Package 'doseSens' reference manual

Title:	Conduct Sensitivity Analysis with Continuous Exposures and Binary Outcomes
Description:	Performs sensitivity analysis for the sharp null and attributable effects in matched studies with continuous exposures and binary outcomes as described in Zhang, Small, Heng (2024) <arXiv:2401.06909>. Two of the functions require installation of the 'Gurobi' optimizer. Please see <https://www.gurobi.com/documentation/9.0/refman/ins_the_r_package.html> for guidance.
Authors:	Jeffrey Zhang [aut, cre]
Maintainer:	Jeffrey Zhang <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0
Built:	2025-03-01 02:49:24 UTC
Source:	https://github.com/jzhang1937/dosesens

Title

Description

Title

Usage

apply_permutation_to_matrix(M, p)
apply_permutation_to_matrix(M, p)

Arguments

`M`	a square matrix of dimension n
`p`	a permuted version of the vector from 1:n

Value

a length n vector with ith entry corresponding to the i, pi entry in M

Separable algorithm for threshold attributable effect in a sensitivity analysis with at most one over-exposed unit in each matched set. For a greater than alternative, finds the 'a' matched sets that most decrease the mean and/or variance.

Description

Separable algorithm for threshold attributable effect in a sensitivity analysis with at most one over-exposed unit in each matched set. For a greater than alternative, finds the 'a' matched sets that most decrease the mean and/or variance.

Usage

binary_thresh_attribute(
  Z,
  Q,
  index,
  gamma,
  thresh = 0,
  a = 1,
  trans = identity,
  mc = 50000
)
binary_thresh_attribute(
  Z,
  Q,
  index,
  gamma,
  thresh = 0,
  a = 1,
  trans = identity,
  mc = 50000
)

Arguments

`Z`	A length N vector of (nonnegative) observed doses.
`Q`	A length N vector of observed binary outcomes.
`index`	A length N vector of indices indicating matched set membership.
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.
`thresh`	The dose threshold for the TAE.
`a`	The number of attributable effects to test for.
`trans`	The transformation of the doses to use for the test statistic. Default is the identity function.
`mc`	Number of monte-carlo samples if testing the sharp null, i.e. a = 0.

Value

Either "reject" if the value a is deemed not plausible/compatible, "feasible" if the value a is deemed so, else a list containing a p-value and dataframe of matched sets that have contribution to the test statistic sorted in order of smallest mean reduction followed by smallest variance reduction.

Examples

# Load the data
data <- treat_out_match
# Solve by the separable algorithm
solution <- binary_thresh_attribute(data$treat, data$complain, data$match_ind,
gamma = 0, thresh = log(3.5), a = 5, trans = identity)

# Load the data
data <- treat_out_match
# Solve by the separable algorithm
solution <- binary_thresh_attribute(data$treat, data$complain, data$match_ind,
gamma = 0, thresh = log(3.5), a = 5, trans = identity)

A helper that takes a gurobi model object outputted from dose_attributable_general or dose_thresh_attributable_one_sided and changes the Delta parameter. Saves computation time by directly editing the constraint involving Delta without having to reinitialize the other constraints that are kept the same. Outputs a list analogous to output from dose_attributable_general or dose_thresh_attributable_one_sided.

Description

A helper that takes a gurobi model object outputted from dose_attributable_general or dose_thresh_attributable_one_sided and changes the Delta parameter. Saves computation time by directly editing the constraint involving Delta without having to reinitialize the other constraints that are kept the same. Outputs a list analogous to output from dose_attributable_general or dose_thresh_attributable_one_sided.

Usage

change_Delta(model, Delta, direction = "equal", TT)
change_Delta(model, Delta, direction = "equal", TT)

Arguments

`model`	A gurobi model object outputted from dose_attributable_general.
`Delta`	The new Delta to test for.
`direction`	The new direction to test
`TT`	The observed test statistic.

Value

A gurobi model and solution.

