Package 'ludic' reference manual

Title:	Linkage Using Diagnosis Codes
Description:	Probabilistic record linkage without direct identifiers using only diagnosis codes. Method is detailed in: Hejblum, Weber, Liao, Palmer, Churchill, Szolovits, Murphy, Kohane & Cai (2019) <doi: 10.1038/sdata.2018.298> ; Zhang, Hejblum, Weber, Palmer, Churchill, Szolovits, Murphy, Liao, Kohane & Cai (2021) <doi: 10.1101/2021.05.02.21256490>.
Authors:	Boris P Hejblum [aut, cre], Harrison G Zhang [aut], Tianxi Cai [aut]
Maintainer:	Boris P Hejblum <[email protected]>
License:	MIT + file LICENSE
Version:	0.2.0
Built:	2024-11-08 06:46:14 UTC
Source:	CRAN

ludic

Description

Linkage Using Diagnosis Codes

Details

This package implements probabilistic record linkage methods that relies on the use of diagnosis codes only, in the absence of direct identifiers .

Package:	ludic
Type:	Package
Version:	ludic 0.2.0
Date:	2021-08-18
License:	The "MIT License" (MIT)

The main function of ludic is recordLink.

Author(s)

Boris P. Hejblum, Tianxi Cai — Maintainer: Boris P. Hejblum

References

Hejblum BP, Weber G, Liao KP, Palmer N, Churchill S, Szolovits P, Murphy S, Kohane I and Cai T, Probabilistic Record Linkage of De-Identified Research Datasets Using Diagnosis Codes, Scientific Data, 6:180298 (2019). doi:10.1038/sdata.2018.298.

Zhang HG, Hejblum BP, Weber G, Palmer N, Churchill S, Szolovits P, Murphy S, Liao KP, Kohane I and Cai T, ATLAS: An automated association test using probabilistically linked health records with application to genetic studies, JAMIA, in press (2021). doi:10.1101/2021.05.02.21256490.

Fast C++ implementation of agreement vector for the element-wise comparison of 2 matrices

Description

agree_C_sparse uses sparse matrices.

Usage

agree_C(mat_A, mat_B)

agree_C_sparse(mat_A, mat_B)
agree_C(mat_A, mat_B)

agree_C_sparse(mat_A, mat_B)

Arguments

`mat_A`	a `nB x K` matrix of the observations to be matched. Must be integers.
`mat_B`	a `nA x K` matrix of the database into which a match is looked for. Must be integers.

Examples

mat1 <- matrix(round(rnorm(n=1000, sd=1.2)), ncol=10, nrow=100)
mat2 <- rbind(mat1[1:10, ],
             matrix(round(rnorm(n=900, sd=1.2)), ncol=10, nrow=90)
             )
rownames(mat1) <- paste0("A", 1:nrow(mat1))
rownames(mat1) <- paste0("B", 1:nrow(mat1))
mat1 <- 1*(mat1>1)
mat2 <- 1*(mat2>1)

mat1 <- matrix(round(rnorm(n=1000, sd=1.2)), ncol=10, nrow=100)
mat2 <- rbind(mat1[1:10, ],
             matrix(round(rnorm(n=900, sd=1.2)), ncol=10, nrow=90)
             )
rownames(mat1) <- paste0("A", 1:nrow(mat1))
rownames(mat1) <- paste0("B", 1:nrow(mat1))
mat1 <- 1*(mat1>1)
mat2 <- 1*(mat2>1)

Association testing by combining several matching thresholds

Description

Computes association test p-values from a generalized linear model for each considered threshold, and computes a p-value for the combination of all the envisioned thresholds through Fisher's method using perturbation resampling.

