Package 'tipr' reference manual

Title:	Tipping Point Analyses
Description:	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, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups. Building on the methods put forth by Cornfield et al. (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele (2016), we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance.
Authors:	Lucy D'Agostino McGowan [aut, cre] , Malcolm Barrett [aut]
Maintainer:	Lucy D'Agostino McGowan <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.2.9000
Built:	2024-11-18 06:10:21 UTC
Source:	https://github.com/r-causal/tipr

Adjust an observed regression coefficient for a normally distributed confounder

Description

Adjust an observed regression coefficient for a normally distributed confounder

Usage

adjust_coef(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE)
)

adjust_coef_with_continuous(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE)
)
adjust_coef(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE)
)

adjust_coef_with_continuous(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE)
)

Arguments

`effect_observed`	Numeric. Observed exposure - outcome effect from a regression model. This can be the beta coefficient, the lower confidence bound of the beta coefficient, or the upper confidence bound of the beta coefficient.
`exposure_confounder_effect`	Numeric. Estimated difference in scaled means between the unmeasured confounder in the exposed population and unexposed population
`confounder_outcome_effect`	Numeric. Estimated relationship between the unmeasured confounder and the outcome.
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`

Value

Data frame.

Examples

## Update an observed coefficient of 0.5 with an unmeasured confounder
## with a difference in scaled means between exposure groups of 0.2
## and coefficient of 0.3
adjust_coef(0.5, 0.2, 0.3)
## Update an observed coefficient of 0.5 with an unmeasured confounder
## with a difference in scaled means between exposure groups of 0.2
## and coefficient of 0.3
adjust_coef(0.5, 0.2, 0.3)

Adjust an observed coefficient from a regression model with a binary confounder

Description

Adjust an observed coefficient from a regression model with a binary confounder

Usage

adjust_coef_with_binary(
  effect_observed,
  exposed_confounder_prev,
  unexposed_confounder_prev,
  confounder_outcome_effect,
  loglinear = FALSE,
  verbose = getOption("tipr.verbose", TRUE)
)
adjust_coef_with_binary(
  effect_observed,
  exposed_confounder_prev,
  unexposed_confounder_prev,
  confounder_outcome_effect,
  loglinear = FALSE,
  verbose = getOption("tipr.verbose", TRUE)
)

Arguments

`effect_observed`	Numeric. Observed exposure - outcome effect from a loglinear model. This can be the beta coefficient, the lower confidence bound of the beta coefficient, or the upper confidence bound of the beta coefficient.
`exposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the exposed population
`unexposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the unexposed population
`confounder_outcome_effect`	Numeric. Estimated relationship between the unmeasured confounder and the outcome.
`loglinear`	Logical. Calculate the adjusted coefficient from a loglinear model instead of a linear model (the default). When `loglinear = FALSE`, `adjust_coef_with_binary()` is equivalent to `adjust_coef()` where `exposure_confounder_effect` is the difference in prevalences.
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`

Value

Data frame.

Examples

adjust_coef_with_binary(1.1, 0.5, 0.3, 1.3)
adjust_coef_with_binary(1.1, 0.5, 0.3, 1.3)

Adjust a regression coefficient using the partial R2 for an unmeasured confounder-exposure relationship and unmeasured confounder- outcome relationship

Description

This function wraps the sensemakr::adjusted_estimate() and sensemakr::adjusted_se() functions.

Usage

adjust_coef_with_r2(
  effect_observed,
  se,
  df,
  confounder_exposure_r2,
  confounder_outcome_r2,
  verbose = getOption("tipr.verbose", TRUE),
  alpha = 0.05,
  ...
)
adjust_coef_with_r2(
  effect_observed,
  se,
  df,
  confounder_exposure_r2,
  confounder_outcome_r2,
  verbose = getOption("tipr.verbose", TRUE),
  alpha = 0.05,
  ...
)

