Help for package DeltaTools

Title:

DELTA Analytic Tools and Learning Curve Analysis

Version:

0.1.4

Description:

A collection of tools for researchers interested in carrying out parametric estimation of learning curves and device effects based on the publication by Ssemaganda et al. (2025) <doi:10.2147/MDER.S520191> with modified versions of propensity score matching (PSM) and inverse probability of treatment weighting (IPTW).

License:

GPL-2 | GPL-3

Encoding:

UTF-8

RoxygenNote:

8.0.0

Imports:

broom (≥ 1.0.12), caret (≥ 7.0-1), data.table (≥ 1.18.4), DescTools (≥ 0.99.60), dplyr (≥ 1.2.1), gbm (≥ 2.2.3), ggplot2 (≥ 4.0.3), glmnet (≥ 5.0), ldbounds (≥ 2.0.2), lmtest (≥ 0.9-40), MatchIt (≥ 4.7.2), methods, mgcv (≥ 1.9-4), minpack.lm (≥ 1.2-4), parameters (≥ 0.28.3), plotly (≥ 4.12.0), pROC (≥ 1.19.0.1), ResourceSelection (≥ 0.3-6), rms (≥ 8.1-1), ROCR (≥ 1.0-12), sjPlot (≥ 2.9.0), stats, stringr (≥ 1.6.0), tableone (≥ 0.13.2), twang (≥ 2.6.2)

Suggests:

kableExtra (≥ 1.4.0), knitr (≥ 1.51), rmarkdown (≥ 2.31), testthat (≥ 3.0.0)

Config/testthat/edition:

Depends:

R (≥ 3.5.0)

LazyData:

true

NeedsCompilation:

Packaged:

2026-09-17 19:59:04 UTC; amyperkins

Author:

Michael Matheny

[aut, cph], Frederic Resnic

[aut, cph], Henry Ssemaganda

[aut, cph], Jejo Koola

[aut, cph], Amy Perkins

[aut, cph, cre]

Maintainer:

Amy Perkins <amy.perkins@vumc.org>

Repository:

CRAN

Date/Publication:

2026-09-28 08:40:19 UTC

DeltaTools Function: PLCAnalysis

Description

DeltaTools Function: PLCAnalysis

Usage

PLCAnalysis(
  data,
  datasetIdentifier = "Dataset1",
  caseIDFieldNM,
  caseDateFieldNM,
  outcomeFieldNM,
  orderFieldNM,
  operatorFieldNM,
  covariateFieldNMs,
  exposureFieldNM,
  exposureOfInterestNM,
  exposureOfInterestOperatorCaseSeriesFieldNM,
  allowIPTWWeighting = FALSE,
  normalizeIPTWWeights = FALSE,
  useGeneralCovariateFieldNMs = TRUE,
  iptwCovariateFieldNMs = c(),
  allowOperatorClustering = FALSE,
  allowGAMBasedAVS = FALSE,
  learningUnadjustedSignalRequired = TRUE,
  leDetectionAlpha = 0.05,
  lcEstimationAlpha = 0.05,
  bootstraps = 0,
  forceEstimationAsymptoteToZero = TRUE,
  deviceSignalEstimationAlpha = 0.05
)

Arguments

data

The data frame.

datasetIdentifier

A character string with the dataset identifier.

caseIDFieldNM

A character string with the case identifier.

caseDateFieldNM

A character string with the case date field.

outcomeFieldNM

A character string with the outcome.

orderFieldNM

A character string with the case order field.

operatorFieldNM

A character string with the operator field.

covariateFieldNMs

A character vector of covariates to adjust for in the model.

exposureFieldNM

A character string with the exposure.

exposureOfInterestNM

A character string with the exposure value of interest.

exposureOfInterestOperatorCaseSeriesFieldNM

A character string with the exposure of interest operator case series field.

allowIPTWWeighting

Set to TRUE to allow Inverse Probability of Treatment Weighting.

normalizeIPTWWeights

Set to TRUE to normalize IPTW weights.

useGeneralCovariateFieldNMs

Set to TRUE to use general covariate field names for IPTW.

iptwCovariateFieldNMs

A character vector of covariate field names for IPTW, used when useGeneralCovariateFieldNMs=TRUE.

allowOperatorClustering

Set to TRUE to allow operator clustering.

allowGAMBasedAVS

Set to TRUE to allow generalized additive model-based automatic variable selection.

learningUnadjustedSignalRequired

Set to TRUE to require a learning unadjusted signal be detected before attempting to detect the presence of a learning effect.

leDetectionAlpha

The alpha level for learning detection; the default value is 0.05.

lcEstimationAlpha

The alpha level for learning curve estimation; the default value is 0.05.

bootstraps

The default value for the number of bootstrapped samples is 0; may add other options in the future.

forceEstimationAsymptoteToZero

Defaults to TRUE to force the estimation asymptote to zero; may add other options in the future.

deviceSignalEstimationAlpha

The alpha level for device signal estimation; the default value is 0.05.

