D-score for child development

Description

The dscore package implements tools needed to calculate the D-score, a numerical score that summarizes early development in children by one number, the D-score.

User functions

The available functions are:

Built-in data

The package contains the following built-in data:

Acknowledgements

The authors wish to recognize the principal investigators and their study team members for their generous contribution of the data that made this tool possible and the members of the Ki team who directly or indirectly contributed to the study: Amina Abubakar, Claudia R. Lindgren Alves, Orazio Attanasio, Maureen M. Black, Maria Caridad Araujo, Susan M. Chang-Lopez, Gary L. Darmstadt, Bernice M. Doove, Wafaie Fawzi, Lia C.H. Fernald, Günther Fink, Emanuela Galasso, Melissa Gladstone, Sally M. Grantham-McGregor, Cristina Gutierrez de Pineres, Pamela Jervis, Jena Derakhshani Hamadani, Charlotte Hanlon, Simone M. Karam, Gillian Lancaster, Betzy Lozoff, Gareth McCray, Jeffrey R Measelle, Girmay Medhin, Ana M. B. Menezes, Lauren Pisani, Helen Pitchik, Muneera Rasheed, Lisy Ratsifandrihamanana, Sarah Reynolds, Linda Richter, Marta Rubio-Codina, Norbert Schady, Limbika Sengani, Chris Sudfeld, Marcus Waldman, Susan P. Walker, Ann M. Weber and Aisha K. Yousafzai.

This study was supported by the Bill & Melinda Gates Foundation. The contents are the sole responsibility of the authors and may not necessarily represent the official views of the Bill & Melinda Gates Foundation or other agencies that may have supported the primary data studies used in the present study.

Author(s)

Maintainer: Stef van Buuren stef.vanbuuren@tno.nl

Authors:

• Iris Eekhout iris.eekhout@tno.nl

• Arjan Huizing arjan.huizing@tno.nl

• Jonathan Seiden jseiden@g.harvard.edu

References

Jacobusse, G., S. van Buuren, and P.H. Verkerk. 2006. “An Interval Scale for Development of Children Aged 0-2 Years.” Statistics in Medicine 25 (13): 2272–83. https://stefvanbuuren.name/publication/jacobusse-2006/

Van Buuren S (2014). Growth charts of human development. Stat Methods Med Res, 23(4), 346-368. https://stefvanbuuren.name/publication/van-buuren-2014-gc/

Weber AM, Rubio-Codina M, Walker SP, van Buuren S, Eekhout I, Grantham-McGregor S, Caridad Araujo M, Chang SM, Fernald LCH, Hamadani JD, Hanlon A, Karam SM, Lozoff B, Ratsifandrihamanana L, Richter L, Black MM (2019). The D-score: a metric for interpreting the early development of infants and toddlers across global settings. BMJ Global Health, BMJ Global Health 4: e001724. https://gh.bmj.com/content/bmjgh/4/6/e001724.full.pdf.

GSED team (Maureen Black, Kieran Bromley, Vanessa Cavallera (lead author), Jorge Cuartas, Tarun Dua (corresponding author), Iris Eekhout, Gunther Fink, Melissa Gladstone, Katelyn Hepworth, Magdalena Janus, Patricia Kariger, Gillian Lancaster, Dana McCoy, Gareth McCray, Abbie Raikes, Marta Rubio-Codina, Stef van Buuren, Marcus Waldman, Susan Walker and Ann Weber). 2019. “The Global Scale for Early Development (GSED).” Early Childhood Matters. https://earlychildhoodmatters.online/2019/the-global-scale-for-early-development-gsed/

See Also

Useful links:

• https://github.com/d-score/dscore

• https://d-score.org/dscore/

• https://d-score.org/dbook1/

• Report bugs at https://github.com/d-score/dscore/issues

Collection of items fitting the Rasch model

Description

A data frame with administrative information per item with difficulty estimates (tau) from the Rasch model. The item bank provides the basic information to calculate D-scores. The items in the item bank are a subset of all items as collected in builtin_itemtable.

