Help for package maicplus

Title:

Matching Adjusted Indirect Comparison

Version:

0.1.2

Description:

Facilitates performing matching adjusted indirect comparison (MAIC) analysis where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response). The method is described by Signorovitch et al (2012) <doi:10.1016/j.jval.2012.05.004>.

License:

Apache License 2.0

URL:

https://github.com/hta-pharma/maicplus/, https://hta-pharma.github.io/maicplus/

BugReports:

https://github.com/hta-pharma/maicplus/issues

Depends:

R (≥ 4.1)

Imports:

graphics, grDevices, stats, survival, lubridate, matrixStats, MASS, boot, stringr, lmtest, sandwich

Suggests:

knitr, testthat (≥ 2.0), ggplot2, rmarkdown, dplyr, survminer, flexsurv, tibble, vdiffr, checkmate

VignetteBuilder:

knitr

Encoding:

UTF-8

Language:

en-US

LazyData:

true

RoxygenNote:

7.3.2

Config/testthat/edition:

NeedsCompilation:

Packaged:

2025-02-21 14:57:33 UTC; root

Author:

Gregory Chen [aut], Michael Seo [aut], Isaac Gravestock [aut, cre], Miranta Antoniou [ctb], Chrysostomos Kalyvas [ctb], MSD, Inc. [cph, fnd], F. Hoffmann-La Roche AG [cph, fnd]

Maintainer:

Isaac Gravestock <isaac.gravestock@roche.com>

Repository:

CRAN

Date/Publication:

2025-02-21 16:00:02 UTC

maicplus: Matching Adjusted Indirect Comparison

Description

Author(s)

Maintainer: Isaac Gravestock isaac.gravestock@roche.com

Authors:

Gregory Chen xiangyi.gregory.chen@msd.com
Michael Seo michael.seo@roche.com

Other contributors:

Miranta Antoniou miranta.antoniou@roche.com [contributor]
Chrysostomos Kalyvas chrysostomos.kal@gmail.com [contributor]
MSD, Inc. [copyright holder, funder]
F. Hoffmann-La Roche AG [copyright holder, funder]

Binary outcome data from single arm trial

Description

Binary outcome data from single arm trial

Usage

adrs_sat

Format

A data frame with 500 rows and 5 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm.
AVAL: Analysis value, in this dataset an indicator of response.
PARAM: Parameter type of AVAL.
RESPONSE: Indicator of response.

Binary outcome data from two arm trial

Description

Binary outcome data from two arm trial

Usage

adrs_twt

Format

A data frame with 1000 rows and 5 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm, "A", "C".
AVAL: Analysis value, in this dataset an indicator of response.
PARAM: Parameter type of AVAL.
RESPONSE: Indicator of response.

Patient data from single arm study

Description

Patient data from single arm study

Usage

adsl_sat

Format

a data frame with 500 rows and 8 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm.
AGE: Age in years at baseline.
SEX: Sex of patient recorded as character "Male"/"Female".
SMOKE: Smoking status at baseline as integer 1/0.
ECOG0: Indicator of ECOG score = 0 at baseline as integer 1/0.
N_PR_THER: Number of prior therapies received as integer ⁠1, 2, 3, 4⁠.
SEX_MALE: Indicator of SEX == "Male" as numeric 1/0.

Patient data from two arm trial

Description

Patient data from two arm trial

Usage

adsl_twt

Format

A data frame with 1000 rows and 8 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm.
AGE: Age in years at baseline.
SEX: Sex of patient recorded as character "Male"/"Female"
SMOKE: Smoking status at baseline as integer 1/0.
ECOG0: Indicator of ECOG score = 0 at baseline as integer 1/0.
N_PR_THER: Number of prior therapies received as integer ⁠1, 2, 3, 4⁠.
SEX_MALE: Indicator of SEX == "Male" as numeric 1/0

Survival data from single arm trial

Description

Survival data from single arm trial

Usage

adtte_sat

Format

A data frame with 500 rows and 10 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm, "A".
AVAL: Analysis value which in this dataset overall survival time in days.
AVALU: Unit of AVAL.
PARAMCD: Paramater code of AVAL, "OS".
PARAM: Parameter name of AVAL, ⁠"Overall Survival⁠.
CNSR: Censoring indicator 0/1.
TIME: Survival time in days.
EVENT: Event indicator 0/1.

Survival data from two arm trial

Description

Survival data from two arm trial

Usage

adtte_twt

Format

A data frame with 1000 rows and 10 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm, "A", "C".
AVAL: Analysis value which in this dataset overall survival time in days.
AVALU: Unit of AVAL.
PARAMCD: Parameter code of AVAL, "OS".
PARAM: Parameter name of AVAL, ⁠"Overall Survival⁠.
CNSR: Censoring indicator 0/1.
TIME: Survival time in days.
EVENT: Event indicator 0/1.

Aggregate effect modifier data from published study

Description

This data is formatted to be used in center_ipd().

Usage

agd

Format

A data frame with 3 rows and 9 columns:

STUDY: The study name, Study_XXXX
ARM: Study arm name or total
N: Number of observations in study arm
AGE_MEAN: Mean age in study arm
AGE_MEDIAN: Median age in study arm
AGE_SD: Standard deviation of age in study arm
SEX_MALE_COUNT: Number of male patients
ECOG0_COUNT: Number of patients with ECOG score = 0
SMOKE_COUNT: Number of smokers
N_PR_THER_MEDIAN: Median number of prior therapies

Basic Kaplan Meier (KM) plot function

Description

This function can generate a basic KM plot with or without risk set table appended at the bottom. In a single plot, it can include up to 4 KM curves. This depends on number of levels in 'treatment' column in the input data.frame kmdat

Usage

basic_kmplot(
  kmdat,
  endpoint_name = "Time to Event Endpoint",
  time_scale = NULL,
  time_grid = NULL,
  show_risk_set = TRUE,
  main_title = "Kaplan-Meier Curves",
  subplot_heights = NULL,
  suppress_plot_layout = FALSE,
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)

Arguments

kmdat

a data.frame, must consist treatment, time (unit in days), n.risk, censor, surv, similar to an output from maicplus:::survfit_makeup

endpoint_name

a string, name of time to event endpoint, to be show in the last line of title

time_scale

a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or 'days'

time_grid

a numeric vector in the unit of time_scale, risk set table and x axis of the km plot will be defined based on this time grid

show_risk_set

logical, show risk set table or not, TRUE by default

main_title

a string, main title of the KM plot

subplot_heights

a numeric vector, heights argument to graphic::layout(),NULL by default which means user will use the default setting

suppress_plot_layout

logical, suppress the layout setting in this function so that user can specify layout outside of the function, FALSE by default

use_colors

a character vector of length up to 4, colors to the KM curves, it will be passed to col of lines()

use_line_types

a numeric vector of length up to 4, line type to the KM curves, it will be passed to lty of lines()

use_pch_cex

a scalar between 0 and 1, point size to indicate censored individuals on the KM curves, it will be passed to cex of points()

use_pch_alpha

a scalar between 0 and 255, degree of color transparency of points to indicate censored individuals on the KM curves, it will be passed to cex of points()

