Package {rwetools}

Add a README sheet to an openxlsx workbook

Description

Add a README sheet to an openxlsx workbook

Usage

add_readme_sheet(wb, readme_text, verbose = TRUE)

Arguments

Value

Called for its side effect (adds a sheet to wb). Returns NULL invisibly.

Build a Table 1 comparing baseline characteristics between groups

Description

Generates a descriptive Table 1 with counts, percentages, means, SDs, crude differences, standardized mean differences (SMD), and missingness. Supports inverse probability weighting via a user-supplied weight column and optionally saves the output to an Excel file.

Usage

build_table1(
  in_df,
  out_xlsxpath = NULL,
  exposure_var,
  exp_value = 1,
  ref_value = 0,
  use_weights = FALSE,
  weight_var = "psweight",
  cont_vars = NULL,
  cat_vars = NULL,
  binary_vars = NULL,
  drop_vars = NULL,
  drop_varpattern = NULL,
  Var_colname = "Variable",
  Vartype_colname = "Type",
  Total_colname = "Total",
  Exp_colname = "Exp",
  Ref_colname = "Ref",
  CrudeDiff_colname = "Crude_diff",
  StdDiff_colname = "Std_diff",
  MissingTotal_colname = "Missing_Total_N_Pct",
  MissingExp_colname = "Missing_Exp_N_Pct",
  MissingRef_colname = "Missing_Ref_N_Pct",
  ExistingTotal_colname = "Existing_Total_N_denom",
  ExistingExp_colname = NULL,
  ExistingRef_colname = NULL,
  mean_decimal = 2,
  sd_decimal = 2,
  n_decimal = 0,
  pct_decimal = 1,
  use_absolute_values_for_diff = FALSE,
  add_n_of_patients_row = TRUE,
  verbose = TRUE
)

Arguments

Value

Invisibly returns the Table 1 data frame.

Side Effects

When out_xlsxpath is not NULL, creates the output directory (if needed) and writes an Excel workbook.

Examples

csv_path <- system.file("extdata", "sample_data.csv", package = "rwetools")
df <- read.csv(csv_path)

# Unweighted Table 1
tbl <- build_table1(
  in_df        = df,
  exposure_var = "exposure",
  cont_vars    = c("cont1", "cont2", "cont3"),
  binary_vars  = c("binary1", "binary2"),
  cat_vars     = c("cat1", "cat2"),
  verbose      = FALSE
)
head(tbl)


# Weighted Table 1 with Excel output (requires openxlsx)
if (requireNamespace("openxlsx", quietly = TRUE)) {
  df_ps <- estimate_ps(
    in_df        = df,
    exposure_var = "exposure",
    class_vars   = c("cat1", "cat2", "cat3", "cat4"),
    cont_vars    = c("cont1", "cont2", "cont3"),
    verbose      = FALSE
  )
  df_wt <- create_ps_weights(
    in_df         = df_ps,
    exposure_var  = "exposure",
    ps_var        = "ps",
    weight_method = "mw",
    weight_var    = "mw_wt",
    verbose       = FALSE
  )
  out_xlsx <- tempfile(fileext = ".xlsx")
  tbl_wt <- build_table1(
    in_df        = df_wt,
    out_xlsxpath = out_xlsx,
    exposure_var = "exposure",
    use_weights  = TRUE,
    weight_var   = "mw_wt",
    cont_vars    = c("cont1", "cont2", "cont3"),
    binary_vars  = c("binary1", "binary2"),
    cat_vars     = c("cat1", "cat2"),
    verbose      = FALSE
  )
}

Calculate C-statistic (concordance) for PS model discrimination

Description

Uses survival::concordance which is O(n log n) and supports sample weights natively.

Usage

calc_c_statistic(
  exposure_vec,
  ps_vec,
  weights_vec = NULL,
  label = "PS Model",
  verbose = TRUE
)

Arguments

Value

A list with elements c_stat (numeric) and se (numeric), or NULL if survival is not available.

Canonize levels for consistent factor conversion

Description

Converts a vector to a factor with canonized levels. Logical vectors become factor("0", "1"), numeric vectors are coerced via as.character, and character vectors are trimmed.

