PERSON-CENTERED MEMORY and COMMUNICATION INTERVENTIONS for ...
Understanding the time course of interventions - a memory ... · Understanding the time course of...
Transcript of Understanding the time course of interventions - a memory ... · Understanding the time course of...
![Page 1: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/1.jpg)
Understanding the time course of interventions - amemory strategy example
March, 2018
Charles Driver
Max Planck Institute for Human Development
![Page 2: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/2.jpg)
The study
Change in associative and recognition memory across the adult age range,particularly with regards to strategy use.
Longitudinal study with 5 waves – though actually 8 waves!
Within wave, participants shown two lists of 26 word pairs, and tested onrecognition of individual words (items) and pairs of words (associations).
book running
![Page 3: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/3.jpg)
The study
Change in associative and recognition memory across the adult age range,particularly with regards to strategy use.
Longitudinal study with 5 waves – though actually 8 waves!
Within wave, participants shown two lists of 26 word pairs, and tested onrecognition of individual words (items) and pairs of words (associations).
book running
![Page 4: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/4.jpg)
The study
Change in associative and recognition memory across the adult age range,particularly with regards to strategy use.
Longitudinal study with 5 waves – though actually 8 waves!
Within wave, participants shown two lists of 26 word pairs, and tested onrecognition of individual words (items) and pairs of words (associations).
book running
![Page 5: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/5.jpg)
The study
Change in associative and recognition memory across the adult age range,particularly with regards to strategy use.
Longitudinal study with 5 waves – though actually 8 waves!
Within wave, participants shown two lists of 26 word pairs, and tested onrecognition of individual words (items) and pairs of words (associations).
book running
![Page 6: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/6.jpg)
Longitudinal timeline
0 50 100 150 200 250
01
23
45
6Observations
Subject
Yea
rs fr
om s
tudy
sta
rt
Before each of the 3 waves with the short, 2 week interval, deep encodingintervention:
Participants asked to generate, for each pair, a sentence relating the twowords to each other during the study portion, and use that sentence to aidrecognition of words and pairs.
Individual variation in timing too...
![Page 7: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/7.jpg)
Longitudinal timeline
0 50 100 150 200 250
01
23
45
6Observations
Subject
Yea
rs fr
om s
tudy
sta
rt
Before each of the 3 waves with the short, 2 week interval, deep encodingintervention:
Participants asked to generate, for each pair, a sentence relating the twowords to each other during the study portion, and use that sentence to aidrecognition of words and pairs.
Individual variation in timing too...
![Page 8: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/8.jpg)
Longitudinal timeline
0 50 100 150 200 250
01
23
45
6Observations
Subject
Yea
rs fr
om s
tudy
sta
rt
Before each of the 3 waves with the short, 2 week interval, deep encodingintervention:
Participants asked to generate, for each pair, a sentence relating the twowords to each other during the study portion, and use that sentence to aidrecognition of words and pairs.
Individual variation in timing too...
![Page 9: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/9.jpg)
Longitudinal timeline
0 50 100 150 200 250
01
23
45
6Observations
Subject
Yea
rs fr
om s
tudy
sta
rt
Before each of the 3 waves with the short, 2 week interval, deep encodingintervention:
Participants asked to generate, for each pair, a sentence relating the twowords to each other during the study portion, and use that sentence to aidrecognition of words and pairs.
Individual variation in timing too...
![Page 10: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/10.jpg)
Research questions
Does the intervention have lasting effects?
How do the covariates moderate the findings?
![Page 11: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/11.jpg)
Research questions
Does the intervention have lasting effects?
How do the covariates moderate the findings?
![Page 12: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/12.jpg)
Research questions
Does the intervention have lasting effects?
How do the covariates moderate the findings?
![Page 13: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/13.jpg)
Descriptive plots
0 1 2 3 4 5 6
0.0
0.2
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1.0 asfa
Years
asfa
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0 ashr
Years
ashr
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0 itfa
Years
itfa
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0 ithr
Years
ithr
![Page 14: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/14.jpg)
Descriptive plots
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0 asfa
Years
asfa
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0 ashr
Years
ashr
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0 itfa
Years
itfa
0 1 2 3 4 5 6
0.0
0.2
0.4
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0.8
1.0 ithr
Years
ithr
![Page 15: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/15.jpg)
Modelling - hierarchical Bayesian continuous time dynamic model.
Subject level latent dynamics driven by stochastic differential equation:
dη(t) =
(Aη(t) + b + Mχ(t)
)dt + GdW(t) (1)
Observations for each subject are described by:
y(t) = Λη(t) + τ + ε(t) where ε(t) ∼ N(0c ,Θ) (2)
Possible via:
wide, SEM approach.long, Kalman-filter.