Title

Description

Title

Usage

constant_effects_test(
  Z,
  R,
  index,
  gamma = 0,
  theta = 0,
  X = NA,
  estimand_function = extract_OLS,
  gamma_star_vec = NULL
)
constant_effects_test(
  Z,
  R,
  index,
  gamma = 0,
  theta = 0,
  X = NA,
  estimand_function = extract_OLS,
  gamma_star_vec = NULL
)

Arguments

`Z`	A length N vector of observed doses.
`R`	A length N vector of observed outcomes.
`index`	A length N vector of indices indicating matched set membership.
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.
`theta`	The value at which to test the weak null.
`X`	A matrix with I rows and less than I columns that contains covariate information.
`estimand_function`	A function that takes in values z and r and outputs a scalar; this function governs the causal estimand to estimate
`gamma_star_vec`	that contains the maximum ratio of any two probabilities of permutations for each matched set.

Value

A list containing the deviate, p-value, observed value of the test statistic, and conservative standard deviation estimate.

Computes deviation from uniform distribution in total variation distance for a given amount of unmeasured confounding and a greater than alternative with a binary outcome.

Description

Computes deviation from uniform distribution in total variation distance for a given amount of unmeasured confounding and a greater than alternative with a binary outcome.

Usage

dev_TV(Z, Q, index, gamma, direct = "upper")
dev_TV(Z, Q, index, gamma, direct = "upper")

Arguments

`Z`	A length N vector of (nonnegative) observed doses.
`Q`	A length N vector of observed binary outcomes.
`index`	A length N vector of indices indicating matched set membership.
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.
`direct`	The direction of the test - "upper" or "lower"; default is upper.

Value

A vector of length equaling the number of matched sets consisting of the TV distance from the uniform for each matched set at gamma level of unmeasured confounding for the worst-case.

Examples

# Load the data
data <- treat_out_match
# compute total variation distances.
total_variation <- dev_TV(data$treat, data$complain,
data$match_ind, gamma = log(1.5))
# Load the data
data <- treat_out_match
# compute total variation distances.
total_variation <- dev_TV(data$treat, data$complain,
data$match_ind, gamma = log(1.5))

Inference for general attributable effects in sensitivity analysis with continuous exposures and binary outcomes. Gurobi must be installed to use this function.

Description

Inference for general attributable effects in sensitivity analysis with continuous exposures and binary outcomes. Gurobi must be installed to use this function.

Usage

dose_attributable_general(
  Z,
  Q,
  index,
  gamma,
  alpha = 0.05,
  monotone = TRUE,
  Delta,
  sign = "positive",
  trans = identity,
  direction = "equal",
  M = 10000,
  eps = 0.01,
  alternative = "both",
  mv_threshold = NA,
  baseline = 0,
  relax = FALSE,
  feasible = TRUE,
  MIPgap = 0.01,
  WorkLimit = 1000,
  OutputFlag = 0
)
dose_attributable_general(
  Z,
  Q,
  index,
  gamma,
  alpha = 0.05,
  monotone = TRUE,
  Delta,
  sign = "positive",
  trans = identity,
  direction = "equal",
  M = 10000,
  eps = 0.01,
  alternative = "both",
  mv_threshold = NA,
  baseline = 0,
  relax = FALSE,
  feasible = TRUE,
  MIPgap = 0.01,
  WorkLimit = 1000,
  OutputFlag = 0
)

Arguments

`Z`	A length N vector of (nonnegative) observed doses.
`Q`	A length N vector of observed binary outcomes.
`index`	A length N vector of indices indicating matched set membership.
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.
`alpha`	Level of the test.
`monotone`	Whether to impose a monotonicity constraint on the potential outcomes
`Delta`	A numeric for the attributable effect to be tested for.
`sign`	The sign of monotonicity - "positive" or "negative".
`trans`	The transformation of the doses to use for the test statistic. Default is the identity function.
`direction`	A string indicating the direction of testing the attributable effect with respect to Delta; "greater", "equal", or "less"
`M`	The numeric penalty parameter to ensure correct sign relationship.
`eps`	precision parameter for the objective function if solving feasible = "Yes"
`alternative`	For constraining the rejection region using the test statistic with baseline potential outcomes, should it be constructed with "lower" alternative, "upper" alternative, or "both."
`mv_threshold`	The number of allowed monotonicity violations.
`baseline`	The baseline dose level for defining the threshold attributable effect.
`relax`	Whether to solve the continuous relaxation.
`feasible`	Whether to look for a feasible solution or to find the optimal solution.
`MIPgap`	Gurobi parameter specifying the precision of the mixed integer program solution; default is 0.01.
`WorkLimit`	Gurobi parameter specifying the maximum work units before stopping; default is 1000.
`OutputFlag`	0 if Gurobi print output is desired, 1 otherwise; default 0.