Usage

canonize_levels(x)

Arguments

Value

A factor with canonical levels.

Check propensity score assumptions

Description

Internal function used by create_ps_weights and other PS functions to run diagnostic checks on propensity scores, including perfect separation, positivity, overlap, and covariate balance.

Usage

check_ps_assumptions_internal(data, ps_var, exposure, verbose = TRUE)

Arguments

Value

A list of assumption-check results, each element containing a detected flag and relevant summary statistics.

Combine named lists, keeping unique values

Description

Merges multiple named lists by name, concatenating and de-duplicating the values for each shared key. Useful for combining cat_vars_w_levels outputs from multiple calls to get_var_types.

Usage

combine_named_list(...)

Arguments

Value

A single named list with unique, combined values per key.

Create a balance (Love) plot comparing standardized differences

Description

Create a balance (Love) plot comparing standardized differences

Usage

create_love_plot(
  variable_names,
  crude_std_diff,
  adjusted_std_diff,
  crude_label,
  adjusted_label,
  title,
  output_path,
  colors = NULL,
  shapes = NULL,
  use_absolute = FALSE
)

Arguments

Value

Invisibly returns a ggplot object, or invisible(NULL) if ggplot2 is unavailable or no data remain after removing NAs.

Side Effects

Saves the plot to output_path via ggplot2::ggsave.

Calculate PS Fine Stratification Weights, Trim Non-overlapping Regions, and Generate Diagnostics

Description

Combined function that (1) creates PS fine strata, (2) calculates stratification weights, (3) trims non-overlapping PS regions, and optionally (4) builds a crude vs weighted balance table (Table 1) and (5) generates diagnostic plots.

Usage

create_ps_fs_weights(
  in_df,
  out_csvpath = NULL,
  out_xlsxpath_report = NULL,
  out_dir_plots = NULL,
  trim_nonoverlap_region = TRUE,
  exposure_var = "exp",
  exp_value = 1,
  ref_value = 0,
  ps_var = NULL,
  weight_var = "ps_fs_wt",
  number_of_strata = 50,
  stratification_method = c("exposure", "cohort"),
  estimand = c("ATT", "ATE"),
  make_unwt_wt_table1 = FALSE,
  table1_cont_vars = NULL,
  table1_binary_vars = NULL,
  table1_cat_vars = NULL,
  std_diff_threshold = 0.1,
  readme_text = NULL,
  verbose = TRUE
)

Arguments

Value

Invisibly returns the trimmed+weighted data frame.

Side Effects

• Writes a CSV file when out_csvpath is provided.

• Creates directories, writes an Excel diagnostic report, and saves PNG plot files when the corresponding path arguments are supplied.

Examples

csv_path <- system.file("extdata", "sample_data.csv", package = "rwetools")
df_ps <- estimate_ps(
  in_df        = read.csv(csv_path),
  exposure_var = "exposure",
  class_vars   = c("cat1", "cat2", "cat3", "cat4"),
  cont_vars    = c("cont1", "cont2", "cont3"),
  verbose      = FALSE
)

result <- create_ps_fs_weights(
  in_df                 = df_ps,
  exposure_var          = "exposure",
  ps_var                = "ps",
  weight_var            = "fs_wt",
  number_of_strata      = 10,
  stratification_method = "exposure",
  estimand              = "ATT",
  verbose               = FALSE
)
summary(result$fs_wt)

Perform propensity score matching and optionally generate diagnostic reports.

Description

This function performs 1:k nearest neighbor PS matching using MatchIt and generates comprehensive diagnostic reports including assumption checks, balance tables, and plots.