Usage

atlas(
  match_prob,
  y,
  x,
  covar = NULL,
  thresholds = seq(from = 0.1, to = 0.9, by = 0.2),
  nb_perturb = 200,
  dist_family = c("gaussian", "binomial"),
  impute_strategy = c("weighted average", "best")
)
atlas(
  match_prob,
  y,
  x,
  covar = NULL,
  thresholds = seq(from = 0.1, to = 0.9, by = 0.2),
  nb_perturb = 200,
  dist_family = c("gaussian", "binomial"),
  impute_strategy = c("weighted average", "best")
)

Arguments

`match_prob`	matching probabilities matrix (e.g. obtained through `recordLink`) of dimensions `n1 x n2`.
`y`	response variable of length `n1`. Only binary phenotypes are supported at the moment.
`x`	a `matrix` or a `data.frame` of predictors of dimensions `n2 x p`. An intercept is automatically added within the function.
`covar`	a `matrix` or a `data.frame` of variables to be adjusted on in the test of dimensions `n3 x p`. Default is `NULL` in which case there is no adjustment.
`thresholds`	a vector (possibly of length `1`) containing the different threshold to use to call a match. Default is `seq(from = 0.5, to = 0.95, by = 0.05)`.
`nb_perturb`	the number of perturbation used for the p-value combination. Default is 200.
`dist_family`	a character string indicating the distribution family for the glm. Currently, only `'gaussian'` and `'binomial'` are supported. Default is `'gaussian'`.
`impute_strategy`	a character string indicating which strategy to use to impute x from the matching probabilities `match_prob`. Either `"best"` (in which case the highest probable match above the threshold is imputed) or `"weighted average"` (in which case weighted mean is imputed for each individual who has at least one match with a posterior probability above the threshold). Default is `"weighted average"`.

Value

a list containing the following:

influencefn_pvals p-values obtained from influence function perturbations with the covariates as columns and the thresholds as rows, with an additional row at the top for the combination
wald_pvals a matrix containing the p-values obtained from the Wald test with the covariates as columns and the thresholds as rows
ptbed_pvals a list containing, for each covariates, a matrix with the nb_perturb perturbed p-values with the different thresholds as rows
theta_impute a matrix of the estimated coefficients from the glm when imputing the weighted average for covariates (as columns) with the thresholds as rows
sd_theta a matrix of the estimated SD (from the influence function) of the coefficients from the glm when imputing the weighted average for covariates (as columns), with the thresholds as rows
ptbed_theta_impute a list containing, for each covariates, a matrix with the nb_perturb perturbed estimated coefficients from the glm when imputing the weighted average for covariates, with the different thresholds as rows
impute_strategy a character string indicating which impute strategy was used (either "weighted average" or "best")

References

Examples

#rm(list=ls())

n_sims <- 1#5000

mysim <- function(i){
 x <- matrix(ncol=2, nrow=99, stats::rnorm(n=99*2))
 #plot(density(rbeta(n=1000, 1,2)))
 match_prob <- matrix(rbeta(n=103*99, 1, 2), nrow=103, ncol=99)

 #y <- rnorm(n=103, mean = 1, sd = 0.5)
 #return(atlas(match_prob, y, x, dist_family="gaussian")$influencefn_pvals)
 y <- rbinom(n=103, size = 1, prob=0.5)
 return(atlas(match_prob, y, x, dist_family="binomial")$influencefn_pvals)
}
#res <- pbapply::pblapply(1:n_sims, mysim, cl = parallel::detectCores()-1)
res <- lapply(1:n_sims, mysim)

size <- sapply(1:(ncol(res[[1]])-2), 
              FUN = function(i){
           rowMeans(sapply(res, function(m){m[, i]<0.05}), na.rm = TRUE)
           }
)
rownames(size) <- rownames(res[[1]])
colnames(size) <- colnames(res[[1]])[-(-1:0 + ncol(res[[1]]))]
size
#rm(list=ls())

n_sims <- 1#5000

mysim <- function(i){
 x <- matrix(ncol=2, nrow=99, stats::rnorm(n=99*2))
 #plot(density(rbeta(n=1000, 1,2)))
 match_prob <- matrix(rbeta(n=103*99, 1, 2), nrow=103, ncol=99)

 #y <- rnorm(n=103, mean = 1, sd = 0.5)
 #return(atlas(match_prob, y, x, dist_family="gaussian")$influencefn_pvals)
 y <- rbinom(n=103, size = 1, prob=0.5)
 return(atlas(match_prob, y, x, dist_family="binomial")$influencefn_pvals)
}
#res <- pbapply::pblapply(1:n_sims, mysim, cl = parallel::detectCores()-1)
res <- lapply(1:n_sims, mysim)

size <- sapply(1:(ncol(res[[1]])-2), 
              FUN = function(i){
           rowMeans(sapply(res, function(m){m[, i]<0.05}), na.rm = TRUE)
           }
)
rownames(size) <- rownames(res[[1]])
colnames(size) <- colnames(res[[1]])[-(-1:0 + ncol(res[[1]]))]
size

Fisher's rule for combining several p-values

Description

Compute the negative of the log-sum for a vector of p-values.

