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Type: Package
Title: Sequential Trial Emulation
Version: 0.13.1
Description: Implementation of sequential trial emulation for the analysis of observational databases. The 'SEQTaRget' software accommodates time-varying treatments and confounders, as well as binary and failure time outcomes. 'SEQTaRget' allows to compare both static and dynamic strategies, can be used to estimate observational analogs of intention-to-treat and per-protocol effects, and can adjust for potential selection bias induced by losses-to-follow-up. (Paper to come).
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Suggests: rmarkdown, testthat (≥ 3.0.0)
Imports: data.table, doFuture, doRNG, fastglm, future, future.apply, ggplot2, knitr, methods, stringr, survival
Config/testthat/edition: 3
Depends: R (≥ 4.1)
URL: https://causalinference.github.io/SEQuential/
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-09-10 15:45:45 UTC; ryo766
Author: Ryan O'Dea ORCID iD [aut, cre], Alejandro Szmulewicz ORCID iD [aut], Miguel Hernan ORCID iD [aut], The President and Fellows of Harvard College ROR ID [cph]
Maintainer: Ryan O'Dea <ryanodea@hsph.harvard.edu>
Repository: CRAN
Date/Publication: 2025-09-15 08:40:02 UTC

Function to return the internal data from a SEQuential object

Description

Function to return the internal data from a SEQuential object

Usage

SEQ_data(object)

Arguments

object

SEQoutput object

Value

data.table

Simulated observational example data for SEQuential

Description

Simulated observational example data for SEQuential

Usage

SEQdata

Format

A data frame with 54,687 rows and 13 columns:

ID

Integer: Unique ID emulating individual patients

time

Integer: Time of observation, always begins at 0, max time of 59. Should be continuous

eligible

Binary: eligibility criteria for timepoints

outcome

Binary: If an outcome is observed at this time point

tx_init

Binary: If treatment is observed at this time point

sex

Binary: Sex of the emulated patient

N

Numeric: Normal random variable from N\(10,5\)

L

Numeric: 4% continuously increase from U\(0, 1\)

P

Numeric: 2% continuously decrease from U\(9, 10\)

excusedOne

Binary: Once one, always one variable emulating an excuse for treatment switch

excusedZero

Binary: Once zero, always zero variable emulating an excuse for treatment switch

Simulated Lost-to-followup example data for SEQuential

Description

Simulated Lost-to-followup example data for SEQuential

Usage

SEQdata.LTFU

Format

A dataframe with 4,139 rows and 13 columns:

ID

Integer: Unique ID emulating individual patients

time

Integer: Time of observation, always begins at 0, max time of 59; however, if lost-to-followup, time is truncated at a random point

eligible

Binary: eligibility criteria for timepoints

outcome

Binary: If an outcome is observed at this time point

tx_init

Binary: If treatment is observed at this time point

sex

Binary: Sex of the emulated patient

N

Numeric: Normal random variable from N\(10,5\)

L

Numeric: 4% continuously increase from U\(0, 1\)

P

Numeric: 2% continuously decrease from U\(9, 10\)

excusedOne

Binary: Once one, always one variable emulating an excuse for treatment switch

excusedZero

Binary: Once zero, always zero variable emulating an excuse for treatment switch

LTFU

Binary: Flag for losing a simulated ID to followup, if 1 there are no more records of the ID afterwards

Simulated multitreatment example data for SEQuential multinomial models

Description

Simulated multitreatment example data for SEQuential multinomial models

Usage

SEQdata.multitreatment

Format

A dataframe with 5,976 rows and 11 columns:

ID

Integer: Unique ID emulating individual patients

time

Integer: Time of observation, always begins at 0, max time of 59; however, if lost-to-followup, time is truncated at a random point

eligible

Binary: eligibility criteria for timepoints

outcome

Binary: If an outcome is observed at this time point

tx_init

Integer: Which treatment is observed at this time point

sex

Binary: Sex of the emulated patient

N

Numeric: Normal random variable from N\(10,5\)

L

Numeric: 4% continuously increase from U\(0, 1\)