Usage

builtin_itembank

Format

A data.frame with variables:

Details

The difficulty estimates were estimated by a Rasch model. The key indicates the specific Rasch model used to estimate the difficulty. Strictly speaking, one can only compare D-score calculated from the same key.

Note

Updates:

• Dec 01, 2022 - Overwrite labels of gto by correct item order.

• Dec 05, 2022 - Adds key gsed2212, adding instruments gl1 and gs1, and defining correct order for gto

• Jan 05, 2023 - Adds instrument gh1 to key gsed2212

See Also

dscore(), get_tau(), builtin_itemtable()

Examples

# count number of items per instrument in each key
table(builtin_itembank$instrument, builtin_itembank$key)

Collection of items from instruments measuring early child development

Description

The built-in variable builtin_itemtable contains the name and label of items for measuring early child development.

Usage

builtin_itemtable

Format

A data.frame with variables:

Details

The builtin_itemtable is created by script data-raw/R/save_builtin_itemtable.R.

Updates:

• May 30, 2022 - added gto (LF) and gpa (SF) items

• June 1, 2022 - added seven gsd items

• Nov 24, 2022 - Added instruments gs1, gs2

• Dec 01, 2022 - Labels of gto replaced by correct order. Incorrect item order affects analyses done on LF between 20220530 - 20221201 !!!

• Dec 05, 2022 - Redefines gs1 and instrument for Phase 2, removes gs2 (139) Adds gl1 (Long Form Phase 2 items 155)

• Jan 05, 2023 - Adds 55 items from GSED-HF

Author(s)

Compiled by Stef van Buuren using different sources

Available keys for calculating the D-score

Description

A key contains the item difficulty estimates from a given Rasch model. The difficulty estimates (tau) are used to calculate D-scores. D-scores can only be compared when calculated with the same key.

Usage

builtin_keys

Format

builtin_keys is a data.frame with variables:

Note

20240609 SvB: Added builtin_keys table by ⁠data-raw\data\R\save_builtin_keys.R⁠

Collection of age-conditional reference distributions

Description

A data frame containing the age-dependent distribution of the D-score for children aged 0-5 years. The distribution is modelled after the LMS distribution (Cole & Green, 1992) or BCT model (Stasinopoulos & Rigby, 2022) and is equal for both boys and girls. The LMS/BCT values can be used to graph reference charts and to calculate age-conditional Z-scores, also known as the Development-for-Age Z-score (DAZ).

Usage

builtin_references

Format

A data.frame with the following variables:

Details

Here are more details on the reference population: The "dutch" references were calculated from the SMOCC data, and cover age range 0-2.5 years (van Buuren, 2014). The "gcdg" references were calculated from the 15 cohorts of the GCDG-study, and cover age range 0-5 years (Weber, 2019). The "phase1" references were calculated from the GSED Phase 1 validation data (GSED-BGD, GSED-PAK, GSED-TZA) cover age range 2w-3.5 years. The age range 3.5-5 yrs is linearly extrapolated and are only indicative. The "preliminary_standards" were calculated from the GSED Phase 1 validation data (GSED-BGD, GSED-PAK, GSED-TZA) using a subset of children with covariate indicating healthy development.

References

Cole TJ, Green PJ (1992). Smoothing reference centile curves: The LMS method and penalized likelihood. Statistics in Medicine, 11(10), 1305-1319.

Van Buuren S (2014). Growth charts of human development. Stat Methods Med Res, 23(4), 346-368. https://stefvanbuuren.name/publication/van-buuren-2014-gc/

Weber AM, Rubio-Codina M, Walker SP, van Buuren S, Eekhout I, Grantham-McGregor S, Caridad Araujo M, Chang SM, Fernald LCH, Hamadani JD, Hanlon A, Karam SM, Lozoff B, Ratsifandrihamanana L, Richter L, Black MM (2019). The D-score: a metric for interpreting the early development of infants and toddlers across global settings. BMJ Global Health, BMJ Global Health 4: e001724. https://gh.bmj.com/content/bmjgh/4/6/e001724.full.pdf

Stasinopoulos M, Rigby R (2022). gamlss.dist: Distributions for Generalized Additive Models for Location Scale and Shape, R package version 6.0-3, https://CRAN.R-project.org/package=gamlss.dist

See Also

dscore()

Examples

# get an overview of available references per key
table(builtin_references$population, builtin_references$key)

Calculate posterior of ability

Description

If the tauj is not within the range rello - relhi from the dynamic EAP, the procedure ignores the score of item j.