Usage

comb_pvals(pv)
comb_pvals(pv)

Arguments

`pv`	the vector of p-values to be combined together

Details

According to Fisher's rule, if the p-values are correlated, then this does not follow a simple chi-square mixture under the null.

Value

the Fisher combination of the p-values. See Details.

Implementation of Winkler's EM algorithm for Fellegi-Sunter matching method

Description

em_winkler_big implements the same method when the data are too big to compute the agreement matrix. Agreement is then recomputed on the fly each time it is needed. The EM steps are completely done in C++. This decreases the RAM usage (still important though), at the cost of increasing computational time.

Usage

em_winkler(
  data1,
  data2,
  tol = 0.001,
  maxit = 500,
  do_plot = TRUE,
  oneone = FALSE,
  verbose = FALSE
)

em_winkler_big(
  data1,
  data2,
  tol = 0.001,
  maxit = 500,
  do_plot = TRUE,
  oneone = FALSE,
  verbose = FALSE
)
em_winkler(
  data1,
  data2,
  tol = 0.001,
  maxit = 500,
  do_plot = TRUE,
  oneone = FALSE,
  verbose = FALSE
)

em_winkler_big(
  data1,
  data2,
  tol = 0.001,
  maxit = 500,
  do_plot = TRUE,
  oneone = FALSE,
  verbose = FALSE
)

Arguments

`data1`	either a binary (`1` or `0` values only) matrix or binary data frame of dimension `n1 x K` whose rownames are the observation identifiers.
`data2`	either a binary (`1` or `0` values only) matrix or a binary data frame of dimension `n2 x K` whose rownames are the observation identifiers.
`tol`	tolerance for the EM algorithm convergence.
`maxit`	maximum number of iterations for the EM algorithm.
`do_plot`	a logical flag indicating whether a plot should be drawn for the EM convergence. Default is `TRUE`.
`oneone`	a logical flag indicating whether 1-1 matching should be enforced. If `TRUE`, then returned `matchingScores` are only kept for the maximum score per column while lower scores are replace by `threshold-1`. Default is `FALSE` in which case original `matchingScores` are returned.
`verbose`	a logical flag indicating whether intermediate values from the EM algorithm should be printed. Useful for debugging. Default is `FALSE`.

Value

a list containing:

matchingScore a matrix of size n1 x n2 with the matching score for each n1*n2 pair.
threshold_ms threshold value for the matching scores above which pairs are considered true matches.
estim_nbmatch an estimation of the number of true matches (N pairs considered multiplied by p the estimated proportion of true matches from the EM algorithm)
convergence_status a logical flag indicating whether the EM algorithm converged

References

Winkler WE. Using the EM Algorithm for Weight Computation in the Fellegi-Sunter Model of Record Linkage. Proc Sect Surv Res Methods, Am Stat Assoc 1988: 667-71.

Grannis SJ, Overhage JM, Hui S, et al. Analysis of a probabilistic record linkage technique without human review. AMIA 2003 Symp Proc 2003: 259-63.

Examples

mat1 <- matrix(round(rnorm(n=1000, sd=1.2)), ncol=10, nrow=100)
mat2 <- rbind(mat1[1:10, ],
             matrix(round(rnorm(n=900, sd=1.2)), ncol=10, nrow=90)
             )
rownames(mat1) <- paste0("A", 1:nrow(mat1))
rownames(mat1) <- paste0("B", 1:nrow(mat1))
mat1 <- 1*(mat1>1)
mat2 <- 1*(mat2>1)
em_winkler(mat1, mat2)

mat1 <- matrix(round(rnorm(n=1000, sd=1.2)), ncol=10, nrow=100)
mat2 <- rbind(mat1[1:10, ],
             matrix(round(rnorm(n=900, sd=1.2)), ncol=10, nrow=90)
             )
rownames(mat1) <- paste0("A", 1:nrow(mat1))
rownames(mat1) <- paste0("B", 1:nrow(mat1))
mat1 <- 1*(mat1>1)
mat2 <- 1*(mat2>1)
em_winkler(mat1, mat2)