Value

A list containing the following:

`sol`	A gurobi object containing the solution to the optimization. If feasible is TRUE, then failing to find a solution indiciates rejection. If feasible is FALSE, then optimal value greater than zero indiciates rejection.
`attribut`	The attributable effect attained by the least rejectable allocation of potential outcomes and unmeasured confounders.
`search`	A list of length the number of matched sets containing the matrix of compatible baseline potential outcomes in each matched set.
`null_exp`	The null expectation of the pivot attained by the least rejectable allocation of potential outcomes and unmeasured confounders.
`obsT`	The value of the pivot attained by the least rejectable allocation of potential outcomes and unmeasured confounders.
`nps`	A vector with length the number of matched sets containing the size of each matched set.
`start.ind`	A vector with length the number of matched sets containing the starting index of the decision variables pertaining to each matched set in the optimization program.
`exp_upper`	The expectation of the pivot attained by the unmeasured confounders equalling the baseline potential outcome.
`exp_lower`	The expectation of the pivot attained by the unmeasured confounders equalling 1 minus the baseline potential outcome.
`var_upper`	The variance of the pivot attained by the unmeasured confounders equalling the baseline potential outcome.
`var_lower`	The variance of the pivot attained by the unmeasured confounders equalling 1 minus the baseline potential outcome.
`dose`	A list of length the number of matched sets containing the vector of doses observed in each matched set.
`model`	The initialized gurobi model.

Examples

# To run the following example, Gurobi must be installed.
# Load the data
data <- treat_out_match
# Make a threshold at log(3.5) transformation function.
above = function(Z) { return(Z > log(3.5)) }
# Solve the mixed-integer program.
solution = dose_attributable_general(data$treat,
data$complain, data$match_ind, gamma=log(1),
alpha = 0.1, monotone = TRUE, trans = above,
Delta = 5, direction = "less", M = 10000, eps = 0.0001,
alternative = "upper", relax = FALSE, feasible = FALSE)

# To run the following example, Gurobi must be installed.
# Load the data
data <- treat_out_match
# Make a threshold at log(3.5) transformation function.
above = function(Z) { return(Z > log(3.5)) }
# Solve the mixed-integer program.
solution = dose_attributable_general(data$treat,
data$complain, data$match_ind, gamma=log(1),
alpha = 0.1, monotone = TRUE, trans = above,
Delta = 5, direction = "less", M = 10000, eps = 0.0001,
alternative = "upper", relax = FALSE, feasible = FALSE)

Sharp null monte-carlo sensitivity analysis for continuous exposures and binary outcomes.

Description

Sharp null monte-carlo sensitivity analysis for continuous exposures and binary outcomes.

Usage

dose_sensitivity_mc_gen(
  Z,
  Q,
  index,
  mc,
  gamma,
  weights = NA,
  obsT = NULL,
  trans = identity,
  direct = "upper",
  seed = 1,
  verbose = FALSE
)
dose_sensitivity_mc_gen(
  Z,
  Q,
  index,
  mc,
  gamma,
  weights = NA,
  obsT = NULL,
  trans = identity,
  direct = "upper",
  seed = 1,
  verbose = FALSE
)

Arguments

`Z`	A length N vector of (nonnegative) observed doses.
`Q`	A length N vector of observed binary outcomes.
`index`	A length N vector of indices indicating matched set membership.
`mc`	An integer for the total number of Monte-Carlo samples desired.
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.
`weights`	Weights for each stratum to apply for the test statistic
`obsT`	The observed value of the test statistic; default is NULL
`trans`	The transformation of the doses to use for the test statistic. Default is the identity function.
`direct`	The direction of the test - "upper" or "lower"; default is upper.
`seed`	seed for random number generation.
`verbose`	Whether to print status updates or not; default is FALSE.

