Class: ModelTerm¶
One term of the model: a column of the design matrix, or the equivalent predictor in a family with no design matrix. A categorical term names its levels and the entities carrying them; a continuous term carries the facts that make a slope interpretable. Nothing here is a fact about any one contrast -- what a contrast did with this term is a Cell on that contrast's Effect. A term with no cell on some contrast is a term that contrast was adjusted for rather than tested.
__
A term is something the measure was regressed on. The quantity being modelled is Analysis.measure and never a term of the model that models it. A table laid out with one row-block per measured parameter -- four diffusion parameters, three frequency bands -- is reporting one analysis per parameter over one measure each, not one analysis over a factor whose levels are the parameters.
classDiagram
class ModelTerm
click ModelTerm href "../ModelTerm/"
ModelTerm : assessment
ModelTerm --> "0..1" Assessment : assessment
click Assessment href "../Assessment/"
ModelTerm : functional_form
ModelTerm --> "0..1" ExtractedFunctionalForm : functional_form
click ExtractedFunctionalForm href "../ExtractedFunctionalForm/"
ModelTerm : interaction_with
ModelTerm --> "*" ModelTerm : interaction_with
click ModelTerm href "../ModelTerm/"
ModelTerm : levels
ModelTerm --> "*" FactorLevel : levels
click FactorLevel href "../FactorLevel/"
ModelTerm : local_id
ModelTerm : name
ModelTerm --> "1" ExtractedString : name
click ExtractedString href "../ExtractedString/"
ModelTerm : region
ModelTerm --> "0..1" Region : region
click Region href "../Region/"
ModelTerm : source_definition
ModelTerm --> "0..1" ExtractedString : source_definition
click ExtractedString href "../ExtractedString/"
ModelTerm : stable_id
ModelTerm : type
ModelTerm --> "1" ExtractedTermType : type
click ExtractedTermType href "../ExtractedTermType/"
ModelTerm : unit
ModelTerm --> "0..1" ExtractedString : unit
click ExtractedString href "../ExtractedString/"
ModelTerm : variation_level
ModelTerm --> "0..1" ExtractedVariationLevel : variation_level
click ExtractedVariationLevel href "../ExtractedVariationLevel/"
Slots¶
| Name | Cardinality and Range | Description | Inheritance |
|---|---|---|---|
| stable_id | 0..1 String |
UUID minted by the store the first time this entity appears, never by an extr... | direct |
| local_id | 1 String |
Stable identifier, referenced by an Effect's cells and its `mediation |
direct |
| name | 1 ExtractedString |
Term label, unique within the model: a name appearing twice is one term recor... | direct |
| type | 1 ExtractedTermType |
Whether the term is categorical or continuous | direct |
| levels | * FactorLevel |
The levels of a categorical term, and for each the entities carrying it | direct |
| assessment | 0..1 Assessment |
The assessment whose measurements supply a continuous term, **including when ... | direct |
| region | 0..1 Region |
The region whose signal supplies this term: a seed timecourse, an ROI mean co... | direct |
| variation_level | 0..1 ExtractedVariationLevel |
Whether this term moves within a participant or only across the sample | direct |
| functional_form | 0..1 ExtractedFunctionalForm |
Shape of the term's relationship to the measure | direct |
| interaction_with | * ModelTerm |
The other terms this term is a product of -- what makes this column an intera... | direct |
| unit | 0..1 ExtractedString |
The unit a continuous term is measured in, when the source gives one -- years... | direct |
| source_definition | 0..1 ExtractedString |
The source's own definition of the term, and the origin of a continuous term ... | direct |
Usages¶
| used by | used in | type | used |
|---|---|---|---|
| Mediation | mediator | range | ModelTerm |
| Cell | term | range | ModelTerm |
| ModelEstimation | terms | range | ModelTerm |
| ModelTerm | interaction_with | range | ModelTerm |
Identifier and Mapping Information¶
Schema Source¶
- from schema: https://neurostuff.org/schemas/neuroimaging-study-extraction
Mappings¶
| Mapping Type | Mapped Value |
|---|---|
| self | neurostudy_ex:ModelTerm |
| native | neurostudy_ex:ModelTerm |
LinkML Source¶
Direct¶
name: ModelTerm
description: 'One term of the model: a column of the design matrix, or the equivalent
predictor in a family with no design matrix. A categorical term names its levels
and the entities carrying them; a continuous term carries the facts that make a
slope interpretable. Nothing here is a fact about any one contrast -- what a contrast
did with this term is a `Cell` on that contrast''s Effect. A term with no cell on
some contrast is a term that contrast was adjusted for rather than tested.