Usage

calculate_posterior(scores, tau, qp, scale, mu, sd, relhi, rello)

Arguments

Value

A list with three elements:

Since ⁠dscore V40.1⁠ the function does not return the "start" element.

Author(s)

Stef van Buuren, Arjan Huizing, 2020

Median D-score from the default references for the given key

Description

Returns the age-interpolated median of the D-score of the default reference for a given key.

Usage

count_mu(t, key, prior_mean_NA = NA_real_)

Arguments

Details

Do not use this function if you want the median D-score for a specific reference.

DEPRECATED in dscore 1.9.6

Value

A vector of length length(t) with the median of the default reference population for the key.

Median of Dutch references

Description

Returns the age-interpolated median of the Dutch references (van Buuren 2014). The working range is 0-3 years. This function is used to set prior mean under key "dutch".

Usage

count_mu_dutch(t)

Arguments

Value

A vector of length length(t) with the median of the Dutch references.

Note

Internal function. Called by dscore()

Examples

dscore:::count_mu_dutch(0:2)

Median of GCDG references

Description

Returns the age-interpolated median of the GCDG references (Weber et al, 2019). The working range is 0-4 years. This function is used to set prior mean under keys "gcdg" and "gsed1912".

Usage

count_mu_gcdg(t)

Arguments

Value

A vector of length length(t) with the median of the GCDG references.

Note

Internal function. Called by dscore()

Examples

dscore:::count_mu_gcdg(0:2)

Median of phase1 references

Description

Returns the age-interpolated median of the phase1 references based on LF & SF in GSED-BGD, GSED-PAK, GSED-TZA. This function is used to set prior mean under keys "293_0" and "gsed2212".

Usage

count_mu_phase1(t)

Arguments

Details

The interpolation is done in two rounds. First round: Calculate D-scores using .gcdg prior-mean, calculate reference, estimate round 1 parameters used in this function. Round 2: Calculate D-score using round 1 estimates as the prior mean (most differences are within 0.1 D-score points), recalculate references, estimate round 2 parameters used in this function.

Round 1: Count model: <= 9MN: 21.3449 + 26.4916 t + 7.0251(t + 0.2) Count model: > 9Mn & <= 3.5 YR: 14.69947 - 12.18636 t + 69.11675(t + 0.92) Linear model: > 3.5 YRS: 61.40956 + 3.80904 t

Round 2: Count model: < 9MND: 20.5883 + 27.3376 t + 6.4254(t + 0.2) Count model: > 9MND & < 3.5 YR: 14.63748 - 12.11774 t + 69.05463(t + 0.92) Linear model: > 3.5 YRS: 61.37967 + 3.83513 t

The working range is 0-3.5 years. After the age of 3.5 years, the function will increase at an arbitrary rate of 3.8 D-score points per year.

Value

A vector of length length(t) with the median of the GCDG references.

Note

Internal function. Called by dscore()

Author(s)

Stef van Buuren, on behalf of GSED project

Examples

dscore:::count_mu_phase1(0:5)

Median of preliminary_standards

Description

Returns the age-interpolated median of the preliminary_standards based on LF & SF in GSED-BGD, GSED-PAK, GSED-TZA. This function is used to set prior mean under key "gsed2406".

Usage

count_mu_preliminary_standards(t)

Arguments

Value

A vector of length length(t) with the median of the GCDG references.

Note

Internal function. Called by dscore()

Author(s)

Stef van Buuren, on behalf of GSED project

Examples

dscore:::count_mu_preliminary_standards(0:5)

Calculate Development-for-Age Z-score (DAZ)

Description

The daz() function calculated the Development-for-Age Z-score (DAZ). The DAZ represents a child's D-score after adjusting for age by an external age-conditional reference.