Usage

create_ps_matched_cohort(
  in_df = NULL,
  in_csvpath = NULL,
  out_csvpath_matcheddata = NULL,
  out_csvpath_crudedata_w_matchindicator = NULL,
  out_xlsxpath_report = NULL,
  out_dir_plots = NULL,
  exposure_var = "exp",
  exp_value = 1,
  ref_value = 0,
  ps_var = "ps",
  ratio = 1,
  caliper = 0.2,
  replace = FALSE,
  make_crude_matched_table1 = FALSE,
  table1_cont_vars = NULL,
  table1_binary_vars = NULL,
  table1_cat_vars = NULL,
  std_diff_threshold = 0.1,
  readme_text = NULL,
  verbose = TRUE
)

Arguments

Value

A data frame containing the matched cohort only (crude data with match indicator can be saved to CSV via out_csvpath_crudedata_w_matchindicator).

Side Effects

• Writes matched-cohort and/or crude-data CSV files when the corresponding path arguments are provided.

• Creates directories, writes an Excel diagnostic report, and saves PNG plot files when the corresponding path arguments are supplied.

Examples

# Requires MatchIt
if (requireNamespace("MatchIt", quietly = TRUE)) {
  csv_path <- system.file("extdata", "sample_data.csv", package = "rwetools")
  df_ps <- estimate_ps(
    in_df        = read.csv(csv_path),
    exposure_var = "exposure",
    class_vars   = c("cat1", "cat2", "cat3", "cat4"),
    cont_vars    = c("cont1", "cont2", "cont3"),
    verbose      = FALSE
  )

  matched <- create_ps_matched_cohort(
    in_df        = df_ps,
    exposure_var = "exposure",
    ps_var       = "ps",
    ratio        = 1,
    caliper      = 0.2,
    verbose      = FALSE
  )
  nrow(matched)
}

Calculate propensity score weights and optionally generate diagnostic reports.

Description

This function calculates various types of propensity score weights (matching weights, overlap weights, IPTW, stabilized IPTW) and adds them to a dataset that already contains propensity scores. Optionally generates comprehensive diagnostic reports including assumption checks, balance tables, and plots.

Usage

create_ps_weights(
  in_df = NULL,
  in_csvpath = NULL,
  out_csvpath = NULL,
  out_xlsxpath_report = NULL,
  out_dir_plots = NULL,
  exposure_var = "exp",
  exp_value = 1,
  ref_value = 0,
  ps_var = "ps",
  weight_method = c("mw", "ow", "iptw", "stabiptw"),
  weight_var = "psweight",
  estimand = c("ATT", "ATE", "ATO"),
  make_unwt_wt_table1 = FALSE,
  table1_cont_vars = NULL,
  table1_binary_vars = NULL,
  table1_cat_vars = NULL,
  std_diff_threshold = 0.1,
  readme_text = NULL,
  verbose = TRUE
)

Arguments

Value

A data frame with the weight column added. Also saves to CSV if path specified.

Side Effects

• Writes a CSV file when out_csvpath is provided.

• Creates directories, writes an Excel diagnostic report, and saves PNG plot files when the corresponding path arguments are supplied.

Examples

csv_path <- system.file("extdata", "sample_data.csv", package = "rwetools")
df_ps <- estimate_ps(
  in_df        = read.csv(csv_path),
  exposure_var = "exposure",
  class_vars   = c("cat1", "cat2", "cat3", "cat4"),
  cont_vars    = c("cont1", "cont2", "cont3"),
  verbose      = FALSE
)

# Add matching weights
df_mw <- create_ps_weights(
  in_df         = df_ps,
  exposure_var  = "exposure",
  ps_var        = "ps",
  weight_method = "mw",
  weight_var    = "mw_wt",
  verbose       = FALSE
)
summary(df_mw$mw_wt)


# Add IPTW weights with Excel diagnostic report (requires openxlsx, ggplot2)
if (requireNamespace("openxlsx", quietly = TRUE) &&
    requireNamespace("ggplot2", quietly = TRUE)) {
  out_xlsx <- tempfile(fileext = ".xlsx")
  out_dir  <- tempdir()
  df_iptw  <- create_ps_weights(
    in_df               = df_ps,
    exposure_var        = "exposure",
    ps_var              = "ps",
    weight_method       = "iptw",
    weight_var          = "iptw_wt",
    estimand            = "ATE",
    out_xlsxpath_report = out_xlsx,
    make_unwt_wt_table1 = TRUE,
    out_dir_plots       = out_dir,
    verbose             = FALSE
  )
}

Estimate Incidence Rates and Hazard Ratios

Description

This function estimates incidence rates and hazard ratios for time-to-event outcomes. It can perform unweighted analysis, weighted (propensity score adjusted) analysis, or both, using potentially different datasets for each analysis type. Incidence rate CIs are estimated using Poisson distribution.

