Title:	Workflow for Cluster Randomised Trials with Spillover
Version:	1.3.2
Maintainer:	Thomas Smith <Thomas-a.Smith@unibas.ch>
Description:	Design, workflow and statistical analysis of Cluster Randomised Trials of (health) interventions where there may be spillover between the arms (see https://thomasasmith.github.io/index.html).
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
Additional_repositories:	https://inla.r-inla-download.org/R/stable/
Imports:	ggplot2, stats, utils, MASS, tidyr, magrittr, dplyr, OOR, lme4, sf, Matrix, spatstat.geom, spatstat.random, jagsUI, TSP, gee, sp
Suggests:	knitr, rmarkdown, INLA, testthat (≥ 2.0.0)
Config/testthat/edition:	2
Depends:	R (≥ 3.5.0)
NeedsCompilation:	no
Packaged:	2025-01-27 18:02:10 UTC; smith
Author:	Thomas Smith [aut, cre, cph], Lea Multerer [ctb], Mariah Silkey [ctb]
Repository:	CRAN
Date/Publication:	2025-01-27 18:20:11 UTC

CRTspat: Workflow for Cluster Randomised Trials with Spillover

Description

Design, workflow and statistical analysis of Cluster Randomised Trials of (health) interventions where there may be spillover between the arms (see https://thomasasmith.github.io/index.html).

Author(s)

Maintainer: Thomas Smith Thomas-a.Smith@unibas.ch (ORCID) [copyright holder]

Other contributors:

• Lea Multerer [contributor]

• Mariah Silkey [contributor]

Analysis of cluster randomized trial with spillover

Description

CRTanalysis carries out a statistical analysis of a cluster randomized trial (CRT).

Usage

CRTanalysis(
  trial,
  method = "GEE",
  distance = "nearestDiscord",
  scale_par = NULL,
  cfunc = "L",
  link = "logit",
  numerator = "num",
  denominator = "denom",
  excludeBuffer = FALSE,
  alpha = 0.05,
  baselineOnly = FALSE,
  baselineNumerator = "base_num",
  baselineDenominator = "base_denom",
  personalProtection = FALSE,
  clusterEffects = TRUE,
  spatialEffects = FALSE,
  requireMesh = FALSE,
  inla_mesh = NULL
)

Arguments

Details

CRTanalysis is a wrapper for the statistical analysis packages: gee, INLA, jagsUI, and the t.test function of package stats.

The wrapper does not provide an interface to the full functionality of these packages. It is specific for typical analyses of cluster randomized trials with geographical clustering. Further details are provided in the vignette.

The key results of the analyses can be extracted using a summary() of the output list. The model_object in the output list is the usual output from the statistical analysis routine, and can be also be inspected with summary(), or analysed using stats::fitted() for purposes of evaluation of model fit etc..

For models with a complementary log-log link function specified with link = "cloglog". the numerator must be coded as 0 or 1. Technically the binomial denominator is then 1. The value of denominator is used as a rate multiplier.

With the "INLA" and "MCMC" methods 'iid' random effects are used to model extra-Poisson variation.

Interval estimates for the coefficient of variation of the cluster level outcome are calculated using the method of Vangel (1996).

Value

list of class CRTanalysis containing the following results of the analysis:

• description : description of the dataset

• method : statistical method

• pt_ests : point estimates

• int_ests : interval estimates

• model_object : object returned by the fitting routine

• spillover : function values and statistics describing the estimated spillover

Examples

example <- readdata('exampleCRT.txt')
# Analysis of test dataset by t-test
exampleT <- CRTanalysis(example, method = "T")
summary(exampleT)
# Standard GEE analysis of test dataset ignoring spillover
exampleGEE <- CRTanalysis(example, method = "GEE")
summary(exampleGEE)
# LME4 analysis with error function spillover function
exampleLME4 <- CRTanalysis(example, method = "LME4", cfunc = "P")
summary(exampleLME4)

Power and sample size calculations for a cluster randomized trial

Description

CRTpower carries out power and sample size calculations for cluster randomized trials.