Usage

daz(d, x, reference_table = NULL, dec = 3, verbose = FALSE)

zad(z, x, reference_table = NULL, dec = 2, verbose = FALSE)

Arguments

Details

The zad() is the inverse of daz(): Given age and the Z-score, it finds the raw D-score.

Note 1: The Box-Cox Cole and Green (BCCG) and Box-Cox t (BCT) distributions model only positive D-score values. To increase robustness, the daz() and zad() functions will round up any D-scores lower than 1.0 to 1.0.

Note 2: The daz() and zad() function call modified version of the pBCT() and qBCT() functions from gamlss for better handling of NA's and rounding.

Value

Unnamed numeric vector with Z-scores of length length(d).

Unnamed numeric vector with D-scores of length length(z).

Author(s)

Stef van Buuren

References

Cole TJ, Green PJ (1992). Smoothing reference centile curves: The LMS method and penalized likelihood. Statistics in Medicine, 11(10), 1305-1319.

See Also

dscore()

Examples

# using default reference and key
daz(d = c(35, 50), x = c(0.5, 1.0))

# print out names of the used reference table
daz(d = c(35, 50), x = c(0.5, 1.0), verbose = TRUE)

# using the default reference in key gcdg
reftab <- get_reference(key = "gcdg")
daz(d = c(35, 50), x = c(0.5, 1.0), reference_table = reftab)

# using Dutch reference in default key
reftab <- get_reference(population = "dutch", verbose = TRUE)
daz(d = c(35, 50), x = c(0.5, 1.0), reference_table = reftab)
# population median at ages 0.5, 1 and 2 years, default reference
zad(z = rep(0, 3), x = c(0.5, 1, 2))

# population median at ages 0.5, 1 and 2 years, gcdg key
reftab <- get_reference(key = "gcdg", verbose = TRUE)
zad(z = rep(0, 3), x = c(0.5, 1, 2), reference_table = reftab)

# population median at ages 0.5, 1 and 2 years, dutch key
reftab <- get_reference(key = "dutch", verbose = TRUE)
zad(z = rep(0, 3), x = c(0.5, 1, 2), reference = reftab)

Decomposes item names into their four components

Description

This utility function decomposes item names into components: instrument, domain, mode and number

Usage

decompose_itemnames(x)

Arguments

Details

The gsed-naming convention is as follows. Position 1-3 codes the instrument, position 4-5 codes the domain, position 6 codes direct/caregiver/message, positions 7-9 is a item sequence number.

Value

A data.frame with length(x) rows and four columns, named: instrument, domain, mode, and number.

Author(s)

Stef van Buuren

References

https://docs.google.com/spreadsheets/d/1zLsSW9CzqshL8ubb7K5R9987jF4YGDVAW_NBw1hR2aQ/edit#gid=0

See Also

sort_itemnames()

Examples

itemnames <- c("aqigmc028", "grihsd219", "", "by1mdd157", "mdsgmd006")
decompose_itemnames(itemnames)

D-score estimation

Description

The dscore() function estimates the following quantities: D-score, a numeric score that quantifies child development by one number, Development-for-Age Z-score (DAZ) that corrects the D-score for age, standard error of measurement (SEM) of the D-score.

Usage

dscore(
  data,
  items = names(data),
  key = NULL,
  population = NULL,
  xname = "age",
  xunit = c("decimal", "days", "months"),
  prepend = NULL,
  itembank = NULL,
  metric = c("dscore", "logit"),
  prior_mean = NULL,
  prior_mean_NA = NULL,
  prior_sd = NULL,
  prior_sd_NA = NULL,
  transform = NULL,
  qp = NULL,
  dec = c(2L, 3L),
  relevance = c(-Inf, Inf),
  algorithm = c("current", "1.8.7"),
  verbose = FALSE
)

dscore_posterior(
  data,
  items = names(data),
  key = NULL,
  population = NULL,
  xname = "age",
  xunit = c("decimal", "days", "months"),
  prepend = NULL,
  itembank = NULL,
  metric = c("dscore", "logit"),
  prior_mean = NULL,
  prior_mean_NA = NULL,
  prior_sd = NULL,
  prior_sd_NA = NULL,
  transform = NULL,
  qp = NULL,
  dec = c(2L, 3L),
  relevance = c(-Inf, Inf),
  algorithm = c("current", "1.8.7"),
  verbose = FALSE
)