Usage

estimate_hr_ir(
  in_df_unwt = NULL,
  in_df_wted = NULL,
  out_xlsxpath = NULL,
  exposure_var = "exp",
  exp_value = 1,
  ref_value = 0,
  outcome_var = NULL,
  weight_var = NULL,
  survival_time = NULL,
  time_unit = c("days", "years"),
  ir_per_pyears = 1000,
  confidence_level = 0.95,
  readme_text = NULL,
  stratify_by = NULL,
  verbose = TRUE
)

Arguments

Details

The function allows flexible analysis configurations:

• Unweighted only: Set in_df_unwt = data, in_df_wted = NULL

• Weighted only: Set in_df_unwt = NULL, in_df_wted = data

• Both: Provide both datasets (can be the same or different populations)

For unweighted analysis:

• Uses standard Cox regression without weights

• Can be applied to matched data, trimmed data, or full cohort

For weighted analysis:

• Uses weighted Cox regression with specified weights

• Typically applied to PS-weighted data (IPTW, MW, OW, FS weights)

Stratification (stratify_by parameter): When stratify_by is specified:

• Cox model: Fits a stratified Cox proportional hazards model using strata() in the model formula. This allows separate baseline hazard functions for each stratum while estimating a common treatment effect (HR) across strata.

• IR/IRD: Uses direct standardization with total cohort person-time distribution as standard weights.

  • IR_std = sum(Rate_k * W_k) where W_k = PT_total_k / PT_total

  • Variance via delta method: Var = sum(W_k^2 * d_k / PT_k^2)

  • IRD_std = IR_std_exposed - IR_std_unexposed

  • CI for standardized IR uses log-transformation to ensure positive bounds

ir_per_pyears parameter: Controls the person-year multiplier for incidence rates and incidence rate differences. For example:

• ir_per_pyears = 1000: rates expressed per 1,000 person-years (default)

• ir_per_pyears = 100000: rates expressed per 100,000 person-years Output column names are fixed (e.g., IR_per_Npy_Unwt, IR_per_Npy_Wted). The multiplier value is recorded in the Analysis Summary sheet.

Value

Invisibly returns a list containing:

• incidence_rates: Data frame with event counts, person-years, and incidence rates

• hazard_ratios: Data frame with unweighted and/or weighted HR estimates

• unweighted_model: The unweighted Cox model object (if unweighted analysis performed)

• weighted_model: The weighted Cox model object (if weighted analysis performed)

• stratum_details_unwt: Data frame with stratum-level details (if stratified, unweighted)

• stratum_details_wted: Data frame with stratum-level details (if stratified, weighted) Also saves results to Excel file when out_xlsxpath is provided

Side Effects

Creates the output directory and writes an Excel workbook when out_xlsxpath is provided.

Examples

csv_path <- system.file("extdata", "sample_data.csv", package = "rwetools")
df <- read.csv(csv_path)

# Unweighted analysis only
result <- estimate_hr_ir(
  in_df_unwt    = df,
  in_df_wted    = NULL,
  exposure_var  = "exposure",
  outcome_var   = "outcome",
  survival_time = "follow_up_days",
  time_unit     = "days",
  ir_per_pyears = 1000,
  verbose       = FALSE
)
result$incidence_rates


