Create a synthetic population of young people attending primary mental health services
We created a basic synthetic dataset of to represent a clinical youth mental health sample.
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Model module libraries
Bundles of readyforwhatsnext modules are distributed as R libraries.
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Add metadata to datasets of individual human records
Appending appropriate metadata to datasets of individual unit records can facilitate partial automation of some modelling tasks. This tutorial describes how a module from the youthvars R package can help you to add metadata to a youth mental health dataset so that it can be more readily used by other readyforwhatsnext modules.
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Modules pipeline
Unreleased software and other preliminary work is currently being developed into readyforwhatsnext model modules.
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Validate variable total scores
Vector based classes can be used to help validate variable values. This tutorial describes how to do that with sub-module classes exported as part of the youthvars R package.
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Find open access model data
Tools from the ready4 framework library can be used to search for relevant open access readyforwhatsnext model data collections and datasets.
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Standardise Variable Values With Fuzzy Logic And Correspondence Tables
Costing health economic datasets is an activity that can involve repeated use of lookup tables. This tutorial describes how a module from the costly R package can help you to use a combination of fuzzy logic and correspondence tables to standardise variable values and thus facilitate partial automation of costing algorithms.
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Standardise Variable Values With Lookup Codes
This tutorial describes how a module from the costly R package can help you to use lookup codes to standardise variable values and thus facilitate partial automation of costing algorithms.
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Score health utility
Using modules from the scorz R package, individual responses to a multi-attribute utility instrument survey can be converted into health utility total scores. This tutorial describes how to do for adolescent AQoL-6D health utility.
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Explore candidate utility mapping models
Using modules from the specific R package, it is possible to undertake an exploratory utility mapping analysis. This tutorial illustrates a hypotehtical example of exploring how to map to EQ-5D health utility.
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Implement a utility mapping study
Using modules from the TTU R package, it is possible to implement a fully reproducible utility mapping study. This tutorial illustrates the main steps using a hypothetical AQoL-6D utility mapping study.
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Example 1: Predict health utility from psychological and functional measures (PHQ-9 and SOFAS)
This tutorial illustrates the main steps for predicting AQoL-6D utility from psychological and functional measures using a longitudinal dataset in long format.
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Example 2: Predict health utility from psychological measures (PHQ-9 and GAD-7)
This tutorial illustrates the main steps for predicting AQoL-6D utility from two psychological measures using a longitudinal dataset in wide format.
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Find and deploy utility mapping models
Using tools (soon to be formalised into ready4 modules) from the youthu R package, it is possible to find and deploy relevant utility mapping algorithms.
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Design a Discrete Choice Experiment
We used functions (soon to be formalised into ready4 modules) from the mychoice R package to design to a discrete choice experiment.
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Use utility mapping algorithms to help implement cost-utility analyses
Using tools (soon to be formalised into ready4 framework modules) from the youthu R package, it is possible to use utility mapping algorithms to help implement cost-utility analyses. This tutorial illustrates the main steps for doing so using psychological and functional measures collected on clinical samples of young people.
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Develop choice models
Using tools (soon to be formalised into ready4 framework modules) from the mychoice R package, it is possible to develop choice models from responses to a discrete choice experiment survey.
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Analyse the results of a Discrete Choice Experiment
Using functions (soon to be formalised into ready4 framework modules) from the mychoice R package, it is possible to develop choice models from responses to a discrete choice experiment survey.
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Develop health utility mapping algorithms
Using modules from the TTU, youthvars, scorz and specific libraries, we developed utility mapping algorithms from a sample of young people attending primary mental health care services.
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Predict health utility
Using functions (soon to be formalised into ready4 framework modules) from the youthu R package, we predicted health utility for a synthetic population of young people attending primary mental health care services.
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Pipeline of people modules
Current unreleased work to develop modules for modelling the characteristics, relationships, behaviours, risk factors and outcomes of young people and those important to them.
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Pipeline of places modules
Current unreleased work to develop modules for modelling the demographic, environmental and proximity drivers of access, equity and outcomes in youth mental health.
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Pipeline of platforms modules
Current unreleased work to develop modules for modelling the optimal staffing and configuration of support services for young people.
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Pipeline of programs modules
Current preliminary work to develop modules for modelling the affordability, value for money and appropriate targeting of interventions for young people.
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Predicting Quality Adjusted Life Years in young people attending primary mental health services
Applying model modules from the Spring To Life model of people to map psychological and functional measures to AQoL-6D health utility