Arguments

Details

The scoring algorithm is based on the method by Bock and Mislevy (1982). The method uses Bayes rule to update a prior ability into a posterior ability.

The item names should correspond to the "gsed" lexicon.

A key is defined by the set of estimated item difficulties.

As a general rule, one should only compare D-scores that are calculated using the same key and the same set of quadrature points. For calculating D-scores on new data, the advice is to use the default, which currently is "gsed2406".

The default starting prior is a mean calculated from a so-called "Count model" that describes mean D-score as a function of age. The The Count models are implemented in the function ⁠[get_mu()]⁠. By default, the spread of the starting prior is 5 D-score points around the mean D-score, which corresponds to approximately 1.5 to 2 times the normal spread of child of a given age. The starting prior is informative for very short test (say <5 items), but has little impact on the posterior for larger tests.

Value

The dscore() function returns a data.frame with nrow(data) rows. Optionally, the first block of columns can be copied to the result by using prepend. The second block consists of the following columns:

The D-score in column d is a linear scale, with values usually ranging from 0 to 100. The D-score is NA if age is missing or if age is lower than -1/12. It is possible to calculate D-scores for cases with missing ages by setting prior_mean_NA and prior_sd_NA to some reasonable value, e.g., prior_mean_NA = 50 and prior_sd_NA = 20, for the sample at hand.

The SEM is a positive number that quantifies the uncertainty of the D-score. It is NA if the D-score is NA.

The DAZ in column daz is a Z-score that corrects the D-score for age. It is NA when there are no reference values for the given age, or when the D-score is extremely unlikely to be valid at the given age.

Advanced applications: The dscore_posterior() function returns a data frame with nrow(data) rows and length(qp) plus prepended columns with the full posterior density of the D-score at each quadrature point. If no valid responses are found, dscore_posterior() returns the prior density. Versions prior to 1.8.5 returned a matrix (instead of a data.frame). Code that depends on the result being a matrix may break and may need adaptation.

Author(s)

Stef van Buuren, Iris Eekhout, Arjan Huizing (2022)

References

Bock DD, Mislevy RJ (1982). Adaptive EAP Estimation of Ability in a Microcomputer Environment. Applied Psychological Measurement, 6(4), 431-444.

Van Buuren S (2014). Growth charts of human development. Stat Methods Med Res, 23(4), 346-368. https://stefvanbuuren.name/publication/van-buuren-2014-gc/

Weber AM, Rubio-Codina M, Walker SP, van Buuren S, Eekhout I, Grantham-McGregor S, Caridad Araujo M, Chang SM, Fernald LCH, Hamadani JD, Hanlon A, Karam SM, Lozoff B, Ratsifandrihamanana L, Richter L, Black MM (2019). The D-score: a metric for interpreting the early development of infants and toddlers across global settings. BMJ Global Health, BMJ Global Health 4: e001724. https://gh.bmj.com/content/bmjgh/4/6/e001724.full.pdf

See Also

builtin_keys(), builtin_itembank(), builtin_itemtable(), builtin_references(), get_tau(), posterior(), milestones()

Examples

# using all defaults and properly formatted data
ds <- dscore(milestones)
head(ds)

# step-by-step example
data <- data.frame(
  id = c(
    "Jane", "Martin", "ID-3", "No. 4", "Five", "6",
    NA_character_, as.character(8:10)
  ),
  age = rep(round(21 / 365.25, 4), 10),
  ddifmd001 = c(NA, NA, 0, 0, 0, 1, 0, 1, 1, 1),
  ddicmm029 = c(NA, NA, NA, 0, 1, 0, 1, 0, 1, 1),
  ddigmd053 = c(NA, 0, 0, 1, 0, 0, 1, 1, 0, 1)
)
items <- names(data)[3:5]