Usage

CRTpower(
  trial = NULL,
  locations = NULL,
  alpha = 0.05,
  desiredPower = 0.8,
  effect = NULL,
  yC = NULL,
  outcome_type = "d",
  sigma2 = NULL,
  denominator = 1,
  N = 1,
  ICC = NULL,
  cv_percent = NULL,
  c = NULL,
  sd_h = 0,
  spillover_interval = 0,
  contaminate_pop_pr = 0,
  distance_distribution = "normal"
)

Arguments

Details

Power and sample size calculations are for an unmatched two-arm trial. For counts or event rate data the formula of Hayes & Bennett (1999), Int. J. Epi., 28(2) pp319–326 is used. This requires as an input the between cluster coefficient of variation (cv_percent). For continuous outcomes and proportions the formulae of Hemming et al, 2011 are used. These make use of the intra-cluster correlation in the outcome (ICC) as an input. If the coefficient of variation and not the ICC is supplied then the intra-cluster correlation is computed from the coefficient of variation using the formulae from Hayes & Moulton. If incompatible values for ICC and cv_percent are supplied then the value of the ICC is used.

The calculations do not consider any loss in power due to loss to follow-up and by default there is no adjustment for effects of spillover.

Spillover bias can be allowed for using a diffusion model of mosquito movement. If no location or arm assignment information is available then contaminate_pop_pr is used to parameterize the model using a normal approximation for the distribution of distance to discordant locations.

If a trial data frame or 'CRTsp' object is input then this is used to determine the number of locations. If this input object contains cluster assignments then the numbers and sizes of clusters in the input data are used to estimate the power. If spillover_interval > 0 and distance_distribution = 'empirical' then effects of spillover are incorporated into the power calculations based on the empirical distribution of distances to the nearest discordant location. (If distance_distribution is not equal to 'empirical' then the distribution of distances is assumed to be normal.

If geolocations are not input then power and sample size calculations are based on the scalar input parameters.

If buffer zones have been specified in the 'CRTsp' object then separate calculations are made for the core area and for the full site.

The output is an object of class 'CRTsp' containing any input trial data frame and values for:

• The required numbers of clusters to achieve the specified power.

• The design effect based on the input ICC.

• Calculations of the power ignoring any bias caused by loss to follow-up etc.
  

• Calculations of delta, the expected spillover bias.

Value

A list of class 'CRTsp' object comprising the input data, cluster and arm assignments, trial description and results of power calculations

Examples

{# Power calculations for a binary outcome without input geolocations
examplePower1 <- CRTpower(locations = 3000, ICC = 0.10, effect = 0.4, alpha = 0.05,
    outcome_type = 'd', desiredPower = 0.8, yC=0.35, c = 20, sd_h = 5)
summary(examplePower1)
# Power calculations for a rate outcome without input geolocations
examplePower2 <- CRTpower(locations = 2000, cv_percent = 40, effect = 0.4, denominator = 2.5,
    alpha = 0.05, outcome_type = 'e', desiredPower = 0.8, yC = 0.35, c = 20, sd_h=5)
summary(examplePower2)
# Example with input geolocations
examplePower3 <- CRTpower(trial = readdata('example_site.csv'), desiredPower = 0.8,
    effect=0.4, yC=0.35, outcome_type = 'd', ICC = 0.05, c = 20)
summary(examplePower3)
# Example with input geolocations, randomisation, and spillover
example4 <- randomizeCRT(specify_clusters(trial = readdata('example_site.csv'), c = 20))
examplePower4 <- CRTpower(trial = example4, desiredPower = 0.8,
    effect=0.4, yC=0.35, outcome_type = 'd', ICC = 0.05, contaminate_pop_pr = 0.3)
summary(examplePower4)
}

Create or update a "CRTsp" object

Description

CRTsp coerces data frames containing co-ordinates and location attributes into objects of class "CRTsp" or creates a new "CRTsp" object by simulating a set of Cartesian co-ordinates for use as the locations in a simulated trial site

Usage

CRTsp(
  x = NULL,
  design = NULL,
  geoscale = NULL,
  locations = NULL,
  kappa = NULL,
  mu = NULL,
  geometry = "point"
)

Arguments

Details

If a data frame or "CRTsp" object is input then the output "CRTsp" object is validated, a description of the geography is computed and power calculations are carried out.

