Package 'pcatsAPIclientR'

Title: 'PCATS' API Client
Description: Provides an R interface to the 'PCATS' API <https://pcats.research.cchmc.org/api/__docs__/>, allowing R users to submit tasks and retrieve results.
Authors: Bin Huang [aut], Chen Chen [aut], Michal Kouril [aut, cre]
Maintainer: Michal Kouril <[email protected]>
License: GNU General Public License
Version: 1.1.0
Built: 2024-11-15 04:36:27 UTC
Source: https://github.com/cran/pcatsAPIclientR

Help Index

Performs a data analysis for data with adaptive treatments.

Description

Performs Bayesian's Gaussian process regression or Bayesian additive regression tree for data with adaptive treatment(s).

Usage

dynamicGP(
  datafile = NULL,
  dataref = NULL,
  method = "BART",
  stg1.outcome,
  stg1.treatment,
  stg1.x.explanatory = NULL,
  stg1.x.confounding = NULL,
  stg1.tr.hte = NULL,
  stg1.tr.values = NULL,
  stg1.tr.type = "Discrete",
  stg1.time,
  stg1.time.value = NULL,
  stg1.outcome.type = "Continuous",
  stg1.outcome.bound_censor = "neither",
  stg1.outcome.lb = NULL,
  stg1.outcome.ub = NULL,
  stg1.outcome.censor.lv = NULL,
  stg1.outcome.censor.uv = NULL,
  stg1.outcome.censor.yn = NULL,
  stg1.outcome.link = "identity",
  stg1.c.margin = NULL,
  stg2.outcome,
  stg2.treatment,
  stg2.x.explanatory = NULL,
  stg2.x.confounding = NULL,
  stg2.tr1.hte = NULL,
  stg2.tr2.hte = NULL,
  stg2.tr.values = NULL,
  stg2.tr.type = "Discrete",
  stg2.time,
  stg2.time.value = NULL,
  stg2.outcome.type = "Continuous",
  stg2.outcome.bound_censor = "neither",
  stg2.outcome.lb = NULL,
  stg2.outcome.ub = NULL,
  stg2.outcome.censor.lv = NULL,
  stg2.outcome.censor.uv = NULL,
  stg2.outcome.censor.yn = NULL,
  stg2.outcome.link = "identity",
  stg2.c.margin = NULL,
  burn.num = 500,
  mcmc.num = 500,
  x.categorical = NULL,
  mi.datafile = NULL,
  mi.dataref = NULL,
  sheet = NULL,
  mi.sheet = NULL,
  seed = 5000,
  token = NULL,
  use.cache = NULL
)

Arguments

datafile

File to upload (.csv or .xls)
dataref

Reference to already uploaded file.
method

The method to be used. "GP" for GP method and "BART" for BART method. The default value is "BART".
stg1.outcome

The name of the intermediate outcome variable for stage 1.
stg1.treatment

The name of the treatment variable for stage 1.
stg1.x.explanatory

A vector of the name of the explanatory variables for stage 1.
stg1.x.confounding

A vector of the name of the confounding variables for stage 1.
stg1.tr.hte

An optional vector specifying categorical variables which may have heterogeneous treatment effect with the treatment variable for stage 1.
stg1.tr.values

User-defined values for the calculation of ATE if the treatment variable is continuous for stage 1.
stg1.tr.type

The type of treatment at stage 1. "Continuous" for continuous treatment and "Discrete" for categorical treatment. The default value is "Discrete".
stg1.time

Time variable.
stg1.time.value

Pre-specified time exposure.
stg1.outcome.type

Intermediate outcome type ("Continuous" or "Discrete") for stage 1.
stg1.outcome.bound_censor

The default value is "neither". "neither" if the intermediate outcome is not bounded or censored. "bounded" if the intermediate outcome is bounded. "censored" if the intermediate outcome is censored.
stg1.outcome.lb

Stage 1 lower bound if the intermediate outcome is bounded.
stg1.outcome.ub

Stage 1 upper bound if the intermediate outcome is bounded.
stg1.outcome.censor.lv

lower variable of censored interval if the intermediate outcome is censored.
stg1.outcome.censor.uv

upper variable of censored interval if the intermediate outcome is censored.
stg1.outcome.censor.yn

Censoring variable if the intermediate outcome is censored.
stg1.outcome.link

function for the intermediate outcome; the default value is “identity”. "identity" if no transformation needed. "log" for log transformation. "logit" for logit transformation.
stg1.c.margin