Value

a KM plot with or without risk set table appended at the bottom, with up to 4 KM curves

Examples

library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)

combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")
kmdat <- do.call(rbind, survfit_makeup(kmobj))
kmdat$treatment <- factor(kmdat$treatment)

# without risk set table
basic_kmplot(kmdat,
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  show_risk_set = FALSE,
  main_title = "Kaplan-Meier Curves",
  subplot_heights = NULL,
  suppress_plot_layout = FALSE,
  use_colors = NULL,
  use_line_types = NULL
)

# with risk set table
basic_kmplot(kmdat,
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  show_risk_set = TRUE,
  main_title = "Kaplan-Meier Curves",
  subplot_heights = NULL,
  suppress_plot_layout = FALSE,
  use_colors = NULL,
  use_line_types = NULL
)

Basic Kaplan Meier (KM) plot function using ggplot

Description

This function generates a basic KM plot using ggplot.

Usage

basic_kmplot2(
  kmlist,
  kmlist_name,
  endpoint_name = "Time to Event Endpoint",
  show_risk_set = TRUE,
  main_title = "Kaplan-Meier Curves",
  break_x_by = NULL,
  censor = TRUE,
  xlab = "Time",
  xlim = NULL,
  use_colors = NULL,
  use_line_types = NULL
)

Arguments

kmlist

a list of survfit object

kmlist_name

a vector indicating the treatment names of each survfit object

endpoint_name

a string, name of time to event endpoint, to be show in the last line of title

show_risk_set

logical, show risk set table or not, TRUE by default

main_title

a string, main title of the KM plot

break_x_by

bin parameter for survminer

censor

indicator to include censor information

xlab

label name for x-axis of the plot

xlim

x limit for the x-axis of the plot

use_colors

a character vector of length up to 4, colors to the KM curves, it will be passed to 'col' of lines()

use_line_types

a numeric vector of length up to 4, line type to the KM curves, it will be passed to lty of lines()

Value

A Kaplan-Meier plot object created with survminer::ggsurvplot().

Examples

library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)

kmobj_A <- survfit(Surv(TIME, EVENT) ~ ARM,
  data = adtte_sat,
  conf.type = "log-log"
)

kmobj_B <- survfit(Surv(TIME, EVENT) ~ ARM,
  data = pseudo_ipd_sat,
  conf.type = "log-log"
)

kmlist <- list(kmobj_A = kmobj_A, kmobj_B = kmobj_B)
kmlist_name <- c("A", "B")

basic_kmplot2(kmlist, kmlist_name)

Bucher method for combining treatment effects

Description

Given two treatment effects of A vs. C and B vs. C derive the treatment effects of A vs. B using the Bucher method. Two-sided confidence interval and Z-test p-value are also calculated. Treatment effects and standard errors should be in log scale for hazard ratio, odds ratio, and risk ratio. Treatment effects and standard errors should be in natural scale for risk difference and mean difference.

Usage

bucher(trt, com, conf_lv = 0.95)

## S3 method for class 'maicplus_bucher'
print(x, ci_digits = 2, pval_digits = 3, exponentiate = FALSE, ...)

Arguments

trt

a list of two scalars for the study with the experimental arm. 'est' is the point estimate and 'se' is the standard error of the treatment effect. For time-to-event data, 'est' and 'se' should be point estimate and standard error of the log hazard ratio. For binary data, 'est' and 'se' should be point estimate and standard error of the log odds ratio, log risk ratio, or risk difference. For continuous data, 'est' and 'se' should be point estimate and standard error of the mean difference.

com

same as trt, but for the study with the control arm

conf_lv

a numerical scalar, prescribe confidence level to derive two-sided confidence interval for the treatment effect

x

maicplus_bucher object

ci_digits

an integer, number of decimal places for point estimate and derived confidence limits

pval_digits

an integer, number of decimal places to display Z-test p-value

exponentiate

whether the treatment effect and confidence interval should be exponentiated. This applies to relative treatment effects. Default is set to false.

...

not used

Value

a list with 5 elements,

est: a scalar, point estimate of the treatment effect
se: a scalar, standard error of the treatment effect
ci_l: a scalar, lower confidence limit of a two-sided CI with prescribed nominal level by conf_lv
ci_u: a scalar, upper confidence limit of a two-sided CI with prescribed nominal level by conf_lv
pval: p-value of Z-test, with null hypothesis that est is zero

Methods (by generic)

print(maicplus_bucher): Print method for maicplus_bucher objects

Examples

trt <- list(est = log(1.1), se = 0.2)
com <- list(est = log(1.3), se = 0.18)
result <- bucher(trt, com, conf_lv = 0.9)
print(result, ci_digits = 3, pval_digits = 3)

Calculate Statistics for Weight Plot Legend

Description

Calculates ESS reduction and median weights which is used to create legend for weights plot

Usage

calculate_weights_legend(weighted_data)

Arguments

weighted_data

object returned after calculating weights using estimate_weights

Value

list of ESS, ESS reduction, median value of scaled and unscaled weights, and missing count

Examples

data("weighted_sat")
calculate_weights_legend(weighted_sat)

Center individual patient data (IPD) variables using aggregate data averages

Description

This function subtracts IPD variables (prognostic variables and/or effect modifiers) by the aggregate data averages. This centering is needed in order to calculate weights. IPD and aggregate data variable names should match.

Usage

center_ipd(ipd, agd)

Arguments

ipd

IPD variable names should match the aggregate data names without the suffix. This would involve either changing the aggregate data name or the ipd name. For instance, if we binarize SEX variable with MALE as a reference using dummize_ipd, function names the new variable as SEX_MALE. In this case, SEX_MALE should also be available in the aggregate data.

agd

pre-processed aggregate data which contain STUDY, ARM, and N. Variable names should be followed by legal suffixes (i.e. MEAN, MEDIAN, SD, or PROP). Note that COUNT suffix is no longer accepted.

