Tools

• Multibiasmeta (R package) - Mathur M
• Conducts corrections and sensitivity analyses for within-study and/or across-study biases in meta-analyses. 
• phacking (R package) – Mathur M 
• Conducts sensitivity analysis for p-hacking in meta-analyses. 
• PublicationBias (R package) – Mathur M
• Conducts sensitivity analysis for publication bias in meta-analyses. 
• EValue (R package) – Mathur M
• Conducts sensitivity analysis for unmeasured confounding for observational studies and meta-analysis, and conducts analogous sensitivity analyses for selection bias. 
• Sobol sensitivity analysis (R package) – Lu R
• Sobol Sensitivity is a comprehensive, statistical approach to compute sobol sensitivity indices in sensitivity analysis. 
• EVALUE (Stata module) - Mathur M
• Calculates the E-value, a measure of sensitivity to unmeasured confounding.
• E-value calculator website (GUI) – Mathur M
• Calculates the E-value, a measure of sensitivity to unmeansured confounding

 

• Second primary lung cancer-risk assessment tool (ShinyApp) – Han S
•     Second Primary Lung Cancer Risk Assessment Tool (SPLC-RAT) is a web-based risk prediction tool for second primary lung cancer (SPLC) among patients with initial primary lung cancer (IPLC) who ever smoked before lung cancer diagnosis. By simply entering patients’ demographic data, tumor characteristics, and smoking history, the estimates for 5-year and 10-year risks of developing SPLC after initial diagnosis of lung cancer can be provided. 
• Risk assessment for metastasis to brain outcome (ShinyApp) – Han S
•     Brain metastasis is common in lung cancer, and treatment of brain metastasis can lead to significant morbidity. Although early detection of brain metastasis may improve outcomes, there is no current clinical consensus on how to identify high-risk patients for brain magnetic resonance imaging (MRI) surveillance. Our model, Risk Assessment for Metastasis to Brain Outcome (RAMBO), utilizes clinical and tumor genomic characteristics at the time of diagnosis to predict risk of future brain metastasis development. 
• Random forest model for CVD risk prediction (ShinyApp) – Lu R
• A comprehensive health-risk prediction tool for postmenopause women (ShinyApp) – Hedlin H

• Multibiasmeta (R package) – Mathur M
• Conducts corrections and sensitivity analyses for within-study and/or across-study biases in meta-analyses. 
• Metabias (R package) – Mathur M
• Provides common components (classes, methods, documentation) for meta-analysis packages. 
• phacking (R package) – Mathur M 
• Conducts sensitivity analysis for p-hacking in meta-analyses. 
• PublicationBias (R package) – Mathur M
• Conducts sensitivity analysis for publication bias in meta-analyses. 
• MetaUtility (R package) – Mathur M 
•  Contains functions to estimate the proportion of effects stronger than a threshold of scientific importance, to make effect size conversions, and to compute and format estimates and inference. 
• EValue (R package) – Mathur M
• Conducts sensitivity analysis for unmeasured confounding for observational studies and meta-analysis, and conducts analogous sensitivity analyses for selection bias. 
• NRejections (R package) – Mathur M
• Computes metrics of outcome-wide evidence strength for studies testing multiple correlated outcomes. 
• Replicate (R package) – Mathur M
• Conducts statistical analyses for multisite replication projects. 
• GhostKnockoff (R package)  -  He Z
• R-package for multiple knockoff inference using summary statistics, e.g. Z-scores.  
• The knockoff inference is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. This package provides a procedure which performs knockoff inference without ever constructing individual knockoffs (GhostKnockoff).  
• It additionally supports multiple knockoff inference for improved stability and reproducibility. Moreover, it supports meta-analysis of multiple overlapping studies. 

Regression-based causal mediation analysis with a treatment-mediator interaction term. 

KnockoffScreen (R package) – He Z

R-package for Identification of Putative Causal Loci in Whole-Genome Sequencing Data via Knockoff Statistics. An efficient knockoff generator for genetic data 

• DMC Registry (Web interface) – Garcia A, Hedlin H, Lee J, Desai M
• COVID Endpoint Registry (Web interface) - Garcia A, Hedlin H, Lee J, Desai M

KnockoffScreen (R package) – He Z

R-package for Identification of Putative Causal Loci in Whole-Genome Sequencing Data via Knockoff Statistics. An efficient knockoff generator for genetic data 

LGRF (R package) – He Z

R-package for the Longitudinal Genetic Random Field model (LGRF). Test the association between a longitudinally measured quantitative outcome and a set of genetic variants in a gene/region 

LGEWIS (R package) – He Z 

R-package for longitudinal gene-environment-wide interaction studies. Single variant/set-based score tests for genetic association/gene-environment interaction in studies with time varying outcomes and environmental exposures 

FSTpackage (R package) – He Z

R-package for unified sequence-based association tests allowing for multiple functional annotation scores. Individual gene / gene-set / genome-wide scan for coding and noncoding variants using summary statistics (available for meta-analysis) and functional genomics data 

GenoNet (Web interface) - He Z

Web interface for predicting cell type specific functional consequences of non-coding variation across 127 tissues/cell types. [NA1] 

Truncnormbayes (R package) – Mathur M

Finds the posterior modes for the mean and standard deviation for a truncated normal distribution. 

Qualtrics Mousetracker (R package) – Mathur M

Software in R, Javascript, and Qualtrics to design and analyze psychology experiments that use fine-grained mouse-tracking measures of perceptual category competition. 

SimTimeVar (R package) – Mathur M, Kapphahn K , Garcia A, Desai M 

Simulates a longitudinal dataset with time-varying covariates with user-specified correlation structure. 

Stratamatch (R package) – Lee J

A pilot matching design to automatically stratify and match large datasets. 

Statistical Analysis Plan Templete (WORD)

Statistical Analysis Plan Templete (PDF)