If geoscale, locations, kappa and mu are specified then a new trial dataframe is constructed corresponding to a novel simulated human settlement pattern. This is generated using the Thomas algorithm (rThomas) in spatstat.random allowing the user to defined the density of locations and degree of spatial clustering. The resulting trial data frame comprises a set of Cartesian coordinates centred at the origin.

Value

A list of class "CRTsp" containing the following components:

Examples

{# Generate a simulated area with 10,000 locations
example_area = CRTsp(geoscale = 1, locations=10000, kappa=3, mu=40)
summary(example_area)
}

Export of GIS layer from 'CRTsp'

Description

CRTwrite exports a simple features object in a GIS format

Usage

CRTwrite(
  object,
  dsn,
  feature = "clusters",
  buffer_width,
  maskbuffer = 0.2,
  ...
)

Arguments

Details

'sf::write_sf' is used to format the output. The function returns TRUE on success, FALSE on failure, invisibly.

If the input object contains a 'centroid' then this is used to compute lat long coordinates, which are assigned the "WGS84" coordinate reference system. Otherwise the objects have equirectangular co-ordinates with centroid (0,0).

If feature = 'buffer' then buffer width determination is as described under plotCRT().

The output vector objects are constructed by forming a Voronoi tessellation of polygons around each of the locations and combining these polygons. The polygons on the outside of the study area extend outwards to an external rectangle. The 'mask' is used to mask out the areas of these polygons that are at a distance > maskbuffer from the nearest location.

Value

obj, invisibly

Examples

        tmpdir = tempdir()
        dsn <- paste0(tmpdir,'/arms')
        CRTwrite(readdata('exampleCRT.txt'), dsn = dsn, feature = 'arms',
        driver = 'ESRI Shapefile', maskbuffer = 0.2)
    

Aggregate data across records with duplicated locations

Description

aggregateCRT aggregates data from a "CRTsp" object or trial data frame containing multiple records with the same location, and outputs a list of class "CRTsp" containing single values for each location, for both the coordinates and the auxiliary variables.

Usage

aggregateCRT(trial, auxiliaries = NULL)

Arguments

Details

Variables that in the trial dataframe that are not included in auxiliaries are retained in the output algorithm "CRTsp" object, with the value corresponding to that of the first record for the location in the input data frame

Value

A list of class "CRTsp"

Examples

{
trial <- readdata('example_site.csv')
trial$base_denom <- 1
aggregated <- aggregateCRT(trial, auxiliaries = c("RDT_test_result","base_denom"))
}

Anonymize locations of a trial site

Description

anonymize_site transforms coordinates to remove potential identification information.

Usage

anonymize_site(trial, ID = NULL, latvar = "lat", longvar = "long")

Arguments

Details

The coordinates are transformed to support confidentiality of information linked to households by replacing precise geo-locations with transformed co-ordinates which preserve distances but not positions. The input may have either lat long or x,y coordinates. The function first searches for any lat long co-ordinates and converts these to x,y Cartesian coordinates. These are then are rotated by a random angle about a random origin. The returned object has transformed co-ordinates re-centred at the origin. Centroids stored in the "CRTsp" object are removed. Other data are unchanged.

Value

A list of class "CRTsp".