Arguments

`effect_observed`	Numeric. Observed exposure - outcome effect from a regression model. This is the point estimate (beta coefficient)
`se`	Numeric. Standard error of the `effect_observed` in the previous parameter.
`df`	Numeric positive value. Residual degrees of freedom for the model used to estimate the observed exposure - outcome effect. This is the total number of observations minus the number of parameters estimated in your model. Often for models estimated with an intercept this is N - k - 1 where k is the number of predictors in the model.
`confounder_exposure_r2`	Numeric value between 0 and 1. The assumed partial R2 of the unobserved confounder with the exposure given the measured covariates.
`confounder_outcome_r2`	Numeric value between 0 and 1. The assumed partial R2 of the unobserved confounder with the outcome given the exposure and the measured covariates.
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`alpha`	Significance level. Default = `0.05`.
`...`	Optional arguments passed to the `sensemakr::adjusted_estimate()` function.

Value

A data frame.

References

Carlos Cinelli, Jeremy Ferwerda and Chad Hazlett (2021). sensemakr: Sensitivity Analysis Tools for Regression Models. R package version 0.1.4. https://CRAN.R-project.org/package=sensemakr

Examples

adjust_coef_with_r2(0.5, 0.1, 102, 0.05, 0.1)
adjust_coef_with_r2(0.5, 0.1, 102, 0.05, 0.1)

Adjust an observed hazard ratio for a normally distributed confounder

Description

Adjust an observed hazard ratio for a normally distributed confounder

Usage

adjust_hr(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)

adjust_hr_with_continuous(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)
adjust_hr(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)

adjust_hr_with_continuous(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome hazard ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposure_confounder_effect`	Numeric. Estimated difference in scaled means between the unmeasured confounder in the exposed population and unexposed population
`confounder_outcome_effect`	Numeric. Estimated relationship between the unmeasured confounder and the outcome.
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`hr_correction`	Logical. Indicates whether to use a correction factor. The methods used for this function are based on risk ratios. For rare outcomes, a hazard ratio approximates a risk ratio. For common outcomes, a correction factor is needed. If you have a common outcome (>15%), set this to `TRUE`. Default: `FALSE`.

Value

Data frame.

Examples

adjust_hr(0.9, -0.9, 1.3)
adjust_hr(0.9, -0.9, 1.3)

Adjust an observed hazard ratio with a binary confounder

Description

Adjust an observed hazard ratio with a binary confounder

Usage

adjust_hr_with_binary(
  effect_observed,
  exposed_confounder_prev,
  unexposed_confounder_prev,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)
adjust_hr_with_binary(
  effect_observed,
  exposed_confounder_prev,
  unexposed_confounder_prev,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome hazard ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the exposed population
`unexposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`hr_correction`	Logical. Indicates whether to use a correction factor. The methods used for this function are based on risk ratios. For rare outcomes, a hazard ratio approximates a risk ratio. For common outcomes, a correction factor is needed. If you have a common outcome (>15%), set this to `TRUE`. Default: `FALSE`.

Value

Data frame.

Examples

adjust_hr_with_binary(0.8, 0.1, 0.5, 1.8)
adjust_hr_with_binary(0.8, 0.1, 0.5, 1.8)

Adjust an observed odds ratio for a normally distributed confounder

Description

Adjust an observed odds ratio for a normally distributed confounder

Usage

adjust_or(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)

adjust_or_with_continuous(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)
adjust_or(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)

adjust_or_with_continuous(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome odds ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposure_confounder_effect`	Numeric. Estimated difference in scaled means between the unmeasured confounder in the exposed population and unexposed population
`confounder_outcome_effect`	Numeric. Estimated relationship between the unmeasured confounder and the outcome.
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`or_correction`	Logical. Indicates whether to use a correction factor. The methods used for this function are based on risk ratios. For rare outcomes, an odds ratio approximates a risk ratio. For common outcomes, a correction factor is needed. If you have a common outcome (>15%), set this to `TRUE`. Default: `FALSE`.

Value

Data frame.