A term is something the measure was regressed *on*. The quantity being modelled
is `Analysis.measure` and never a term of the model that models it. A table laid
out with one row-block per measured parameter -- four diffusion parameters, three
frequency bands -- is reporting one analysis per parameter over one measure each,
not one analysis over a factor whose levels are the parameters.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction
rank: 1000
attributes:
stable_id:
name: stable_id
description: UUID minted by the store the first time this entity appears, never
by an extractor, and kept across re-runs so a later extraction matches the same
entity.
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
domain_of:
- Arm
- Timepoint
- Group
- Task
- Condition
- Assessment
- Region
- Acquisition
- Device
- Analysis
- Measure
- Preprocessing
- ModelEstimation
- ModelTerm
- InferenceSettings
- Table
- CoordinateSet
- Study
range: string
pattern: ^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$
local_id:
name: local_id
description: Stable identifier, referenced by an Effect's `cells` and its `mediation.mediator`,
and by `interaction_with`.
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
identifier: true
domain_of:
- Arm
- Timepoint
- Group
- Task
- Condition
- Assessment
- Region
- Acquisition
- Device
- Analysis
- Measure
- Preprocessing
- ModelEstimation
- ModelTerm
- InferenceSettings
- Table
- CoordinateSet
- Study
range: string
required: true
name:
name: name
description: 'Term label, unique within the model: a name appearing twice is one
term recorded twice. For a categorical term this is the factor''s identity.
Often a label the source never writes. A paper reporting 1-back, 2-back and
3-back conditions states the levels and states that they vary together, but
frequently names no axis; `load` is then a name supplied for a grouping the
source does make. That is permitted, within two limits: the levels must be source-stated,
and the name must not assert a comparison or a construct the paper does not.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
domain_of:
- Arm
- Timepoint
- Group
- Task
- Condition
- Assessment
- Region
- Analysis
- ModelTerm
- ExternalDataset
- PerformanceMetric
range: ExtractedString
required: true
type:
name: type
description: 'Whether the term is categorical or continuous. Categorical when
the column distinguishes named cells of the design -- occasions, cohorts, conditions,
arms, regions -- whether or not the paper names the axis; `levels` is then required.
Continuous when the column carries a measured value and the model fits a slope.
Small as it is, this field settles four others: whether `levels` exists, whether
a `Cell` here takes a `level`, whether the term can be crossed, and which branch
of the derived kind fires. A term recorded continuous is one no contrast can
be built across.
**A term whose name states a comparison -- `pre > post`, `A versus B` -- is
almost always a categorical factor written down from its contrast''s side.**
The design matrix distinguished two occasions, cohorts or conditions; the paper
labelled the difference and left the axis unnamed. Record the factor with a
level per side and let the cells carry the direction (`representing-models.md`
ยง5.6). Collapsed into one continuous column, nothing says what was compared
and the effect reads as a slope.
