scholarly journals Time-to-event data with time-varying biomarkers measured only at study entry, with applications to Alzheimer's disease

2017 ◽  
Vol 37 (6) ◽  
pp. 914-932
Author(s):  
Catherine Lee ◽  
Rebecca A. Betensky ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Study Entry ◽  
Time Varying ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

scholarly journals Prediction of Conversion to Alzheimer’s Disease with Longitudinal Measures and Time-To-Event Data

2017 ◽  
Vol 58 (2) ◽  
pp. 361-371 ◽  
Author(s):  
Kan Li ◽  
Wenyaw Chan ◽  
Rachelle S. Doody ◽  
Joseph Quinn ◽  
Sheng Luo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

scholarly journals Functional joint model for longitudinal and time-to-event data: an application to Alzheimer's disease

2017 ◽  
Vol 36 (22) ◽  
pp. 3560-3572 ◽  
Author(s):  
Kan Li ◽  
Sheng Luo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Joint Model ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

scholarly journals A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data

2018 ◽  
Vol 14 (5) ◽  
pp. 644-651 ◽  
Author(s):  
Kan Li ◽  
Richard O'Brien ◽  
Michael Lutz ◽  
Sheng Luo ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prognostic Model ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Model Of Alzheimer’S Disease

scholarly journals Joint modelling of longitudinal and time-to-event data: An illustration using CD4 count and mortality in a cohort of patients initiated on antiretroviral therapy

2020 ◽  
Author(s):  
Nobuhle Nokubonga Mchunu ◽  
Henry Mwambi ◽  
Tarylee Reddy ◽  
Nonhlanhla Yende-Zuma ◽  
Kogieleum Naidoo
Keyword(s):  
Cox Model ◽  
Joint Model ◽  
Cd4 Count ◽  
Time Varying ◽  
Event Data ◽  
Time To Event ◽  
Joint Modelling ◽  
Risk Of Death ◽  
Time To Event Data ◽  
Over Time

Abstract Background: Modelling of longitudinal biomarkers and time-to-event data are important to monitor disease progression. However, these two variables are traditionally analyzed separately or time-varying Cox models are used. The former strategy fails to recognize the shared random-effects from the two processes while the latter assumes that longitudinal biomarkers are exogenous covariates, resulting in inefficient or biased estimates for the time-to-event model. Therefore, we used joint modelling for longitudinal and time-to-event data to assess the effect of longitudinal CD4 count on mortality. Methods: We studied 4014 patients from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) who initiated ART between June 2004 and August 2013. We used proportional hazards regression model to assess the effect of baseline characteristics (excluding CD4 count) on mortality, and linear mixed effect models to evaluate the effect of baseline characteristics on the CD4 count evolution over time. Thereafter, the two analytical approaches were amalgamated to form an advanced joint model for studying the effect of longitudinal CD4 count on mortality. To illustrate the virtues of the joint model, the results from the joint model were compared to those from the time-varying Cox model. Results: Using joint modelling, we found that lower CD4 count over time was associated with a 1.3-fold increase in the risk of death, (HR: 1.34, 95% CI: 1.27-1.42). Whereas, results from the time-varying Cox model showed lower CD4 count over time was associated with a 1.2-fold increase in the risk of death, (HR: 1.17, 95% CI: 1.12-1.23). Conclusions: Joint modelling enabled the assessment of the effect of longitudinal CD4 count on mortality while correcting for shared random effects between longitudinal and time-to-event models. In the era of universal test and treat, the evaluation of CD4 count is still crucial for guiding the initiation and discontinuation of opportunistic infections prophylaxis and assessment of late presenting patients. CD4 count can also be used when immunological failure is suspected as we have shown that it is associated with mortality. Keywords: Time-to-event data; longitudinal data; joint models; CD4 count; mortality; bias

scholarly journals Evaluating principal surrogate endpoints with time-to-event data accounting for time-varying treatment efficacy

Biostatistics ◽  
2013 ◽  
Vol 15 (2) ◽  
pp. 251-265 ◽  
Author(s):  
E. E. Gabriel ◽  
P. B. Gilbert
Keyword(s):  
Treatment Efficacy ◽  
Surrogate Endpoints ◽  
Time Varying ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

scholarly journals Joint modelling of longitudinal and time-to-event data: an illustration using CD4 count and mortality in a cohort of patients initiated on antiretroviral therapy

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Nobuhle N. Mchunu ◽  
Henry G. Mwambi ◽  
Tarylee Reddy ◽  
Nonhlanhla Yende-Zuma ◽  
Kogieleum Naidoo
Keyword(s):  
Cox Model ◽  
Joint Model ◽  
Cd4 Count ◽  
Time Varying ◽  
Event Data ◽  
Time To Event ◽  
Joint Modelling ◽  
Risk Of Death ◽  
Time To Event Data ◽  
Over Time

Abstract Background Modelling of longitudinal biomarkers and time-to-event data are important to monitor disease progression. However, these two variables are traditionally analyzed separately or time-varying Cox models are used. The former strategy fails to recognize the shared random-effects from the two processes while the latter assumes that longitudinal biomarkers are exogenous covariates, resulting in inefficient or biased estimates for the time-to-event model. Therefore, we used joint modelling for longitudinal and time-to-event data to assess the effect of longitudinal CD4 count on mortality. Methods We studied 4014 patients from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) who initiated ART between June 2004 and August 2013. We used proportional hazards regression model to assess the effect of baseline characteristics (excluding CD4 count) on mortality, and linear mixed effect models to evaluate the effect of baseline characteristics on the CD4 count evolution over time. Thereafter, the two analytical approaches were amalgamated to form an advanced joint model for studying the effect of longitudinal CD4 count on mortality. To illustrate the virtues of the joint model, the results from the joint model were compared to those from the time-varying Cox model. Results Using joint modelling, we found that lower CD4 count over time was associated with a 1.3-fold increase in the risk of death, (HR: 1.34, 95% CI: 1.27-1.42). Whereas, results from the time-varying Cox model showed lower CD4 count over time was associated with a 1.2-fold increase in the risk of death, (HR: 1.17, 95% CI: 1.12-1.23). Conclusions Joint modelling enabled the assessment of the effect of longitudinal CD4 count on mortality while correcting for shared random effects between longitudinal and time-to-event models. In the era of universal test and treat, the evaluation of CD4 count is still crucial for guiding the initiation and discontinuation of opportunistic infections prophylaxis and assessment of late presenting patients. CD4 count can also be used when immunological failure is suspected as we have shown that it is associated with mortality.

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