# third item is not part of the default key
get_tau(items, verbose = TRUE)

# calculate D-score
dscore(data)

# prepend id variable to output
dscore(data, prepend = "id")

# or prepend all data
# dscore(data, prepend = colnames(data))

# calculate full posterior
p <- dscore_posterior(data)

# check that rows sum to 1
rowSums(p)

# plot full posterior for measurement 7
barplot(as.matrix(p[7, 12:36]),
  names = 1:25,
  xlab = "D-score", ylab = "Density", col = "grey",
  main = "Full D-score posterior for measurement in row 7",
  sub = "D-score (EAP) = 11.58, SEM = 3.99")

# plot P10, P50 and P90 of D-score references
g <- expand.grid(age = seq(0.1, 4, 0.1), p = c(0.1, 0.5, 0.9))
d <- zad(z = qnorm(g$p), x = g$age, verbose = TRUE)
matplot(
  x = matrix(g$age, ncol = 3), y = matrix(d, ncol = 3), type = "l",
  lty = 1, col = "blue", xlab = "Age (years)", ylab = "D-score",
  main = "D-score preliminary standards: P10, P50 and P90")
abline(h = seq(10, 80, 10), v = seq(0, 4, 0.5), col = "gray", lty = 2)

# add measurements made on very preterms, ga < 32 weeks
ds <- dscore(milestones)
points(x = ds$a, y = ds$d, pch = 19, col = "red")

Get age equivalents of items that have a difficulty estimate

Description

This function calculates the ages at which a certain percent in the reference population passes the items.

Usage

get_age_equivalent(
  items,
  pct = c(10, 50, 90),
  key = NULL,
  population = NULL,
  transform = NULL,
  itembank = dscore::builtin_itembank,
  xunit = c("decimal", "days", "months"),
  verbose = FALSE
)

Arguments

Value

data.frame with four columns: item, d (D-score), pct (percentile), and a (age-equivalent, in xunit units).

Note

The function internally defines a scale factor given the key.

Examples

get_age_equivalent(c("gpagmc018", "gtogmd026", "ddicmm050"))

Extract item names

Description

The get_itemnames() function matches names against the 9-code template. This is useful for quickly selecting names of items from a larger set of names.

Usage

get_itemnames(
  x,
  instrument = NULL,
  domain = NULL,
  mode = NULL,
  number = NULL,
  strict = FALSE,
  itemtable = NULL,
  order = "idnm"
)

Arguments

Details

The gsed-naming convention is as follows. Position 1-3 codes the instrument, position 4-5 codes the domain, position 6 codes direct/caregiver/message, positions 7-9 is a item sequence number.

Value

A vector with names of items

Author(s)

Stef van Buuren 2020

See Also

sort_itemnames()

Examples

itemnames <- c("aqigmc028", "grihsd219", "", "age", "mdsgmd999")

# filter out impossible names
get_itemnames(itemnames)
get_itemnames(itemnames, strict = TRUE)

# only items from specific instruments
get_itemnames(itemnames, instrument = c("aqi", "mds"))
get_itemnames(itemnames, instrument = c("aqi", "mds"), strict = TRUE)

# get all items from the se domain of iyo instrument
get_itemnames(domain = "se", instrument = "iyo")

# get all item from the se domain with direct assessment mode
get_itemnames(domain = "se", mode = "d")

# get all item numbers 70 and 73 from gm domain
get_itemnames(number = c(70, 73), domain = "gm")

Get a subset of items from the itemtable

Description

The builtin_itemtable object in the dscore package contains basic meta-information about items: a name, the equate group, and the item label. The get_itemtable() function returns a subset of items in the itemtable.

Usage

get_itemtable(items = NULL, itemtable = NULL, decompose = FALSE)

Arguments

Value

A data.frame with seven columns.

