Finding specific modules and sub-modules
How to find individual readyforwhatsnext modules and sub-modules.
Tag: Model
Find model modules and datasets
How to find individual modules, module libraries, dataset collections and datasets.
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Use readyforwhastsnext model modules
How to use readyforwhatsnext model modules to model the people, places, platforms and programs that shape young people’s mental health.
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Installing readyforwhatsnext model modules
To implement a modelling analysis with readyforwhatsnext you need to install model module R libraries.
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People module library releases
Releases of module libraries for modelling people (collectively, the Spring To Life model).
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Places module library releases
Releases of module libraries for modelling places (collectively, the Springtides model).
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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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Model youth choices
Replication programs for designing, analysing and reporting discrete choice experiments.
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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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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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Modules for modelling people
Modules to model the characteristics, relationships, behaviours, risk factors and outcomes of young people and individuals who interact with young people are collectively referred to as the “Spring To Life” sub-model. A table summarising Spring To Life module libraries for modelling people is available. Additional information (e.g. tutorials and blog articles) about currently available Spring To Life modules is labelled with the “model-modules-people” tag. Resources about Spring To Life datasets are tagged with “data-datasets-people”. Brief information about additional unreleased Spring To Life modules that are in development is also available.
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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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Modules for modelling places
Modules for spatio-temporal modelling of the environments that shape young people’s mental health are collectively referred to as the “Springtides” sub-model. Both Springtides module libraries for modelling places that are available are highly preliminary and lack tutorials to demonstrate their use. A deprecated app built using these libraries is available for illustration purposes. Resources relating to preliminary and unreleased modules for the Springtides model is tagged with the “model-modules-places” tag and those relating to compatible datasets are tagged with “data-datasets-places”. Brief information on unreleased work in progress module libraries are also available.
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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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Modules for modelling platforms
Modules that model the processes, eligibility requirements, staffing and configurations of youth service platforms are collectively referred to as the “First Bounce” sub-model. No First Bounce modules are yet available - see details on unreleased work in progress.
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Modules for modelling programs
Modules for modelling the efficacy, cost-effectiveness and budget impact of youth mental health programs (e.g. interventions for prevention, treatment and wellbeing) are collectively referred to as the “On Target” sub-model. There are currently two development releases of On Target module libraries for modelling programs but both are highly preliminary. Resources (including tutorials) relating to these module libaries is tagged with “model-modules-programs”.
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Tutorials
Learn how to find and use readyforwhatsnext modules and datasets.
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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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Predicting the spatial epidemiology of emerging mental disorders
We previously developed a user interface for the epidemiology modules of our Springtides model of places.
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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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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
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Modelling the mental health impacts of COVID-19
Applying the Springtides model to predict the potential mental health burden of COVID-19.
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readyforwhatsnext - conference posters now available
Initial set of academic posters relating to the development of the readyforwhatsnext model.