Frequentist SEM allows individual variation in intercepts,
With Bayesian formulation, everything can vary.
![Page 16: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/16.jpg)
Modelling - hierarchical Bayesian continuous time dynamic model.
Subject level latent dynamics driven by stochastic differential equation:
dη(t) =
(Aη(t) + b + Mχ(t)
)dt + GdW(t) (1)
Observations for each subject are described by:
y(t) = Λη(t) + τ + ε(t) where ε(t) ∼ N(0c ,Θ) (2)
Possible via:
wide, SEM approach.long, Kalman-filter.
Frequentist SEM allows individual variation in intercepts,
With Bayesian formulation, everything can vary.
![Page 17: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/17.jpg)
Modelling - hierarchical Bayesian continuous time dynamic model.
Subject level latent dynamics driven by stochastic differential equation:
dη(t) =
(Aη(t) + b + Mχ(t)
)dt + GdW(t) (1)
Observations for each subject are described by:
y(t) = Λη(t) + τ + ε(t) where ε(t) ∼ N(0c ,Θ) (2)
Possible via:
wide, SEM approach.long, Kalman-filter.
Frequentist SEM allows individual variation in intercepts,
With Bayesian formulation, everything can vary.
![Page 18: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/18.jpg)
Modelling - hierarchical Bayesian continuous time dynamic model.
Subject level latent dynamics driven by stochastic differential equation:
dη(t) =
(Aη(t) + b + Mχ(t)
)dt + GdW(t) (1)
Observations for each subject are described by:
y(t) = Λη(t) + τ + ε(t) where ε(t) ∼ N(0c ,Θ) (2)
Possible via:
wide, SEM approach.long, Kalman-filter.
Frequentist SEM allows individual variation in intercepts,
With Bayesian formulation, everything can vary.
![Page 19: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/19.jpg)
Modelling - hierarchical Bayesian continuous time dynamic model.
Subject level latent dynamics driven by stochastic differential equation:
dη(t) =
(Aη(t) + b + Mχ(t)
)dt + GdW(t) (1)
Observations for each subject are described by:
y(t) = Λη(t) + τ + ε(t) where ε(t) ∼ N(0c ,Θ) (2)
Possible via:wide, SEM approach.
long, Kalman-filter.
Frequentist SEM allows individual variation in intercepts,
With Bayesian formulation, everything can vary.
![Page 20: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/20.jpg)
Modelling - hierarchical Bayesian continuous time dynamic model.
Subject level latent dynamics driven by stochastic differential equation:
dη(t) =
(Aη(t) + b + Mχ(t)
)dt + GdW(t) (1)
Observations for each subject are described by:
y(t) = Λη(t) + τ + ε(t) where ε(t) ∼ N(0c ,Θ) (2)
Possible via:wide, SEM approach.long, Kalman-filter.
Frequentist SEM allows individual variation in intercepts,
With Bayesian formulation, everything can vary.
![Page 21: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/21.jpg)
Modelling - hierarchical Bayesian continuous time dynamic model.
Subject level latent dynamics driven by stochastic differential equation:
dη(t) =
(Aη(t) + b + Mχ(t)
)dt + GdW(t) (1)
Observations for each subject are described by:
y(t) = Λη(t) + τ + ε(t) where ε(t) ∼ N(0c ,Θ) (2)
Possible via:wide, SEM approach.long, Kalman-filter.
Frequentist SEM allows individual variation in intercepts,
With Bayesian formulation, everything can vary.
![Page 22: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/22.jpg)
Modelling - hierarchical Bayesian continuous time dynamic model.
Subject level latent dynamics driven by stochastic differential equation:
dη(t) =
(Aη(t) + b + Mχ(t)
)dt + GdW(t) (1)
Observations for each subject are described by:
y(t) = Λη(t) + τ + ε(t) where ε(t) ∼ N(0c ,Θ) (2)
Possible via:wide, SEM approach.long, Kalman-filter.
Frequentist SEM allows individual variation in intercepts,
With Bayesian formulation, everything can vary.
![Page 23: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/23.jpg)
Modelling – take 2
Four latent memory factors asfa, ashr, itfa, ithr, each measured by twonoisy indicators per wave.
Change over time in these latent factors is modelled with an initialintercept, a linear slope, and a stochastic portion to account formeaningful but unpredictable (according to our model) change.
On top of this, we estimate an intervention process, and the effect of thisprocess on each of the four memory factors.