The exception is a column genuinely holding one number per participant -- a
difference score, a percent change entered as a covariate. That is continuous,
and `source_definition` records the subtraction it came from.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
domain_of:
- Measure
- ModelTerm
range: ExtractedTermType
required: true
levels:
name: levels
description: 'The levels of a categorical term, and for each the entities carrying
it. This is the whole of what a factor is: its level vocabulary, and which cohort,
condition, occasion, or arm occupies each cell. Record a level the source names
even when no reported contrast uses it -- a three-level factor contrasted at
two levels is still a three-level factor. Empty for a continuous term.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
domain_of:
- ModelTerm
range: FactorLevel
multivalued: true
inlined_as_list: true
assessment:
name: assessment
description: 'The assessment whose measurements supply a continuous term, **including
when the column is derived from them rather than being one of them**: a change
score, a percent change, a composite of subscales, a residualized score. The
instrument is still where the values came from, and one administration or several
makes no difference to that -- `region` says the same by example, an ROI mean
and a PPI regressor both naming their region.
One instrument, one record, however many columns it supplies: two subscale scores
of one questionnaire each name it, and which subscale a column carries is the
term''s `name`.
Unset only when the values come from somewhere the study does not record as
an Assessment -- age, dose, a motion parameter, cardiac phase, scanner drift
-- in which case `source_definition` names the origin. A term with neither is
a column whose origin the record does not state.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
domain_of:
- ModelTerm
range: Assessment
inlined: false
region:
name: region
description: 'The region whose signal supplies this term: a seed timecourse, an
ROI mean covariate, a PPI physiological regressor. The counterpart of `assessment`
for a column drawn from a place in the brain rather than an instrument.
Singular, so it does not serve a factor that *compares* regions: that is one
term with several levels, and the regions hang off `FactorLevel.regions`.
The test is whether the column''s numbers came out of that place. A region that
only bounds where inference ran -- an explicit mask, a search volume, an atlas
the analysis was restricted to -- is `Analysis.regions`, and no term draws anything
from it. Naming it here says the column *was* that region''s signal, which for
a voxelwise fit inside a mask is false, and it says so while `Analysis.regions`
goes on saying the true thing, so the two never visibly disagree.
Leave it empty on a categorical term. Regions are carried by a factor''s levels,
on `FactorLevel.regions`, and a factor comparing places has at least two: one
level named for a mask is a search space written as a comparison the study never
made.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
domain_of:
- ModelTerm
range: Region
inlined: false
variation_level:
name: variation_level
description: 'Whether this term moves within a participant or only across the
sample. Populate it for **any** term: a repeated-measures factor is within-subject
and a diagnosis factor is between-subject in exactly the sense a trial-wise
modulator and an age covariate are.
For a continuous term it is what separates a parametric modulation from a cross-subject
regression, and the derived kind of any effect testing it reads this on that
basis. For a categorical factor it feeds no derivation, so setting it changes
no other value; it is the factor''s scope, and two studies that crossed a factor
within participants and between them did not estimate the same thing.
A categorical factor''s levels usually settle it, and the two should agree:
levels naming `groups` are between-subject, since a participant belongs to one
cohort; levels naming `timepoints` are within-subject, since an occasion is
when the same people were measured again; levels naming `conditions` are within-subject
in the ordinary repeated-measures case; levels naming `arms` follow `StudyDesign.assignment_structure`,
crossover being within and parallel between.
A level the vocabulary cannot name is written here as free text; the derived
kind then cannot choose between a modulation and a regression, so it is undetermined
for that record rather than wrong.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
domain_of:
- ModelTerm
range: ExtractedVariationLevel
functional_form:
name: functional_form
description: 'Shape of the term''s relationship to the measure. A squared term
with a negative coefficient is an inverted-U relationship, not a negative association,
so non-linear and transformed terms must not be recorded as linear. A model
fitting both a linear and a quadratic form of the same measurement has two terms,
not one.
Not where a product is recorded: a non-empty `interaction_with` is what makes
a column a product.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
domain_of:
- ModelTerm
range: ExtractedFunctionalForm
interaction_with:
name: interaction_with
description: 'The other terms this term is a product of -- what makes this column
an interaction column. Populate it when the source names an interaction term
in one model; leave it empty when the source describes a regression on a contrast.
The terms it may name are those of this model and of the stages it reaches through
`inputs_from`, which is how a group stage crosses a cohort factor with a first-level
condition: the product column is declared where it was fitted and names the
lower stage''s column directly.