Value

centered ipd using aggregate level data averages

Examples

data(adsl_sat)
data(agd)
agd <- process_agd(agd)
ipd_centered <- center_ipd(ipd = adsl_sat, agd = agd)

Centered patient data from single arm trial

Description

Centered patient data from single arm trial

Usage

centered_ipd_sat

Format

A data frame with 500 rows and 14 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm.
AGE: Age in years at baseline.
SEX: Sex of patient recorded as character "Male"/"Female".
SMOKE: Smoking status at baseline as integer 1/0.
ECOG0: Indicator of ECOG score = 0 at baseline as integer 1/0.
N_PR_THER: Number of prior therapies received as integer ⁠1, 2, 3, 4⁠.
SEX_MALE: Indicator of SEX == "Male" as numeric 1/0.
AGE_CENTERED: Age in years at baseline relative to average in aggregate data agd.
AGE_MEDIAN_CENTERED: AGE greater/less than MEDIAN_AGE in agd coded as 1/0 and then centered at 0.5.
AGE_SQUARED_CENTERED: AGE squared and centered with respect to the AGE in agd. The squared age in the aggregate data is derived from the E(X^2) term in the variance formula.
SEX_MALE_CENTERED: SEX_MALE centered by the proportion of male patients in agd
ECOG0_CENTERED: ECOG0 centered by the proportion of ECOG0 in agd
SMOKE_CENTERED: SMOKE centered by the proportion of SMOKE in agd
N_PR_THER_MEDIAN_CENTERED: N_PR_THER centered by the median in agd.

Centered patient data from two arm trial

Description

Centered patient data from two arm trial

Usage

centered_ipd_twt

Format

A data frame with 1000 rows and 14 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm.
AGE: Age in years at baseline.
SEX: Sex of patient recorded as character "Male"/"Female".
SMOKE: Smoking status at baseline as integer 1/0.
ECOG0: Indicator of ECOG score = 0 at baseline as integer 1/0.
N_PR_THER: Number of prior therapies received as integer ⁠1, 2, 3, 4⁠.
SEX_MALE: Indicator of SEX == "Male" as numeric 1/0.
AGE_CENTERED: Age in years at baseline relative to average in aggregate data agd.
AGE_MEDIAN_CENTERED: AGE greater/less than MEDIAN_AGE in agd coded as 1/0 and then centered at 0.5.
AGE_SQUARED_CENTERED: AGE squared and centered with respect to the AGE in agd. The squared age in the aggregate data is derived from the E(X^2) term in the variance formula.
SEX_MALE_CENTERED: SEX_MALE centered by the proportion of male patients in agd
ECOG0_CENTERED: ECOG0 centered by the proportion of ECOG0 in agd
SMOKE_CENTERED: SMOKE centered by the proportion of SMOKE in agd
N_PR_THER_MEDIAN_CENTERED: N_PR_THER centered by the median in agd.

Check to see if weights are optimized correctly

Description

This function checks to see if the optimization is done properly by checking the covariate averages before and after adjustment. In case of ties when calculating median, we return the mean of the two numbers. For more details, see ties parameter in matrixStats::weightedMedian.

Usage

check_weights(weighted_data, processed_agd)

## S3 method for class 'maicplus_check_weights'
print(
  x,
  mean_digits = 2,
  prop_digits = 2,
  sd_digits = 3,
  digits = getOption("digits"),
  ...
)

Arguments

weighted_data

object returned after calculating weights using estimate_weights

processed_agd

a data frame, object returned after using process_agd or aggregated data following the same naming convention

x

object from check_weights

mean_digits

number of digits for rounding mean columns in the output

prop_digits

number of digits for rounding proportion columns in the output

sd_digits

number of digits for rounding mean columns in the output

digits

minimal number of significant digits, see print.default.

...

further arguments to print.data.frame

Value

data.frame of weighted and unweighted covariate averages of the IPD, average of aggregate data, and sum of inner products of covariate x_i and the weights (exp(x_i\beta))

Methods (by generic)

print(maicplus_check_weights): Print method for check_weights objects

Examples

data(weighted_sat)
data(agd)
check_weights(weighted_sat, process_agd(agd))

Create dummy variables from categorical variables in an individual patient data (ipd)

Description

This is a convenient function to convert categorical variables into dummy binary variables. This would be especially useful if the variable has more than two factors. Note that the original variable is kept after a variable is dummized.

Usage

dummize_ipd(raw_ipd, dummize_cols, dummize_ref_level)

Arguments

raw_ipd

ipd data that contains variable to dummize

dummize_cols

vector of column names to binarize

dummize_ref_level

vector of reference level of the variables to binarize

Value

ipd with dummized columns

Examples

data(adsl_twt)
dummize_ipd(adsl_twt, dummize_cols = c("SEX"), dummize_ref_level = c("Male"))

Note on Expected Sample Size Reduction

Description

Note on Expected Sample Size Reduction

Usage

ess_footnote_text(width = 0.9 * getOption("width"))

Arguments

width

Number of characters to break string into new lines (⁠\n⁠).

Value

A character string

Derive individual weights in the matching step of MAIC

Description

Assuming data is properly processed, this function takes individual patient data (IPD) with centered covariates (effect modifiers and/or prognostic variables) as input, and generates weights for each individual in IPD trial to match the covariates in aggregate data.

The plot function displays individuals weights with key summary in top right legend that includes median weight, effective sample size (ESS), and reduction percentage (what percent ESS is reduced from the original sample size). There are two options of plotting: base R plot and ggplot. The default for base R plot is to plot unscaled and scaled separately. The default for ggplot is to plot unscaled and scaled weights on a same plot.

Usage

estimate_weights(
  data,
  centered_colnames = NULL,
  start_val = 0,
  method = "BFGS",
  n_boot_iteration = NULL,
  set_seed_boot = 1234,
  boot_strata = "ARM",
  ...
)

## S3 method for class 'maicplus_estimate_weights'
plot(
  x,
  ggplot = FALSE,
  bin_col = "#6ECEB2",
  vline_col = "#688CE8",
  main_title = NULL,
  scaled_weights = TRUE,
  bins = 50,
  ...
)

Arguments

data

a numeric matrix, centered covariates of IPD, no missing value in any cell is allowed

centered_colnames

a character or numeric vector (column indicators) of centered covariates

start_val

a scalar, the starting value for all coefficients of the propensity score regression

method

a string, name of the optimization algorithm (see 'method' argument of base::optim()) The default is "BFGS", other options are "Nelder-Mead", "CG", "L-BFGS-B", "SANN", and "Brent"

n_boot_iteration

an integer, number of bootstrap iterations. By default is NULL which means bootstrapping procedure will not be triggered, and hence the element "boot" of output list object will be NULL.

set_seed_boot

a scalar, the random seed for conducting the bootstrapping, only relevant if n_boot_iteration is not NULL. By default, use seed 1234

boot_strata

a character vector of column names in data that defines the strata for bootstrapping. This ensures that samples are drawn proportionally from each defined stratum. If NULL, no stratification during bootstrapping process. By default, it is "ARM"

...

