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‎DESCRIPTION‎

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(2016), we can use these quantities to assess how an unmeasured
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confounder may tip our result to insignificance.
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License: MIT + file LICENSE
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BugReports: https://github.com/LucyMcGowan/tipr/issues
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BugReports: https://github.com/r-causal/tipr/issues
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Depends:
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R (>= 2.10)
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Imports:

‎README.Rmd‎

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# tipr: R tools for tipping point sensitivity analyses <img src="man/figures/logo.png" align="right" height="138" />
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<!-- badges: start -->
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[![R-CMD-check](https://github.com/LucyMcGowan/tipr/workflows/R-CMD-check/badge.svg)](https://github.com/LucyMcGowan/tipr/actions)
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[![R-CMD-check](https://github.com/r-causal/tipr/workflows/R-CMD-check/badge.svg)](https://github.com/r-causal/tipr/actions)
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[![DOI](https://joss.theoj.org/papers/10.21105/joss.04495/status.svg)](https://doi.org/10.21105/joss.04495)
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<!-- badges: end -->
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**Authors:** [Lucy D'Agostino McGowan](https://www.lucymcgowan.com/)<br/>
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**License:** [MIT](https://opensource.org/licenses/MIT)
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**License:** [MIT](https://opensource.org/licenses/mit/)
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## Installation