# Weighted analysis with Excel output (requires openxlsx)
if (requireNamespace("openxlsx", quietly = TRUE)) {
  df_ps <- estimate_ps(
    in_df        = df,
    exposure_var = "exposure",
    class_vars   = c("cat1", "cat2", "cat3", "cat4"),
    cont_vars    = c("cont1", "cont2", "cont3"),
    verbose      = FALSE
  )
  df_wt <- create_ps_weights(
    in_df         = df_ps,
    exposure_var  = "exposure",
    ps_var        = "ps",
    weight_method = "mw",
    weight_var    = "mw_wt",
    verbose       = FALSE
  )
  out_xlsx <- tempfile(fileext = ".xlsx")
  result2 <- estimate_hr_ir(
    in_df_unwt    = df,
    in_df_wted    = df_wt,
    out_xlsxpath  = out_xlsx,
    exposure_var  = "exposure",
    outcome_var   = "outcome",
    survival_time = "follow_up_days",
    weight_var    = "mw_wt",
    ir_per_pyears = 1000,
    verbose       = FALSE
  )
}

Calculate propensity scores and add them to the dataset

Description

This function calculates propensity scores using logistic regression and adds them as a new column to the dataset. It can output both an R data frame and/or CSV file. Additionally, it can save odds ratio table from the PS model to Excel or data frame.

Usage

estimate_ps(
  in_df = NULL,
  in_csvpath = NULL,
  out_csvpath = NULL,
  out_xlsxpath_odds_ratio = NULL,
  exposure_var = "exposure",
  exp_value = 1,
  ref_value = 0,
  class_vars = NULL,
  cont_vars = NULL,
  ps_var = "ps",
  interactions = NULL,
  exclude_vars_w_extreme_distribution = FALSE,
  verbose = TRUE
)

Arguments

Value

A data frame with the PS column added. Also saves to CSV if path specified.

Side Effects

• Writes a CSV file when out_csvpath is provided.

• Writes an Excel file with odds-ratio table when out_xlsxpath_odds_ratio is provided.

Examples

csv_path <- system.file("extdata", "sample_data.csv", package = "rwetools")
df <- read.csv(csv_path)

# Basic usage: estimate PS and add it as a new column
result <- estimate_ps(
  in_df        = df,
  exposure_var = "exposure",
  class_vars   = c("cat1", "cat2", "cat3", "cat4"),
  cont_vars    = c("cont1", "cont2", "cont3"),
  verbose      = FALSE
)
head(result$ps)


# With CSV output and Excel OR table (requires openxlsx)
if (requireNamespace("openxlsx", quietly = TRUE)) {
  out_csv  <- tempfile(fileext = ".csv")
  out_xlsx <- tempfile(fileext = ".xlsx")
  result2 <- estimate_ps(
    in_df                               = df,
    out_csvpath                         = out_csv,
    out_xlsxpath_odds_ratio             = out_xlsx,
    exposure_var                        = "exposure",
    class_vars                          = c("cat1", "cat2"),
    cont_vars                           = c("cont1", "cont2"),
    exclude_vars_w_extreme_distribution = TRUE,
    verbose                             = FALSE
  )
}

Estimate Risk Ratios and Risk Differences using Cumulative Incidence

Description

This function estimates risk ratios (RR) and risk differences (RD) for time-to-event outcomes using cumulative incidence at a specified timepoint. Two estimators are supported:

Kaplan-Meier (KM)

Standard 1 - S(t) cumulative incidence. Appropriate when there are no competing risks or competing events are treated as censored.

Aalen-Johansen (AJ)

Cumulative incidence function accounting for competing risks. Produces unbiased risk estimates in the presence of competing events by treating them as a separate absorbing state rather than censoring. Requires a competing event indicator via competing_event_var. Uses multi-state survfit internally (survival >= 3.1).

It can perform unweighted analysis, weighted (propensity score adjusted) analysis, or both, using potentially different datasets for each analysis type. Confidence intervals can be estimated using bootstrapping, analytical (delta method / Greenwood), or both.

Usage

estimate_rr_rd(
  in_df_unwt = NULL,
  in_df_wted = NULL,
  out_xlsxpath = NULL,
  exposure_var = "exp",
  exp_value = 1,
  ref_value = 0,
  outcome_var = NULL,
  weight_var = NULL,
  survival_time = NULL,
  time_unit = c("days", "months", "years"),
  rr_rd_at_timepoint = 365,
  rr_rd_per_individuals = 1000,
  confidence_level = 0.95,
  conf_int_method = c("bootstrap", "analytical", "both"),
  risk_estimator = c("KM", "AJ"),
  competing_event_var = NULL,
  bootstrap_count = 500,
  stratify_by = NULL,
  n_cores = NULL,
  seed = NULL,
  readme_text = NULL,
  verbose = TRUE
)

Arguments

Details

When stratify_by is specified, the function performs direct standardization: stratum-specific risks are combined using the total population distribution as the standard. Bootstrap resampling is stratified to preserve stratum structure.

