Calculate phecode-specific weights for phenotype risk scores

This is typically the second step of an analysis using phenotype risk scores, the next is getScores().

Usage

getWeights(
  demos,
  phecodeOccurrences,
  method = c("prevalence", "logistic", "cox", "loglinear", "prevalence_precalc"),
  methodFormula = NULL,
  negativeWeights = FALSE,
  dopar = FALSE
)

Arguments

demos: A data.table having one row per person in the cohort. Must have a column person_id. When the cox method is used, demos must have columns first_age and last_age corresponding to first and last age of visit (in years).
phecodeOccurrences: A data.table of phecode occurrences for each person in the cohort. Must have columns person_id and phecode under the "prevalence" or "logistic" methods, columns person_id, phecode, and num_occurrences under the "loglinear" method, and columns person_id, phecode, and occurrence_age under the "cox" method. num_occurrences refers to the number of unique dates a phecode was recorded for a person. occurrence_age refers to the first age (in years) a person acquired a phecode.
method: A string indicating the statistical model for calculating weights.
methodFormula: A formula representing the right-hand side of the model corresponding to method. All terms in the formula must correspond to columns in demos. A method formula is not required for the "prevalence" and "prevalence_precalc" methods. Do not use age-related covariates with the "cox" method.
negativeWeights: Logical indicating whether to allow negative weights for individuals with no occurrences of a phecode. This option is not required for the "loglinear" method since under this method, individuals with a nonzero phecode occurrence can also have negative weights.
dopar: Logical indicating whether to run calculations in parallel if a parallel backend is already set up, e.g., using doParallel::registerDoParallel(). Recommended to minimize runtime.

Value

A data.table with columns person_id, phecode, pred, and w. The column pred represents a different quantity depending on method. Under the "prevalence" method, it is fraction of the cohort that has at least one occurrence of the given phecode. The "prevalence_precalc" method is similar to the "prevalence" method but pred is calculated based on EHR data from the Vanderbilt University Medical Center. Under "logistic" or "cox" method, it is the predicted probability of given individual having a given phecode based on methodFormula. Under the "loglinear" method, it is the predicted log2(num_occurrences + 1) of a given phecode for a given individual based on methodFormula. For the "prevalence", "prevalence_precalc", "cox", and "logistic" methods, weight is calculated as -log10(pred)

when an individual has non-zero phecode occurrence and log10(1 - pred)

when an individual has zero phecode occurrence. For the "loglinear" method

weight is calculated as the difference between the observed log2(num_occurrences + 1) and pred.

Examples

library('data.table')
library('survival')

# map ICD codes to phecodes
phecodeOccurrences = getPhecodeOccurrences(icdSample)

# calculate weights using the prevalence method
weightsPrev = getWeights(demoSample, phecodeOccurrences)

# calculate weights using the prevalence method
# (assign negative weights to those with zero phecode occurrence)
weightsPrevNeg = getWeights(
  demoSample, phecodeOccurrences, negativeWeights = TRUE)

# calculate weights using the logistic method
weightsLogistic = getWeights(
  demoSample, phecodeOccurrences, method = 'logistic', methodFormula = ~ sex)

# calculate weights using the loglinear method
phecodeOccurrences2 = phecodeOccurrences[, .(
  num_occurrences = uniqueN(entry_date)), by = .(person_id, phecode)]
weightsLoglinear = getWeights(
  demoSample, phecodeOccurrences2, method = 'loglinear', methodFormula = ~ sex)

# calculate weights using the cox method
phecodeOccurrences3 = phecodeOccurrences[, .(
  first_occurrence_date = min(entry_date)) , by = .(person_id, phecode)]
phecodeOccurrences3 = merge(
  phecodeOccurrences3, demoSample[, .(person_id, dob)], by = 'person_id')
phecodeOccurrences3[,
  occurrence_age := as.numeric((first_occurrence_date - dob)/365.25)]
#>      person_id phecode first_occurrence_date        dob occurrence_age
#>   1:         1     365            2014-03-10 1925-05-24       88.79670
#>   2:         1     366            2014-11-07 1925-05-24       89.45926
#>   3:         1     735            2014-05-28 1925-05-24       89.01299
#>   4:         1   735.1            2014-05-28 1925-05-24       89.01299
#>   5:         2     442            2011-10-10 1976-06-05       35.34601
#>  ---                                                                  
#> 100:        20     531            2017-02-15 2006-09-25       10.39358
#> 101:        20   531.1            2017-02-15 2006-09-25       10.39358
#> 102:        20     661            2019-07-01 2006-09-25       12.76456
#> 103:        20     755            2017-05-10 2006-09-25       10.62356
#> 104:        20   755.3            2017-05-10 2006-09-25       10.62356
phecodeOccurrences3[, `:=`(first_occurrence_date = NULL, dob = NULL)]
#>      person_id phecode occurrence_age
#>   1:         1     365       88.79670
#>   2:         1     366       89.45926
#>   3:         1     735       89.01299
#>   4:         1   735.1       89.01299
#>   5:         2     442       35.34601
#>  ---                                 
#> 100:        20     531       10.39358
#> 101:        20   531.1       10.39358
#> 102:        20     661       12.76456
#> 103:        20     755       10.62356
#> 104:        20   755.3       10.62356
demoSample3 = demoSample[, .(
  person_id, sex,
  first_age = as.numeric((first_visit_date - dob)/365.25),
  last_age = as.numeric((last_visit_date - dob)/365.25))]
weightsCox = getWeights(
  demoSample3, phecodeOccurrences3, method = 'cox', methodFormula = ~ sex)
#> Warning: Ran out of iterations and did not converge
#> Warning: Ran out of iterations and did not converge
#> Warning: Ran out of iterations and did not converge
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#> Warning: Loglik converged before variable  1 ; beta may be infinite. 
#> Warning: Loglik converged before variable  1 ; beta may be infinite. 
#> Warning: Ran out of iterations and did not converge
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#> Warning: Loglik converged before variable  1 ; beta may be infinite. 
#> Warning: Ran out of iterations and did not converge
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#> Warning: Ran out of iterations and did not converge
#> Warning: Loglik converged before variable  1 ; beta may be infinite. 
#> Warning: Ran out of iterations and did not converge
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#> Warning: Ran out of iterations and did not converge
#> Warning: Ran out of iterations and did not converge
#> Warning: Ran out of iterations and did not converge
#> Warning: Ran out of iterations and did not converge
#> Warning: Loglik converged before variable  1 ; beta may be infinite. 
#> Warning: Loglik converged before variable  1 ; beta may be infinite. 
#> Warning: Loglik converged before variable  1 ; beta may be infinite. 
#> Warning: Ran out of iterations and did not converge
#> Warning: Ran out of iterations and did not converge

# calculate weights using pre-calculated weights based on data from
# Vanderbilt University Medical Center
weightsPreCalc = getWeights(
  demoSample, phecodeOccurrences, method = 'prevalence_precalc')

Calculate phecode-specific weights for phenotype risk scores

Usage

Arguments

Value

See also

Examples