The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, a plausible residual effect size for an unmeasured continuous or binary confounder, and a realistic mean difference or prevalence difference for this hypothetical confounder. Building on the methods put forth by Lin, Psaty, & Kronmal (1998) DOI:10.2307/2533848, we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance, rendering the study inconclusive.
| Version: | 0.2.0 |
| Imports: | glue, tibble, purrr |
| Suggests: | testthat, broom, dplyr, MASS |
| Published: | 2020-11-16 |
| Author: | Lucy D'Agostino McGowan |
| Maintainer: | Lucy D'Agostino McGowan <lucydagostino at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | tipr results |
| Reference manual: | tipr.pdf |
| Package source: | tipr_0.2.0.tar.gz |
| Windows binaries: | r-devel: tipr_0.2.0.zip, r-release: tipr_0.2.0.zip, r-oldrel: tipr_0.2.0.zip |
| macOS binaries: | r-release: tipr_0.2.0.tgz, r-oldrel: tipr_0.2.0.tgz |
| Old sources: | tipr archive |
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