Value

A list containing two objects:

`mc`	A length mc vector containing the monte-carlo samples of the test statistic.
`p`	The monte-carlo p-value.

Examples

# Load the data
data <- treat_out_match
# Make a threshold at log(3.5) transformation function.
above = function(Z) { return(Z > log(3.5)) }
# Conduct randomization test.
solution <- dose_sensitivity_mc_gen(data$treat, data$complain, data$match_ind,
mc = 250, gamma = 0, trans = above)

# Load the data
data <- treat_out_match
# Make a threshold at log(3.5) transformation function.
above = function(Z) { return(Z > log(3.5)) }
# Conduct randomization test.
solution <- dose_sensitivity_mc_gen(data$treat, data$complain, data$match_ind,
mc = 250, gamma = 0, trans = above)

Inference for threshold attributable effects in sensitivity analysis with continuous exposures and binary outcomes. Gurobi must be installed to use this function.

Description

Inference for threshold attributable effects in sensitivity analysis with continuous exposures and binary outcomes. Gurobi must be installed to use this function.

Usage

dose_thresh_attributable_one_sided(
  Z,
  Q,
  index,
  gamma,
  alpha = 0.05,
  monotone = TRUE,
  Delta,
  sign = "positive",
  direction = "equal",
  threshold = 0,
  M = 10000,
  eps = 0.01,
  mv_threshold = NA,
  baseline = 0,
  relax = FALSE,
  feasible = TRUE,
  MIPgap = 0.01,
  WorkLimit = 1000,
  OutputFlag = 0
)
dose_thresh_attributable_one_sided(
  Z,
  Q,
  index,
  gamma,
  alpha = 0.05,
  monotone = TRUE,
  Delta,
  sign = "positive",
  direction = "equal",
  threshold = 0,
  M = 10000,
  eps = 0.01,
  mv_threshold = NA,
  baseline = 0,
  relax = FALSE,
  feasible = TRUE,
  MIPgap = 0.01,
  WorkLimit = 1000,
  OutputFlag = 0
)

Arguments

`Z`	A length N vector of (nonnegative) observed doses.
`Q`	A length N vector of observed binary outcomes.
`index`	A length N vector of indices indicating matched set membership.
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.
`alpha`	Level of the test.
`monotone`	Whether to impose a monotonicity constraint on the potential outcomes
`Delta`	A numeric for the attributable effect to be tested for.
`sign`	The sign of monotonicity - "positive" or "negative".
`direction`	A string indicating the direction of testing the attributable effect with respect to Delta; "greater", "equal", or "less"
`threshold`	The threshold for the TAE.
`M`	The numeric penalty parameter to ensure correct sign relationship.
`eps`	precision parameter for the objective function if solving feasible = "Yes"
`mv_threshold`	The number of allowed monotonicity violations.
`baseline`	The baseline dose level for defining the threshold attributable effect.
`relax`	Whether to solve the continuous relaxation.
`feasible`	Whether to look for a feasible solution or to find the optima.
`MIPgap`	Gurobi parameter specifying the precision of the mixed integer program solution; default is 0.01.
`WorkLimit`	Gurobi parameter specifying the maximum work units before stopping; default is 1000.
`OutputFlag`	0 if Gurobi print output is desired, 1 otherwise; default 0.