A crossing of two factors is normally read off the cells, since a factor compared
against itself carries a positive cell and a negative one. A continuous term
has no levels and so cannot be crossed that way, which makes this slot the only
record of a moderation such as `age x group`; the derived kind reads it before
`variation_level`.
A product of terms that are all categorical and all crossed is legal but decides
nothing, the crossed cells already saying what it says, and is flagged for review.
A product column declares no `levels` of its own; a signed cell on one with
a continuous component may name a level of the factor, saying whose slope the
sign describes. Such a column is not a covariate of a contrast whose cells name
all of its components.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
domain_of:
- ModelTerm
range: ModelTerm
multivalued: true
inlined: false
unit:
name: unit
description: The unit a continuous term is measured in, when the source gives
one -- years for age, mg for a dose. It is what makes a reported slope interpretable.
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
domain_of:
- Measure
- ModelTerm
range: ExtractedString
source_definition:
name: source_definition
description: 'The source''s own definition of the term, and the origin of a continuous
term that no Assessment supplies, such as age, dose, a motion parameter, cardiac
phase, or scanner drift.
**Required for a derived column, whatever its `assessment` says.** A change
score names its instrument in `assessment` and its derivation here, in the source''s
own words and including the occasions it spans -- "percent reduction in BDI,
(post-pre)/pre, from baseline to post-treatment". Nothing else can carry those
occasions: `FactorLevel.timepoints` names the occasions a factor *compares*,
and a derived column has no levels to hang them on, so where a study has several
post-intervention occasions this is the only thing separating a change to the
endpoint from a change to a later follow-up.
Not the shape of the fitted relationship, which is `functional_form`: a percent
change entered linearly is a linear term computed from a difference, so recording
the construction as the form would lose whether the fit was linear or quadratic.
A transform *of the measure* the vocabulary cannot name still goes in functional_form.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
domain_of:
- ModelTerm
range: ExtractedString
Induced¶
name: ModelTerm
description: 'One term of the model: a column of the design matrix, or the equivalent
predictor in a family with no design matrix. A categorical term names its levels
and the entities carrying them; a continuous term carries the facts that make a
slope interpretable. Nothing here is a fact about any one contrast -- what a contrast
did with this term is a `Cell` on that contrast''s Effect. A term with no cell on
some contrast is a term that contrast was adjusted for rather than tested.
A term is something the measure was regressed *on*. The quantity being modelled
is `Analysis.measure` and never a term of the model that models it. A table laid
out with one row-block per measured parameter -- four diffusion parameters, three
frequency bands -- is reporting one analysis per parameter over one measure each,
not one analysis over a factor whose levels are the parameters.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction
rank: 1000
attributes:
stable_id:
name: stable_id
description: UUID minted by the store the first time this entity appears, never
by an extractor, and kept across re-runs so a later extraction matches the same
entity.
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
owner: ModelTerm
domain_of:
- Arm
- Timepoint
- Group
- Task
- Condition
- Assessment
- Region
- Acquisition
- Device
- Analysis
- Measure
- Preprocessing
- ModelEstimation
- ModelTerm
- InferenceSettings
- Table
- CoordinateSet
- Study
range: string
pattern: ^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$
local_id:
name: local_id
description: Stable identifier, referenced by an Effect's `cells` and its `mediation.mediator`,
and by `interaction_with`.
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
identifier: true
owner: ModelTerm
domain_of:
- Arm
- Timepoint
- Group
- Task
- Condition
- Assessment
- Region
- Acquisition
- Device
- Analysis
- Measure
- Preprocessing
- ModelEstimation
- ModelTerm
- InferenceSettings
- Table
- CoordinateSet
- Study
range: string
required: true
name:
name: name
description: 'Term label, unique within the model: a name appearing twice is one
term recorded twice. For a categorical term this is the factor''s identity.