The function proceeds as follows:

1. Fits cumulative incidence curves by exposure group using the selected estimator

2. Extracts cumulative incidence (risk) at the specified timepoint

3. Computes Risk Ratio (RR) = Risk_exposed / Risk_reference

4. Computes Risk Difference (RD) = Risk_exposed - Risk_reference

5. Estimates confidence intervals using the selected method

Kaplan-Meier (KM) estimator: Risk = 1 - S(t), where S(t) is the KM survival estimate. Competing events, if present, are treated as censored, which can overestimate cumulative incidence.

Aalen-Johansen (AJ) estimator: Cumulative incidence function from a multi-state model via survfit with Surv(time, factor(status)). Competing events are modelled as a separate absorbing state, producing unbiased risk estimates. Variance is based on the counting-process variance estimator (Aalen, 1978).

Analytical CI method:

• RR: delta method on log scale. Var(log(RR)) = Var(R_exp)/R_exp^2 + Var(R_ref)/R_ref^2. CI = exp(log(RR) +/- z * SE(log(RR))).

• RD: Var(RD) = (Var(R_exp) + Var(R_ref)) * per_n^2. CI = RD +/- z * SE(RD).

• Variance from survfit (Greenwood for KM, counting-process for AJ).

Bootstrap CI method: Percentile bootstrap with parallel computation via parLapply using L'Ecuyer-CMRG random number streams. Pass an integer to seed for reproducible bootstrap CIs; by default (seed = NULL) the caller's current RNG state is used and not modified.

When stratify_by is specified (Direct Standardization):

1. Stratum weights w_k = N_k / N_total from the total population

2. Within each stratum k, risk R_{ik} is computed for each exposure group i

3. Standardized risk: Risk_std_i = Sum_k(R_{ik} * w_k)

4. RR = Risk_std_1 / Risk_std_0; RD = Risk_std_1 - Risk_std_0

5. Analytical SE: Var(Risk_std_i) = Sum_k(w_k^2 * Var(R_{ik}))

6. Bootstrap CIs use stratified resampling with fixed original population weights

Value

Invisibly returns a list containing:

estimates

Data frame with RR, RD, confidence intervals, and Risk_Estimator ("KM" or "AJ").

cumulative_incidence

Data frame with cumulative incidence by group. Columns: Risk (probability), Risk_LCI / Risk_UCI (log-transformed CI), Risk_SE (SE on 0-1 scale), Risk_Var (variance), RiskperN (scaled risk), RiskperN_LCI / RiskperN_UCI (scaled CI), and Risk_Estimator.

stratum_details

(if stratify_by specified) Data frame with stratum-level results and Risk_Estimator.

unweighted_bootstrap

(if applicable) Bootstrap results matrix.

weighted_bootstrap

(if applicable) Bootstrap results matrix.

Also saves results to Excel file when out_xlsxpath is provided.

Side Effects

Creates the output directory and writes an Excel workbook when out_xlsxpath is provided.

Examples

csv_path <- system.file("extdata", "sample_data.csv", package = "rwetools")
df <- read.csv(csv_path)

# KM with analytical CIs (fast, no bootstrap)
result <- estimate_rr_rd(
  in_df_unwt            = df,
  in_df_wted            = NULL,
  exposure_var          = "exposure",
  outcome_var           = "outcome",
  survival_time         = "follow_up_days",
  time_unit             = "days",
  rr_rd_at_timepoint    = 365,
  rr_rd_per_individuals = 1000,
  conf_int_method       = "analytical",
  verbose               = FALSE
)
result$estimates