An optional vector of user-defined values of c for PrTE at stage 1.
stg2.outcome

The name of the outcome variable for stage 2.
stg2.treatment

The name of the treatment variable for stage 2.
stg2.x.explanatory

A vector of the name of the explanatory variables for stage 2.
stg2.x.confounding

A vector of the name of the confounding variables for stage 2.
stg2.tr1.hte

At stage 2, an optional vector specifying categorical variables which may have heterogeneous treatment effect with the stage 1 treatment variable
stg2.tr2.hte

At stage 2, an optional vector specifying categorical variables which may have heterogeneous treatment effect with the stage 2 treatment variable
stg2.tr.values

User-defined values for the calculation of ATE if the treatment variable is continuous for stage 2.
stg2.tr.type

The type of treatment at stage 2. "Continuous" for continuous treatment and "Discrete" for categorical treatment. The default value is "Discrete".
stg2.time

Time variable.
stg2.time.value

Pre-specified time exposure.
stg2.outcome.type

Outcome type ("Continuous" or "Discrete") for stage 2.
stg2.outcome.bound_censor

The default value is "neither". "neither" if the intermediate outcome is not bounded or censored. "bounded" if the intermediate outcome is bounded. "censored" if the intermediate outcome is censored.
stg2.outcome.lb

Stage 2 lower bound if the outcome is bounded.
stg2.outcome.ub

Stage 2 upper bound if the outcome is bounded.
stg2.outcome.censor.lv

lower variable of censored interval if the outcome is censored.
stg2.outcome.censor.uv

upper variable of censored interval if the outcome is censored.
stg2.outcome.censor.yn

Censoring variable if the outcome is censored.
stg2.outcome.link

function for the outcome; the default value is “identity”. "identity" if no transformation needed. "log" for log transformation. "logit" for logit transformation.
stg2.c.margin

An optional vector of user-defined values of c for PrTE at stage 2.
burn.num

numeric; the number of MCMC 'burn-in' samples, i.e. number of MCMC to be discarded. The default value is 500.
mcmc.num

numeric; the number of MCMC samples after 'burn-in'. The default value is 500.
x.categorical

A vector of the name of categorical variables in data.
mi.datafile

File to upload (.csv or .xls) that contains the imputed data in the model.
mi.dataref

Reference to already uploaded file that contains the imputed data in the model.
sheet

If datafile or dataref points to an Excel file this variable specifies which sheet to load.
mi.sheet

If mi.datafile or mi.dataurl points to an Excel file this variable specifies which sheet to load.
seed

Sets the seed. The default value is 5000.
token

Authentication token.
use.cache

Use cached results (default True).

Value

jobid

Get conditional average treatment effect for data with two time points.

Description

Estimate the conditional average treatment effect of user-specified treatment groups.

Usage

dynamicGP.cate(
  jobid,
  x,
  control.tr,
  treat.tr,
  c.margin = NULL,
  token = NULL,
  use.cache = NULL
)

Arguments

jobid

job id of the "dynamicGP".
x

The name of variable which may have the heterogeneous treatment effect. x should be a categorical variable.
control.tr

A vector of the values of the treatment variables at all stages as the reference group.
treat.tr

A vector of the values of the treatment variables at all stages compared to the reference group.
c.margin

An optional vector of user-defined values of c for PrCTE.
token

Authentication token.
use.cache

Use cached results (default True).

Details

The contrast of potential outcomes for the reference group and the treatment group is estimated at a list of x values if x is not a factor. If x is a factor, the conditional average treatment effect is estimated at each value of levels of x.

Value

jobid

Note

The conditional average treatment effect is estimated based on the sample data. The observations with missing covariates in the model are excluded. For the unspecified variables in the model, the observed data is used to estimate the conditional average treatment effect.

Return job status

Description

Return status of the previously submitted job

Usage

job_status(jobid, token = NULL)

Arguments

jobid

Job ID of the previously submitted job
token

Authentication token.

Value

status

Return plot URL

Description

Return plot URL

Usage

ploturl(jobid, plottype = "", token = NULL)

Arguments

jobid

Job ID of the previously submitted job
plottype

Plot type
token

Authentication token.

Value

url

Print job results

Description

Return formatted string with job results

Usage

printgp(jobid, token = NULL)

Arguments

jobid

Job ID of the previously submitted job
token

Authentication token.

Value

formatted text

Return job results

Description

Return job results

Usage

results(jobid, token = NULL)

Arguments

jobid

Job ID of the previously submitted job
token

Authentication token.

Value

results

Performs a data analysis for data with non-adaptive treatment(s).

Description

Performs Bayesian's Gaussian process regression or Bayesian additive regression tree for data with non-adaptive treatment(s).