Examples

#Rotate and reflect test site locations
transformedTestlocations <- anonymize_site(trial =  readdata("exampleCRT.txt"))

Extract model coefficients

Description

coef.CRTanalysis method for extracting model fitted values

Usage

## S3 method for class 'CRTanalysis'
coef(object, ...)

Arguments

Value

the model coefficients returned by the statistical model run within the CRTanalysis function

Examples

{example <- readdata('exampleCRT.txt')
exampleGEE <- CRTanalysis(example, method = "GEE")
coef(exampleGEE)
}

Compute distance or surround values for a cluster randomized trial

Description

compute_distance computes distance or surround values for a cluster randomized trial (CRT)

Usage

compute_distance(
  trial,
  distance = "nearestDiscord",
  scale_par = NULL,
  auxiliary = NULL
)

Arguments

Details

For each selected distance measure, the function first checks whether the variable is already present, and carries out the calculations only if the corresponding field is absent from the trial data frame.

If distance = "nearestDiscord" is selected the computed values are Euclidean distances assigned a positive sign for the intervention arm of the trial, and a negative sign for the control arm.

If distance = "distanceAssigned" is selected the computed values are Euclidean distances to the nearest pixel in the auxiliary "CRTsp" object.

If distance = "disc" is specified, the disc statistic is computed for each location as the number of locations within the specified radius that are in the intervention arm (Anaya-Izquierdo & Alexander(2020)). The input value of scale_par is stored in the design list of the output "CRTsp" object. Recalculation is carried out if the input value of scale_par differs from the one in the input design list. The value of the the surround calculated based on intervened locations is divided by the value of the surround calculated on the basis of all locations, so the value returned is a proportion.

If distance = "kern" is specified, the Normal curve with standard deviation scale_par is used to simulate diffusion of the intervention effect by Euclidean distance. For each location in the trial, the contributions of all intervened locations are summed. As with distance = "disc", when distance = "kern" the surround calculated based on intervened locations is divided by the value of the surround calculated on the basis of all locations, so the value returned is a proportion.

If either distance = "hdep" or distance = "sdep" is specified then both the simplicial depth and Tukey half space depth are calculated using the algorithm of Rousseeuw & Ruts(1996). The half-depth probability within the intervention cloud (di) is computed with respect to other locations in the intervention arm (Anaya-Izquierdo & Alexander(2020)). The half-depth within the half-depth within the control cloud (dc) is also computed. CRTspat returns the proportion di/(dc + di).

If an auxiliary auxiliary "CRTsp" object is specified then either distanceAssigned or nearestDiscord (the default) is computed with respect to the assignments in the auxiliary. If the auxiliary is a grid with design$geometry set to 'triangle', 'square' or 'hexagon' then the distance is computed to the edge of the nearest grid pixel in the discordant arm (using a circular approximation for the perimeter) rather than to the point location itself.

Value

The input "CRTsp" object with additional column(s) added to the trial data frame with variable name corresponding to the input value of distance.

Examples

{
# Calculate the disc with a radius of 0.5 km
exampletrial <- compute_distance(trial = readdata('exampleCRT.txt'),
distance = 'disc', scale_par = 0.5)
}

Create INLA mesh for spatial analysis

Description

compute_mesh create objects required for INLA analysis of an object of class "CRTsp".

Usage

compute_mesh(
  trial = trial,
  offset = -0.1,
  max.edge = 0.25,
  inla.alpha = 2,
  maskbuffer = 0.5,
  pixel = 0.5
)

Arguments

Details

compute_mesh carries out the computationally intensive steps required for setting-up an INLA analysis of an object of class "CRTsp", creating the prediction mesh and the projection matrices. The mesh can be reused for different models fitted to the same geography. The computational resources required depend largely on the resolution of the prediction mesh. The prediction mesh is thinned to include only pixels centred at a distance less than maskbuffer from the nearest point.
A warning may be generated if the Matrix library is not loaded.

Value

list

• prediction Data frame containing the prediction points and covariate values

• A projection matrix from the observations to the mesh nodes.

• Ap projection matrix from the prediction points to the mesh nodes.