Value

A list containing the following:

`sol`	A gurobi object containing the solution to the optimization. If feasible is TRUE, then failing to find a solution indiciates rejection. If feasible is FALSE, then optimal value greater than zero indiciates rejection.
`attribut`	The attributable effect attained by the least rejectable allocation of potential outcomes and unmeasured confounders.
`search`	A list of length the number of matched sets containing the matrix of compatible baseline potential outcomes in each matched set.
`null_exp`	The null expectation of the pivot attained by the least rejectable allocation of potential outcomes and unmeasured confounders.
`obsT`	The value of the pivot attained by the least rejectable allocation of potential outcomes and unmeasured confounders.
`nps`	A vector with length the number of matched sets containing the size of each matched set.
`start.ind`	A vector with length the number of matched sets containing the starting index of the decision variables pertaining to each matched set in the optimization program.
`exp_upper`	The expectation of the pivot attained by the unmeasured confounders equalling the baseline potential outcome.
`var_upper`	The variance of the pivot attained by the unmeasured confounders equalling the baseline potential outcome.
`dose`	A list of length the number of matched sets containing the vector of doses observed in each matched set.
`model`	The initialized gurobi model.

Examples

# To run the following example, Gurobi must be installed.
# Load the data
data <- treat_out_match
# Solve the mixed-integer program.
solution = dose_thresh_attributable_one_sided(data$treat,
data$complain, data$match_ind,
gamma=log(1), alpha = 0.1, monotone = TRUE, Delta = 5,
 direction = "less", threshold = log(3.5),M = 10000,
 eps = 0.0001,relax = FALSE, feasible = FALSE)
# To run the following example, Gurobi must be installed.
# Load the data
data <- treat_out_match
# Solve the mixed-integer program.
solution = dose_thresh_attributable_one_sided(data$treat,
data$complain, data$match_ind,
gamma=log(1), alpha = 0.1, monotone = TRUE, Delta = 5,
 direction = "less", threshold = log(3.5),M = 10000,
 eps = 0.0001,relax = FALSE, feasible = FALSE)

Title

Description

Title

Usage

extract_above_threshold_vs_baseline(z, r, threshold)
extract_above_threshold_vs_baseline(z, r, threshold)

Arguments

`z`	a vector of doses
`r`	a vector of outcomes
`threshold`	a dose threshold

Value

the average of the outcomes with dose z above threshold c minus the average of the outcomes r.

Title

Description

Title

Usage

extract_below_threshold_vs_baseline(z, r, threshold)
extract_below_threshold_vs_baseline(z, r, threshold)

Arguments

`z`	a vector of doses
`r`	a vector of outcomes
`threshold`	a dose threshold

Value

the average of the outcomes with dose z below threshold c minus the average of the outcomes r.

Title

Description

Title

Usage

extract_max_vs_baseline(z, r)
extract_max_vs_baseline(z, r)

Arguments

`z`	a vector of doses
`r`	a vector of outcomes

Value

the outcome r corresponding to the largest dose z minus the average of the outcomes r.

Title

Description

Title

Usage

extract_min_vs_baseline(z, r)
extract_min_vs_baseline(z, r)

Arguments

`z`	a vector of doses
`r`	a vector of outcomes

Value

the outcome r corresponding to the smallest dose z minus the average of the outcomes r.

Title

Description

Title

Usage

extract_OLS(z, r)
extract_OLS(z, r)

Arguments

`z`	a vector of doses
`r`	a vector of outcomes

Value

the OLS regression coefficient from regressing r on z.

Title

Description

Title

Usage

extract_stochastic_intervention(z, r, s)
extract_stochastic_intervention(z, r, s)

Arguments

`z`	a vector of doses
`r`	a vector of outcomes
`s`	a set of weights, summing to 1

Value

the inner product of s and r

Title

Description

Title

Usage

extract_threshold_effect(z, r, threshold)
extract_threshold_effect(z, r, threshold)

Arguments

`z`	a vector of doses
`r`	a vector of outcomes
`threshold`	a dose threshold

Value

the average of the outcomes with dose z above threshold c minus the average of the outcomes with dose z below the threshold c.

Title

Description

Title

Usage

extract_threshold_effect_function(threshold = 0)
extract_threshold_effect_function(threshold = 0)

Arguments

threshold

a dose threshold

Value

A function that corresponds to extract_threshold_effect with the given threshold

Title

Description

Title

Usage

max_expectation(z, gamma, f_pi, with_variance = FALSE)
max_expectation(z, gamma, f_pi, with_variance = FALSE)

Arguments

`z`	vector of doses of length n
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.
`f_pi`	a vector of length n! that contains the value of the test statistic under each of the n! permutations of z, with order of f_pi determined by first sorting z into increasing order and calling gtools::permutations on z.
`with_variance`	whether to return the variance along with the worst-case expectation, default is FALSE.