Examples

adjust_or(1.2, 0.9, 1.3)
adjust_or(1.2, 0.9, 1.3)

Adjust an observed odds ratio with a binary confounder

Description

Adjust an observed odds ratio with a binary confounder

Usage

adjust_or_with_binary(
  effect_observed,
  exposed_confounder_prev,
  unexposed_confounder_prev,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)
adjust_or_with_binary(
  effect_observed,
  exposed_confounder_prev,
  unexposed_confounder_prev,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome odds ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the exposed population
`unexposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`or_correction`	Logical. Indicates whether to use a correction factor. The methods used for this function are based on risk ratios. For rare outcomes, an odds ratio approximates a risk ratio. For common outcomes, a correction factor is needed. If you have a common outcome (>15%), set this to `TRUE`. Default: `FALSE`.

Value

Data frame.

Examples

adjust_or_with_binary(3, 1, 0, 3)
adjust_or_with_binary(3, 1, 0, 3, or_correction = TRUE)
adjust_or_with_binary(3, 1, 0, 3)
adjust_or_with_binary(3, 1, 0, 3, or_correction = TRUE)

Adjust an observed risk ratio for a normally distributed confounder

Description

Adjust an observed risk ratio for a normally distributed confounder

Usage

adjust_rr(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = TRUE
)

adjust_rr_with_continuous(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = TRUE
)
adjust_rr(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = TRUE
)

adjust_rr_with_continuous(
  effect_observed,
  exposure_confounder_effect,
  confounder_outcome_effect,
  verbose = TRUE
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome risk ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposure_confounder_effect`	Numeric. Estimated difference in scaled means between the unmeasured confounder in the exposed population and unexposed population
`confounder_outcome_effect`	Numeric. Estimated relationship between the unmeasured confounder and the outcome.
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`

Value

Data frame.

Examples

adjust_rr(1.2, 0.5, 1.1)
adjust_rr(1.2, 0.5, 1.1)

Adjust an observed risk ratio with a binary confounder

Description

Adjust an observed risk ratio with a binary confounder

Usage

adjust_rr_with_binary(
  effect_observed,
  exposed_confounder_prev,
  unexposed_confounder_prev,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE)
)
adjust_rr_with_binary(
  effect_observed,
  exposed_confounder_prev,
  unexposed_confounder_prev,
  confounder_outcome_effect,
  verbose = getOption("tipr.verbose", TRUE)
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome risk ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the exposed population
`unexposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`

Value

Data frame.

Examples

adjust_rr_with_binary(1.1, 0.5, 0.3, 1.3)
adjust_rr_with_binary(1.1, 0.5, 0.3, 1.3)

Calculate an E-value

Description

Calculate an E-value

Usage

e_value(effect_observed)
e_value(effect_observed)

Arguments

effect_observed

Numeric positive value. Observed exposure - outcome effect (assumed to be the exponentiated coefficient, so a risk ratio, odds ratio, or hazard ratio). This can be the point estimate, lower confidence bound, or upper confidence bound.

Value

Numeric value

Examples

e_value(0.9)
e_value(1.3)
e_value(0.9)
e_value(1.3)

Example Data (Continuous Outcome)

Description

A data set simulated with two Normally distributed confounders, one "measured" and one "unmeasured", an exposure, and outcome. The "true" causal effect of the exposure on the outcome, accounting for both the measured and unmeasured confounders, should be 0.

Usage

exdata_continuous
exdata_continuous

Format

A data frame with 2,000 rows and 4 columns:

.unmeasured_confounder: A simulated unmeasured confounder
measured_confounder: A simulated measured confounder
exposure
outcome

Example Data (Risk Ratio)

Description

Usage

exdata_rr
exdata_rr

Format

A data frame with 2,000 rows and 4 columns:

.unmeasured_confounder: A simulated unmeasured confounder
measured_confounder: A simulated measured confounder
exposure
outcome

Order observed bias data frame for plotting

Description

Order observed bias data frame for plotting

Usage

observed_bias_order(d, by)
observed_bias_order(d, by)

Arguments

`d`	Observed bias data frame. Must have columns `dropped` and `type`
`by`	Character. Variable in `d` to order by.