See Also

get_labels(), get_itemnames()

Examples

head(get_itemtable(), 3)
get_itemtable(LETTERS[1:3], "")

Get labels for items

Description

The get_labels() function obtains the item labels for a specified set of items.

Usage

get_labels(items = NULL, trim = NULL, itemtable = NULL)

Arguments

Value

A named character vector with length(items) elements with item labels, in the same order as in items.

See Also

builtin_itemtable(), get_itemnames()

Examples

# get labels of first two Macarthur items
get_labels(get_itemnames(instrument = "mac", number = 1:2), trim = 40)

Median D-score from the base population for a given key

Description

Returns the age-interpolated median of the D-score of the default reference for a given key.

Usage

get_mu(t, key, prior_mean_NA = NA_real_)

Arguments

Details

Use get_reference() for more options.

Value

A vector of length length(t) with the median of the default reference population for the key.

Get D-score reference

Description

The get_reference() function selects the D-score reference distribution.

Usage

get_reference(
  population = NULL,
  key = NULL,
  references = dscore::builtin_references,
  verbose = FALSE,
  ...
)

Arguments

Value

A data.frame with the LMS reference values.

Note

No references for population "gsed" exist. The function will silently rewrite population = "gsed" into to the population = "gsed".

The "dutch" reference was published in Van Buuren (2014) The "gcdg" was calculated from 15 cohorts with direct observations (Weber, 2019). The "phase1" references were calculated from the GSED Phase 1 validation data (GSED-BGD, GSED-PAK, GSED-TZA) cover age range 2w-3.5 years. The age range 3.5-5 yrs is linearly extrapolated and are only indicative. The "preliminary_standards" references were calculated from the GSED Phase 1 validation using a subset of children with healthy development.

References

Van Buuren S (2014). Growth charts of human development. Stat Methods Med Res, 23(4), 346-368.

Weber AM, Rubio-Codina M, Walker SP, van Buuren S, Eekhout I, Grantham-McGregor S, Caridad Araujo M, Chang SM, Fernald LCH, Hamadani JD, Hanlon A, Karam SM, Lozoff B, Ratsifandrihamanana L, Richter L, Black MM (2019). The D-score: a metric for interpreting the early development of infants and toddlers across global settings. BMJ Global Health, BMJ Global Health 4: e001724. https://gh.bmj.com/content/bmjgh/4/6/e001724.full.pdf.

See Also

builtin_references()

Examples

# see key-population combinations of builtin_references
table(builtin_references$key, builtin_references$population)

# get the default reference
reftab <- get_reference()
head(reftab, 2)

# get the default reference for the key "gsed2212"
reftab <- get_reference(key = "gsed2212", verbose = TRUE)

# get dutch reference for default key
reftab <- get_reference(population = "dutch", verbose = TRUE)

# loading a non-existing reference yields zero rows
reftab <- get_reference(population = "france", verbose = TRUE)
nrow(reftab)

Obtain difficulty parameters from item bank

Description

Searches the item bank for matching items, and returns the difficulty estimates. Matching is done by item name. Comparisons are done in lower case.

Usage

get_tau(
  items,
  key = NULL,
  itembank = dscore::builtin_itembank,
  verbose = FALSE
)

Arguments

Value

A named vector with the difficulty estimate per item with length(items) elements.

Author(s)

Stef van Buuren 2020

See Also

builtin_itembank(), dscore()

Examples

# difficulty levels in the GHAP lexicon
get_tau(items = c("ddifmd001", "DDigmd052", "xyz"))

Sample of 10 children from the GSED Phase 1 study

Description

A demo dataset with developmental scores at the item level for 10 random children from the GSED Phase 1 data.

Usage

gsample

Format

A data.frame with 10 rows and 295 variables:

There are 138 gpa items (item gpamoc008 (clench fists) removed) from GSED SF and and 155 gto items from GSED LF.

See Also

dscore()

Examples

head(gsample)

Outcomes on developmental milestones for preterm-born children

Description

A demo dataset with developmental scores at the item level for a set of 27 preterm children.

