Package 'nhscancerwaits'

Title: NHS Cancer Waiting-Time Analysis, Benchmarking and Multilevel Modelling
Description: Provides tools for importing, harmonising, cleaning, analysing, benchmarking and visualising National Health Service (NHS) England Cancer Waiting Times data. The package supports national performance monitoring, provider-level benchmarking and cancer pathway comparisons through key performance indicator summaries, provider filtering, clustering analyses, mixed-effects regression models, variance decomposition, intraclass correlation coefficient estimation, adjusted provider performance estimation and sensitivity analyses. Functions are included for exploratory analysis, publication-ready visualisations and spreadsheet exports, supporting reproducible health services research, cancer services evaluation, quality improvement and assessment of waiting-time performance across healthcare organisations.
Authors: Muhammad Zahir Khan [aut, cre] (ORCID: <https://orcid.org/0009-0005-7645-8960>)
Maintainer: Muhammad Zahir Khan <[email protected]>
License: MIT + file LICENSE
Version: 1.0.1
Built: 2026-06-20 09:18:58 UTC
Source: https://github.com/zerish12/nhscancerwaits

Help Index

Calculate Intraclass Correlation Coefficient

Description

Calculates the intraclass correlation coefficient from a fitted mixed-effects model.

Usage

calculate_icc(model)

Arguments

model

A fitted mixed-effects model from lme4::lmer().

Value

A data frame with provider variance, residual variance, total variance and ICC.

Calculate Provider Clustering Silhouette Score

Description

Calculates the average silhouette score for provider clusters.

Usage

calculate_silhouette_score(clustered_data)

Arguments

clustered_data

Data frame from cluster_providers().

Value

Average silhouette score.

Check Mixed-Model Performance Diagnostics

Description

Returns standard model diagnostics using the performance package.

Usage

check_cwt_model(model)

Arguments

model

A fitted mixed-effects model.

Value

A model diagnostic object.

Clean NHS Cancer Waiting-Time Data

Description

Standardises column names, provider fields, reporting dates, performance values and activity denominators.

Usage

clean_cwt_data(data)

Arguments

data

A data frame of NHS Cancer Waiting-Time records.

Value

A cleaned data frame.

Cluster Providers by Performance and Activity

Description

Groups providers into performance clusters using provider-level summaries.

Usage

cluster_providers(
  data,
  provider_var = "provider_code",
  performance_var = "performance_percent",
  activity_var = "total_treated",
  k = 3
)

Arguments

data

A cleaned and filtered cancer waiting-time data frame.
provider_var

Provider column. Default is "provider_code".
performance_var

Performance percentage column. Default is "performance_percent".
activity_var

Activity denominator column. Default is "total_treated".
k

Number of clusters. Default is 3.

Value

A data frame containing provider summaries and cluster labels.

Export Analysis Tables to Excel

Description

Exports one or more analysis tables to an Excel workbook.

Usage

export_excel_tables(tables, path = "nhscancerwaits_outputs.xlsx")

Arguments

tables

A named list of data frames.
path

Output Excel file path. Default is "nhscancerwaits_outputs.xlsx".

Value

The output file path, invisibly.

Export Provider Rankings to Excel

Description

Exports adjusted provider effects or provider ranking tables to Excel.

Usage

export_provider_rankings(provider_rankings, path = "provider_rankings.xlsx")

Arguments

provider_rankings

A provider ranking data frame.
path

Output Excel file path. Default is "provider_rankings.xlsx".

Value

The output file path, invisibly.

Extract Fixed Effects from Mixed Model

Description

Extract Fixed Effects from Mixed Model

Usage

extract_model_effects(model)

Arguments

model

A fitted mixed-effects model.

Value

A data frame of fixed-effect estimates.

Extract Adjusted Provider Effects

Description

Extracts provider-specific random effects from a fitted mixed-effects model.

Usage

extract_provider_effects(model, provider_name = "provider_code")

Arguments

model

A fitted model from fit_cwt_mixed_model().
provider_name

Name of provider grouping variable.

Value

A data frame of adjusted provider effects.

Filter Providers for Reliable Analysis

Description

Keeps providers with sufficient activity, repeated observations and stable performance values.

Usage

filter_providers(
  data,
  provider_var = "provider_code",
  activity_var = "total_treated",
  performance_var = "performance_percent",
  min_mean_activity = 20,
  min_observations = 5,
  max_cv = 0.5
)

Arguments

data

A cleaned cancer waiting-time data frame.
provider_var

Provider code column. Default is "provider_code".
activity_var

Activity denominator column. Default is "total_treated".
performance_var

Performance percentage column. Default is "performance_percent".
min_mean_activity

Minimum mean activity per provider.
min_observations

Minimum number of observations per provider.
max_cv

Maximum coefficient of variation.

Value

A filtered data frame.

Fit Mixed-Effects Model for Cancer Waiting-Time Performance

Description

Fits a random-intercept mixed-effects model with provider as the grouping variable.

Usage

fit_cwt_mixed_model(
  data,
  performance_var = "performance_percent",
  month_var = "month_index",
  pathway_var = "cancer_type",
  provider_var = "provider_code"
)

Arguments

data

A cleaned and filtered cancer waiting-time data frame.
performance_var

Outcome column. Default is "performance_percent".
month_var

Month index column. Default is "month_index".
pathway_var

Cancer pathway column. Default is "cancer_type".
provider_var

Provider column. Default is "provider_code".

Value

A fitted lmerMod model object.

Load NHS Cancer Waiting-Time CSV Files

Description

Reads one or more NHS Cancer Waiting-Time CSV files and combines them into a single data frame.

Usage

load_cwt_data(files)

Arguments

files

Character vector of CSV file paths.