• indexs index set for the SPDE model

• spde SPDE model

• pixel pixel size (km)

Examples

{
# low resolution mesh for test dataset
library(Matrix)
library(sp)
example <- readdata('exampleCRT.txt')
exampleMesh=compute_mesh(example, pixel = 0.5)
}

Extract model fitted values

Description

fitted.CRTanalysis method for extracting model fitted values

Usage

## S3 method for class 'CRTanalysis'
fitted(object, ...)

Arguments

Value

the fitted values returned by the statistical model run within the CRTanalysis function

Examples

{example <- readdata('exampleCRT.txt')
exampleGEE <- CRTanalysis(example, method = "GEE")
fitted_values <- fitted(exampleGEE)
}

Convert lat long co-ordinates to x,y

Description

latlong_as_xy converts co-ordinates expressed as decimal degrees into x,y

Usage

latlong_as_xy(trial, latvar = "lat", longvar = "long")

Arguments

Details

The output object contains the input locations replaced with Cartesian coordinates in units of km, centred on (0,0), corresponding to using the equirectangular projection (valid for small areas). Other data are unchanged.

Value

A list of class "CRTsp" containing the following components:

Examples

examplexy <- latlong_as_xy(readdata("example_latlong.csv"))

Graphical displays of the geography of a CRT

Description

plotCRT returns graphical displays of the geography of a CRT or of the results of statistical analyses of a CRT

Usage

plotCRT(
  object,
  map = FALSE,
  distance = "nearestDiscord",
  fill = "arms",
  showLocations = FALSE,
  showClusterBoundaries = TRUE,
  showClusterLabels = FALSE,
  showBuffer = FALSE,
  cpalette = NULL,
  buffer_width = NULL,
  maskbuffer = 0.2,
  labelsize = 4,
  legend.position = NULL
)

Arguments

Details

If map = FALSE and the input is a trial data frame or a CRTsp object, containing a randomisation to arms, a stacked bar chart of the outcome grouped by the specified distance is produced. If the specified distance has not yet been calculated an error is returned.

If map = FALSE and the input is a CRTanalysis object a plot of the estimated spillover function is generated. The fitted spillover function is plotted as a continuous blue line against the measure the surround or of the distance to the nearest discordant location. Using the same axes, data summaries are plotted for ten categories of distance from the boundary. Both the average of the outcome and confidence intervals are plotted.

• For analyses with logit link function the outcome is plotted as a proportion.
  

• For analyses with log or cloglog link function the data are plotted on a scale of the Williams mean (mean of exp(log(x + 1))) - 1) rescaled so that the median matches the fitted curve at the midpoint.
  

If map = TRUE a thematic map corresponding to the value of fill is generated.

• fill = 'clusters' or leads to thematic map showing the locations of the clusters

• fill = 'arms' leads to a thematic map showing the geography of the randomization

• fill = 'distance' leads to a raster plot of the distance to the nearest discordant location.

• fill = 'prediction' leads to a raster plot of predictions from an 'INLA' model.

If showBuffer = TRUE the map is overlaid with a grey transparent layer showing which areas are within a defined distance of the boundary between the arms. Possibilities are:

• If the trial has not been randomised or if showBuffer = FALSE no buffer is displayed

• If buffer_width takes a positive value then buffers of this width are displayed irrespective of any pre-specified or spillover limits.

• If the input is a 'CRTanalysis' and spillover limits have been estimated by an 'LME4' or 'INLA' model then these limits are used to define the displayed buffer.

• If buffer_width is not specified and no spillover limits are available, then any pre-specified buffer (e.g. one generated by specify_buffer()) is displayed.

A message is output indicating which of these possibilities applies.