Additional control parameters passed to stats::optim.

x

object from estimate_weights

ggplot

indicator to print base weights plot or ggplot weights plot

bin_col

a string, color for the bins of histogram

vline_col

a string, color for the vertical line in the histogram

main_title

title of the plot. For ggplot, name of scaled weights plot and unscaled weights plot, respectively.

scaled_weights

(base plot only) an indicator for using scaled weights instead of regular weights

bins

(ggplot only) number of bin parameter to use

Value

a list with the following 4 elements,

data: a data.frame, includes the input data with appended column 'weights' and 'scaled_weights'. Scaled weights has a summation to be the number of rows in data that has no missing value in any of the effect modifiers
centered_colnames: column names of centered effect modifiers in data
nr_missing: number of rows in data that has at least 1 missing value in specified centered effect modifiers
ess: effective sample size, square of sum divided by sum of squares
opt: R object returned by base::optim(), for assess convergence and other details
boot_strata: 'strata' from a boot::boot object
boot_seed: column names in data of the stratification factors
boot: a n by 2 by k array or NA, where n equals to number of rows in data, and k equals n_boot_iteration. The 2 columns in the second dimension include a column of numeric indexes of the rows in data that are selected at a bootstrapping iteration and a column of weights. boot is NA when argument n_boot_iteration is set as NULL

Methods (by generic)

plot(maicplus_estimate_weights): Plot method for estimate_weights objects

Examples

data(centered_ipd_sat)
centered_colnames <- grep("_CENTERED", colnames(centered_ipd_sat), value = TRUE)
weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames)

# To later estimate bootstrap confidence intervals, we calculate the weights
# for the bootstrap samples:
weighted_data_boot <- estimate_weights(
  data = centered_ipd_sat, centered_colnames = centered_colnames, n_boot_iteration = 100
)

plot(weighted_sat)

if (requireNamespace("ggplot2")) {
  plot(weighted_sat, ggplot = TRUE)
}

helper function: transform TTE ADaM data to suitable input for survival R package

Description

helper function: transform TTE ADaM data to suitable input for survival R package

Usage

ext_tte_transfer(dd, time_scale = "months", trt = NULL)

Arguments

dd

data frame, ADTTE read via haven::read_sas

time_scale

a character string, 'years', 'months', 'weeks' or 'days', time unit of median survival time

trt

values to include in treatment column

Value

a data frame that can be used as input to survival::Surv

Calculate standard error from the reported confidence interval.

Description

Comparator studies often only report confidence interval of the treatment effects. This function calculates standard error of the treatment effect given the reported confidence interval. For relative treatment effect (i.e. hazard ratio, odds ratio, and risk ratio), the function would log the confidence interval. For risk difference and mean difference, we do not log the confidence interval. The option to log the confidence interval is controlled by 'log' parameter.

Usage

find_SE_from_CI(CI_lower = NULL, CI_upper = NULL, CI_perc = 0.95, log = TRUE)

Arguments

CI_lower

Reported lower percentile value of the treatment effect

CI_upper

Reported upper percentile value of the treatment effect

CI_perc

Percentage of confidence interval reported

log

Whether the confidence interval should be logged. For relative treatment effect, log should be applied because estimated log treatment effect is approximately normally distributed.

Value

Standard error of log relative treatment effect if 'log' is true and standard error of the treatment effect if 'log' is false

Examples

find_SE_from_CI(CI_lower = 0.55, CI_upper = 0.90, CI_perc = 0.95)

Create pseudo IPD given aggregated binary data

Description

Create pseudo IPD given aggregated binary data

Usage

get_pseudo_ipd_binary(binary_agd, format = c("stacked", "unstacked"))

Arguments

binary_agd

a data.frame that take different formats depending on format

format

a string, "stacked" or "unstacked"

Value

a data.frame of pseudo binary IPD, with columns USUBJID, ARM, RESPONSE

Examples

# example of unstacked
testdat <- data.frame(Yes = 280, No = 120)
rownames(testdat) <- "B"
get_pseudo_ipd_binary(
  binary_agd = testdat,
  format = "unstacked"
)

# example of stacked
get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = rep("B", 2),
    RESPONSE = c("YES", "NO"),
    COUNT = c(280, 120)
  ),
  format = "stacked"
)

Convert Time Values Using Scaling Factors

Description

Convert Time Values Using Scaling Factors

Usage

get_time_as(times, as = NULL)

Arguments

times

Numeric time values

as

A time scale to convert to. One of "days", "weeks", "months", "years"

Value

Returns a numeric vector calculated from times / get_time_conversion(factor = as)

Examples

get_time_as(50, as = "months")

Helper function to summarize outputs from glm fit

Description

Helper function to summarize outputs from glm fit

Usage

glm_makeup(binobj, legend = "before matching", weighted = FALSE)

Arguments

binobj

returned object from stats::glm

legend

label to indicate the binary fit

weighted

logical flag indicating whether weights have been applied in the glm fit

Value

A data.frame containing a summary of the number of events and subjects in a logistic regression model.

Examples

data(adrs_sat)
pseudo_adrs <- get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = rep("B", 2),
    RESPONSE = c("YES", "NO"),
    COUNT = c(280, 120)
  ),
  format = "stacked"
)
pseudo_adrs$RESPONSE <- as.numeric(pseudo_adrs$RESPONSE)
combined_data <- rbind(adrs_sat[, c("USUBJID", "ARM", "RESPONSE")], pseudo_adrs)
combined_data$ARM <- as.factor(combined_data$ARM)
binobj_dat <- stats::glm(RESPONSE ~ ARM, combined_data, family = binomial("logit"))
glm_makeup(binobj_dat)

Kaplan Meier (KM) plot function for anchored and unanchored cases

Description

It is wrapper function of basic_kmplot. The argument setting is similar to maic_anchored and maic_unanchored, and it is used in those two functions.