P

Numeric: 2% continuously decrease from U\(9, 10\)

excusedOne

Binary: Once one, always one variable emulating an excuse for treatment switch

excusedZero

Binary: Once zero, always zero variable emulating an excuse for treatment switch

Estimate the (very rough) time to run SEQuential analysis on current machine

Description

Estimate the (very rough) time to run SEQuential analysis on current machine

Usage

SEQestimate(
  data,
  id.col,
  time.col,
  eligible.col,
  treatment.col,
  outcome.col,
  time_varying.cols = list(),
  fixed.cols = list(),
  method,
  options,
  verbose = TRUE
)

Arguments

data

data.frame or data.table, if not already expanded with SEQexpand, will preform expansion according to arguments passed to either params or ...

id.col

String: column name of the id column

time.col

String: column name of the time column

eligible.col

String: column name of the eligibility column

treatment.col

String: column name of the treatment column

outcome.col

String: column name of the outcome column

time_varying.cols

List: column names for time varying columns

fixed.cols

List: column names for fixed columns

method

String: method of analysis to preform

options

List: optional list of parameters from SEQopts

verbose

Logical: if TRUE, cats progress to console

Value

A list of (very rough) estimates for the time required for SEQuential containing:

Creates an expanded dataset for use with SEQuential

Description

Creates an expanded dataset for use with SEQuential

Usage

SEQexpand(params)

Arguments

params

SEQparams object built in the SEQuential function

Parameter Builder for SEQuential Model and Estimates

Description

Parameter Builder for SEQuential Model and Estimates

Usage

SEQopts(
  bootstrap = FALSE,
  bootstrap.nboot = 100,
  bootstrap.sample = 0.8,
  cense = NA,
  cense.denominator = NA,
  cense.eligible = NA,
  cense.numerator = NA,
  compevent = NA,
  covariates = NA,
  data.return = FALSE,
  denominator = NA,
  deviation = FALSE,
  deviation.col = NA,
  deviation.conditions = c(NA, NA),
  deviation.excused = FALSE,
  deviation.excused_cols = c(NA, NA),
  excused = FALSE,
  excused.cols = c(NA, NA),
  fastglm.method = 2L,
  followup.class = FALSE,
  followup.include = TRUE,
  followup.max = Inf,
  followup.min = -Inf,
  followup.spline = FALSE,
  hazard = FALSE,
  indicator.baseline = "_bas",
  indicator.squared = "_sq",
  km.curves = FALSE,
  multinomial = FALSE,
  ncores = parallel::detectCores() - 1,
  nthreads = data.table::getDTthreads(),
  numerator = NA,
  parallel = FALSE,
  plot.colors = c("#F8766D", "#00BFC4", "#555555"),
  plot.labels = NA,
  plot.subtitle = NA,
  plot.title = NA,
  plot.type = "survival",
  seed = NULL,
  selection.first_trial = FALSE,
  selection.prob = 0.8,
  selection.random = FALSE,
  subgroup = NA,
  survival.max = Inf,
  treat.level = c(0, 1),
  trial.include = TRUE,
  weight.eligible_cols = c(),
  weight.lower = -Inf,
  weight.lag_condition = TRUE,
  weight.p99 = FALSE,
  weight.preexpansion = TRUE,
  weight.upper = Inf,
  weighted = FALSE
)

Arguments

bootstrap

Logical: defines if SEQuential should run bootstrapping, default is FALSE

bootstrap.nboot

Integer: number of bootstraps

bootstrap.sample

Numeric: percentage of data to use when bootstrapping, should in [0, 1], default is 0.8

cense

String: column name for additional censoring variable, e.g. loss-to-follow-up

cense.denominator

String: censoring denominator covariates to the right hand side of a formula object

cense.eligible

String: column name for indicator column defining which rows to use for censoring model

cense.numerator

String: censoring numerator covariates to the right hand side of a formula object

compevent

String: column name for competing event indicator

covariates

String: covariates to the right hand side of a formula object

data.return

Logical: whether to return the expanded dataframe with weighting information

denominator

String: denominator covariates to the right hand side of a to formula object

deviation

Logical: create switch based on deviation from column deviation.col

deviation.col

Character: column name for deviation

deviation.conditions

Character list: RHS evaluations of the same length as treat.levels

deviation.excused

Logical: whether deviations should be excused by deviation.excused_cols

deviation.excused_cols

Character list: excused columns for deviation switches

excused

Logical: in the case of censoring, whether there is an excused condition

excused.cols

List: list of column names for treatment switch excuses - should be the same length, and ordered the same as treat.level