Usage

milestones

Format

A data.frame with 100 rows and 62 variables:

See Also

dscore()

Examples

head(milestones)

Normalize distribution

Description

Normalizes the distribution so that the total mass equals 1.

Usage

normalize(d, qp)

Arguments

Value

A vector of length(d) elements with the prior density estimate at each quadature point.

Note

: Internal function

Examples

dscore:::normalize(c(5, 10, 5), qp = c(0, 1, 2))

sum(dscore:::normalize(rnorm(5), qp = 1:5))

Calculate posterior for one item given score, difficulty and prior

Description

Calculate posterior for one item given score, difficulty and prior

Usage

posterior(score, tau, prior, qp, scale)

Arguments

Details

This function assumes that the difficulties have been estimated by a binary Rasch model, e.g. by rasch.pairwise.itemcluster() of the sirt package.

Value

A vector of length length(prior)

Note

: Internal function

Author(s)

Stef van Buuren, Arjan Huizing, 2020

See Also

dscore()

Rename items from gcdg into gsed lexicon

Description

Function rename_gcdg_gsed() translates item names in the gcdg lexicon to item names in the gsed lexicon.

Usage

rename_gcdg_gsed(x, copy = TRUE)

Arguments

Details

The gsed-naming convention is as follows. Position 1-3 codes the instrument, position 4-5 codes the domain, position 6 codes direct/caregiver/message, positions 7-9 is a item sequence number.

The function currently support ASQ-I (aqi), Barrera-Moncade (bar), Batelle (bat), Bayley I (by1), Bayley II (by2), Bayley III (by3), Dutch Development Instrument (ddi), Denver (den), Griffith (gri), MacArthur (mac), WHO milestones (mds), Mullen (mul), pegboard (peg), South African Griffith (sgr), Stanford Binet (sbi), Tepsi (tep), Vineland (vin).

In cases where the domain of the items isn't clear (vin, bar), the domain is coded as 'xx'.

Value

A character vector of length length(x) with gcdg item names replaced by gsed item name.

Author(s)

Iris Eekhout, Stef van Buuren

References

https://docs.google.com/spreadsheets/d/1zLsSW9CzqshL8ubb7K5R9987jF4YGDVAW_NBw1hR2aQ/edit#gid=0

Examples

from <- c(
  "ag28", "gh2_19", "a14ps4", "b1m157", "mil6",
  "bm19", "a16fm4", "n22", "ag9", "gh6_5"
)
to <- rename_gcdg_gsed(from, copy = FALSE)
to

Sample of 10 children from GSED HF

Description

A demo dataset with developmental scores at the item level for 10 random children from the GSED Phase 1 data.

Usage

sample_hf

Format

A data.frame with 10 rows and 57 variables:

Sample data for 55 gpa items forming GSED HF V1

See Also

dscore()

Examples

head(sample_hf)

Sample of 10 children from gto (LF)

Description

A demo dataset with developmental scores at the item level for 10 random children from the GSED Phase 1 data.

Usage

sample_lf

Format

A data.frame with 10 rows and 157 variables:

Sample data for 155 gto items from GSED SF

See Also

dscore()

Examples

head(sample_lf)

Sample of 10 children from gpa (SF)

Description

A demo dataset with developmental scores at the item level for 10 random children from the GSED Phase 1 data.

Usage

sample_sf

Format

A data.frame with 10 rows and 141 variables:

Sample data for 139 gpa items from GSED SF

See Also

dscore()

Examples

head(sample_sf)

Sorts item names according to user-specified priority

Description

This function sorts the item names according to instrument, domain, mode and number. The user can specify the sorting order.

Usage

sort_itemnames(x, order = "idnm")

order_itemnames(x, order = "idnm")

Arguments

Value

sort_itemnames() return a character vector with length(x) sorted elements. order_itemnames() return an integer vector of length length(x) with positions of the sorted elements.

Author(s)

Stef van Buuren

See Also

decompose_itemnames()

Examples

itemnames <- c("aqigmc028", "grihsd219", "", "by1mdd157", "mdsgmd006")
sort_itemnames(itemnames)