C++ implementation of the pseudo-likelihood computation

Description

loglikC_bin implements an even faster C++ implementation of the pseudo-likelihood computation for binary variables

loglikC_bin_wDates implements a C++ implementation of the pseudo-likelihood computation for binary variables with dates

Usage

loglikC_bin(Bmat, Amat, eps_p, eps_n, piA, piB)

loglikC_bin_wDates(Bmat, Amat, Bdates, Adates, eps_p, eps_n, piA, piB)

loglikratioC_diff_arbitrary(Bmat, Amat, d_max, cost)
loglikC_bin(Bmat, Amat, eps_p, eps_n, piA, piB)

loglikC_bin_wDates(Bmat, Amat, Bdates, Adates, eps_p, eps_n, piA, piB)

loglikratioC_diff_arbitrary(Bmat, Amat, d_max, cost)

Arguments

`Bmat`	`K x nB` matrix of the observations to be matched.
`Amat`	`nA x K` matrix the database into which a match is looked for.
`eps_p`	a vector of length `K` giving the prior discrepancy rate expected from A to B for the positives, for each variable.
`eps_n`	a vector of length `K` giving the prior discrepancy rate expected from A to B for the negatives, for each variable.
`piA`	a vector of length `K` giving the prior probabilities of observing each variable in A.
`piB`	a vector of length `K` giving the prior probabilities of observing each variable in B.
`Bdates`	`nB x K` matrix of the dates for each observations to be matched.
`Adates`	`nA x K` matrix of the dates for database into which a match is looked for.
`d_max`	a numeric vector of length `K` giving the minimum difference from which it is considered a discrepancy.
`cost`	a numeric vector of length `K` giving the arbitrary cost of discrepancy.

Fast C++ computation of the final posterior probabilities in the E-M Winkler's method

Description

matchingScore_C_sparse_big implements a version using sparse matrices. It has a better management of memory but is a little bit slower (indicated for big matrices)

Usage

matchingScore_C(agreemat, m, u, nA, nB)

matchingScore_C_sparse_big(mat_A, mat_B, m, u)
matchingScore_C(agreemat, m, u, nA, nB)

matchingScore_C_sparse_big(mat_A, mat_B, m, u)

Arguments

`agreemat`	binary sparse matrix of dimensions `N x K` containing the agreement rows for each pair of potential matches.
`m`	vector of length `K` containing the agreement weights.
`u`	vector of length `K` containing the disagreement weights.
`nA`	integer indicating the number of observations to be matched.
`nB`	integer indicating the number of observations to be matched with.
`mat_A`	a `nB x K` matrix of the observations to be matched.
`mat_B`	a `nA x K` matrix of the database into which a match is looked for.

Compute the matching probabilities for each pair of observations

Description

C++ version: for each observations in (1:n), all the matching probabilities are computed for the p possible pairs.

Usage

matchProbs_rank_full_C(computed_dist, prop_match)
matchProbs_rank_full_C(computed_dist, prop_match)

Arguments

`computed_dist`	a `n x p` matrix of computed distances used for ranking.
`prop_match`	a priori proportion of matches ("rho_1")

Value

a n x p matrix containing the matching probabilities for each pair

Compute p-values for a Z-score

Description

Compute p-values for a Z-score assuming normal distribution of the z-score under the null Hypothesis H0

Usage

pval_zscore(beta, sigma)
pval_zscore(beta, sigma)

Arguments

`beta`	the estimate
`sigma`	estimate's estimated variance

Value

the p-value

Anonymized binarized diagnosis codes from RA study.

Description

An anonymized version of the binarized diagnosis code data from the RA1 and RA2 datasets, over both 6-year and 11-year time span.