Value

A list containing relevant messages about detection of a learning effect and device signal, modeling summaries of the learning unadjusted signal, modeling summaries of the learning adjusted signal, learning detection summaries, and the GAM plot (if applicable).

Examples

# Retrospective Parametric Learning Curve Analysis
# Learning effect not detected, unadjusted device signal detected
plc <- PLCAnalysis(data=data.plc,
                   datasetIdentifier="Dataset1",
                   caseIDFieldNM="Patient",
                   caseDateFieldNM="ProcDate",
                   outcomeFieldNM="Outcome_Final",
                   orderFieldNM="CaseOrder_All",
                   operatorFieldNM="Operator",
                   covariateFieldNMs=c("Pt_F1", "Pt_F2", "Pt_F3", "Pt_F4", "Pt_F5",
                                       "Pt_F6", "Pt_F7", "Pt_F8", "Pt_F9", "Pt_F10",
                                       "Pt_F11", "Pt_F12", "Pt_F13", "Pt_F14", "Pt_F15",
                                       "Pt_F16", "Pt_F17", "Pt_F18", "Pt_F19", "Pt_F20",
                                       "Pt_F21", "Op_F1", "Op_F2", "Inst_F1", "Inst_F2",
                                       "Inst_F3"),
                   exposureFieldNM="Device",
                   exposureOfInterestNM="B",
                   exposureOfInterestOperatorCaseSeriesFieldNM="CaseOrder_Op_DevB")

Parametric Learning Curve Analysis example data

Description

A synthetic patient population generated for learning effect and device signal detection using this function.

Usage

data.plc

Format

`data.plc`

A data frame with 11,808 rows and 41 columns:

Patient: Patient identifier
Operator: Operator identifier
Institution: Institution identifier
CaseOrder_All: Case Order across all observations
CaseOrder_Inst: Case Order across that institution
CaseOrder_Op: Case Order across that operator
ProcDate: Procedure date
CasePeriod: Time period during which the case procedure occurred
OutcomePeriod: Time period during which the case outcome occurred
CaseOrder_Inst_DevA: Case Order across that institution and Device A
CaseOrder_Inst_DevB: Case Order across that institution and Device B
CaseOrder_Op_DevA: Case Order across that operator and Device A
CaseOrder_Op_DevB: Case Order across that oeprator and Device B
Device: Device placed during the procedure
Outcome_Final: Outcome variable
Pt_F1: Patient factor 1
Pt_F2: Patient factor 2
Pt_F3: Patient factor 3
Pt_F4: Patient factor 4
Pt_F5: Patient factor 5
Pt_F6: Patient factor 6
Pt_F7: Patient factor 7
Pt_F8: Patient factor 8
Pt_F9: Patient factor 9
Pt_F10: Patient factor 10
Pt_F11: Patient factor 11
Pt_F12: Patient factor 12
Pt_F13: Patient factor 13
Pt_F14: Patient factor 14
Pt_F15: Patient factor 15
Pt_F16: Patient factor 16
Pt_F17: Patient factor 17
Pt_F18: Patient factor 18
Pt_F19: Patient factor 19
Pt_F20: Patient factor 20
Pt_F21: Patient factor 21
Op_F1: Operator factor 1
Op_F2: Operator factor 2
Inst_F1: Institution factor 1
Inst_F2: Institution factor 2
Inst_F3: Institution factor 3

Propensity Score Analysis and Inverse Probability of Treatment Weighting example data

Description

A synthetic patient population generated for use with the functions propensity score matching or inverse probability of treatment weighting.