Usage

kmplot(
  weights_object,
  tte_ipd,
  tte_pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common = NULL,
  normalize_weights = FALSE,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  km_conf_type = "log-log",
  km_layout = c("all", "by_trial", "by_arm"),
  ...
)

Arguments

weights_object

an object returned by estimate_weight

tte_ipd

a data frame of individual patient data (IPD) of internal trial, contain at least "USUBJID", "EVENT", "TIME" columns and a column indicating treatment assignment

tte_pseudo_ipd

a data frame of pseudo IPD by digitized KM curves of external trial (for time-to-event endpoint), contain at least "EVENT", "TIME"

trt_ipd

a string, name of the interested investigation arm in internal trial dat_igd (real IPD)

trt_agd

a string, name of the interested investigation arm in external trial dat_pseudo (pseudo IPD)

trt_common

a string, name of the common comparator in internal and external trial, by default is NULL, indicating unanchored case

normalize_weights

logical, default is FALSE. If TRUE, scaled_weights (normalized weights) in weights_object$data will be used.

trt_var_ipd

a string, column name in tte_ipd that contains the treatment assignment

trt_var_agd

a string, column name in tte_pseudo_ipd that contains the treatment assignment

km_conf_type

a string, pass to conf.type of survfit

km_layout

a string, only applicable for unanchored case (trt_common = NULL), indicated the desired layout of output KM curve.

...

other arguments in basic_kmplot

Value

In unanchored case, a KM plot with risk set table. In anchored case, depending on km_layout,

if "by_trial", 2 by 1 plot, first all KM curves (incl. weighted) in IPD trial, and then KM curves in AgD trial, with risk set table.
if "by_arm", 2 by 1 plot, first KM curves of trt_agd and trt_ipd (with and without weights), and then KM curves of trt_common in AgD trial and IPD trial (with and without weights). Risk set table is appended.
if "all", 2 by 2 plot, all plots in "by_trial" and "by_arm" without risk set table appended.

Examples

# unanchored example using kmplot
data(weighted_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

kmplot(
  weights_object = weighted_sat,
  tte_ipd = adtte_sat,
  tte_pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = NULL,
  km_conf_type = "log-log",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)
# anchored example using kmplot
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

# plot by trial
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_trial",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)

# plot by arm
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_arm",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)

# plot all
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "all",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)

Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot

Description

This is wrapper function of basic_kmplot2. The argument setting is similar to maic_anchored and maic_unanchored, and it is used in those two functions.

Usage

kmplot2(
  weights_object,
  tte_ipd,
  tte_pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common = NULL,
  normalize_weights = FALSE,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  km_conf_type = "log-log",
  km_layout = c("all", "by_trial", "by_arm"),
  time_scale,
  ...
)

Arguments

weights_object

an object returned by estimate_weight

tte_ipd

a data frame of individual patient data (IPD) of internal trial, contain at least "USUBJID", "EVENT", "TIME" columns and a column indicating treatment assignment

tte_pseudo_ipd

a data frame of pseudo IPD by digitized KM curves of external trial (for time-to-event endpoint), contain at least "EVENT", "TIME"

trt_ipd

a string, name of the interested investigation arm in internal trial dat_igd (real IPD)

trt_agd

a string, name of the interested investigation arm in external trial dat_pseudo (pseudo IPD)

trt_common

a string, name of the common comparator in internal and external trial, by default is NULL, indicating unanchored case

normalize_weights

logical, default is FALSE. If TRUE, scaled_weights (normalized weights) in weights_object$data will be used.

trt_var_ipd

a string, column name in tte_ipd that contains the treatment assignment

trt_var_agd

a string, column name in tte_pseudo_ipd that contains the treatment assignment

km_conf_type

a string, pass to conf.type of survfit

km_layout

a string, only applicable for unanchored case (trt_common = NULL), indicated the desired layout of output KM curve.

time_scale

a string, time unit of median survival time, taking a value of 'years', 'months', weeks' or 'days'

...

other arguments in basic_kmplot2

Value

In unanchored case, a KM plot with risk set table. In anchored case, depending on km_layout,

if "by_trial", 2 by 1 plot, first all KM curves (incl. weighted) in IPD trial, and then KM curves in AgD trial, with risk set table.
if "by_arm", 2 by 1 plot, first KM curves of trt_agd and trt_ipd (with and without weights), and then KM curves of trt_common in AgD trial and IPD trial (with and without weights). Risk set table is appended.
if "all", 2 by 2 plot, all plots in "by_trial" and "by_arm" without risk set table appended.

Examples

# unanchored example using kmplot2
data(weighted_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

kmplot2(
  weights_object = weighted_sat,
  tte_ipd = adtte_sat,
  tte_pseudo_ipd = pseudo_ipd_sat,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = NULL,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  time_scale = "month",
  break_x_by = 2,
  xlim = c(0, 20)
)
# anchored example using kmplot2
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

# plot by trial
kmplot2(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_trial",
  time_scale = "month",
  break_x_by = 2
)

# plot by arm
kmplot2(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_arm",
  time_scale = "month",
  break_x_by = 2
)

# plot all
kmplot2(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "all",
  time_scale = "month",
  break_x_by = 2,
  xlim = c(0, 20),
  show_risk_set = FALSE
)

Anchored MAIC for binary and time-to-event endpoint

Description

This is a wrapper function to provide adjusted effect estimates and relevant statistics in anchored case (i.e. there is a common comparator arm in the internal and external trial).

Usage

maic_anchored(
  weights_object,
  ipd,
  pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  normalize_weights = FALSE,
  endpoint_type = "tte",
  endpoint_name = "Time to Event Endpoint",
  eff_measure = c("HR", "OR", "RR", "RD"),
  boot_ci_type = c("norm", "basic", "stud", "perc", "bca"),
  time_scale = "months",
  km_conf_type = "log-log",
  binary_robust_cov_type = "HC3"
)

Arguments

weights_object

an object returned by estimate_weight

ipd

a data frame that meet format requirements in 'Details', individual patient data (IPD) of internal trial

pseudo_ipd

a data frame, pseudo IPD from digitized KM curve of external trial (for time-to-event endpoint) or from contingency table (for binary endpoint)

trt_ipd

a string, name of the interested investigation arm in internal trial ipd (internal IPD)

trt_agd

a string, name of the interested investigation arm in external trial pseudo_ipd (pseudo IPD)

trt_common

a string, name of the common comparator in internal and external trial

trt_var_ipd

a string, column name in ipd that contains the treatment assignment

trt_var_agd

a string, column name in ipd that contains the treatment assignment

normalize_weights

logical, default is FALSE. If TRUE, scaled_weights (normalized weights) in weights_object$data will be used.

endpoint_type

a string, one out of the following "binary", "tte" (time to event)

endpoint_name

a string, name of time to event endpoint, to be show in the last line of title

eff_measure

a string, "RD" (risk difference), "OR" (odds ratio), "RR" (relative risk) for a binary endpoint; "HR" for a time-to-event endpoint. By default is NULL, "OR" is used for binary case, otherwise "HR" is used.

boot_ci_type

a string, one of c("norm","basic", "stud", "perc", "bca") to select the type of bootstrap confidence interval. See boot::boot.ci for more details.

time_scale

a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or 'days'. NOTE: it is assumed that values in TIME column of ipd and pseudo_ipd is in the unit of days

km_conf_type

a string, pass to conf.type of survfit

binary_robust_cov_type

a string to pass to argument type of sandwich::vcovHC, see possible options in the documentation of that function. Default is "HC3"

Details

It is required that input ipd and pseudo_ipd to have the following columns. This function is not sensitive to upper or lower case of letters in column names.