Value

Data frame in the correct order

Create a data frame to assist with creating an observed bias plot

Description

Create a data frame to assist with creating an observed bias plot

Usage

observed_bias_tbl(ps_mod, outcome_mod, drop_list = NULL)
observed_bias_tbl(ps_mod, outcome_mod, drop_list = NULL)

Arguments

`ps_mod`	Model object for the propensity score model
`outcome_mod`	Model object for the outcome model
`drop_list`	Named list of covariates or groups of covariates to drop if `NULL`, will default to dropping each covariate one at a time.

Value

Data frame with the following columns:

dropped: The covariate or group of covariates that were dropped
type: Explanation of dropped, whether it refers to a single covariate (covariate) or a group of covariates (group)
ps_formula: The new formula for the updated propensity score model
outcome_formula: The new formula for the updated outcome model
ps_model: The new model object for the updated propensity score model
p: The updated propensity score

Examples

ps_mod <- glm(am ~ mpg + cyl + I(hp^2), data = mtcars)
outcome_mod <- lm(qsec ~ am + hp + disp + wt, data = mtcars)
observed_bias_tbl(
 ps_mod,
 outcome_mod,
 drop_list = list(
   group_one = c("mpg", "hp"),
   group_two = c("cyl", "wt")
   )
 )
ps_mod <- glm(am ~ mpg + cyl + I(hp^2), data = mtcars)
outcome_mod <- lm(qsec ~ am + hp + disp + wt, data = mtcars)
observed_bias_tbl(
 ps_mod,
 outcome_mod,
 drop_list = list(
   group_one = c("mpg", "hp"),
   group_two = c("cyl", "wt")
   )
 )

Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder

Description

Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder

Usage

observed_bias_tip(
  tip,
  point_estimate,
  lb,
  ub,
  tip_desc = "Hypothetical unmeasured confounder"
)
observed_bias_tip(
  tip,
  point_estimate,
  lb,
  ub,
  tip_desc = "Hypothetical unmeasured confounder"
)

Arguments

`tip`	Numeric. Value you would like to tip to.
`point_estimate`	Numeric. Result estimate from the full model.
`lb`	Numeric. Result lower bound from the full model.
`ub`	Numeric. Result upper bound from the full model.
`tip_desc`	Character. A description of the tipping point.

Value

A data frame with five columns:

dropped: the input from tip_desc
type: Explanation of dropped, here tip to clarify that this was calculated as a tipping point.
point_estimate: the shifted point estimate
lb: the shifted lower bound
ub: the shifted upper bound

Calculate the Observed Covariate E-value

Description

Calculate the Observed Covariate E-value

Usage

observed_covariate_e_value(lb, ub, lb_adj, ub_adj, transform = NULL)
observed_covariate_e_value(lb, ub, lb_adj, ub_adj, transform = NULL)

Arguments

`lb`	Numeric. The lower bound of the full model
`ub`	Numeric. The upper bound of the full model
`lb_adj`	Numeric. The lower bound of the adjusted model
`ub_adj`	Numeric. The upper bound of the adjusted model
`transform`	Character. If your effect is an odds ratio or hazard ratio, this will perform the transformation suggested by VanderWeele and Ding. Allowed values are: "OR" "HR"

Value

The Observed Covariate E-value

Robustness value

Description

This function wraps the sensemakr::robustness_value() function

Usage

r_value(effect_observed, se, df, ...)
r_value(effect_observed, se, df, ...)

Arguments

`effect_observed`	Numeric. Observed exposure - outcome effect from a regression model. This is the point estimate (beta coefficient)
`se`	Numeric. Standard error of the `effect_observed` in the previous parameter.
`df`	Numeric positive value. Residual degrees of freedom for the model used to estimate the observed exposure - outcome effect. This is the total number of observations minus the number of parameters estimated in your model. Often for models estimated with an intercept this is N - k - 1 where k is the number of predictors in the model.
`...`	Optional arguments passed to the `sensemakr::robustness_value()` function.

