Selection 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
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-06
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- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 6
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 83%
- UsingSAMBA HTML
Downloads
Dependencies
Installing SAMBA adds 10 packages (5 with compiled code, 11 MB of source tarballs). Heaviest: survey 2.3 MB, optimx 2.2 MB, nloptr 2.1 MB.
Code & Tests
Datasets
People & History
Previously maintained by Alexander Rix (0.9.0).
Pick two releases to compare their code metrics.
- 1.0.0Latest
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 0.9.02020-02-20
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2020-02-20
- Total releases
- 2 / 6 yrs
- License
- GPL-3 OSI
- Bundled data
- 52 KB / 1 file
- Download size
- 425 KB
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