fastglm.method

Integer: decomposition method for fastglm (1-QR, 2-Cholesky, 3-LDLT, 4-QR.FPIV)

followup.class

Logical: treat followup as a class, e.g. expands every time to it's own indicator column

followup.include

Logical: whether or not to include 'followup' and 'followup_squared' in the outcome model

followup.max

Numeric: maximum time to expand about, default is Inf (no maximum)

followup.min

Numeric: minimum time to expand aboud, default is -Inf (no minimum)

followup.spline

Logical: treat followup as a cubic spline

hazard

Logical: hazard error calculation instead of survival estimation

indicator.baseline

String: identifier for baseline variables in covariates, numerator, denominator - intended as an override

indicator.squared

String: identifier for squared variables in covariates, numerator, denominator - intended as an override

km.curves

Logical: Kaplan-Meier survival curve creation and data return

multinomial

Logical: whether to expect multilevel treatment values

ncores

Integer: number of cores to use in parallel processing, default is one less than system max

nthreads

Integer: number of threads to use for data.table processing

numerator

String: numerator covariates to the right hand side of a to formula object

parallel

Logical: define if the SEQuential process is run in parallel, default is FALSE

plot.colors

Character: Colors for output plot if km.curves = TRUE, defaulted to ggplot2 defaults

plot.labels

Character: Color labels for output plot if km.curves = TRUE in order e.g. c("risk.0", "risk.1")

plot.subtitle

Character: Subtitle for output plot if km.curves = TRUE

plot.title

Character: Title for output plot if km.curves = TRUE

plot.type

Character: Type of plot to create if km.curves = TRUE, available options are 'survival', 'risk', and 'inc' (in the case of censoring)

seed

Integer: starting seed

selection.first_trial

Logical: selects only the first eligible trial in the expanded dataset

selection.prob

Numeric: percent of total IDs to select for selection.random, should be bound [0, 1]

selection.random

Logical: randomly selects IDs with replacement to run analysis

subgroup

Character: Column name to stratify outcome models on

survival.max

Numeric: maximum time for survival curves, default is Inf (no maximum)

treat.level

List: treatment levels to compare

trial.include

Logical: whether or not to include 'trial' and 'trial_squared' in the outcome model

weight.eligible_cols

List: list of column names for indicator columns defining which weights are eligible for weight models - in order of treat.level

weight.lower

Numeric: weights truncated at lower end at this weight

weight.lag_condition

Logical: whether weights should be conditioned on treatment lag value

weight.p99

Logical: forces weight truncation at 1st and 99th percentile weights, will override provided weight.upper and weight.lower

weight.preexpansion

Logical: whether weighting should be done on pre-expanded data

weight.upper

Numeric: weights truncated at upper end at this weight

weighted

Logical: whether or not to preform weighted analysis, default is FALSE

Value

An object of class 'SEQopts'

An S4 class of user options to feed into the SEQuential processes and estimates This class should match SEQopts in file SEQopts.R

Description

An S4 class of user options to feed into the SEQuential processes and estimates This class should match SEQopts in file SEQopts.R

An S4 class used to hold the outputs for the SEQuential process

Description

An S4 class used to hold the outputs for the SEQuential process

Slots

params

SEQparams object

outcome

outcome covariates

numerator

numerator covariates

denominator

denominator covariates

outcome.model

list of length bootstrap.nboot containing outcome coefficients

hazard

hazard ratio

survival.curve

ggplot object for the survival curves

survival.data

data.table of survival data

risk.difference

risk difference calculated from survival data

risk.ratio

risk ratio calculated from survival data

time

time in minutes used for the SEQuential process

weight.statistics

information from the weighting process, containing weight coefficients and weight statistics

info

list of outcome and switch information (if applicable)

ce.model

list of competing event models if compevent is specified, NA otherwise

An internal S4 class to carry around parameters during the SEQuential process - inherits user facing parameters from SEQopts

Description

An internal S4 class to carry around parameters during the SEQuential process - inherits user facing parameters from SEQopts

Slots

data

pre expansion data

DT

post expansion data

id

id column as defined by the user

time

time column as defined by the user

eligible

eligible column as defined by the user

treatment

treatment column as defined by the user

time_varying

list of time varying columns as defined by the user

fixed

list of fixed columns as defined by the user

method

method of analysis as defined by the user

SEQuential trial emulation

Description

'SEQuential' is an all-in-one API to SEQuential analysis, returning a SEQoutput object of results. More specific examples can be found on pages at https://causalinference.github.io/SEQuential/