Value

Numeric. Robustness value

References

Carlos Cinelli, Jeremy Ferwerda and Chad Hazlett (2021). sensemakr: Sensitivity Analysis Tools for Regression Models. R package version 0.1.4. https://CRAN.R-project.org/package=sensemakr

Examples

r_value(0.5, 0.1, 102)
r_value(0.5, 0.1, 102)

Tip a result with a normally distributed confounder.

Description

choose one of the following, and the other will be estimated:

exposure_confounder_effect
confounder_outcome_effect

Usage

tip(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  correction_factor = "none"
)

tip_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  correction_factor = "none"
)

tip_c(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  correction_factor = "none"
)
tip(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  correction_factor = "none"
)

tip_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  correction_factor = "none"
)

tip_c(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  correction_factor = "none"
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome effect (assumed to be the exponentiated coefficient, so a risk ratio, odds ratio, or hazard ratio). This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposure_confounder_effect`	Numeric. Estimated difference in scaled means between the unmeasured confounder in the exposed population and unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`correction_factor`	Character string. Options are "none", "hr", "or". For common outcomes (>15%), the odds ratio or hazard ratio is not a good estimate for the risk ratio. In these cases, we can apply a correction factor. If you are supplying a hazard ratio for a common outcome, set this to "hr"; if you are supplying an odds ratio for a common outcome, set this to "or"; if you are supplying a risk ratio or your outcome is rare, set this to "none" (default).

Value

Data frame.

Examples

## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99)

## Example with broom
if (requireNamespace("broom", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {
  glm(am ~ mpg, data = mtcars, family = "binomial") %>%
   broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%
   dplyr::filter(term == "mpg") %>%
   dplyr::pull(conf.low) %>%
   tip(confounder_outcome_effect = 2.5)
}
## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99)

## Example with broom
if (requireNamespace("broom", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {
  glm(am ~ mpg, data = mtcars, family = "binomial") %>%
   broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%
   dplyr::filter(term == "mpg") %>%
   dplyr::pull(conf.low) %>%
   tip(confounder_outcome_effect = 2.5)
}

Tip a linear model coefficient with a continuous confounder.

Description

choose one of the following, and the other will be estimated:

exposure_confounder_effect
confounder_outcome_effect

Usage

tip_coef(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)

tip_coef_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)
tip_coef(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)

tip_coef_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)

Arguments

`effect_observed`	Numeric. Observed exposure - outcome effect from a regression model. This can be the beta coefficient, the lower confidence bound of the beta coefficient, or the upper confidence bound of the beta coefficient.
`exposure_confounder_effect`	Numeric. Estimated scaled mean difference between the unmeasured confounder in the exposed population and unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`

Value

Data frame.

Examples

## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_coef(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_coef(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = -0.05)

## Example with broom
if (requireNamespace("broom", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {
  lm(wt ~ mpg, data = mtcars) %>%
   broom::tidy(conf.int = TRUE) %>%
   dplyr::filter(term == "mpg") %>%
   dplyr::pull(conf.low) %>%
   tip_coef(confounder_outcome_effect = 2.5)
}
## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_coef(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_coef(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = -0.05)

## Example with broom
if (requireNamespace("broom", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {
  lm(wt ~ mpg, data = mtcars) %>%
   broom::tidy(conf.int = TRUE) %>%
   dplyr::filter(term == "mpg") %>%
   dplyr::pull(conf.low) %>%
   tip_coef(confounder_outcome_effect = 2.5)
}

Tip a regression coefficient using the partial R2 for an unmeasured confounder-exposure relationship and unmeasured confounder- outcome relationship

Description

Choose one of the following, and the other will be estimated:

confounder_exposure_r2
confounder_outcome_r2

Usage

tip_coef_with_r2(
  effect_observed,
  se,
  df,
  confounder_exposure_r2 = NULL,
  confounder_outcome_r2 = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  alpha = 0.05,
  tip_bound = FALSE,
  ...
)
tip_coef_with_r2(
  effect_observed,
  se,
  df,
  confounder_exposure_r2 = NULL,
  confounder_outcome_r2 = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  alpha = 0.05,
  tip_bound = FALSE,
  ...
)