USUBJID - character, unique subject ID
ARM - character or factor, treatment indicator, column name does not have to be 'ARM'. User specify in trt_var_ipd and trt_var_agd

For time-to-event analysis, the follow columns are required:

EVENT - numeric, 1 for censored/death, 0 otherwise
TIME - numeric column, observation time of the EVENT; unit in days

For binary outcomes:

RESPONSE - numeric, 1 for event occurred, 0 otherwise

Value

A list, contains 'descriptive' and 'inferential'

Examples

# Anchored example using maic_anchored for time-to-event data
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

result_tte <- maic_anchored(
  weights_object = weighted_twt,
  ipd = adtte_twt,
  pseudo_ipd = pseudo_ipd_twt,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  endpoint_name = "Overall Survival",
  endpoint_type = "tte",
  eff_measure = "HR",
  time_scale = "month",
  km_conf_type = "log-log",
)
result_tte$descriptive$summary
result_tte$inferential$summary
# Anchored example using maic_anchored for binary outcome
data(weighted_twt)
data(adrs_twt)

# Reported summary data
pseudo_adrs <- get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = c("B", "C", "B", "C"),
    RESPONSE = c("YES", "YES", "NO", "NO"),
    COUNT = c(280, 120, 200, 200)
  ),
  format = "stacked"
)

# inferential result
result_binary <- maic_anchored(
  weights_object = weighted_twt,
  ipd = adrs_twt,
  pseudo_ipd = pseudo_adrs,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  endpoint_name = "Binary Event",
  endpoint_type = "binary",
  eff_measure = "OR"
)

result_binary$descriptive$summary
result_binary$inferential$summary

Unanchored MAIC for binary and time-to-event endpoint

Description

This is a wrapper function to provide adjusted effect estimates and relevant statistics in unanchored case (i.e. there is no common comparator arm in the internal and external trial).

Usage

maic_unanchored(
  weights_object,
  ipd,
  pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  normalize_weights = FALSE,
  endpoint_type = "tte",
  endpoint_name = "Time to Event Endpoint",
  eff_measure = c("HR", "OR", "RR", "RD"),
  boot_ci_type = c("norm", "basic", "stud", "perc", "bca"),
  time_scale = "months",
  km_conf_type = "log-log",
  binary_robust_cov_type = "HC3"
)

Arguments

weights_object

an object returned by estimate_weight

ipd

a data frame that meet format requirements in 'Details', individual patient data (IPD) of internal trial

pseudo_ipd

a data frame, pseudo IPD from digitized KM curve of external trial (for time-to-event endpoint) or from contingency table (for binary endpoint)

trt_ipd

a string, name of the interested investigation arm in internal trial dat_igd (real IPD)

trt_agd

a string, name of the interested investigation arm in external trial pseudo_ipd (pseudo IPD)

trt_var_ipd

a string, column name in ipd that contains the treatment assignment

trt_var_agd

a string, column name in ipd that contains the treatment assignment

normalize_weights

logical, default is FALSE. If TRUE, scaled_weights (normalized weights) in weights_object$data will be used.

endpoint_type

a string, one out of the following "binary", "tte" (time to event)

endpoint_name

a string, name of time to event endpoint, to be show in the last line of title

eff_measure

boot_ci_type

a string, one of c("norm","basic", "stud", "perc", "bca") to select the type of bootstrap confidence interval. See boot::boot.ci for more details.

time_scale

a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or 'days'. NOTE: it is assumed that values in TIME column of ipd and pseudo_ipd is in the unit of days

km_conf_type

a string, pass to conf.type of survfit

binary_robust_cov_type

a string to pass to argument type of sandwich::vcovHC, see possible options in the documentation of that function. Default is "HC3"

Details

For time-to-event analysis, it is required that input ipd and pseudo_ipd to have the following columns. This function is not sensitive to upper or lower case of letters in column names.

USUBJID - character, unique subject ID
ARM - character or factor, treatment indicator, column name does not have to be 'ARM'. User specify in trt_var_ipd and trt_var_agd
EVENT - numeric, 1 for censored/death, 0 for otherwise
TIME - numeric column, observation time of the EVENT; unit in days

Value

A list, contains 'descriptive' and 'inferential'

Examples

#
# unanchored example using maic_unanchored for time-to-event data
#
data(centered_ipd_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

#### derive weights
weighted_data <- estimate_weights(
  data = centered_ipd_sat,
  centered_colnames = grep("_CENTERED$", names(centered_ipd_sat)),
  start_val = 0,
  method = "BFGS"
)

weighted_data2 <- estimate_weights(
  data = centered_ipd_sat,
  centered_colnames = grep("_CENTERED$", names(centered_ipd_sat)),
  start_val = 0,
  method = "BFGS",
  n_boot_iteration = 100,
  set_seed_boot = 1234
)

# inferential result
result <- maic_unanchored(
  weights_object = weighted_data,
  ipd = adtte_sat,
  pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  endpoint_name = "Overall Survival",
  endpoint_type = "tte",
  eff_measure = "HR",
  time_scale = "month",
  km_conf_type = "log-log"
)
result$descriptive$summary
result$inferential$summary

result_boot <- maic_unanchored(
  weights_object = weighted_data2,
  ipd = adtte_sat,
  pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  endpoint_name = "Overall Survival",
  endpoint_type = "tte",
  eff_measure = "HR",
  time_scale = "month",
  km_conf_type = "log-log"
)
result$descriptive$summary
result$inferential$summary
#
# unanchored example using maic_unanchored for binary outcome
#

data(centered_ipd_sat)
data(adrs_sat)

centered_ipd_sat
centered_colnames <- grep("_CENTERED$", colnames(centered_ipd_sat), value = TRUE)
weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames)
weighted_data2 <- estimate_weights(
  data = centered_ipd_sat, centered_colnames = centered_colnames,
  n_boot_iteration = 100
)

# get dummy binary IPD
pseudo_adrs <- get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = rep("B", 2),
    RESPONSE = c("YES", "NO"),
    COUNT = c(280, 120)
  ),
  format = "stacked"
)

