Package 'PRP'

Cardiovascular disease impact on the mortality of COVID-19

Description

This is a dataset containing several effect estimates and their standard errors for the impact of cardivascular disease on the mortality of COVID-19 in the literature.

Usage

data("mortality")

Format

An object of class data.frame with 6 rows and 3 columns.

Examples

data("mortality")

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Posterior Predictive Replication p-value Calculation

Description

Posterior Predictive Replication p-value Calculation

Usage

posterior_prp(
  beta,
  se,
  L = 1000,
  r_vec = c(0, 8e-04, 0.006, 0.024),
  test = Q,
  print_test_dist = FALSE
)

Arguments

beta	A vector, containing the estimates in the original study and the replication study.
se	A vector, containing the standard errors of the estimates in the original study and the replication study.
L	A value, determining the times of repeating simulation.
r_vec	A vector, defining the prior reproducible model. Each r value corresponds to a probability of sign consistency.
test	A function designed to calculate the test quantity, the default one is the Cochran's Q test statistics.
print_test_dist	A boolean, determining whether the simulated test statistics value difference will be plot as a histogram or not. Default is False.

Value

A list with the following components:

grid	Detailed grid values for the hyperparameters.
test_statistics	The test statistics used in calculating the replication p-value.
n_sim	The L value.
test_stats_dif	The difference between the simulated test statistics quantity and the original value.
pvalue	The resulting posterior predictive replicaiton p-value.

Examples

data("mortality")
res = posterior_prp(beta = mortality$beta, se = mortality$se, test=Q)
names(res)
print(res$pvalue)

---

Prior Predictive Replication p-value Calculation

Description

Assessing the prior predictive distribution and calculating the replication p-value based on it.

Usage

prior_prp(
  beta,
  se,
  r_vec = c(0, 8e-04, 0.006, 0.024),
  test = "two_sided",
  report_PI = FALSE
)

Arguments

beta	A 2-D vector, containing the estimates in the original study and the replication study.
se	A 2-D vector, containing the standard errors of the estimates in the original study and the replication study.
r_vec	A vector, defining the prior reproducible model. Each r value corresponds to a probability of sign consistency.
test	A string, determining which test statistics to utilize. If not specified, the default two-sided one will be used.
report_PI	A boolean, denoting whether the 95% predictive interval for the estimates be reported or not. This option is only valid for two-sided test statistics. The default is FALSE.

Value

A list with the following components:

grid	The detailed grid values for the hyperparameters.
test_statistics	The test statistics used in calculating the replication p-value.
pvalue	The resulting prior predictive replicaiton p-value.
predictive_interval	The 95% predictive interval if required.

Examples

data("RPP_filtered")
attach(RPP_filtered)
rpp_pval<-sapply(1:nrow(RPP_filtered),function(x)
  prior_prp(beta=c(beta_orig[x], beta_rep[x]),se=c(se_orig[x],  se_rep[x]))$pvalue)

---

Sign consistency probability and the value for r parameter 1-1 transformation

Description

This function transforms the probability of simulated beta_j having the same sign with the underlying true effect barbeta to the corresponding heterogeneity r parameter value.

Usage

prob_to_r(p)

Arguments

p	A value, the required probability of sign consistency.

Value

The corresponding heterogeneity parameter value.

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Filtered RPP data

Description

This contains the RP:P data from the Open Science Collaboration project after filtering.

Usage

data("RPP_filtered")

Format

An object of class data.frame with 73 rows and 5 columns.

Examples

data("RPP_filtered")

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Cardiovascular disease impact on the severe case rate of COVID-19

Description

This is a dataset containing several effect estimates and their standard errors for the impact of cardiovascular disease on the severe case rate of COVID-19 in the literature.

Usage

data("severity")

Format

An object of class data.frame with 6 rows and 3 columns.

Examples

data("severity")

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