Value

graphics object produced by the ggplot2 package

Examples

{example <- readdata('exampleCRT.txt')
#Plot of data by distance
plotCRT(example)
#Map of locations only
plotCRT(example, map = TRUE, fill = 'none', showLocations = TRUE,
           showClusterBoundaries=FALSE, maskbuffer=0.2)
#show cluster boundaries and number clusters
plotCRT(example, map = TRUE, fill ='none', showClusterBoundaries=TRUE,
           showClusterLabels=TRUE, maskbuffer=0.2, labelsize = 2)
#show clusters in colour
plotCRT(example, map = TRUE, fill = 'clusters', showClusterLabels = TRUE,
          labelsize=2, maskbuffer=0.2)
#show arms
plotCRT(example, map = TRUE,
fill = 'arms', maskbuffer=0.2, legend.position=c(0.8,0.8))
#spillover plot
analysis <- CRTanalysis(example)
 plotCRT(analysis, map = FALSE)
}

Model predictions

Description

predict.CRTanalysis method for extracting model predictions

Usage

## S3 method for class 'CRTanalysis'
predict(object, ...)

Arguments

Value

the model predictions returned by the statistical model run within the CRTanalysis function

Examples

{example <- readdata('exampleCRT.txt')
exampleGEE <- CRTanalysis(example, method = "GEE")
predictions <- predict(exampleGEE)
}#'

Randomize a two-armed cluster trial

Description

randomizeCRT carries out randomization of clusters and augments the trial data frame with assignments to arms

Usage

randomizeCRT(
  trial,
  matchedPair = FALSE,
  baselineNumerator = "base_num",
  baselineDenominator = "base_denom"
)

Arguments

Value

A list of class "CRTsp" containing the following components:

Examples

# Randomize the clusters in an example trial
exampleCRT <- randomizeCRT(trial = readdata('exampleCRT.txt'), matchedPair = TRUE)

Read example dataset

Description

readdata reads a file from the package library of example datasets

Usage

readdata(filename)

Arguments

Details

The input file name should include the extension (either .csv or .txt). The resulting object is a data frame if the extension is .csv.

Value

R object corresponding to the text file

Examples

exampleCRT <- readdata('exampleCRT.txt')

Extract model residuals

Description

residuals.CRTanalysis method for extracting model residuals

Usage

## S3 method for class 'CRTanalysis'
residuals(object, ...)

Arguments

Value

the residuals from the statistical model run within the CRTanalysis function

Examples

{example <- readdata('exampleCRT.txt')
exampleGEE <- CRTanalysis(example, method = "GEE")
residuals <- residuals(exampleGEE)
}

Simulation of cluster randomized trial with spillover

Description

simulateCRT generates simulated data for a cluster randomized trial (CRT) with geographic spillover between arms.

Usage

simulateCRT(
  trial = NULL,
  effect = 0,
  outcome0 = NULL,
  generateBaseline = TRUE,
  matchedPair = TRUE,
  scale = "proportion",
  baselineNumerator = "base_num",
  baselineDenominator = "base_denom",
  denominator = NULL,
  ICC_inp = NULL,
  kernels = 200,
  sigma_m = NULL,
  spillover_interval = NULL,
  tol = 0.005
)

Arguments

Details

Synthetic data are generated by sampling around the values of variable propensity, which is a numerical vector (taking positive values) of length equal to the number of locations. There are three ways in which propensity can arise:

1. propensity can be provided as part of the input trial object.

2. Baseline numerators and denominators (values of baselineNumerator and baselineDenominator may be provided. propensity is then generated as the numerator:denominator ratio for each location in the input object

3. Otherwise propensity is generated using a 2D Normal kernel density. The OOR::StoSOO is used to achieve an intra-cluster correlation coefficient (ICC) that approximates the value of 'ICC_inp' by searching for an appropriate value of the kernel bandwidth.

num[i], the synthetic outcome for location i is simulated with expectation:

E(num[i]) = outcome0[i] * propensity[i] * denom[i] * (1 - effect*I[i])/mean(outcome0[] * propensity[])

The sampling distribution of num[i] depends on the value of scale as follows:

• scale=’continuous’: Values of num are sampled from a Normal distributions with means E(num[i]) and variance determined by the fitting to ICC_inp.
  

• scale=’count’: Simulated events are allocated to locations via multivariate hypergeometric distributions parameterised with E(num[i]).
  