Usage

SEQuential(
  data,
  id.col,
  time.col,
  eligible.col,
  treatment.col,
  outcome.col,
  time_varying.cols = list(),
  fixed.cols = list(),
  method,
  options,
  verbose = TRUE
)

Arguments

data

data.frame or data.table, if not already expanded with SEQexpand, will preform expansion according to arguments passed to either params or ...

id.col

String: column name of the id column

time.col

String: column name of the time column

eligible.col

String: column name of the eligibility column

treatment.col

String: column name of the treatment column

outcome.col

String: column name of the outcome column

time_varying.cols

List: column names for time varying columns

fixed.cols

List: column names for fixed columns

method

String: method of analysis to preform

options

List: optional list of parameters from SEQopts

verbose

Logical: if TRUE, cats progress to console

Details

Implemention of sequential trial emulation for the analysis of observational databases. The SEQuential software accommodates time-varying treatments and confounders, as well as binary and failure time outcomes. SEQ allows to compare both static and dynamic strategies, can be used to estimate observational analogs of intention-to-treat and per-protocol effects, and can adjust for potential selection bias induced by losses-to-follow-up.

Value

An S4 object of class SEQoutput

Examples


data <- SEQdata
model <- SEQuential(data, id.col = "ID", 
                          time.col = "time", 
                          eligible.col = "eligible",
                          treatment.col = "tx_init",
                          outcome.col = "outcome",
                          time_varying.cols = c("N", "L", "P"),
                          fixed.cols = "sex",
                          method = "ITT", 
                          options = SEQopts())

An internal S4 class to help transfer weight statistics out of internal_weights

Description

An internal S4 class to help transfer weight statistics out of internal_weights

Slots

weights

a data.table containing the estimated weights, either pre or post expansion

coef.n0

numerator zero model

coef.n1

numerator one model

coef.d0

denominator zero model

coef.d1

denominator one model

coef.ncense

numerator censoring model

coef.dcense

denominator censoring model

Function to clean out non needed elements from fastglm return

Description

Function to clean out non needed elements from fastglm return

Usage

clean_fastglm(model)

Arguments

model

a fastglm model

Function to return competing event models from a SEQuential object

Description

Function to return competing event models from a SEQuential object

Usage

compevent(object)

Arguments

object

SEQoutput object

Value

list of fastglm objects

Retrieves Outcome, Numerator, and Denominator Covariates

Description

Retrieves Outcome, Numerator, and Denominator Covariates

Usage

covariates(object)

Arguments

object

object of class SEQoutput

Value

list of SEQuential covariates

Internal Function to create 'default' loss-to-followup formula

Description

Internal Function to create 'default' loss-to-followup formula

Usage

create.default.LTFU.covariates(params, type)

Internal Function to create 'default' formula

Description

Internal Function to create 'default' formula

Usage

create.default.covariates(params)

Internal Function to create 'default' weighting formula

Description

Internal Function to create 'default' weighting formula

Usage

create.default.weight.covariates(params, type)

Internal function to pull Risk Ratio and Risk Difference from data when km.curves = TRUE

Description

Internal function to pull Risk Ratio and Risk Difference from data when km.curves = TRUE

Usage

create.risk(data, params)

Retrieves Denominator Models from SEQuential object

Description

Retrieves Denominator Models from SEQuential object

Usage

denominator(object)

Arguments

object

object of class SEQoutput

Value

List of both numerator models

Function to return diagnostic tables from a SEQuential object

Description

Function to return diagnostic tables from a SEQuential object

Usage

diagnostics(object)

Arguments

object

SEQoutput object

Value

list of diagnostic tables

Nicely cleans time for readability

Description

Nicely cleans time for readability

Usage

## S3 method for class 'time'
format(seconds)

Function to return hazard ratios from a SEQuential object

Description

Function to return hazard ratios from a SEQuential object

Usage

hazard_ratio(object)

Arguments

object

SEQoutput object

Value

list of hazard ratios

Helper Function to inline predict a fastglm object

Description

Helper Function to inline predict a fastglm object

Usage

inline.pred(
  model,
  newdata,
  params,
  type,
  case = "default",
  multi = FALSE,
  target = NULL
)

