Scripts

The scripts are divided into five sets:

  1. Common variables and functions

  2. Data setup

  3. Main analyses

  4. Simulation validation

  5. Plotting

Script names have a three-digit prefix indicating their set (first digit) and running order.

Within each set, the x00 and x01 script contain common objects and functions respectively. This helps keep the code separate and clean.

1xx scripts: common

Common functions and a re-write of several gamlss functions (that had issues within our pipeline).

  • 100.common-variables.r

  • 101.common-functions.r

    • Create.Folders(): Create folder structure for a specific subset

    • Check.Attributes(): Utility function for subsets of data

  • 102.gamlss-recode.r

    • bfpNA(): Custom version of bfp() that can handle `NA`s

    • GGalt(): Custom version of GG() (GAMLSS family) with robust GGalt$mean() and GGalt$variance()

These scripts are sourced in later scripts, they define common variables/objects and functions.

Importantly, they also include a re-write of several gamlss functions to address numerical instability (these may not be necessary in a novel replication, however they are required for using our output fitted objects).

2xx scripts: data setup

Import and clean the data ready for the gamlss fitting. Also, generate simulated dataset (called omega) used for validation.

  • 200.variables.r

  • 201.functions.r

    • None

  • 211.data-setup.r

    • Custom script to load and clean the raw data, outputs are SUBSET.rds and model objects

  • 220.simulation-omega-setup.r

    • Custom script to create simulated data, outputs are SUBSET.rds and model objects

Each data script is a custom

3xx scripts:

These scripts perform the substantial calculations and model fitting.

  • 300.variables.r

  • 301.functions.r
    • Fit.Function(): Calls Extract.Wrapper()

      • gamlssWrapper(): Simple wrapper around gamlss() to ensure consistent calls

      • Extract.Summary(): Generate consistent summary of subset/dataset

      • Extract.Param(): Create custom ParamObj (new class of object) from gamlss() output

      • Extract.Wrapper(): Combined call of gamlssWrapper(), Extract.Summary() and Extract.Param()

      • Save.Extracted(): Save gamlss() output objects, called from within Fit.Function()

    • Make.bfpNA.model.from.extract(): Convert fp() into bfpNA(), i.e. define fractional polynomial explicitly

    • Find.Models.To.Fit(): Find candidate models under MODEL folder

    • Make.Longitudinal(): For longitudinal follow-up, generate subject-specific summary measures, e.g. IQR

    • Boot.Function(): Generate a bootstrap replicate dataset and call Extract.Wrapper()

    • ValidateCleanInput(): Check dataset conforms to ParamObj

    • Apply.Param(): Use ParamObj to generate predictions for a dataset (calls ValidateCleanInput())

    • Apply.FitAndBoot(): Calls Apply.Param() on all bootstrap replicate ParamObj

    • Load.Subset.Wrapper(): Load multiple elements into HOLDER object

    • Calc.Expanded(): Wrapper calling Ranef.MLE.Func() and Add.New.Ranefs()

      • Find.Fitted.Levels(): Compare ParamObj with dataset to find studies with fitted random-effects

      • Find.Missing.Levels(): Compare ParamObj with dataset to find studies with missing/unknown random-effects

      • Ranef.MLE.Func(): Estimate random-effects using maximum likelihood (using dXX from GAMLSS family)

      • Add.New.Ranefs(): Expand a ParamObj with new study random-effects

  • 310.fitting.r

    • Uses Fit.Function()

  • 320.best-fit.r

    • Extracts BIC values from fitted models and selects the best (makes a copy or symlink as MODEL.rds)

  • 330.bootstrapping.r

    • Uses Boot.Function()

  • 340.bootstrap-merge.r

    • Merges separate bootstrap outputs into BOOT.EXTRACT.rds

  • 350.calc-derived.r

    • Uses Apply.Param() and Apply.FitandBoot() to create all derived curves and outputs (for lifespan and study-specific curves) saved as DERIVED.rds

  • 350.calc-novel.r

    • Uses Calc.Expanded() to estimate random-effects for novel data saved as FIT.EXPANDED.rds (for fit and bootstrap replicates)

Main scripts, these fit the gamlss model(s), select the best (via BIC), perform bootstrapping, and calculate all necessary derived values.

5xx scripts: plotting

Some example plotting scripts using GNU R’s base graphics. The article uses “nicer” plots generated using ggplot2 (not included in this repository).

  • 500.plotting-variables.r

  • 501.plotting-functions.r

    • None

  • 510.plotting.r

    • Example plots using DERIVED.rds object

Plotting functions, these only use the DERIVED.rds and the fitted objects from novel data (FIT.EXPANDED.rds).