# unanchored binary MAIC, with CI based on sandwich estimator
maic_unanchored(
  weights_object = weighted_data,
  ipd = adrs_sat,
  pseudo_ipd = pseudo_adrs,
  trt_ipd = "A",
  trt_agd = "B",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_type = "binary",
  endpoint_name = "Binary Endpoint",
  eff_measure = "RR",
  # binary specific args
  binary_robust_cov_type = "HC3"
)

# unanchored binary MAIC, with bootstrapped CI
maic_unanchored(
  weights_object = weighted_data2,
  ipd = adrs_sat,
  pseudo_ipd = pseudo_adrs,
  trt_ipd = "A",
  trt_agd = "B",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_type = "binary",
  endpoint_name = "Binary Endpoint",
  eff_measure = "RR",
  # binary specific args
  binary_robust_cov_type = "HC3"
)

#---------------------------------

Helper function to retrieve median survival time from a `survival::survfit` object

Description

Extract and display median survival time with confidence interval

Usage

medSurv_makeup(km_fit, legend = "before matching", time_scale)

Arguments

km_fit

returned object from survival::survfit

legend

a character string, name used in 'type' column in returned data frame

time_scale

a character string, 'years', 'months', 'weeks' or 'days', time unit of median survival time

Value

a data frame with a index column 'type', median survival time and confidence interval

Examples

data(adtte_sat)
data(pseudo_ipd_sat)
library(survival)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")

# Derive median survival time
medSurv <- medSurv_makeup(kmobj, legend = "before matching", time_scale = "day")
medSurv

Diagnosis plot of proportional hazard assumption for anchored and unanchored

Description

Diagnosis plot of proportional hazard assumption for anchored and unanchored

Usage

ph_diagplot(
  weights_object,
  tte_ipd,
  tte_pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common = NULL,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Time to Event Endpoint",
  time_scale,
  zph_transform = "log",
  zph_log_hazard = TRUE
)

Arguments

weights_object

an object returned by estimate_weight

tte_ipd

a data frame of individual patient data (IPD) of internal trial, contain at least "USUBJID", "EVENT", "TIME" columns and a column indicating treatment assignment

tte_pseudo_ipd

a data frame of pseudo IPD by digitized KM curves of external trial (for time-to-event endpoint), contain at least "EVENT", "TIME"

trt_ipd

a string, name of the interested investigation arm in internal trial tte_ipd (real IPD)

trt_agd

a string, name of the interested investigation arm in external trial tte_pseudo_ipd (pseudo IPD)

trt_common

a string, name of the common comparator in internal and external trial, by default is NULL, indicating unanchored case

trt_var_ipd

a string, column name in tte_ipd that contains the treatment assignment

trt_var_agd

a string, column name in tte_pseudo_ipd that contains the treatment assignment

endpoint_name

a string, name of time to event endpoint, to be show in the last line of title

time_scale

a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or 'days'

zph_transform

a string, pass to survival::cox.zph, default is "log"

zph_log_hazard

a logical, if TRUE (default), y axis of the time dependent hazard function is log-hazard, otherwise, hazard.

Value

a 3 by 2 plot, include log-cumulative hazard plot, time dependent hazard function and unscaled Schoenfeld residual plot, before and after matching

Examples

# unanchored example using ph_diagplot
data(weighted_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

ph_diagplot(
  weights_object = weighted_sat,
  tte_ipd = adtte_sat,
  tte_pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = NULL,
  endpoint_name = "Overall Survival",
  time_scale = "week",
  zph_transform = "log",
  zph_log_hazard = TRUE
)
# anchored example using ph_diagplot
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

ph_diagplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  endpoint_name = "Overall Survival",
  time_scale = "week",
  zph_transform = "log",
  zph_log_hazard = TRUE
)

PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time

Description

This plot is also known as log negative log survival rate.

Usage

ph_diagplot_lch(
  km_fit,
  time_scale,
  log_time = TRUE,
  endpoint_name = "",
  subtitle = "",
  exclude_censor = TRUE
)

Arguments

km_fit

returned object from survival::survfit

time_scale

a character string, 'years', 'months', 'weeks' or 'days', time unit of median survival time

log_time

logical, TRUE (default) or FALSE

endpoint_name

a character string, name of the endpoint

subtitle

a character string, subtitle of the plot

exclude_censor

logical, should censored data point be plotted

Details

a diagnosis plot for proportional hazard assumption, versus log-time (default) or time

Value

a plot of log cumulative hazard rate

Examples

library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")
ph_diagplot_lch(kmobj,
  time_scale = "month", log_time = TRUE,
  endpoint_name = "OS", subtitle = "(Before Matching)"
)

PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit

Description

PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit

Usage

ph_diagplot_schoenfeld(
  coxobj,
  time_scale = "months",
  log_time = TRUE,
  endpoint_name = "",
  subtitle = ""
)

Arguments

coxobj

object returned from coxph

time_scale

a character string, 'years', 'months', 'weeks' or 'days', time unit of median survival time

log_time

logical, TRUE (default) or FALSE

endpoint_name

a character string, name of the endpoint

subtitle

a character string, subtitle of the plot

Value

a plot of Schoenfeld residuals

Examples

library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data)
ph_diagplot_schoenfeld(unweighted_cox,
  time_scale = "month", log_time = TRUE,
  endpoint_name = "OS", subtitle = "(Before Matching)"
)

Plot MAIC weights in a histogram with key statistics in legend

Description

Generates a base R histogram of weights. Default is to plot either unscaled or scaled weights and not both.

Usage

plot_weights_base(
  weighted_data,
  bin_col,
  vline_col,
  main_title,
  scaled_weights
)

Arguments

weighted_data

object returned after calculating weights using estimate_weights

bin_col

a string, color for the bins of histogram

vline_col

a string, color for the vertical line in the histogram

main_title

title of the plot

scaled_weights

an indicator for using scaled weights instead of regular weights

Value

a plot of unscaled or scaled weights

Examples

plot_weights_base(weighted_sat,
  bin_col = "#6ECEB2",
  vline_col = "#688CE8",
  main_title = c("Scaled Individual Weights", "Unscaled Individual Weights"),
  scaled_weights = TRUE
)

Plot MAIC weights in a histogram with key statistics in legend using `ggplot2`

Description

Generates a ggplot histogram of weights. Default is to plot both unscaled and scaled weights on a same graph.