Usage

data(RA)
data(RA)

Format

5 objects

RA1_6y: an integer matrix of 0s and 1s containing 4,936 renamed diagnosis codes for 26,681 patients from the dataset RA1 recorded over a 6-year time span.
RA2_6y: an integer matrix of 0s and 1s containing 4,936 renamed diagnosis codes for 5,707 patients from the dataset RA2 recorded over a 6-year time span.
RA1_11y: an integer matrix of 0s and 1s containing 5,593 renamed diagnosis codes for 26,687 patients from the dataset RA1 recorded over a 11-year time span.
RA2_11y: an integer matrix of 0s and 1s containing 5,593 renamed diagnosis codes for 6,394 patients from the dataset RA2 recorded over a 11-year time span.
silverstandard_truematches: a character matrix with two columns containing the identifiers of the 3,831 pairs of silver-standard matches.

Details

The ICD-9 diagnosis codes have also been masked and randomly reordered, replaced by meaningless names. Finally, the silver-standard matching pairs are also provided to allow the benchmarking of methods for probabilistic record linkage using diagnosis codes.

References

Liao, K. P. et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care & Research 62, 1120-1127 (2010). doi:10.1002/acr.20184

Liao, K. P. et al. Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts. PLoS ONE 10, e0136651 (2015). doi:10.1371/journal.pone.0136651

Examples


if(interactive()){
rm(list=ls())
library(ludic)
data(RA)
res_match_6y <- recordLink(data1 = RA1_6y, data2 = RA2_6y, 
                          eps_plus = 0.01, eps_minus = 0.01,
                          aggreg_2ways ="mean",
                          min_prev = 0,
                          use_diff = FALSE)

res_match_11y <- recordLink(data1 = RA1_11y, data2 = RA2_11y, 
                           eps_plus = 0.01, eps_minus = 0.01,
                           aggreg_2ways ="mean",
                           min_prev = 0,
                           use_diff = FALSE)


print.res_matching <- function(res, threshold=0.9, ref=silverstandard_truematches){
 have_match_row <- rowSums(res>threshold)
 have_match_col <- colSums(res>threshold)
 bestmatched_pairs_all <- cbind.data.frame(
   "D1"=rownames(res)[apply(res[,which(have_match_col>0), drop=FALSE], 2, which.max)],
   "D2"=names(have_match_col)[which(have_match_col>0)]
 )
 nTM_all <- nrow(ref)
 nP_all <- nrow(bestmatched_pairs_all)
 TPR_all <- sum(apply(bestmatched_pairs_all, 1, paste0, collapse="") 
                %in% apply(ref, 1, paste0, collapse=""))/nTM_all
 PPV_all <- sum(apply(bestmatched_pairs_all, 1, paste0, collapse="") 
                %in% apply(ref, 1, paste0, collapse=""))/nP_all
 cat("threshold: ", threshold, 
     "\nnb matched: ", nP_all,"; nb true matches: ", nTM_all, 
     "\nTPR: ", TPR_all, ";   PPV: ", PPV_all, "\n\n", sep="")
}
print.res_matching(res_match_6y)
print.res_matching(res_match_11y)

}

if(interactive()){
rm(list=ls())
library(ludic)
data(RA)
res_match_6y <- recordLink(data1 = RA1_6y, data2 = RA2_6y, 
                          eps_plus = 0.01, eps_minus = 0.01,
                          aggreg_2ways ="mean",
                          min_prev = 0,
                          use_diff = FALSE)

res_match_11y <- recordLink(data1 = RA1_11y, data2 = RA2_11y, 
                           eps_plus = 0.01, eps_minus = 0.01,
                           aggreg_2ways ="mean",
                           min_prev = 0,
                           use_diff = FALSE)


print.res_matching <- function(res, threshold=0.9, ref=silverstandard_truematches){
 have_match_row <- rowSums(res>threshold)
 have_match_col <- colSums(res>threshold)
 bestmatched_pairs_all <- cbind.data.frame(
   "D1"=rownames(res)[apply(res[,which(have_match_col>0), drop=FALSE], 2, which.max)],
   "D2"=names(have_match_col)[which(have_match_col>0)]
 )
 nTM_all <- nrow(ref)
 nP_all <- nrow(bestmatched_pairs_all)
 TPR_all <- sum(apply(bestmatched_pairs_all, 1, paste0, collapse="") 
                %in% apply(ref, 1, paste0, collapse=""))/nTM_all
 PPV_all <- sum(apply(bestmatched_pairs_all, 1, paste0, collapse="") 
                %in% apply(ref, 1, paste0, collapse=""))/nP_all
 cat("threshold: ", threshold, 
     "\nnb matched: ", nP_all,"; nb true matches: ", nTM_all, 
     "\nTPR: ", TPR_all, ";   PPV: ", PPV_all, "\n\n", sep="")
}
print.res_matching(res_match_6y)
print.res_matching(res_match_11y)