Often a label the source never writes. A paper reporting 1-back, 2-back and
3-back conditions states the levels and states that they vary together, but
frequently names no axis; `load` is then a name supplied for a grouping the
source does make. That is permitted, within two limits: the levels must be source-stated,
and the name must not assert a comparison or a construct the paper does not.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
owner: ModelTerm
domain_of:
- Arm
- Timepoint
- Group
- Task
- Condition
- Assessment
- Region
- Analysis
- ModelTerm
- ExternalDataset
- PerformanceMetric
range: ExtractedString
required: true
type:
name: type
description: 'Whether the term is categorical or continuous. Categorical when
the column distinguishes named cells of the design -- occasions, cohorts, conditions,
arms, regions -- whether or not the paper names the axis; `levels` is then required.
Continuous when the column carries a measured value and the model fits a slope.
Small as it is, this field settles four others: whether `levels` exists, whether
a `Cell` here takes a `level`, whether the term can be crossed, and which branch
of the derived kind fires. A term recorded continuous is one no contrast can
be built across.
**A term whose name states a comparison -- `pre > post`, `A versus B` -- is
almost always a categorical factor written down from its contrast''s side.**
The design matrix distinguished two occasions, cohorts or conditions; the paper
labelled the difference and left the axis unnamed. Record the factor with a
level per side and let the cells carry the direction (`representing-models.md`
ยง5.6). Collapsed into one continuous column, nothing says what was compared
and the effect reads as a slope.
The exception is a column genuinely holding one number per participant -- a
difference score, a percent change entered as a covariate. That is continuous,
and `source_definition` records the subtraction it came from.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
owner: ModelTerm
domain_of:
- Measure
- ModelTerm
range: ExtractedTermType
required: true
levels:
name: levels
description: 'The levels of a categorical term, and for each the entities carrying
it. This is the whole of what a factor is: its level vocabulary, and which cohort,
condition, occasion, or arm occupies each cell. Record a level the source names
even when no reported contrast uses it -- a three-level factor contrasted at
two levels is still a three-level factor. Empty for a continuous term.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
owner: ModelTerm
domain_of:
- ModelTerm
range: FactorLevel
multivalued: true
inlined: true
inlined_as_list: true
assessment:
name: assessment
description: 'The assessment whose measurements supply a continuous term, **including
when the column is derived from them rather than being one of them**: a change
score, a percent change, a composite of subscales, a residualized score. The
instrument is still where the values came from, and one administration or several
makes no difference to that -- `region` says the same by example, an ROI mean
and a PPI regressor both naming their region.
One instrument, one record, however many columns it supplies: two subscale scores
of one questionnaire each name it, and which subscale a column carries is the
term''s `name`.
Unset only when the values come from somewhere the study does not record as
an Assessment -- age, dose, a motion parameter, cardiac phase, scanner drift
-- in which case `source_definition` names the origin. A term with neither is
a column whose origin the record does not state.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
owner: ModelTerm
domain_of:
- ModelTerm
range: Assessment
inlined: false
region:
name: region
description: 'The region whose signal supplies this term: a seed timecourse, an
ROI mean covariate, a PPI physiological regressor. The counterpart of `assessment`
for a column drawn from a place in the brain rather than an instrument.
Singular, so it does not serve a factor that *compares* regions: that is one
term with several levels, and the regions hang off `FactorLevel.regions`.
The test is whether the column''s numbers came out of that place. A region that
only bounds where inference ran -- an explicit mask, a search volume, an atlas
the analysis was restricted to -- is `Analysis.regions`, and no term draws anything
from it. Naming it here says the column *was* that region''s signal, which for
a voxelwise fit inside a mask is false, and it says so while `Analysis.regions`
goes on saying the true thing, so the two never visibly disagree.
Leave it empty on a categorical term. Regions are carried by a factor''s levels,
on `FactorLevel.regions`, and a factor comparing places has at least two: one
level named for a mask is a search space written as a comparison the study never
made.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
owner: ModelTerm
domain_of:
- ModelTerm
range: Region
inlined: false
variation_level:
name: variation_level
description: 'Whether this term moves within a participant or only across the
sample. Populate it for **any** term: a repeated-measures factor is within-subject
and a diagnosis factor is between-subject in exactly the sense a trial-wise
modulator and an age covariate are.