Arguments

model

a fastglm object

newdata

filler for a .SD from data.table

params

parameter from SEQuential

type

type of prediction

Internal analysis tool for handling parallelization/bootstrapping on multiple OS types

Description

Internal analysis tool for handling parallelization/bootstrapping on multiple OS types

Usage

internal.analysis(params)

Generic function to format a dataset for hazard ratio calculation

Description

Generic function to format a dataset for hazard ratio calculation

Usage

internal.hazard(model, params)

Internal function for fitting outcome models

Description

Internal function for fitting outcome models

Usage

internal.model(data, params)

Plotting for survival curves

Description

Plotting for survival curves

Usage

internal.plot(survival.data, params)

Arguments

survival.data

Dataframe containing survival information

params

Params passed around SEQuential

Internal function for creating survival curves

Description

Internal function for creating survival curves

Usage

internal.survival(params, outcome)

Internal function for defining weights

Description

Internal function for defining weights

Usage

internal.weights(DT, data, params)

Arguments

DT

data.table after expansion

data

data.table for data before expansion

params

object of class SEQparams (defined in SEQuential)

Function to print kaplan-meier curves

Description

Function to print kaplan-meier curves

Usage

km_curve(
  object,
  plot.type = "survival",
  plot.title,
  plot.subtitle,
  plot.labels,
  plot.colors
)

Arguments

object

SEQoutput object to plot

plot.type

character: type of plot to print

plot.title

character: defines the title of the plot

plot.subtitle

character: plot subtitle

plot.labels

length 2 character: plot labels

plot.colors

length 2 character: plot colors

Value

ggplot object of plot plot.type

Function to return survival data from a SEQuential object

Description

Function to return survival data from a SEQuential object

Usage

km_data(object)

Arguments

object

SEQoutput object

Value

list of dataframes of survival values

Helper function for nested logistic

Description

Helper function for nested logistic

Usage

multinomial(X, y, family = quasibinomial(), method)

Helper to predict from the nested logistic

Description

Helper to predict from the nested logistic

Usage

multinomial.predict(model, X, target = NULL)

Helper function to get the summary table from multinomial

Description

Helper function to get the summary table from multinomial

Usage

multinomial.summary(model)

Retrieves Numerator Models from SEQuential object

Description

Retrieves Numerator Models from SEQuential object

Usage

numerator(object)

Arguments

object

object of class SEQoutput

Value

List of both numerator models

Retrieves Outcome Models from SEQuential object

Description

Retrieves Outcome Models from SEQuential object

Usage

outcome(object)

Arguments

object

object of class SEQoutput

Value

List of all outcome models

Parameter Helper

Description

Parameter Helper

Usage

parameter.setter(
  data,
  DT,
  id.col,
  time.col,
  eligible.col,
  outcome.col,
  treatment.col,
  time_varying.cols,
  fixed.cols,
  method,
  opts,
  verbose
)

Simplifies parameters down for later use

Description

Simplifies parameters down for later use

Usage

parameter.simplifier(params)

Helper function to prepare data for fastglm

Description

Helper function to prepare data for fastglm

Usage

prepare.data(weight, params, type, model, case)

Arguments

weight

data after undergoing preparation

params

parameter from SEQuential

type

type of model, e.g. d0 = "denominator"

model

model number, e.g. d0 = "zero model"

Output constructor

Description

Output constructor

Usage

prepare.output(
  params,
  WDT,
  outcome,
  weights,
  hazard,
  survival.plot,
  survival.data,
  survival.ce,
  risk,
  runtime,
  info
)

Function to return risk information from a SEQuential object

Description

Function to return risk information from a SEQuential object

Usage

risk_comparison(object)

Arguments

object

SEQoutput object

Value

a data frame of risk information at end of followup (risk ratios, risk differences and confidence intervals, if bootstrapped)

Function to return risk information from a SEQuential object

Description

Function to return risk information from a SEQuential object

Usage

risk_data(object)

Arguments

object

SEQoutput object

Value

a data table of risk information at every followup

Show method for S4 object - SEQoutput.

Description

Show method for S4 object - SEQoutput.

Usage

## S4 method for signature 'SEQoutput'
show(object)

Arguments

object

A SEQoutput object - usually generated from SEQuential

Value

No return value, sends information about SEQoutput to the console

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.