}

Probabilistic Patient Record Linkage

Description

Probabilistic Patient Record Linkage

Usage

recordLink(
  data1,
  data2,
  dates1 = NULL,
  dates2 = NULL,
  eps_plus,
  eps_minus,
  aggreg_2ways = "mean",
  min_prev = 0.01,
  data1_cont2diff = NULL,
  data2_cont2diff = NULL,
  d_max,
  use_diff = TRUE
)
recordLink(
  data1,
  data2,
  dates1 = NULL,
  dates2 = NULL,
  eps_plus,
  eps_minus,
  aggreg_2ways = "mean",
  min_prev = 0.01,
  data1_cont2diff = NULL,
  data2_cont2diff = NULL,
  d_max,
  use_diff = TRUE
)

Arguments

`data1`	either a binary (`1` or `0` values only) matrix or binary data frame of dimension `n1 x K` whose rownames are the observation identifiers.
`data2`	either a binary (`1` or `0` values only) matrix or a binary data frame of dimension `n2 x K` whose rownames are the observation identifiers. Columns should be in the same order as in `data1`.
`dates1`	matrix or dataframe of dimension `n1 x K` including the concatenated dates intervals for each corresponding diagnosis codes in `data1`. Default is `NULL` in which case dates are not used.
`dates2`	matrix or dataframe of dimension `n2 x K` including the concatenated dates intervals for each corresponding diagnosis codes in `data2`. Default is `NULL` in which case dates are not used. See details.
`eps_plus`	discrepancy rate between `data1` and `data2`
`eps_minus`	discrepancy rate between `data2` and `data1`
`aggreg_2ways`	a character string indicating how to merge the posterior two probability matrices obtained for each of the 2 databases. Four possibility are currently implemented: `"maxnorm"`, `"max"`, `"min"`, `"mean"` and `"prod"`. Default is `"mean"`.
`min_prev`	minimum prevalence for the variables used in matching. Default is 1%.
`data1_cont2diff`	either a matrix or dataframe of continuous features, such as age, for which the similarity measure uses the difference with `data2_cont2diff`, whose rownames are . Default is `NULL`.
`data2_cont2diff`	either a matrix or dataframe of continuous features, such as age, for which the similarity measure uses the difference with `data2_cont1diff`, whose rownames are . Default is `NULL`.
`d_max`	a numeric vector of length `K` giving the minimum difference from which it is considered a discrepancy.
`use_diff`	logical flag indicating whether continuous differentiable variables should be used in the

Details

Dates: the use of dates1 and dates2 requires that at least one date interval matches across dates1 and dates2 for claiming an agreement on a diagnosis code between data1 and data2, in addition of having that very same code recorded in both.

Value

a matrix of size n1 x n2 with the posterior probability of matching for each n1*n2 pair

References

Examples

set.seed(123)
ncodes <- 500
npat <- 200
incid <- abs(rnorm(n=ncodes, 0.15, 0.07))
bin_codes <- rbinom(n=npat*ncodes, size=1,  prob=rep(incid, npat))
bin_codes_mat <- matrix(bin_codes, ncol=ncodes, byrow = TRUE)
data1_ex <- bin_codes_mat[1:(npat/2+npat/10),]
data2_ex <- bin_codes_mat[c(1:(npat/10), (npat/2+npat/10 + 1):npat), ]
rownames(data1_ex) <- paste0("ID", 1:(npat/2+npat/10), "_data1")
rownames(data2_ex) <- paste0("ID", c(1:(npat/10), (npat/2+npat/10 + 1):npat), "_data2")

if(interactive()){
res <- recordLink(data1 = data1_ex, data2 = data2_ex, 
                 use_diff = FALSE, eps_minus = 0.01, eps_plus = 0.01)
round(res[c(1:3, 19:23), c(1:3, 19:23)], 3)
}