Usage

data.ps

Format

`data.ps`

A data frame with 29,778 rows and 24 columns:

Hypertension: Hypertension indicator
Mortality30D: 30-day mortality indicator
Aspirin: Aspirin use indicator
Warfarin: Warfarin use indicator
CreatPreProc_mgdl: Pre-procedure serum creatinine level in mg/dL
FluoroMins: Fluoroscopy time in minutes
NSTEMIatPresent: Non-ST-Elevation Myocardial Infarction at presentation indicator
ProcDate: Procedure date
NumVesselsIndexPCI: Number of blocked vessels at index percutaneous coronary intervention
Patient: Patient identifier
Readmission: Hospital readmission indicator
PriorPCI: History of percutaneous coronary intervention indicator
Emergent: Emergent case indicator
TotalAdmitPCI: Total admitted percutaneous coronary interventions
ChrLungDis: Chronic lung disease indicator
Smoker: Smoker indicator
Female: Female sex
Dialysis: Dialysis indicator
Diabetes: Diabetes indicator
PeripheralArterialDis: Peripheral arterial disease indicator
RepeatProc: Repeat procedure indicator
DeviceB: Device B indicator
Age: Patient age in years
BMI: Patient BMI in kg/m2

DeltaTools Function: iptwDT

Description

DeltaTools Function: iptwDT

Usage

iptwDT(
  data,
  outcomes,
  exposure,
  covariates.factor,
  covariates.continuous,
  identifier,
  link = "logit",
  avs = TRUE,
  lambda = "lambda.min",
  postmatch.adjusted.model = FALSE,
  allow.trimming = TRUE,
  allow.stabilization = TRUE,
  lower.cutoff = 5,
  upper.cutoff = 95,
  smd.t1 = 0.1,
  smd.t2 = 0.25
)

Arguments

data

The data frame.

outcomes

A character vector of one or more outcomes.

exposure

A character string with the exposure.

covariates.factor

A character vector of factor covariates.

covariates.continuous

A character vector of continuous covariates.

identifier

A character string with the record identifier.

link

Specify the link function as "logit" or "probit".

avs

Set to TRUE to run automatic variable selection.

lambda

Specify lambda as "lambda.min" or "lambda.1se".

postmatch.adjusted.model

Set to TRUE to adjust for additional variables in a logistic regression model on the matched cohort.

allow.trimming

Set to TRUE to trim weights.

allow.stabilization

Set to TRUE to stabilize weights.

lower.cutoff

Quantile for lower cutoff to trim weights, only used if allow.trimming = TRUE. Otherwise, specify NULL.

upper.cutoff

Quantile for upper cutoff to trim weights, only used if allow.trimming = TRUE. Otherwise, specify NULL.

smd.t1

Standardized mean difference threshold 1 (more conservative).

smd.t2

Standardized mean difference threshold 2 (less conservative).

Value

A list containing relevant messages about the weighting and modeling process, descriptive statistics for the unweighted and weighted cohorts stratified by exposure variable, covariate balance assessment, and logistic regression modeling summaries.

Examples


# Inverse Probability of Treatment Weighting with automatic variable selection and
# trimmed, stabilized weights
wt <- iptwDT(data=data.ps,
             outcomes=c("Mortality30D", "Readmission", "RepeatProc"),
             exposure="DeviceB",
             covariates.factor=c("Aspirin", "ChrLungDis", "Diabetes", "Dialysis",
                                 "Emergent", "Female", "Hypertension", "NSTEMIatPresent",
                                 "PeripheralArterialDis", "PriorPCI", "Smoker", "Warfarin"),
             covariates.continuous=c("Age", "BMI", "CreatPreProc_mgdl", "FluoroMins",
                                     "NumVesselsIndexPCI", "TotalAdmitPCI"),
             identifier="Patient")

DeltaTools Function: propensityScoreAnalysis

Description

DeltaTools Function: propensityScoreAnalysis

Usage

propensityScoreAnalysis(
  data,
  outcomes,
  exposure,
  covariates.factor,
  covariates.continuous,
  identifier,
  datefield,
  link = "logit",
  avs = TRUE,
  period.type = "quarter",
  alpha = 0.05,
  m.days = 90,
  m.type = "closest",
  lambda = "lambda.min",
  match.date.range = FALSE,
  m.method = "nearest",
  m.dist = "glm",
  m.dist.opt = list(),
  m.est = "ATT",
  m.exact = NULL,
  m.mahvars = NULL,
  m.antiexact = NULL,
  m.discard = "none",
  m.reestimate = FALSE,
  m.s.weights = NULL,
  m.replace = FALSE,
  m.order = "largest",
  m.caliper = 0.01,
  m.ratio = 1,
  m.verbose = FALSE,
  m.include.obj = FALSE,
  postmatch.adjusted.model = FALSE
)

