SAMBA
1.0.0Selection and Misclassification Bias Adjustment for Logistic Regression Models
Overview
Health research using data from electronic health records (EHR) has gained popularity, but misclassification of EHR-derived disease status and lack of representativeness of the study sample can result in substantial bias in effect estimates and can impact power and type I error for association tests. Here, the assumed target of inference is the relationship between binary disease status and predictors modeled using a logistic regression model. 'SAMBA' implements several methods for obtaining bias-corrected point estimates along with valid standard errors as proposed in Beesley and Mukherjee (2020) doi:10.1111/biom.13400, Biometrics.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-06-0613 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 83%
Downloads
Dependencies
Code & Tests
Datasets
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- 1.0.0Latest
- RR 4.6.0 released · 2026-04-24
- 0.9.02026-03-10
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2020-02-20
- Total releases
- 2 / 6 yrs
- License
- GPL-3 OSI
- Bundled data
- 52 KB / 1 file
- Download size
- 243 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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