set.seed(123)
ncodes <- 500
npat <- 200
incid <- abs(rnorm(n=ncodes, 0.15, 0.07))
bin_codes <- rbinom(n=npat*ncodes, size=1,  prob=rep(incid, npat))
bin_codes_mat <- matrix(bin_codes, ncol=ncodes, byrow = TRUE)
data1_ex <- bin_codes_mat[1:(npat/2+npat/10),]
data2_ex <- bin_codes_mat[c(1:(npat/10), (npat/2+npat/10 + 1):npat), ]
rownames(data1_ex) <- paste0("ID", 1:(npat/2+npat/10), "_data1")
rownames(data2_ex) <- paste0("ID", c(1:(npat/10), (npat/2+npat/10 + 1):npat), "_data2")

if(interactive()){
res <- recordLink(data1 = data1_ex, data2 = data2_ex, 
                 use_diff = FALSE, eps_minus = 0.01, eps_plus = 0.01)
round(res[c(1:3, 19:23), c(1:3, 19:23)], 3)
}

Association testing using Han & Lahiri estimating equations and jackknife approach

Description

Association testing using Han & Lahiri estimating equations and jackknife approach

Usage

test_han2018(
  match_prob,
  y,
  x,
  covar_y = NULL,
  covar_x = NULL,
  jackknife_nrep = 100,
  jackknife_blocksize = max(floor(min(length(y), nrow(x))/jackknife_nrep), 1),
  methods = c("F", "M", "M2"),
  dist_family = c("gaussian", "binomial")
)
test_han2018(
  match_prob,
  y,
  x,
  covar_y = NULL,
  covar_x = NULL,
  jackknife_nrep = 100,
  jackknife_blocksize = max(floor(min(length(y), nrow(x))/jackknife_nrep), 1),
  methods = c("F", "M", "M2"),
  dist_family = c("gaussian", "binomial")
)

Arguments

`match_prob`	matching probabilities matrix (e.g. obtained through `recordLink`) of dimensions `n1 x n2`.
`y`	response variable of length `n1`. Only binary or gaussian phenotypes are supported at the moment.
`x`	a `matrix` or a `data.frame` of predictors of dimensions `n2 x p`. An intercept is automatically added within the function.
`covar_y`	a `matrix` or a `data.frame` of predictors of dimensions `n1 x q1`. An intercept is automatically added within the function.
`covar_x`	a `matrix` or a `data.frame` of predictors of dimensions `n2 x q2`. An intercept is automatically added within the function.
`jackknife_nrep`	the number of jackknife repetitions. Default is 100 (from Han et al.).
`jackknife_blocksize`	the number of observations to remove in each jackknife.
`methods`	a character vector which must be a subset of `("F", "M", "M2")` indicating which estimator from Han et al. 2018 should be computed. Default is all 3.
`dist_family`	a character string indicating the distribution family for the glm. Currently, only `'gaussian'` and `'binomial'` are supported. Default is `'gaussian'`.

Value

a list containing the following for each estimator in methods:

beta a vector containing the p estimated coefficients
varcov the p x p variance-covariance matrix of the beta coefficients
zscores a vector containing the p Z-scores
pval the corresponding Gaussian assumption p-values

References

Han, Y., and Lahiri, P. (2019) Statistical Analysis with Linked Data. International Statistical Review, 87: S139– S157. doi:10.1111/insr.12295.

Examples

# rm(list=ls())
# n_sims <- 500
# res <- pbapply::pblapply(1:n_sims, function(n){
# nx <- 99
# ny <- 103
# x <- matrix(ncol=2, nrow=ny, stats::rnorm(n=ny*2))
# 
# #plot(density(rbeta(n=1000, 1,2)))
# match_prob <- diag(ny)[, 1:nx]#matrix(rbeta(n=ny*nx, 1, 2), nrow=ny, ncol=99)
# 
# covar_y <- matrix(rnorm(n=ny, 1, 0.5), ncol=1)
# covar_x <- matrix(ncol=3, nrow=ny, stats::rnorm(n=ny*3))
# 
# #y <- rnorm(n=ny, mean = x %*% c(2,-3)  + covar_x %*% rep(0.2, ncol(covar_x)) + 0.5*covar_y, 0.5)
# y <- rbinom(n=ny, 1, prob=expit(x %*% c(2,-3)  + covar_x %*% 
#             rep(0.2, ncol(covar_x)) + 0.5*covar_y))
# #glm(y~0+x+covar_y+covar_x, family = "binomial")
# return(
# #test_han2018(match_prob, y, x, jackknife_blocksize = 10, covar_x = NULL, covar_y = NULL)
# test_han2018(match_prob, y[1:ny], x[1:nx, ], dist_family = "binomial", 
#              jackknife_blocksize = 10, covar_x = covar_x[1:nx, ], 
#              covar_y = covar_y[1:ny, , drop=FALSE])
# )
# }, cl=parallel::detectCores()-1)
# pvals_F <- sapply(lapply(res, "[[", "F"), "[[", "beta")
# pvals_M <- sapply(lapply(res, "[[", "M"), "[[", "beta")
# pvals_M2 <- sapply(lapply(res, "[[", "M2"), "[[", "beta")
# quantile(pvals_F)
# quantile(pvals_M)
# quantile(pvals_M2)
# rowMeans(pvals_F<0.05)
# rowMeans(pvals_M<0.05)
# rowMeans(pvals_M2<0.05)