‎README.md‎

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<!-- badges: start -->
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[![R-CMD-check](https://github.com/LucyMcGowan/tipr/workflows/R-CMD-check/badge.svg)](https://github.com/LucyMcGowan/tipr/actions)
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[![R-CMD-check](https://github.com/r-causal/tipr/workflows/R-CMD-check/badge.svg)](https://github.com/r-causal/tipr/actions)
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[![DOI](https://joss.theoj.org/papers/10.21105/joss.04495/status.svg)](https://doi.org/10.21105/joss.04495)
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<!-- badges: end -->
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**Authors:** [Lucy D’Agostino
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McGowan](https://www.lucymcgowan.com/)<br/> **License:**
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[MIT](https://opensource.org/licenses/MIT)
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[MIT](https://opensource.org/licenses/mit/)
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## Installation
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tip(effect_observed = 1.5, exposure_confounder_effect = 0.5)
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```
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## The observed effect (1.5) WOULD be tipped by 1 unmeasured confounder
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## with the following specifications:
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## * estimated difference in scaled means between the unmeasured confounder
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## in the exposed population and unexposed population: 0.5
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## * estimated relationship between the unmeasured confounder and the outcome: 2.25
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## ℹ The observed effect (1.5) WOULD be tipped by 1 unmeasured confounder with the
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## following specifications:
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## • estimated difference in scaled means between the unmeasured confounder in the
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## exposed population and unexposed population: 0.5
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## • estimated relationship between the unmeasured confounder and the outcome:
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## 2.25
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## # A tibble: 1 × 5
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## effect_adjusted effect_observed exposure_confounder_effect confounde…¹ n_unm…²
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## <dbl> <dbl> <dbl> <dbl> <dbl>
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## 1 1 1.5 0.5 2.25 1
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## # … with abbreviated variable names ¹​confounder_outcome_effect,
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## # ²​n_unmeasured_confounders
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## effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…²
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## <dbl> <dbl> <dbl> <dbl>
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## 1 1 1.5 0.5 2.25
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## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect
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## # ℹ 1 more variable: n_unmeasured_confounders <dbl>
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A hypothetical unobserved continuous confounder a scaled mean difference
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between exposure groups of `0.5` would need a relationship of at least
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tip(effect_observed = 1.09, exposure_confounder_effect = 0.5)
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```
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## The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder
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## with the following specifications:
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## * estimated difference in scaled means between the unmeasured confounder
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## in the exposed population and unexposed population: 0.5
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## * estimated relationship between the unmeasured confounder and the outcome: 1.19
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## ℹ The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder with
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## the following specifications:
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## • estimated difference in scaled means between the unmeasured confounder in the
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## exposed population and unexposed population: 0.5
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## • estimated relationship between the unmeasured confounder and the outcome:
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## 1.19
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## # A tibble: 1 × 5
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## effect_adjusted effect_observed exposure_confounder_effect confounde…¹ n_unm…²
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## <dbl> <dbl> <dbl> <dbl> <dbl>
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## 1 1 1.09 0.5 1.19 1
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## # … with abbreviated variable names ¹​confounder_outcome_effect,
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## # ²​n_unmeasured_confounders
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## effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…²
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## <dbl> <dbl> <dbl> <dbl>
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## 1 1 1.09 0.5 1.19
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## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect
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## # ℹ 1 more variable: n_unmeasured_confounders <dbl>
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A hypothetical unobserved continuous confounder a scaled mean difference
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between exposure groups of `0.5` would need a relationship of at least
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```
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## # A tibble: 4 × 7
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## term estimate std.error statistic p.value conf.low conf.…¹
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## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
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## 1 (Intercept) 0.0245 0.163 -22.7 1.49e-114 0.0176 0.0334
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## 2 exposure 0.921 0.172 -0.477 6.34e- 1 0.660 1.30
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## 3 measured_confounder 2.44 0.0746 11.9 6.95e- 33 2.11 2.82
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## 4 .unmeasured_confounder 2.42 0.0742 11.9 1.35e- 32 2.09 2.80
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## # … with abbreviated variable name ¹​conf.high
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## term estimate std.error statistic p.value conf.low conf.high
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## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
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## 1 (Intercept) 0.0245 0.163 -22.7 1.49e-114 0.0176 0.0334
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## 2 exposure 0.921 0.172 -0.477 6.34e- 1 0.660 1.30
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## 3 measured_confounder 2.44 0.0746 11.9 6.95e- 33 2.11 2.82
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## 4 .unmeasured_confoun… 2.42 0.0742 11.9 1.35e- 32 2.09 2.80
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Notice here the `.unmeasured_confounder` effect is 2.42 (which is
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greater than the 2.25 we calculated that would be needed to render our
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unexposed_confounder_prev = 0.10)
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```
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## The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder
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## with the following specifications:
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## * estimated prevalence of the unmeasured confounder in the exposed population: 0.25
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## * estimated prevalence of the unmeasured confounder in the unexposed population: 0.1
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## * estimated relationship between the unmeasured confounder and the outcome: 1.64
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## ℹ The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder with
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## the following specifications:
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## • estimated prevalence of the unmeasured confounder in the exposed population:
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## 0.25
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## • estimated prevalence of the unmeasured confounder in the unexposed
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## population: 0.1
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## • estimated relationship between the unmeasured confounder and the outcome:
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## 1.64
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## # A tibble: 1 × 6
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## effect_adjusted effect_observed exposed_confounder_p…¹ unexp…² confo…³ n_unm…⁴
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## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
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## 1 1 1.09 0.25 0.1 1.64 1
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## # … with abbreviated variable names ¹​exposed_confounder_prev,
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## # ²​unexposed_confounder_prev, ³​confounder_outcome_effect,
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## # ⁴​n_unmeasured_confounders
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## effect_adjusted effect_observed exposed_confounder_prev unexposed_confounder…¹
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## <dbl> <dbl> <dbl> <dbl>
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## 1 1 1.09 0.25 0.1
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## # ℹ abbreviated name: ¹​unexposed_confounder_prev
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## # ℹ 2 more variables: confounder_outcome_effect <dbl>,
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## # n_unmeasured_confounders <dbl>
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A hypothetical unobserved binary confounder that is prevalent in 10% of
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the unexposed population and 25% of the exposed population would need to
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confounder_outcome_effect = 1.05)
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```
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## The observed effect (1.09) WOULD be tipped by 7 unmeasured confounders
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## with the following specifications:
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## * estimated difference in scaled means between the unmeasured confounder
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## in the exposed population and unexposed population: 0.25
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## * estimated relationship between the unmeasured confounder and the outcome: 1.05
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## ℹ The observed effect (1.09) WOULD be tipped by 7 unmeasured confounders with
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## the following specifications:
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## • estimated difference in scaled means between the unmeasured confounder in the
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## exposed population and unexposed population: 0.25
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## • estimated relationship between the unmeasured confounder and the outcome:
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## 1.05
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## # A tibble: 1 × 5
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## effect_adjusted effect_observed exposure_confounder_effect confounde…¹ n_unm…²
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## <dbl> <dbl> <dbl> <dbl> <dbl>
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## 1 1 1.09 0.25 1.05 7.07
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## # … with abbreviated variable names ¹​confounder_outcome_effect,
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## # ²​n_unmeasured_confounders
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## effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…²
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## <dbl> <dbl> <dbl> <dbl>
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## 1 1 1.09 0.25 1.05
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## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect
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## # ℹ 1 more variable: n_unmeasured_confounders <dbl>
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It would take about `7` independent standardized Normal unmeasured
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confounders with a mean difference between exposure groups of 0.25 and a
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}
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```
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## The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder
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## with the following specifications:
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## * estimated difference in scaled means between the unmeasured confounder
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## in the exposed population and unexposed population: 0.09
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## * estimated relationship between the unmeasured confounder and the outcome: 2.5
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## ℹ The observed effect (1.09) WOULD be tipped by 1 unmeasured confounder with
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## the following specifications:
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## • estimated difference in scaled means between the unmeasured confounder in the
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## exposed population and unexposed population: 0.09
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## • estimated relationship between the unmeasured confounder and the outcome: 2.5
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## # A tibble: 1 × 5
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## effect_adjusted effect_observed exposure_confounder_effect confounde…¹ n_unm…²
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## <dbl> <dbl> <dbl> <dbl> <dbl>
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## 1 1 1.09 0.0907 2.5 1
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## # … with abbreviated variable names ¹​confounder_outcome_effect,
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## # ²​n_unmeasured_confounders
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## effect_adjusted effect_observed exposure_confounder_e…¹ confounder_outcome_e…²
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## <dbl> <dbl> <dbl> <dbl>
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## 1 1 1.09 0.0907 2.5
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## # ℹ abbreviated names: ¹​exposure_confounder_effect, ²​confounder_outcome_effect
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## # ℹ 1 more variable: n_unmeasured_confounders <dbl>
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## Code of Conduct
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