Arguments

`effect_observed`	Numeric. Observed exposure - outcome effect from a regression model. This is the point estimate (beta coefficient)
`se`	Numeric. Standard error of the `effect_observed` in the previous parameter.
`df`	Numeric positive value. Residual degrees of freedom for the model used to estimate the observed exposure - outcome effect. This is the total number of observations minus the number of parameters estimated in your model. Often for models estimated with an intercept this is N - k - 1 where k is the number of predictors in the model.
`confounder_exposure_r2`	Numeric value between 0 and 1. The assumed partial R2 of the unobserved confounder with the exposure given the measured covariates.
`confounder_outcome_r2`	Numeric value between 0 and 1. The assumed partial R2 of the unobserved confounder with the outcome given the exposure and the measured covariates.
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`alpha`	Significance level. Default = `0.05`.
`tip_bound`	Do you want to tip at the bound? Default = `FALSE`, will tip at the point estimate
`...`	Optional arguments passed to the `sensemakr::adjusted_estimate()` function.

Value

A data frame.

Examples

tip_coef_with_r2(0.5, 0.1, 102, 0.5)
tip_coef_with_r2(0.5, 0.1, 102, 0.5)

Tip an observed hazard ratio with a normally distributed confounder.

Description

choose one of the following, and the other will be estimated:

exposure_confounder_effect
confounder_outcome_effect

Usage

tip_hr(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)

tip_hr_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)
tip_hr(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)

tip_hr_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome hazard ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposure_confounder_effect`	Numeric. Estimated difference in scaled means between the unmeasured confounder in the exposed population and unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`hr_correction`	Logical. Indicates whether to use a correction factor. The methods used for this function are based on risk ratios. For rare outcomes, a hazard ratio approximates a risk ratio. For common outcomes, a correction factor is needed. If you have a common outcome (>15%), set this to `TRUE`. Default: `FALSE`.

Value

Data frame.

Examples

## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_hr(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_hr(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99)

## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_hr(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_hr(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99)

Tip an observed hazard ratio with a binary confounder.

Description

Choose two of the following three to specify, and the third will be estimated:

exposed_confounder_prev
unexposed_confounder_prev
confounder_outcome_effect

Alternatively, specify all three and the function will return the number of unmeasured confounders specified needed to tip the analysis.

Usage

tip_hr_with_binary(
  effect_observed,
  exposed_confounder_prev = NULL,
  unexposed_confounder_prev = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)
tip_hr_with_binary(
  effect_observed,
  exposed_confounder_prev = NULL,
  unexposed_confounder_prev = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  hr_correction = FALSE
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome hazard ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the exposed population
`unexposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`hr_correction`	Logical. Indicates whether to use a correction factor. The methods used for this function are based on risk ratios. For rare outcomes, a hazard ratio approximates a risk ratio. For common outcomes, a correction factor is needed. If you have a common outcome (>15%), set this to `TRUE`. Default: `FALSE`.

Value

Data frame.

Examples

tip_hr_with_binary(0.9, 0.9, 0.1)
tip_hr_with_binary(0.9, 0.9, 0.1)

Tip an observed odds ratio with a normally distributed confounder.

Description

choose one of the following, and the other will be estimated:

exposure_confounder_effect
confounder_outcome_effect

Usage

tip_or(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)

tip_or_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)
tip_or(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)

tip_or_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome odds ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposure_confounder_effect`	Numeric. Estimated difference in scaled means between the unmeasured confounder in the exposed population and unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`or_correction`	Logical. Indicates whether to use a correction factor. The methods used for this function are based on risk ratios. For rare outcomes, an odds ratio approximates a risk ratio. For common outcomes, a correction factor is needed. If you have a common outcome (>15%), set this to `TRUE`. Default: `FALSE`.

Value

Data frame.