For a continuous term it is what separates a parametric modulation from a cross-subject
regression, and the derived kind of any effect testing it reads this on that
basis. For a categorical factor it feeds no derivation, so setting it changes
no other value; it is the factor''s scope, and two studies that crossed a factor
within participants and between them did not estimate the same thing.
A categorical factor''s levels usually settle it, and the two should agree:
levels naming `groups` are between-subject, since a participant belongs to one
cohort; levels naming `timepoints` are within-subject, since an occasion is
when the same people were measured again; levels naming `conditions` are within-subject
in the ordinary repeated-measures case; levels naming `arms` follow `StudyDesign.assignment_structure`,
crossover being within and parallel between.
A level the vocabulary cannot name is written here as free text; the derived
kind then cannot choose between a modulation and a regression, so it is undetermined
for that record rather than wrong.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
owner: ModelTerm
domain_of:
- ModelTerm
range: ExtractedVariationLevel
functional_form:
name: functional_form
description: 'Shape of the term''s relationship to the measure. A squared term
with a negative coefficient is an inverted-U relationship, not a negative association,
so non-linear and transformed terms must not be recorded as linear. A model
fitting both a linear and a quadratic form of the same measurement has two terms,
not one.
Not where a product is recorded: a non-empty `interaction_with` is what makes
a column a product.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
owner: ModelTerm
domain_of:
- ModelTerm
range: ExtractedFunctionalForm
interaction_with:
name: interaction_with
description: 'The other terms this term is a product of -- what makes this column
an interaction column. Populate it when the source names an interaction term
in one model; leave it empty when the source describes a regression on a contrast.
The terms it may name are those of this model and of the stages it reaches through
`inputs_from`, which is how a group stage crosses a cohort factor with a first-level
condition: the product column is declared where it was fitted and names the
lower stage''s column directly.
A crossing of two factors is normally read off the cells, since a factor compared
against itself carries a positive cell and a negative one. A continuous term
has no levels and so cannot be crossed that way, which makes this slot the only
record of a moderation such as `age x group`; the derived kind reads it before
`variation_level`.
A product of terms that are all categorical and all crossed is legal but decides
nothing, the crossed cells already saying what it says, and is flagged for review.
A product column declares no `levels` of its own; a signed cell on one with
a continuous component may name a level of the factor, saying whose slope the
sign describes. Such a column is not a covariate of a contrast whose cells name
all of its components.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
owner: ModelTerm
domain_of:
- ModelTerm
range: ModelTerm
multivalued: true
inlined: false
unit:
name: unit
description: The unit a continuous term is measured in, when the source gives
one -- years for age, mg for a dose. It is what makes a reported slope interpretable.
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
owner: ModelTerm
domain_of:
- Measure
- ModelTerm
range: ExtractedString
source_definition:
name: source_definition
description: 'The source''s own definition of the term, and the origin of a continuous
term that no Assessment supplies, such as age, dose, a motion parameter, cardiac
phase, or scanner drift.
**Required for a derived column, whatever its `assessment` says.** A change
score names its instrument in `assessment` and its derivation here, in the source''s
own words and including the occasions it spans -- "percent reduction in BDI,
(post-pre)/pre, from baseline to post-treatment". Nothing else can carry those
occasions: `FactorLevel.timepoints` names the occasions a factor *compares*,
and a derived column has no levels to hang them on, so where a study has several
post-intervention occasions this is the only thing separating a change to the
endpoint from a change to a later follow-up.
Not the shape of the fitted relationship, which is `functional_form`: a percent
change entered linearly is a linear term computed from a difference, so recording
the construction as the form would lose whether the fit was linear or quadratic.
A transform *of the measure* the vocabulary cannot name still goes in functional_form.'
from_schema: https://neurostuff.org/schemas/neuroimaging-study-extraction/analysis
rank: 1000
owner: ModelTerm
domain_of:
- ModelTerm
range: ExtractedString