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" `method`

s, 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
#> 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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')
```