Arguments

data

The data frame.

outcomes

A character vector of one or more outcomes.

exposure

A character string with the exposure.

covariates.factor

A character vector of factor covariates.

covariates.continuous

A character vector of continuous covariates.

identifier

A character string with the record identifier.

datefield

The observation date if matching within a date range.

link

Specify the link function as "logit" or "probit".

avs

Set to TRUE to run automatic variable selection.

period.type

Specifies time period as "week", "month", "quarter", "half-year", or "year"; specify "none" if not matching within a date range.

alpha

The probability of a Type I error.

m.days

Radius for time period, in days, if matching within a date range.

m.type

Specify matching type as "closest" or "random".

lambda

Specify lambda as "lambda.min" or "lambda.1se".

match.date.range

Set to TRUE to match within a date range.

m.method

Specify matching method as "nearest", "optimal", "full", "quick", "genetic", "cem", "exact", "cardinality", or "subclass".

m.dist

Specify the distance measure as "glm", "mahalanobis", a vector of distance measures, or a matrix of pairwise distances.

m.dist.opt

Specify a list of additional arguments for the function that estimates the distance measure. Otherwise, specify an empty list.

m.est

Specify the target estimand as "ATT" (Average Treatment Effect on the Treated), "ATC" (Average Treatment Effect on the Controls), or "ATE" (Average Treatment Effect).

m.exact

Specify a string with the variables for which exact matching should be used or specify a one-sided formula with the variables on the right-hand side. Otherwise, specify NULL.

m.mahvars

Specify a string with the variables on which Mahalanobis distance matching should be used or specify a one-sided formula with the variables on the right-hand side. Otherwise, specify NULL.

m.antiexact

Specify a string with the names of variables for which anti-exact matching should be used or specify a one-sided formula with the variables on the right-hand side. Otherwise, specify NULL.

m.discard

Specify whether "none", "treated", "control", or "both" units should be discarded when the propensity scores fall outside the common support region.

m.reestimate

Set to TRUE to re-estimate propensity scores in the sample that remains, only used if m.discard is not "none".

m.s.weights

Specify a numeric vector of sampling weights for the propensity score models and balance statistics, a string containing the variable to be used, or a one-sided formula with the variable on the right-hand side. Otherwise, specify NULL.

m.replace

Set to TRUE to match with replacement.

m.order

Specify matching order as "largest" or "data".

m.caliper

A numeric vector of caliper widths for each variable. Otherwise, NULL for no caliper.

m.ratio

Specify an integer for the number of control cases to match with each treated case in k:1 matching.

m.verbose

Set to TRUE to print information on the matching process to the console.

m.include.obj

Set to TRUE to include objects generated during the matching process in the output.

postmatch.adjusted.model

Set to TRUE to adjust for additional variables in a logistic regression model on the matched cohort.

Value

A list containing relevant messages about the matching process, summaries of unmatched and matched cohort variable balance assessment, descriptive statistics, logistic regression modeling summaries, proportional difference summaries, and observed vs expected summaries.

Examples


# Match within date range and without replacement using a standard caliper
ps <- propensityScoreAnalysis(data=data.ps,
                              outcomes=c("Mortality30D", "Readmission", "RepeatProc"),
                              exposure="DeviceB",
                              covariates.factor=c("Aspirin", "ChrLungDis", "Diabetes",
                                                  "Dialysis", "Emergent", "Female",
                                                  "Hypertension", "NSTEMIatPresent",
                                                  "PeripheralArterialDis", "PriorPCI",
                                                  "Smoker", "Warfarin"),
                              covariates.continuous=c("Age", "BMI", "CreatPreProc_mgdl",
                                                      "FluoroMins", "NumVesselsIndexPCI",
                                                      "TotalAdmitPCI"),
                              identifier="Patient",
                              datefield="ProcDate",
                              match.date.range=TRUE,
                              m.replace=FALSE,
                              m.caliper=0.2)

Package {DeltaTools}

DeltaTools Function: PLCAnalysis

Description

Usage

Arguments

Value

Examples

Parametric Learning Curve Analysis example data

Description

Usage

Format

data.plc

Propensity Score Analysis and Inverse Probability of Treatment Weighting example data

Description

Usage

Format

data.ps

DeltaTools Function: iptwDT

Description

Usage

Arguments

Value

Examples

DeltaTools Function: propensityScoreAnalysis

Description

Usage

Arguments

Value

Examples

`data.plc`

`data.ps`