Osteosarcoma Intergroup study (MRC BO06/EORTC 80931). Comparison is performed between two partial logistic artificial neural networks (PLANN original by Biganzoli et al. 1998, Statistics in medicine, 17(10), 1169-1186 and PLANN extended by Kantidakis et al. 2020 BMC medical research methodology, 20(1), 1-14) as well as Cox models for 20, 40, 61, and 80% censoring. Survival times are generated from a log-normal distribution. Models are contrasted in terms of C-index, Brier score at 0-5 years, Integrated Brier Score (IBS) at 5 years, and miscalibration at 2 and 5 years. Endpoint of interest is overall survival. Note: PLANN original/extended are tuned based on IBS at 5 years and C-index.
ehsanx/VoIPred