# KM with bootstrap CIs (slow) and competing risk (AJ estimator)
result2 <- estimate_rr_rd(
  in_df_unwt            = df,
  in_df_wted            = NULL,
  exposure_var          = "exposure",
  outcome_var           = "outcome",
  survival_time         = "follow_up_days",
  time_unit             = "days",
  rr_rd_at_timepoint    = 365,
  rr_rd_per_individuals = 1000,
  risk_estimator        = "AJ",
  competing_event_var   = "competing_event",
  conf_int_method       = "bootstrap",
  bootstrap_count       = 100,
  n_cores               = 1,
  verbose               = FALSE
)

Format mean and standard deviation as "mean (SD)"

Description

Format mean and standard deviation as "mean (SD)"

Usage

fmt_mean_sd(m, s, mean_digits = 2, sd_digits = 2, use_abs = FALSE)

Arguments

Value

Character string in the form "mean (sd)", or "NA" when m is NA.

Format count and percentage as "n (pct%)"

Description

Format count and percentage as "n (pct%)"

Usage

fmt_n_pct(n, d, n_digits = 0, pct_digits = 1, use_abs = FALSE)

Arguments

Value

Character string in the form "n (pct%)", or "NA" when inputs are missing or d == 0.

Format a numeric value as a fixed-point string

Description

Format a numeric value as a fixed-point string

Usage

fmt_num(x, digits = 3, use_abs = FALSE)

Arguments

Value

Character string, or "NA" when x is NA.

Format a proportion as a percentage string

Description

Format a proportion as a percentage string

Usage

fmt_pct(p, digits = 1, use_abs = FALSE)

Arguments

Value

Character string in the form "xx.x%", or "–" when p is NA.

Classify variables as continuous, binary, or categorical

Description

Inspects one or more data frames to determine the type of each variable using a combination of pattern-based rules and data-driven heuristics.

Usage

get_var_types(df_list, max_cat_levels = 12)

Arguments

Value

A named list with elements:

cat_vars

Sorted character vector of categorical variable names.

cat_vars_w_levels

Named list mapping each categorical variable to its sorted unique levels.

binary_vars

Sorted character vector of binary (0/1) variable names.

cont_vars

Sorted character vector of continuous variable names.

Compute weighted proportion for a single factor level

Description

Compute weighted proportion for a single factor level

Usage

get_weighted_prop(design, var, level)

Arguments

Value

Numeric scalar: the weighted proportion, 0 if the level is absent, or NA_real_ on error.

Compute weighted mean, SD, and total weight from a survey design

Description

Compute weighted mean, SD, and total weight from a survey design

Usage

get_weighted_stats(des, var_name)

Arguments

Value

A named numeric vector with elements mean, sd, and sum_w. Returns c(mean = NA, sd = NA, sum_w = 0) when all values are NA.

Get non-baseline factor levels

Description

Returns all factor levels except those commonly used as a baseline (e.g., "0", "No", "None", "FALSE").

Usage

levels_excl_zero(f)

Arguments

Value

Character vector of non-baseline levels.

Process a single categorical or binary variable for Table 1

Description

Computes weighted counts, percentages, crude differences, and standardized mean differences for each level of a categorical or binary variable. Uses stats::xtabs to compute the full weighted cross-tabulation once per variable, then extracts per-level results efficiently.

Usage

process_catbin_direct(
  v,
  dat,
  levels_to_show = c("all", "non_zero"),
  predefined_levels = NULL,
  N_all,
  N_ref,
  N_tx,
  idx_ref,
  idx_tx,
  w_vec,
  grp_vec,
  using_w,
  n_decimal = 0,
  pct_decimal = 1,
  use_abs = FALSE,
  calculate_existing = FALSE,
  verbose = TRUE
)

Arguments

Value

A matrix of list rows (one per level), or NULL if the variable is not in dat or has no displayable levels.

Summarize propensity-score distribution by exposure group

Description

Summarize propensity-score distribution by exposure group

Usage

summarize_ps_by_group(ps_vals, group_vals, group_labels = NULL)

Arguments

Value

data.frame with one row per group containing n, mean, SD, min, quartiles, and max of the propensity score.