Usage

staticGP(
  datafile = NULL,
  dataref = NULL,
  method = "BART",
  outcome,
  outcome.type = "Continuous",
  outcome.bound_censor = "neither",
  outcome.lb = NULL,
  outcome.ub = NULL,
  outcome.censor.yn = NULL,
  outcome.censor.lv = NULL,
  outcome.censor.uv = NULL,
  outcome.link = "identity",
  treatment,
  x.explanatory = NULL,
  x.confounding = NULL,
  tr.type = "Discrete",
  tr.values = NULL,
  c.margin = NULL,
  tr.hte = NULL,
  time,
  time.value = NULL,
  burn.num = 500,
  mcmc.num = 500,
  x.categorical = NULL,
  mi.datafile = NULL,
  mi.dataref = NULL,
  sheet = NULL,
  mi.sheet = NULL,
  seed = 5000,
  token = NULL,
  use.cache = NULL
)

Arguments

datafile

File to upload (.csv or .xls)
dataref

Reference to already uploaded file.
method

The method to be used. "GP" for GP method and "BART" for BART method. The default value is "BART".
outcome

The name of the outcome variable.
outcome.type

Outcome type ("Continuous" or "Discrete"). The default value is "Continuous".
outcome.bound_censor

The default value is "neither". "neither" if the outcome is not bounded or censored. "bounded" if the outcome is bounded. "censored" if the outcome is censored.
outcome.lb

Putting a lower bound if the outcome is bounded.
outcome.ub

Putting a upper bound if the outcome is bounded.
outcome.censor.yn

Censoring variable if outcome is censored.
outcome.censor.lv

lower variable of censored interval if outcome is censored.
outcome.censor.uv

upper variable of censored interval if outcome is censored.
outcome.link

function for outcome; the default value is "identity". "identity" if no transformation needed. "log" for log transformation. "logit" for logit transformation.
treatment

The vector of the name of the treatment variables. Users can input at most two treatment variables.
x.explanatory

The vector of the name of the explanatory variables.
x.confounding

The vector of the name of the confounding variables.
tr.type

The type of the first treatment. "Continuous" for continuous treatment and "Discrete" for categorical treatment. The default value is "Discrete".
tr.values

user-defined values for the calculation of ATE if the first treatment variable is continuous
c.margin

An optional vector of user-defined values of c for PrTE.
tr.hte

An optional vector specifying variables which may have heterogeneous treatment effect with the first treatment variable
time

Time variable.
time.value

Pre-specified time exposure.
burn.num

numeric; the number of MCMC 'burn-in' samples, i.e. number of MCMC to be discarded. The default value is 500.
mcmc.num

numeric; the number of MCMC samples after 'burn-in'. The default value is 500.
x.categorical

A vector of the name of categorical variables in data.
mi.datafile

File to upload (.csv or .xls) that contains the imputed data in the model.
mi.dataref

Reference to already uploaded file that contains the imputed data in the model.
sheet

If datafile or dataref points to an Excel file this variable specifies which sheet to load.
mi.sheet

If mi.datafile or mi.dataurl points to an Excel file this variable specifies which sheet to load.
seed

Sets the seed. The default value is 5000.
token

Authentication token.
use.cache

Use cached results (default True).

Value

jobid

Get conditional average treatment effect

Description

Estimate the conditional average treatment effect of user-specified treatment groups.

Usage

staticGP.cate(
  jobid,
  x,
  control.tr,
  treat.tr,
  c.margin = NULL,
  token = NULL,
  use.cache = NULL
)

Arguments

jobid

job id of the "staticGP".
x

The name of a categorical variable which may have the heterogeneous treatment effect.
control.tr

The value of the treatment variable as the reference group.
treat.tr

The value of the treatment variable compared to the reference group.
c.margin

An optional vector of user-defined values of c for PrCTE.
token

Authentication token.
use.cache

Use cached results (default True).

Details

The contrast of potential outcomes for the reference group and the treatment group is estimated at each value of x.

Value

Return jobid

Note

The conditional average treatment effect is estimated based on the sample data. The observations with missing covariates in the model are excluded. For the unspecified variables in the model, the original data is used to estimate the conditional average treatment effect.

Upload a file

Description

Upload a file

Usage

uploadfile(filename, token = NULL)

Arguments

filename

Filename of a file to upload
token

Authentication token.

Value

Backend filename reference

Wait while the job status is pending

Description

Return when the job status is finished (either successfully or otherwise)

Usage

wait_for_result(jobid, token = NULL)

Arguments

jobid

Job ID of the previously submitted job
token

Authentication token.

Value

status