# rm(list=ls())
# n_sims <- 500
# res <- pbapply::pblapply(1:n_sims, function(n){
# nx <- 99
# ny <- 103
# x <- matrix(ncol=2, nrow=ny, stats::rnorm(n=ny*2))
# 
# #plot(density(rbeta(n=1000, 1,2)))
# match_prob <- diag(ny)[, 1:nx]#matrix(rbeta(n=ny*nx, 1, 2), nrow=ny, ncol=99)
# 
# covar_y <- matrix(rnorm(n=ny, 1, 0.5), ncol=1)
# covar_x <- matrix(ncol=3, nrow=ny, stats::rnorm(n=ny*3))
# 
# #y <- rnorm(n=ny, mean = x %*% c(2,-3)  + covar_x %*% rep(0.2, ncol(covar_x)) + 0.5*covar_y, 0.5)
# y <- rbinom(n=ny, 1, prob=expit(x %*% c(2,-3)  + covar_x %*% 
#             rep(0.2, ncol(covar_x)) + 0.5*covar_y))
# #glm(y~0+x+covar_y+covar_x, family = "binomial")
# return(
# #test_han2018(match_prob, y, x, jackknife_blocksize = 10, covar_x = NULL, covar_y = NULL)
# test_han2018(match_prob, y[1:ny], x[1:nx, ], dist_family = "binomial", 
#              jackknife_blocksize = 10, covar_x = covar_x[1:nx, ], 
#              covar_y = covar_y[1:ny, , drop=FALSE])
# )
# }, cl=parallel::detectCores()-1)
# pvals_F <- sapply(lapply(res, "[[", "F"), "[[", "beta")
# pvals_M <- sapply(lapply(res, "[[", "M"), "[[", "beta")
# pvals_M2 <- sapply(lapply(res, "[[", "M2"), "[[", "beta")
# quantile(pvals_F)
# quantile(pvals_M)
# quantile(pvals_M2)
# rowMeans(pvals_F<0.05)
# rowMeans(pvals_M<0.05)
# rowMeans(pvals_M2<0.05)

Package 'ludic'

Help Index

ludic

Description

Details

Author(s)

References

Fast C++ implementation of agreement vector for the element-wise comparison of 2 matrices

Description

Usage

Arguments

Examples

Association testing by combining several matching thresholds

Description

Usage

Arguments

Value

References

Examples

Fisher's rule for combining several p-values

Description

Usage

Arguments

Details

Value

Implementation of Winkler's EM algorithm for Fellegi-Sunter matching method

Description

Usage

Arguments

Value

References

Examples

C++ implementation of the pseudo-likelihood computation

Description

Usage

Arguments

Fast C++ computation of the final posterior probabilities in the E-M Winkler's method

Description

Usage

Arguments

Compute the matching probabilities for each pair of observations

Description

Usage

Arguments

Value

Compute p-values for a Z-score

Description

Usage

Arguments

Value

Anonymized binarized diagnosis codes from RA study.

Description

Usage

Format

Details

References

Examples

Probabilistic Patient Record Linkage

Description

Usage

Arguments

Details

Value

References

Examples

Association testing using Han & Lahiri estimating equations and jackknife approach

Description

Usage

Arguments

Value

References

Examples