0 1 2 3 4 5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Subject 1, Age 75, Sex F, WkMz 1.55, METSz -0.81
Time
asfa
![Page 24: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/24.jpg)
Modelling – take 2
Four latent memory factors asfa, ashr, itfa, ithr, each measured by twonoisy indicators per wave.
Change over time in these latent factors is modelled with an initialintercept, a linear slope, and a stochastic portion to account formeaningful but unpredictable (according to our model) change.
On top of this, we estimate an intervention process, and the effect of thisprocess on each of the four memory factors.
0 1 2 3 4 5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Subject 1, Age 75, Sex F, WkMz 1.55, METSz -0.81
Time
asfa
![Page 25: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/25.jpg)
Modelling – take 2
Four latent memory factors asfa, ashr, itfa, ithr, each measured by twonoisy indicators per wave.
Change over time in these latent factors is modelled with an initialintercept, a linear slope, and a stochastic portion to account formeaningful but unpredictable (according to our model) change.
On top of this, we estimate an intervention process, and the effect of thisprocess on each of the four memory factors.
0 1 2 3 4 5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Subject 1, Age 75, Sex F, WkMz 1.55, METSz -0.81
Time
asfa
![Page 26: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/26.jpg)
Modelling – take 2
Four latent memory factors asfa, ashr, itfa, ithr, each measured by twonoisy indicators per wave.
Change over time in these latent factors is modelled with an initialintercept, a linear slope, and a stochastic portion to account formeaningful but unpredictable (according to our model) change.
On top of this, we estimate an intervention process, and the effect of thisprocess on each of the four memory factors.
0 1 2 3 4 5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Subject 1, Age 75, Sex F, WkMz 1.55, METSz -0.81
Time
asfa
![Page 27: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/27.jpg)
What do we gain with such an approach to interventions?
Continuous time accounts for variability in time interval betweenobservations.
Intervention as dynamic process allows estimating unknown shape /persistence parameters.
Hierarchical approach accounts for, allows understanding of, individualvariability.
![Page 28: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/28.jpg)
What do we gain with such an approach to interventions?
Continuous time accounts for variability in time interval betweenobservations.
Intervention as dynamic process allows estimating unknown shape /persistence parameters.
Hierarchical approach accounts for, allows understanding of, individualvariability.
![Page 29: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/29.jpg)
What do we gain with such an approach to interventions?
Continuous time accounts for variability in time interval betweenobservations.
Intervention as dynamic process allows estimating unknown shape /persistence parameters.
Hierarchical approach accounts for, allows understanding of, individualvariability.
![Page 30: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/30.jpg)
What do we gain with such an approach to interventions?
Continuous time accounts for variability in time interval betweenobservations.
Intervention as dynamic process allows estimating unknown shape /persistence parameters.
Hierarchical approach accounts for, allows understanding of, individualvariability.
![Page 31: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/31.jpg)
Results
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
asfa
Years
asfa
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
ashr
Years
ashr
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
itfa
Years
itfa
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
ithr
Years
ithr
Sig. intervention improvement for asfa, ashr, itfa.
Unclear if intervention effect persists across years – probably not in general.
Intervention gives greater gains for worse performers.
![Page 32: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/32.jpg)
Results
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
asfa
Years
asfa
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
ashr
Years
ashr
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
itfa
Years
itfa
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
ithr
Years
ithr
Sig. intervention improvement for asfa, ashr, itfa.
Unclear if intervention effect persists across years – probably not in general.
Intervention gives greater gains for worse performers.
![Page 33: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/33.jpg)
Results
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
asfa
Years
asfa
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
ashr
Years
ashr
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
itfa
Years
itfa
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
ithr
Years
ithr
Sig. intervention improvement for asfa, ashr, itfa.
Unclear if intervention effect persists across years – probably not in general.
Intervention gives greater gains for worse performers.
![Page 34: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/34.jpg)
Results
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
asfa
Years
asfa
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
ashr
Years
ashr
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
itfa
Years
itfa
0 1 2 3 4 5 6
0.0
0.2
0.4
0.6
0.8
1.0
ithr
Years
ithr
Sig. intervention improvement for asfa, ashr, itfa.
Unclear if intervention effect persists across years – probably not in general.
Intervention gives greater gains for worse performers.
