SF-12 Scoring Using Australian Norms.R

# -------------------------------------------------------------------------
# Project: SF-12 v2.0 Scoring using Australian Population Weights
#
# Coded by: Sam Mancuso
# Web: sammancuso.com
#
# Date: 9 November 2017
# Version: 2.00
# Notes: Updated to use dplyr
# -------------------------------------------------------------------------

# Australian Population Weights
# ------------------------------
# Hawthorne, G., Osborne, R., Taylor, A., & Sansoni, J. (2007). The SF36
# Version 2: critical analyses of population weights, scoring algorithms
# and population norms. Quality of Life Research, 16(4), 661-673.
# doi: 10.1007/s11136-006-9154-4

# Assumes variable names conform to survey:
#
# I1, I2A, I2B, I3A, I4A, I4B, I5, I6A, I6B, I6C, and I7

# Intall/Load Required Packages -------------------------------------------
if (!require(pacman)) {
  install.packages("pacman")
}
pacman::p_load(dplyr, magrittr)

# Scoring -----------------------------------------------------------------
dataFrame %<>%
  # Recode items, 1, 5, 6a, and 6b
  mutate(
    I1r = recode(
      I1, 
      `1` = 5, 
      `2` = 4.4, 
      `3` = 3.4, 
      `4` = 2.0, 
      `5` = 1.0
      ),
    I5r = 6 - I5,
    I6Ar = 6 - I6A,
    I6Br = 6 - I6B
  ) %>%
  # Transform raw scores:
  # PF = Physical Functioning
  # RP = Role Physical
  # BP = Bodily Pain
  # GH = General Health
  # VT = Vitality
  # SF = Social Functioning
  # RE = Role Emotional
  # MH = Mental Health
  mutate(
    PF = ((I2A + I2B - 2) / 4) * 100,
    RP = ((I3A + I3B - 2) / 8) * 100,
    BP = ((I5r - 1) / 4) * 100,
    GH = ((I1r - 1) / 4) * 100,
    VT = ((I6Br - 1) / 4) * 100,
    SF = ((I7 - 1) / 4) * 100,
    RE = ((I4A + I4B - 2) / 8) * 100,
    MH = ((I6Ar + I6C - 2) / 8) * 100
  ) %>%
  # Standardisation of SF-12v2 Scores
  mutate(
    PF_Z = (PF - 84.64) / 21.86,
    RP_Z = (RP - 84.41) / 25.13,
    BP_Z = (BP - 76.45) / 21.24,
    GH_Z = (GH - 71.90) / 21.88,
    VT_Z = (VT - 61.12) / 20.80,
    SF_Z = (SF - 86.19) / 22.33,
    RE_Z = (RE - 91.59) / 17.50,
    MH_Z = (MH - 80.63) / 16.99
  ) %>%
  # Physical and Mental Component Scores
  mutate(
    AGG_PHYS = (PF_Z * 0.409) +
      (RP_Z * 0.325) +
      (BP_Z * 0.289) +
      (GH_Z * 0.231) +
      (VT_Z * 0.106) +
      (SF_Z * 0.014) +
      (RE_Z * -.183) +
      (MH_Z * -.205),
    AGG_MENT = (PF_Z * -.224) +
      (RP_Z * -.096) +
      (BP_Z * -.105) +
      (GH_Z * 0.001) +
      (VT_Z * 0.157) +
      (SF_Z * 0.249) +
      (RE_Z * 0.449) +
      (MH_Z * 0.476)
  ) %>%
  # Convert to T-Scores
  mutate(
    PF_T = 50 + (10 * PF_Z),
    RP_T = 50 + (10 * RP_Z),
    BP_T = 50 + (10 * BP_Z),
    GH_T = 50 + (10 * GH_Z),
    VT_T = 50 + (10 * VT_Z),
    SF_T = 50 + (10 * SF_Z),
    RE_T = 50 + (10 * RE_Z),
    MH_T = 50 + (10 * MH_Z),
    AGG_PHYS_T = 50 + (10 * AGG_PHYS),
    AGG_MENT_T = 50 + (10 * AGG_MENT)
  )