Usage

plot_weights_ggplot(weighted_data, bin_col, vline_col, main_title, bins)

Arguments

weighted_data

object returned after calculating weights using estimate_weights

bin_col

a string, color for the bins of histogram

vline_col

a string, color for the vertical line in the histogram

main_title

Name of scaled weights plot and unscaled weights plot, respectively.

bins

number of bin parameter to use

Value

a plot of unscaled and scaled weights

Examples

if (requireNamespace("ggplot2")) {
  plot_weights_ggplot(weighted_sat,
    bin_col = "#6ECEB2",
    vline_col = "#688CE8",
    main_title = c("Scaled Individual Weights", "Unscaled Individual Weights"),
    bins = 50
  )
}

Pre-process aggregate data

Description

This function checks the format of the aggregate data. Data is required to have three columns: STUDY, ARM, and N. Column names that do not have legal suffixes (MEAN, MEDIAN, SD, COUNT, or PROP) are dropped. If a variable is a count variable, it is converted to proportions by dividing the sample size (N). Note, when the count is specified, proportion is always calculated based on the count, that is, specified proportion will be ignored if applicable. If the aggregated data comes from multiple sources (i.e. different analysis population) and sample size differs for each variable, one option is to specify proportion directly instead of count by using suffix _PROP.

Usage

process_agd(raw_agd)

Arguments

raw_agd

raw aggregate data should contain STUDY, ARM, and N. Variable names should be followed by legal suffixes (i.e. MEAN, MEDIAN, SD, COUNT, or PROP).

Value

pre-processed aggregate level data

Examples

data(agd)
agd <- process_agd(agd)

Pseudo individual patient survival data from published study

Description

Pseudo individual patient survival data from published study

Usage

pseudo_ipd_sat

Format

A data frame with 300 rows and 3 columns:

TIME: Survival time in days.
EVENT: Event indicator 0/1.
ARM: Assigned treatment arm, "B".

Pseudo individual patient survival data from published two arm study

Description

Pseudo individual patient survival data from published two arm study

Usage

pseudo_ipd_twt

Format

A data frame with 800 rows and 3 columns:

TIME: Survival time in days.
EVENT: Event indicator 0/1.
ARM: Assigned treatment arm, "B", "C".

Reformat `maicplus_bucher` alike object

Description

Reformat maicplus_bucher alike object

Usage

reformat(
  x,
  ci_digits = 2,
  pval_digits = 3,
  show_pval = TRUE,
  exponentiate = FALSE
)

Arguments

x

a list, structured like a maicplus_bucher object

ci_digits

an integer, number of decimal places for point estimate and derived confidence limits

pval_digits

an integer, number of decimal places to display Z-test p-value

show_pval

a logical value, default is TRUE. If FALSE, p-value will not be output as the second element of the character vector

exponentiate

whether the treatment effect and confidence interval should be exponentiated. This applies to relative treatment effects. Default is set to false.

Get and Set Time Conversion Factors

Description

Get and Set Time Conversion Factors

Usage

set_time_conversion(
  default = "days",
  days = 1,
  weeks = 7,
  months = 365.25/12,
  years = 365.25
)

get_time_conversion(factor = c("days", "weeks", "months", "years"))

Arguments

default

The default time scale, commonly whichever has factor = 1

days

Factor to divide data time units to get time in days

weeks

Factor to divide data time units to get time in weeks

months

Factor to divide data time units to get time in months

years

Factor to divide data time units to get time in years

factor

Time factor to get.

Value

No value returned. Conversion factors are stored internally and used within functions.

Examples

# The default time scale is days:
set_time_conversion(default = "days", days = 1, weeks = 7, months = 365.25 / 12, years = 365.25)

# Set the default time scale to years
set_time_conversion(
  default = "years",
  days = 1 / 365.25,
  weeks = 1 / 52.17857,
  months = 1 / 12,
  years = 1
)

# Get time scale factors:
get_time_conversion("years")
get_time_conversion("weeks")

Helper function to select set of variables used for Kaplan-Meier plot

Description

Helper function to select set of variables used for Kaplan-Meier plot

Usage

survfit_makeup(km_fit, single_trt_name = "treatment")

Arguments

km_fit

returned object from survival::survfit

single_trt_name

name of treatment if no strata are specified in km_fit

Value

a list of data frames of variables from survival::survfit(). Data frame is divided by treatment.

Examples

library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")
survfit_makeup(kmobj)

Weighted object for single arm trial data

Description

Weighted object for single arm trial data

Usage

weighted_sat

Format

A maicplus_estimate_weights object created by estimate_weights() containing

data: patient level data with weights
centered_colnames: Columns used in MAIC
nr_missing: Number of observations with missing data
ess: Expected sample size
opt: Information from optim from weight calculation
boot: Parameters and bootstrap sample weights, NULL in this object

Weighted object for two arm trial data

Description

The weighted patient data for a two arm trial generated from the centered patient data (centered_ipd_twt). It has weights calculated for 100 bootstrap samples.

The object is generated using the following code:

estimate_weights(
  data = centered_ipd_twt,
  centered_colnames = c(
    "AGE_CENTERED",
    "AGE_MEDIAN_CENTERED",
    "AGE_SQUARED_CENTERED",
    "SEX_MALE_CENTERED",
    "ECOG0_CENTERED",
    "SMOKE_CENTERED"
    ),
  n_boot_iteration = 100
 )

Usage

weighted_twt

Format

A maicplus_estimate_weights object created by estimate_weights() containing

data: patient level data with weights
centered_colnames: Columns used in MAIC
nr_missing: Number of observations with missing data
ess: Expected sample size
opt: Information from optim from weight calculation
boot: Parameters and bootstrap sample weights for the 100 samples

maicplus: Matching Adjusted Indirect Comparison

Description

Author(s)

See Also

Binary outcome data from single arm trial

Description

Usage

Format

See Also

Binary outcome data from two arm trial

Description

Usage

Format

See Also

Patient data from single arm study

Description

Usage

Format

See Also

Patient data from two arm trial

Description

Usage

Format

See Also

Survival data from single arm trial

Description

Usage

Format

See Also

Survival data from two arm trial

Description

Usage

Format

See Also

Aggregate effect modifier data from published study

Description

Usage

Format

See Also

Basic Kaplan Meier (KM) plot function

Description

Usage

Arguments

Value

Examples

Basic Kaplan Meier (KM) plot function using ggplot

Description

Usage

Arguments

Value

Examples

Bucher method for combining treatment effects

Description

Usage

Arguments

Value

Methods (by generic)

Examples

Calculate Statistics for Weight Plot Legend

Description

Usage

Arguments

Value

Examples

Center individual patient data (IPD) variables using aggregate data averages

Description

Usage

Arguments

Value

Examples

Centered patient data from single arm trial

Description

Usage

Format

See Also

Centered patient data from two arm trial

Description

Usage

Format

See Also