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DynSurv() - Linear prediction for new patients #76
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I'm not sure I quite follow. jointmodel$sfit is just the Cox model for the
separate time-to-event model ignoring the joint-model component, X(t). So
jointmodel$sfit$linear.predictors is equivalent to fitting a Cox model with
fixed effects and pulling out the linear discriminant term.
…On Fri, 24 Sept 2021 at 14:10, pradeepvirdee ***@***.***> wrote:
Hi Graeme,
I've fit a joint model using mjoint() and stored the model under the
object name "jointmodel". I've obtained dynamic predictions using dynSurv()
on the model building sample but also a validation sample containing data
from new patients. My aim now is to calculate the model's calibration slope
in the validation sample (which involves fitting a Cox model with linear
predictions as the only covariate). For the patients used to build the
model, I can extract their linear predictions from
jointmodel$sfit$linear.predictors. Can I extract the linear prediction for
each new patient in the validation sample? If not, is there a way I can
work this out?
Pradeep
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Yes, I figured that out shortly after posting the question. Sorry, at the time I posted, I thought jointmodel$sfit was a Cox model that included the joint model component. Say I wanted linear predictions from the Cox model that does include the joint model component, is that possible? Sorry if this is a silly question.
Pradeep
Mr Pradeep Virdee
NIHR Doctoral Research Fellow; Medical Statistician
Centre for Statistics in Medicine (CSM),
Botnar Research Centre, NDORMS,
Nuffield Orthopaedic Centre,
University of Oxford,
Windmill Road,
Oxford,
OX3 7LD
Tel: +44 (0)1865 737950
Email: ***@***.***<https://owa.nexus.ox.ac.uk/owa/redir.aspx?C=M9QJvEjCUkuxZIviXHPm2T5wkQ4vR9IIT0wsw9HEKA5FGwCUhzgJWNNdd4XrD5EdWxtXidh_3Ts.&URL=mailto%3apradeep.virdee%40csm.ox.ac.uk>
Web: https://www.ndorms.ox.ac.uk/team/pradeep-virdee
From: Graeme Hickey ***@***.***>
Sent: 16 November 2021 21:49
To: graemeleehickey/joineRML ***@***.***>
Cc: Pradeep Virdee ***@***.***>; Author ***@***.***>
Subject: Re: [graemeleehickey/joineRML] DynSurv() - Linear prediction for new patients (#76)
I'm not sure I quite follow. jointmodel$sfit is just the Cox model for the
separate time-to-event model ignoring the joint-model component, X(t). So
jointmodel$sfit$linear.predictors is equivalent to fitting a Cox model with
fixed effects and pulling out the linear discriminant term.
On Fri, 24 Sept 2021 at 14:10, pradeepvirdee ***@***.***<mailto:***@***.***>> wrote:
Hi Graeme,
I've fit a joint model using mjoint() and stored the model under the
object name "jointmodel". I've obtained dynamic predictions using dynSurv()
on the model building sample but also a validation sample containing data
from new patients. My aim now is to calculate the model's calibration slope
in the validation sample (which involves fitting a Cox model with linear
predictions as the only covariate). For the patients used to build the
model, I can extract their linear predictions from
jointmodel$sfit$linear.predictors. Can I extract the linear prediction for
each new patient in the validation sample? If not, is there a way I can
work this out?
Pradeep
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Since the linear term in the Cox model is a function of a time, you would need to construct it from the coefficients + the time-varying component, m(t).
… On 17 Nov 2021, at 12:04, pradeepvirdee ***@***.***> wrote:
Yes, I figured that out shortly after posting the question. Sorry, at the time I posted, I thought jointmodel$sfit was a Cox model that included the joint model component. Say I wanted linear predictions from the Cox model that does include the joint model component, is that possible? Sorry if this is a silly question.
Pradeep
Mr Pradeep Virdee
NIHR Doctoral Research Fellow; Medical Statistician
Centre for Statistics in Medicine (CSM),
Botnar Research Centre, NDORMS,
Nuffield Orthopaedic Centre,
University of Oxford,
Windmill Road,
Oxford,
OX3 7LD
Tel: +44 (0)1865 737950
Email: ***@***.***<https://owa.nexus.ox.ac.uk/owa/redir.aspx?C=M9QJvEjCUkuxZIviXHPm2T5wkQ4vR9IIT0wsw9HEKA5FGwCUhzgJWNNdd4XrD5EdWxtXidh_3Ts.&URL=mailto%3apradeep.virdee%40csm.ox.ac.uk>
Web: https://www.ndorms.ox.ac.uk/team/pradeep-virdee
From: Graeme Hickey ***@***.***>
Sent: 16 November 2021 21:49
To: graemeleehickey/joineRML ***@***.***>
Cc: Pradeep Virdee ***@***.***>; Author ***@***.***>
Subject: Re: [graemeleehickey/joineRML] DynSurv() - Linear prediction for new patients (#76)
I'm not sure I quite follow. jointmodel$sfit is just the Cox model for the
separate time-to-event model ignoring the joint-model component, X(t). So
jointmodel$sfit$linear.predictors is equivalent to fitting a Cox model with
fixed effects and pulling out the linear discriminant term.
On Fri, 24 Sept 2021 at 14:10, pradeepvirdee ***@***.***<mailto:***@***.***>>
wrote:
> Hi Graeme,
>
> I've fit a joint model using mjoint() and stored the model under the
> object name "jointmodel". I've obtained dynamic predictions using dynSurv()
> on the model building sample but also a validation sample containing data
> from new patients. My aim now is to calculate the model's calibration slope
> in the validation sample (which involves fitting a Cox model with linear
> predictions as the only covariate). For the patients used to build the
> model, I can extract their linear predictions from
> jointmodel$sfit$linear.predictors. Can I extract the linear prediction for
> each new patient in the validation sample? If not, is there a way I can
> work this out?
>
> Pradeep
>
> —
> You are receiving this because you are subscribed to this thread.
> Reply to this email directly, view it on GitHub
> <#76>, or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ABIG3QLZZMIWUJIQ6AVW65TUDR2CTANCNFSM5EV5XB2Q>
> .
>
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Hi Graeme,
I've fit a joint model using mjoint() and stored the model under the object name "jointmodel". I've obtained dynamic predictions using dynSurv() on the model building sample but also a validation sample containing data from new patients. My aim now is to calculate the model's calibration slope in the validation sample (which involves fitting a Cox model with linear predictions as the only covariate). For the patients used to build the model, I can extract their linear predictions from jointmodel$sfit$linear.predictors. Can I extract the linear prediction for each new patient in the validation sample? If not, is there a way I can work this out?
Pradeep
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