Value

a list containing the worst-case expectation, and/or variance and the solution to the optmization problem.

Title

Description

Title

Usage

max_ratio(z, gamma)
max_ratio(z, gamma)

Arguments

`z`	vector of doses
`gamma`	level of unmeasured confounding

Value

the maximum ratio between the probability of two different permutations under the Rosenbaum model with doses z and unmeasured confounding level gamma.

Title

Description

Title

Usage

max_ratio_new(z, gamma)
max_ratio_new(z, gamma)

Arguments

`z`	vector of doses
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.

Value

the maximum ratio between the probability of two different permutations under the Rosenbaum model with doses z and unmeasured confounding level gamma.

Title

Description

Title

Usage

max_ratios_summary(Z, index, gamma)
max_ratios_summary(Z, index, gamma)

Arguments

`Z`	A length N vector of observed doses.
`index`	A length N vector of indices indicating matched set membership.
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.

Value

A vector of length equaling the number of unique indices that contains the maximum ratio between any two permutations for each of the matched sets.

Sharp null sensitivity analysis for continuous exposures and binary outcomes using normal approximation.

Description

Sharp null sensitivity analysis for continuous exposures and binary outcomes using normal approximation.

Usage

normal_test_gen(
  Z,
  Q,
  index,
  gamma,
  trans = identity,
  weights = NA,
  obsT = NULL,
  direct = "upper"
)
normal_test_gen(
  Z,
  Q,
  index,
  gamma,
  trans = identity,
  weights = NA,
  obsT = NULL,
  direct = "upper"
)

Arguments

`Z`	A length N vector of (nonnegative) observed doses.
`Q`	A length N vector of observed binary outcomes.
`index`	A length N vector of indices indicating matched set membership.
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.
`trans`	The transformation of the doses to use for the test statistic. Default is the identity function.
`weights`	Weights to apply for the test statistic
`obsT`	The observed value of the test statistic; default is NULL.
`direct`	The direction of the test - "upper" or "lower"; default is upper.

Value

A list containing the following:

`obsT`	The observed value of the test statistic
`exp`	The worst-case expectation
`var`	The worst-case variance.
`deviate`	The normal approximation deviate.

Examples

# Load the data
data <- treat_out_match
# Make a threshold at log(3.5) transformation function.
above = function(Z) { return(Z > log(3.5)) }
# Conduct randomization test using normal approximation.
solution <- normal_test_gen(data$treat, data$complain, data$match_ind,
gamma = 0, trans = above)
# Load the data
data <- treat_out_match
# Make a threshold at log(3.5) transformation function.
above = function(Z) { return(Z > log(3.5)) }
# Conduct randomization test using normal approximation.
solution <- normal_test_gen(data$treat, data$complain, data$match_ind,
gamma = 0, trans = above)

Title

Description

Title

Usage

prob_bounds(z, gamma)
prob_bounds(z, gamma)

Arguments

`z`	vector of doses
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.

Value

a list containing the maximum and minimum probability of a permutation under the Rosenbaum model with doses z and unmeasured confounding level gamma.

Title

Description

Title

Usage

sharp_double_statistic(z, r, q1, q2)
sharp_double_statistic(z, r, q1, q2)

Arguments

`z`	a vector of doses
`r`	a vector of outcomes
`q1`	a function that transforms the doses z
`q2`	a function that transforms the outcomes r

Value

a vector with values corresponding to the inner product of transformed by q1 permutations of z with transformed by q2 versions of r.

