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Calculate quality of life years

Source: R/calculate_qalys.R
calculate_qalys.Rd

Generic for calculating quality of life years (QALY) metrics for EQ5D survey respondents.

Usage

calculate_qalys(x, ...)

# S3 method for default
calculate_qalys(x, ...)

# S3 method for EQ5D
calculate_qalys(
  x,
  time_index,
  type,
  country,
  units = c("days", "weeks", "months", "quarters", "years"),
  baseline_survey = NULL,
  ...
)

# S3 method for utility
calculate_qalys(
  x,
  time_index,
  units = c("days", "weeks", "months", "quarters", "years"),
  baseline_survey = NULL,
  ...
)

Arguments

x

An R object.

...

Further arguments passed to or from other methods.

time_index

[character] Name of variable in x representing the relative time within the survey framework.

type

[character] Method type(s) used for calculating the value sets.

For EQ5D3L inputs this can be:

  • "TTO", the time trade-off valuation technique;

  • "VAS", the visual analogue scale valuation technique;

  • "RCW", a reverse crosswalk conversion to EQ5D5L values; or

  • "DSU", the NICE Decision Support Unit's model that allows mappings on to EQ5D5L values accounting for both age and sex.

For EQ5D5L inputs this can be:

  • "VT", value sets generated via a EuroQol standardised valuation study protocol;

  • "CW", a crosswalk conversion EQ5D3L values; or

  • "DSU", the NICE Decision Support Unit's model that allows mappings on to EQ5D5L values accounting for both age and sex.

country

[character] Value set countries to use.

units

character The units of the time_index column of x. Can be one of "days", "weeks", "months", "quarters" or "years". Note that the output will always be a QALY (i.e years) irrespective of the unit input.

baseline_survey

(optional) Either a character string specifying the surveyID, to use as a baseline or a data frame. If a data frame, it must have at least two columns; one for the respondentID (with name matching that in x input) and another (of any name) for the associated utility values. If desired you can also specify columns that match on the utility and country type columns of the input x.

Value

A data frame.

Details

The methods provided for utility and EQ5D objects, return two metrics by default:

  • Firstly, a raw QALY. This is the area under the utility curve scaled to the proportion of the year it corresponds to.

  • Secondly, a loss_v_fullhealth value. This represents the loss from perfect health which is calculated by assuming all dimensions are 1 to calculate a full health QALY value.

Optionally, a third metric can also be returned, loss_v_baseline. This represents the loss from a specified baseline utility value.

Note

If a character string baseline_survey argument is given then this must match a surveyID to match against. In this situation the survey is still included in the unadjusted, raw, calculation, prior to the calculation of loss.

Alternatively the baseline_survey argument can be specified as a data frame with a column corresponding to the respondentID and another representing the associated utility. Optionally columns corresponding to the utility country and utility type can be included to allow more granular comparisons. For this specification of baseline, it is not included in the unadjusted, raw, calculation.

Examples


data("EQ5D5L_surveys")
dat <- as_eq5d5l(
    EQ5D5L_surveys,
    surveyID = "surveyID",
    respondentID = "respondentID",
    mobility = "mobility",
    self_care = "self_care",
    usual = "usual",
    pain = "pain",
    anxiety = "anxiety",
    vas = "vas"
)
calculate_qalys(
    dat,
    time_index = "time_index",
    type = "VT", country = c("Denmark", "France")
)
#> # A data frame: 4,000 × 5
#>    respondentID .utility_type .utility_country .qaly .value
#>           <int> <chr>         <chr>            <chr>  <dbl>
#>  1            1 VT            Denmark          raw    0.544
#>  2            1 VT            France           raw    0.573
#>  3            2 VT            Denmark          raw    0.487
#>  4            2 VT            France           raw    0.503
#>  5            3 VT            Denmark          raw    0.530
#>  6            3 VT            France           raw    0.577
#>  7            4 VT            Denmark          raw    0.276
#>  8            4 VT            France           raw    0.430
#>  9            5 VT            Denmark          raw    0.506
#> 10            5 VT            France           raw    0.575
#> # … with 3,990 more rows
#> # ℹ Use `print(n = ...)` to see more rows