Value

A data frame containing combined cancer waiting-time records.

Load NHS Cancer Waiting-Time Excel Files

Description

Reads one or more NHS Cancer Waiting-Time Excel files and combines them into a single data frame.

Usage

load_cwt_excel(files, sheet = 1, .name_repair = "unique")

Arguments

files

Character vector of Excel file paths.
sheet

Sheet name or sheet number. Default is 1.
.name_repair

Passed to readxl::read_excel(). Default is "unique".

Value

A combined tibble with a source_file column.

Reshape Monthly Provider Performance to Wide Format

Description

Creates a provider-by-month performance table.

Usage

pivot_provider_months(
  data,
  provider_var = "provider_code",
  month_var = "reporting_date",
  performance_var = "performance_percent"
)

Arguments

data

A cleaned cancer waiting-time data frame.
provider_var

Provider column.
month_var

Month column.
performance_var

Performance column.

Value

A wide provider-by-month table.

Plot National Cancer Waiting-Time Trends

Description

Creates a monthly performance trend plot.

Usage

plot_national_trends(
  data,
  month_var = "reporting_date",
  performance_var = "performance_percent",
  group_var = "standard"
)

Arguments

data

A cleaned cancer waiting-time data frame.
month_var

Reporting date column. Default is "reporting_date".
performance_var

Performance percentage column. Default is "performance_percent".
group_var

Grouping column. Default is "standard".

Value

A ggplot object.

Plot Adjusted Pathway Performance

Description

Creates a horizontal plot of adjusted predicted performance by cancer pathway.

Usage

plot_pathway_predictions(
  pathway_predictions,
  pathway_var = "cancer_type",
  prediction_var = "predicted_performance"
)

Arguments

pathway_predictions

Data frame from predict_pathway_performance().
pathway_var

Cancer pathway column. Default is "cancer_type".
prediction_var

Predicted performance column. Default is "predicted_performance".

Value

A ggplot object.

Plot Provider Clusters

Description

Creates a scatter plot of provider clusters.

Usage

plot_provider_clusters(clustered_data)

Arguments

clustered_data

Data frame from cluster_providers().

Value

A ggplot object.

Plot Adjusted Provider Effects

Description

Creates a ranked plot of adjusted provider effects.

Usage

plot_provider_effects(
  provider_effects,
  provider_var = "provider_code",
  effect_var = "adjusted_effect"
)

Arguments

provider_effects

Data frame from extract_provider_effects().
provider_var

Provider column. Default is "provider_code".
effect_var

Adjusted effect column. Default is "adjusted_effect".

Value

A ggplot object.

Predict Adjusted Performance by Cancer Pathway

Description

Generates adjusted predicted performance for each cancer pathway from a fitted mixed-effects model.

Usage

predict_pathway_performance(
  model,
  data,
  pathway_var = "cancer_type",
  month_var = "month_index",
  provider_var = "provider_code"
)

Arguments

model

A fitted model from fit_cwt_mixed_model().
data

The data frame used to fit the model.
pathway_var

Cancer pathway column. Default is "cancer_type".
month_var

Month index column. Default is "month_index".
provider_var

Provider column. Default is "provider_code".

Value

A data frame with adjusted predicted pathway performance.

Run Sensitivity Analyses

Description

Runs mixed-effects models across alternative provider-filtering thresholds. If a cohort is too small or a model fails, the function returns a row with status information rather than stopping.

Usage

run_sensitivity_analysis(
  data,
  cohorts = data.frame(cohort = c("Main cohort", "No outlier removal",
    "Stricter cohort"), min_mean_activity = c(20, 20, 30), min_observations = c(5, 5, 5),
    max_cv = c(0.5, 0.75, 0.5)),
  provider_var = "provider_code",
  activity_var = "total_treated",
  performance_var = "performance_percent",
  month_var = "month_index",
  pathway_var = "cancer_type",
  min_providers = 3,
  min_rows = 20
)

Arguments

data

A cleaned cancer waiting-time data frame.
cohorts

A data frame defining sensitivity cohorts.
provider_var

Provider column. Default is "provider_code".
activity_var

Activity denominator column. Default is "total_treated".
performance_var

Performance column. Default is "performance_percent".
month_var

Month index column. Default is "month_index".
pathway_var

Cancer pathway column. Default is "cancer_type".
min_providers

Minimum number of providers required to fit a model.
min_rows

Minimum number of rows required to fit a model.

Value

A data frame summarising model results across cohorts.

Summarise Cancer Waiting-Time KPIs

Description

Calculates summary statistics for cancer waiting-time performance.

Usage

summarise_kpis(
  data,
  group_var = "standard",
  performance_var = "performance_percent"
)

Arguments

data

A cleaned cancer waiting-time data frame.
group_var

Column used to group results. Default is "standard".
performance_var

Performance percentage column. Default is "performance_percent".

Value

A summary data frame.

Summarise Cancer Pathway Performance

Description

Summarise Cancer Pathway Performance

Usage

summarise_pathways(
  data,
  pathway_var = "cancer_type",
  performance_var = "performance_percent"
)

Arguments

data

A cleaned cancer waiting-time data frame.
pathway_var

Cancer pathway column.
performance_var

Performance column.

Value

Pathway-level summary table.

Summarise Provider Performance

Description

Summarise Provider Performance

Usage

summarise_providers(
  data,
  provider_var = "provider_code",
  performance_var = "performance_percent",
  activity_var = "total_treated"
)

Arguments

data

A cleaned cancer waiting-time data frame.
provider_var

Provider column.
performance_var

Performance column.
activity_var

Activity column.

Value

Provider-level summary table.