Title

Description

Title

Usage

sharp_null_double_test(
  Z,
  R,
  index,
  gamma = 0,
  q1 = NA,
  q2 = NA,
  X = NA,
  stratum_weights = rep(NA, nostratum),
  conservative_variance = TRUE,
  double_rank = TRUE
)
sharp_null_double_test(
  Z,
  R,
  index,
  gamma = 0,
  q1 = NA,
  q2 = NA,
  X = NA,
  stratum_weights = rep(NA, nostratum),
  conservative_variance = TRUE,
  double_rank = TRUE
)

Arguments

`Z`	A length N vector of observed doses.
`R`	A length N vector of observed outcomes.
`index`	A length N vector of indices indicating matched set membership.
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.
`q1`	A transformation to apply to the doses.
`q2`	A transformation to apply to the outcomes
`X`	A matrix with I rows and less than I columns that contains covariate information.
`stratum_weights`	A weight vector.
`conservative_variance`	Whether to use the conservative variance or not; default is TRUE.
`double_rank`	Whether to use the ranks of the transformed doses and outcomes; default is TRUE.

Value

A list containing the deviate, p-value, observed value of the test statistic, and conservative standard deviation estimate.

Matched lead dataset

Description

A matched, trimmed dataset of early life lead exposure and juvenile delinquency. There are 2007 matched sets.

Usage

treat_out_match
treat_out_match

Format

`treat_out_match`

A data frame with 4,134 rows and 4 columns:

treat: The log of lead exposure level measured in micrograms per deciliter.
serious: Whether the juvenile comitted a serious offense.
complain: Whether the juvenile comitted an offense worthy of complaint.
match_ind: Matched set membership.

...

Source

https://scholarworks.iu.edu/dspace/handle/2022/25638

Title

Description

Title

Usage

var_est(y, W, H_Q)
var_est(y, W, H_Q)

Arguments

`y`	a vector of length I containing the value of test statistics from each matched set
`W`	weight vector of length I.
`H_Q`	hat matrix corresponding to a matrix Q with dimension I by L.

Value

a conservative estimate of the standard deviation of the test statistic.

Title

Description

Title

Usage

weak_null_test(
  Z,
  R,
  index,
  gamma = 0,
  theta = 0,
  X = NA,
  estimand_function = extract_OLS,
  gamma_star_vec = NULL,
  kappa_inv_vec = NULL
)
weak_null_test(
  Z,
  R,
  index,
  gamma = 0,
  theta = 0,
  X = NA,
  estimand_function = extract_OLS,
  gamma_star_vec = NULL,
  kappa_inv_vec = NULL
)

Arguments

`Z`	A length N vector of observed doses.
`R`	A length N vector of observed outcomes.
`index`	A length N vector of indices indicating matched set membership.
`gamma`	The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.
`theta`	The value at which to test the weak null.
`X`	A matrix with I rows and less than I columns that contains covariate information.
`estimand_function`	A function that takes in values z and r and outputs a scalar; this function governs the causal estimand to estimate
`gamma_star_vec`	A vector that contains the minimum probability of a permutation for each matched set.
`kappa_inv_vec`	A vector that contains the ratio of the maximum probability and minimum probability of a permutation for each matched set.

Value

A list containing the deviate, p-value, observed value of the test statistic, and conservative standard deviation estimate.

Package 'doseSens'

Help Index

Title

Description

Usage

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Separable algorithm for threshold attributable effect in a sensitivity analysis with at most one over-exposed unit in each matched set. For a greater than alternative, finds the 'a' matched sets that most decrease the mean and/or variance.

Description

Usage

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Value

Examples

Description

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Title

Description

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Computes deviation from uniform distribution in total variation distance for a given amount of unmeasured confounding and a greater than alternative with a binary outcome.

Description

Usage

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Value

Examples

Inference for general attributable effects in sensitivity analysis with continuous exposures and binary outcomes. Gurobi must be installed to use this function.

Description

Usage

Arguments

Value

Examples

Sharp null monte-carlo sensitivity analysis for continuous exposures and binary outcomes.

Description

Usage

Arguments

Value

Examples

Inference for threshold attributable effects in sensitivity analysis with continuous exposures and binary outcomes. Gurobi must be installed to use this function.

Description

Usage

Arguments

Value

Examples

Title

Description

Usage

Arguments

Value

Title

Description

Usage

Arguments

Value

Title

Description

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Title

Description

Usage

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Value

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Description

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Title

Description

Usage

Arguments

`treat_out_match`