Examples

## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_or(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_or(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99)

## Example with broom
if (requireNamespace("broom", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {
  glm(am ~ mpg, data = mtcars, family = "binomial") %>%
   broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%
   dplyr::filter(term == "mpg") %>%
   dplyr::pull(conf.low) %>%
   tip_or(confounder_outcome_effect = 2.5, or_correction = TRUE)
}
## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_or(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_or(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99)

## Example with broom
if (requireNamespace("broom", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {
  glm(am ~ mpg, data = mtcars, family = "binomial") %>%
   broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%
   dplyr::filter(term == "mpg") %>%
   dplyr::pull(conf.low) %>%
   tip_or(confounder_outcome_effect = 2.5, or_correction = TRUE)
}

Tip an observed odds ratio with a binary confounder.

Description

Choose two of the following three to specify, and the third will be estimated:

exposed_confounder_prev
unexposed_confounder_prev
confounder_outcome_effect

Alternatively, specify all three and the function will return the number of unmeasured confounders specified needed to tip the analysis.

Usage

tip_or_with_binary(
  effect_observed,
  exposed_confounder_prev = NULL,
  unexposed_confounder_prev = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)
tip_or_with_binary(
  effect_observed,
  exposed_confounder_prev = NULL,
  unexposed_confounder_prev = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  or_correction = FALSE
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome odds ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the exposed population
`unexposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`or_correction`	Logical. Indicates whether to use a correction factor. The methods used for this function are based on risk ratios. For rare outcomes, an odds ratio approximates a risk ratio. For common outcomes, a correction factor is needed. If you have a common outcome (>15%), set this to `TRUE`. Default: `FALSE`.

Value

Data frame.

Examples

tip_or_with_binary(0.9, 0.9, 0.1)
tip_or_with_binary(0.9, 0.9, 0.1)

Tip an observed risk ratio with a normally distributed confounder.

Description

choose one of the following, and the other will be estimated:

exposure_confounder_effect
confounder_outcome_effect

Usage

tip_rr(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)

tip_rr_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)
tip_rr(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)

tip_rr_with_continuous(
  effect_observed,
  exposure_confounder_effect = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome risk ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposure_confounder_effect`	Numeric. Estimated difference in scaled means between the unmeasured confounder in the exposed population and unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`

Value

Data frame.

Examples

## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_rr(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_rr(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99)

## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_rr(1.2, exposure_confounder_effect = -2)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_rr(1.2, exposure_confounder_effect = -2, confounder_outcome_effect = .99)

Tip an observed risk ratio with a binary confounder.

Description

Choose two of the following three to specify, and the third will be estimated:

exposed_confounder_prev
unexposed_confounder_prev
confounder_outcome_effect

Alternatively, specify all three and the function will return the number of unmeasured confounders specified needed to tip the analysis.

Usage

tip_rr_with_binary(
  effect_observed,
  exposed_confounder_prev = NULL,
  unexposed_confounder_prev = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)
tip_rr_with_binary(
  effect_observed,
  exposed_confounder_prev = NULL,
  unexposed_confounder_prev = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE)
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome risk ratio. This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the exposed population
`unexposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`

Tip a result with a binary confounder.

Description

Choose two of the following three to specify, and the third will be estimated:

exposed_confounder_prev
unexposed_confounder_prev
confounder_outcome_effect

Alternatively, specify all three and the function will return the number of unmeasured confounders specified needed to tip the analysis.

Usage

tip_with_binary(
  effect_observed,
  exposed_confounder_prev = NULL,
  unexposed_confounder_prev = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  correction_factor = "none"
)

tip_b(
  effect_observed,
  exposed_confounder_prev = NULL,
  unexposed_confounder_prev = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  correction_factor = "none"
)
tip_with_binary(
  effect_observed,
  exposed_confounder_prev = NULL,
  unexposed_confounder_prev = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  correction_factor = "none"
)

tip_b(
  effect_observed,
  exposed_confounder_prev = NULL,
  unexposed_confounder_prev = NULL,
  confounder_outcome_effect = NULL,
  verbose = getOption("tipr.verbose", TRUE),
  correction_factor = "none"
)