![Page 35: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/35.jpg)
Individual differences - age effects
20 30 40 50 60 70
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5
AgeT1
Effect
TD_asfa_intTD_ashr_intTD_itfa_intTD_ithr_int
20 30 40 50 60 70
−0.2
−0.1
0.0
0.1
0.2
0.3
AgeT1
Effect
slope_asfaslope__ashrslope__itfaslope__ithr
20 30 40 50 60 70
−0.6
−0.4
−0.2
0.0
0.2
0.4
0.6
0.8
AgeT1
Effect
manifestmeans_asfamanifestmeans_ashrmanifestmeans_itfamanifestmeans_ithr
20 30 40 50 60 70
0.6
0.8
1.0
1.2
AgeT1
Effect
manifestvar_asfamanifestvar_ashrmanifestvar_itfamanifestvar_ithr
20 30 40 50 60 70
−10
−8−6
−4−2
02
AgeT1
Effect
dr_memInt
20 30 40 50 60 70
−4−2
02
4
AgeT1
Effect
TD_memInt2TD_memInt3
![Page 36: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/36.jpg)
Individual differences - age effects
20 30 40 50 60 70
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5
AgeT1
Effect
TD_asfa_intTD_ashr_intTD_itfa_intTD_ithr_int
20 30 40 50 60 70−0.2
−0.1
0.0
0.1
0.2
0.3
AgeT1
Effect
slope_asfaslope__ashrslope__itfaslope__ithr
20 30 40 50 60 70
−0.6
−0.4
−0.2
0.0
0.2
0.4
0.6
0.8
AgeT1
Effect
manifestmeans_asfamanifestmeans_ashrmanifestmeans_itfamanifestmeans_ithr
20 30 40 50 60 70
0.6
0.8
1.0
1.2
AgeT1
Effect
manifestvar_asfamanifestvar_ashrmanifestvar_itfamanifestvar_ithr
20 30 40 50 60 70
−10
−8−6
−4−2
02
AgeT1
Effect
dr_memInt
20 30 40 50 60 70
−4−2
02
4
AgeT1
Effect
TD_memInt2TD_memInt3
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Discussion points - different shapes of intervention effect
0 5 10 15 20
0.0
0.2
0.4
0.6
0.8
1.0
Basic impulse effect
Time
Stat
e
Base process
0 5 10 15 20
0.0
0.5
1.0
1.5
2.0
2.5
Persistent level change
Time
Stat
e
Base processInput process
0 5 10 15 20
0.0
0.5
1.0
1.5
Dissipative impulse
Time
Stat
e
Base processInput process
0 5 10 15 20
-0.5
0.0
0.5
1.0
Oscillatory dissipation
Time
Stat
e
Base processInput processMediating input process
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Discussion points - interventions on a trend
0 5 10 15 20
01
23
45
6
Time
Stat
e
Process without interventionProcess with interventionIntervention
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Discussion points - system identification
0 5 10 15 20
−0.
50.
00.
51.
01.
52.
0
Natural state
Time
Sta
te
Process 1Process 2
0 5 10 15 20−
0.5
0.0
0.5
1.0
1.5
2.0
State with interventions
Time
Sta
te
Process 1Process 2
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Discussion points - absolute model fit
Fits are better than saturated covariance structure.
Are there formalised approaches for model fit with person specific meanand or covariance?
Is absolute fit actually important?
![Page 41: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/41.jpg)
Discussion points - absolute model fit
Fits are better than saturated covariance structure.
Are there formalised approaches for model fit with person specific meanand or covariance?
Is absolute fit actually important?
![Page 42: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/42.jpg)
Discussion points - absolute model fit
Fits are better than saturated covariance structure.
Are there formalised approaches for model fit with person specific meanand or covariance?
Is absolute fit actually important?
![Page 43: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/43.jpg)
Summary
The effect of interventions can change in time, and across people.
Analyses and plots via ctsem R package.
Chapter: Understanding the time course of interventions with continuoustime dynamic models.
Thanks!
![Page 44: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/44.jpg)
Summary
The effect of interventions can change in time, and across people.
Analyses and plots via ctsem R package.
Chapter: Understanding the time course of interventions with continuoustime dynamic models.
Thanks!
![Page 45: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/45.jpg)
Summary
The effect of interventions can change in time, and across people.
Analyses and plots via ctsem R package.
Chapter: Understanding the time course of interventions with continuoustime dynamic models.
Thanks!
![Page 46: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/46.jpg)
Summary
The effect of interventions can change in time, and across people.
Analyses and plots via ctsem R package.
Chapter: Understanding the time course of interventions with continuoustime dynamic models.
Thanks!
![Page 47: Understanding the time course of interventions - a memory ... · Understanding the time course of interventions - a memory strategy example March, 2018 Charles Driver Max Planck Institute](https://reader033.fdocuments.net/reader033/viewer/2022050105/5f434f9a32ee0c76ad4ffc63/html5/thumbnails/47.jpg)
Summary
The effect of interventions can change in time, and across people.
Analyses and plots via ctsem R package.
Chapter: Understanding the time course of interventions with continuoustime dynamic models.
Thanks!