• scale=’proportion’: Simulated events are allocated to locations via multinomial distributions parameterised with E(num[i]).
  

denominator may specify a vector of numeric (non-zero) values in the input "CRTsp" or data.frame which is returned as variable denom. It acts as a scale-factor for continuous outcomes, rate-multiplier for counts, or denominator for proportions. For discrete data all values of denom must be > 0.5 and are rounded to the nearest integer in calculations of num.

By default, denom is generated as a vector of ones, leading to simulation of dichotomous outcomes if scale=’proportion’.

If baseline numerators and denominators are provided then the output vectors base_denom and base_num are set to the input values. If baseline numerators and denominators are not provided then the synthetic baseline data are generated by sampling around propensity in the same way as the outcome data, but with the effect size set to zero.

If matchedPair is TRUE then pair-matching on the baseline data will be used in randomization providing there are an even number of clusters. If there are an odd number of clusters then matched pairs are not generated and an unmatched randomization is output.

Either sigma_m or spillover_interval must be provided. If both are provided then the value of sigma_m is overwritten by the standard deviation implicit in the value of spillover_interval. Spillover is simulated as arising from a diffusion-like process.

For further details see Multerer (2021)

Value

A list of class "CRTsp" containing the following components:

Examples

{smalltrial <- readdata('smalltrial.csv')
 simulation <- simulateCRT(smalltrial,
  effect = 0.25,
  ICC_inp = 0.05,
  outcome0 = 0.5,
  matchedPair = FALSE,
  scale = 'proportion',
  sigma_m = 0.6,
  tol = 0.05)
 summary(simulation)
 }

Specification of buffer zone in a cluster randomized trial

Description

specify_buffer specifies a buffer zone in a cluster randomized trial (CRT) by flagging those locations that are within a defined distance of those in the opposite arm.

Usage

specify_buffer(trial, buffer_width = 0)

Arguments

Value

A list of class "CRTsp" containing the following components:

Examples

#Specify a buffer of 200m
exampletrial <- specify_buffer(trial = readdata('exampleCRT.txt'), buffer_width = 0.2)

Assign locations to clusters in a CRT

Description

specify_clusters algorithmically assigns locations to clusters by grouping them geographically

Usage

specify_clusters(
  trial = trial,
  c = NULL,
  h = NULL,
  algorithm = "NN",
  reuseTSP = FALSE,
  auxiliary = NULL
)

Arguments

Details

Either c or h must be specified. If both are specified the input value of c is ignored.

The reuseTSP parameter is used to allow the path to be reused for creating alternative allocations with different cluster sizes.

If an auxiliary auxiliary "CRTsp" object is specified then the other options are ignored and the cluster assignments (and arm assignments if available) are taken from the auxiliary object. The trial data frame is augmented with a column "nearestPixel" containing the distance to boundary of the nearest grid pixel in the auxiliary. If the auxiliary is a grid with design$geometry set to 'triangle', 'square' or 'hexagon' then the distance is computed to the edge of the nearest grid pixel in the discordant arm (using a circular approximation for the perimeter) rather than to the point location itself. If the point is within the pixel then the distance is given a negative sign.

Value

A list of class "CRTsp" containing the following components:

Examples

#Assign clusters of average size h = 40 to a test set of co-ordinates, using the kmeans algorithm
exampletrial <- specify_clusters(trial = readdata('exampleCRT.txt'),
                            h = 40, algorithm = 'kmeans', reuseTSP = FALSE)

Summary of the results of a statistical analysis of a CRT

Description

summary.CRTanalysis generates a summary of a CRTanalysis including the main results

Usage

## S3 method for class 'CRTanalysis'
summary(object, ...)

Arguments

Value

No return value, writes text to the console.

Examples

{example <- readdata('exampleCRT.txt')
exampleT <- CRTanalysis(example, method = "T")
summary(exampleT)
}

Summary description of a "CRTsp" object

Description

summary.CRTsp provides a description of a "CRTsp" object

Usage

## S3 method for class 'CRTsp'
summary(object, maskbuffer = 0.2, ...)

Arguments

Value

No return value, write text to the console.

Examples

summary(CRTsp(readdata('exampleCRT.txt')))