Arguments

`effect_observed`	Numeric positive value. Observed exposure - outcome effect (assumed to be the exponentiated coefficient, so a risk ratio, odds ratio, or hazard ratio). This can be the point estimate, lower confidence bound, or upper confidence bound.
`exposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the exposed population
`unexposed_confounder_prev`	Numeric between 0 and 1. Estimated prevalence of the unmeasured confounder in the unexposed population
`confounder_outcome_effect`	Numeric positive value. Estimated relationship between the unmeasured confounder and the outcome
`verbose`	Logical. Indicates whether to print informative message. Default: `TRUE`
`correction_factor`	Character string. Options are "none", "hr", "or". For common outcomes (>15%), the odds ratio or hazard ratio is not a good estimate for the risk ratio. In these cases, we can apply a correction factor. If you are supplying a hazard ratio for a common outcome, set this to "hr"; if you are supplying an odds ratio for a common outcome, set this to "or"; if you are supplying a risk ratio or your outcome is rare, set this to "none" (default).

Details

tip_b() is an alias for tip_with_binary().

Examples

## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_with_binary(1.2, exposed_confounder_prev = 0.5, unexposed_confounder_prev = 0)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_with_binary(1.2,
  exposed_confounder_prev = 0.5,
  unexposed_confounder_prev = 0,
  confounder_outcome_effect = 1.1)

## Example with broom
if (requireNamespace("broom", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {
  glm(am ~ mpg, data = mtcars, family = "binomial") %>%
   broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%
   dplyr::filter(term == "mpg") %>%
   dplyr::pull(conf.low) %>%
   tip_with_binary(exposed_confounder_prev = 1, confounder_outcome_effect = 1.15)
}
## to estimate the relationship between an unmeasured confounder and outcome
## needed to tip analysis
tip_with_binary(1.2, exposed_confounder_prev = 0.5, unexposed_confounder_prev = 0)

## to estimate the number of unmeasured confounders specified needed to tip
## the analysis
tip_with_binary(1.2,
  exposed_confounder_prev = 0.5,
  unexposed_confounder_prev = 0,
  confounder_outcome_effect = 1.1)

## Example with broom
if (requireNamespace("broom", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {
  glm(am ~ mpg, data = mtcars, family = "binomial") %>%
   broom::tidy(conf.int = TRUE, exponentiate = TRUE) %>%
   dplyr::filter(term == "mpg") %>%
   dplyr::pull(conf.low) %>%
   tip_with_binary(exposed_confounder_prev = 1, confounder_outcome_effect = 1.15)
}

Package 'tipr'

Help Index

Adjust an observed regression coefficient for a normally distributed confounder

Description

Usage

Arguments

Value

Examples

Adjust an observed coefficient from a regression model with a binary confounder

Description

Usage

Arguments

Value

Examples

Adjust a regression coefficient using the partial R2 for an unmeasured confounder-exposure relationship and unmeasured confounder- outcome relationship

Description

Usage

Arguments

Value

References

Examples

Adjust an observed hazard ratio for a normally distributed confounder

Description

Usage

Arguments

Value

Examples

Adjust an observed hazard ratio with a binary confounder

Description

Usage

Arguments

Value

Examples

Adjust an observed odds ratio for a normally distributed confounder

Description

Usage

Arguments

Value

Examples

Adjust an observed odds ratio with a binary confounder

Description

Usage

Arguments

Value

Examples

Adjust an observed risk ratio for a normally distributed confounder

Description

Usage

Arguments

Value

Examples

Adjust an observed risk ratio with a binary confounder

Description

Usage

Arguments

Value

Examples

Calculate an E-value

Description

Usage

Arguments

Value

Examples

Example Data (Continuous Outcome)

Description

Usage

Format

Example Data (Risk Ratio)

Description

Usage

Format

Order observed bias data frame for plotting

Description

Usage

Arguments

Value

Create a data frame to assist with creating an observed bias plot

Description

Usage

Arguments