Evaluating the Mode of Presentation to Hospital and Time to Death/Discharge in Patients with COVID-19 in Southwest Iran: A Joint Modelling Approach

Abstract Within-individual variability of repeatedly-measured exposures may predict later outcomes: e.g. blood pressure (BP) variability (BPV) is an independent cardiovascular risk factor above and beyond mean BP. Since two-stage methods, known to introduce bias, are typically used to investigate such associations, we introduce a joint modelling approach, examining associations of mean BP and BPV across childhood to left ventricular mass (indexed to height; LVMI) in early adulthood with data (collected 1990-2011) from the UK’s Avon Longitudinal Study of Parents and Children cohort. Using multilevel models, we allow BPV to vary between individuals (a “random effect”) as well as to depend on covariates (allowing for heteroscedasticity). We further distinguish within-clinic variability (“measurement error”) from visit-to-visit BPV. BPV was predicted to be greater at older ages, at higher bodyweights, and in females, and was positively correlated with mean BP. BPV had a weak positive association with LVMI (10% increase in within-individual BP variance was predicted to increase LVMI by 0.21% (95% credible interval: -0.23%, 0.69%)), but this association became negative (-0.78%, 95% credible interval: -2.54%, 0.22%)) once the effect of mean BP on LVMI was adjusted for. This joint modelling approach offers a flexible method of relating repeatedly-measured exposures to later outcomes.

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Bayesian Joint Modelling of Disease Progression Marker and Time to Death Event of HIV/AIDS Patients under ART Follow-up

British Journal of Medicine and Medical Research ◽

10.9734/bjmmr/2015/12907 ◽

2015 ◽

Vol 5 (8) ◽

pp. 1034-1043

Author(s):

Gemeda Buta ◽

Ayele Goshu ◽

Hailemichael Worku

Keyword(s):

Disease Progression ◽

Aids Patients ◽

Joint Modelling ◽

Time To Death ◽

Progression Marker ◽

Death Event ◽

Hiv Aids

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Evaluating Quality Adjusted Life Years in the Absence of Standard Utility Values- A Dynamic Joint Modelling Approach

10.1101/2020.05.24.20112102 ◽

2020 ◽

Author(s):

Vishal Deo ◽

Gurprit Grover

Keyword(s):

Joint Model ◽

Monte Carlo Integration ◽

Survival Times ◽

Quality Adjusted Life Years ◽

Joint Modelling ◽

Utility Values ◽

Decision Analytic Modelling ◽

Life Years ◽

Quality Adjusted Life ◽

Modelling Approach

AbstractEstimation of Quality Adjusted Life Years (QALYs) is pivotal towards cost-effectiveness analysis (CEA) of medical interventions. Most of the CEA studies employ multi-state decision analytic modelling approach, where fixed utility values are assigned to each disease state and total QALYs are calculated on the basis of total lengths of stay in each state.In this paper, we have formulated a new approach to CEA by defining utility as a function of a longitudinal covariate which is significantly associated with disease progression. Association parameter between the longitudinal covariate and survival times is estimated through joint modelling of the longitudinal linear mixed effects model and the Weibull accelerated failure time survival model. Metropolis-Hastings algorithm and Monte Carlo integration are used to predict expected survival times of each censored case using the joint model. Fitted longitudinal model is further used to project values of the longitudinal covariate at all time points for each patient. Utility values calculated using these projected covariate values are used to evaluate QALYs for each patient.Retrospective survival data of HIV/ AIDS patients undergoing treatment at the Antiretroviral Therapy centre of Ram Manohar Lohia hospital in New Delhi is used to demonstrate the implementation of the proposed methodology. A simulation exercise is also carried out to gauge the predictive capability of the joint model in projecting the values of the longitudinal covariate.The proposed dynamic approach to calculate QALY provides a promising alternative to the popular multi-state decision analytic modelling approach, especially when the standard utility values for different stages of the concerned disease are not available.

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Definition of baseline renal function in hospitalised patients with multiple preadmission measures of serum creatinine—A joint modelling approach

Nephrology ◽

10.1111/nep.13768 ◽

2020 ◽

Vol 25 (12) ◽

pp. 897-905

Author(s):

Olga Laszczyńska ◽

Milton Severo ◽

Ana Azevedo

Keyword(s):

Renal Function ◽

Serum Creatinine ◽

Joint Modelling ◽

Hospitalised Patients ◽

Definition Of ◽

Baseline Renal Function ◽

Modelling Approach

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Analysis of multivariate longitudinal immuno-epidemiological data using a pairwise joint modelling approach

BMC Immunology ◽

10.1186/s12865-021-00453-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Lawrence Lubyayi ◽

Patrice A. Mawa ◽

Stephen Cose ◽

Alison M. Elliott ◽

Jonathan Levin ◽

...

Keyword(s):

Mixed Models ◽

Repeated Measures ◽

Mixed Model ◽

Epidemiological Data ◽

Parameter Estimates ◽

Joint Modelling ◽

Or Groups ◽

Longitudinal Profiles ◽

Infant Immune Response ◽

Modelling Approach

Abstract Background Immuno-epidemiologists are often faced with multivariate outcomes, measured repeatedly over time. Such data are characterised by complex inter- and intra-outcome relationships which must be accounted for during analysis. Scientific questions of interest might include determining the effect of a treatment on the evolution of all outcomes together, or grouping outcomes that change in the same way. Modelling the different outcomes separately may not be appropriate because it ignores the underlying relationships between outcomes. In such situations, a joint modelling strategy is necessary. This paper describes a pairwise joint modelling approach and discusses its benefits over more simple statistical analysis approaches, with application to data from a study of the response to BCG vaccination in the first year of life, conducted in Entebbe, Uganda. Methods The study aimed to determine the effect of maternal latent Mycobacterium tuberculosis infection (LTBI) on infant immune response (TNF, IFN-γ, IL-13, IL-10, IL-5, IL-17A and IL-2 responses to PPD), following immunisation with BCG. A simple analysis ignoring the correlation structure of multivariate longitudinal data is first shown. Univariate linear mixed models are then used to describe longitudinal profiles of each outcome, and are then combined into a multivariate mixed model, specifying a joint distribution for the random effects to account for correlations between the multiple outcomes. A pairwise joint modelling approach, where all possible pairs of bivariate mixed models are fitted, is then used to obtain parameter estimates. Results Univariate and pairwise longitudinal analysis approaches are consistent in finding that LTBI had no impact on the evolution of cytokine responses to PPD. Estimates from the pairwise joint modelling approach were more precise. Major advantages of the pairwise approach include the opportunity to test for the effect of LTBI on the joint evolution of all, or groups of, outcomes and the ability to estimate association structures of the outcomes. Conclusions The pairwise joint modelling approach reduces the complexity of analysis of high-dimensional multivariate repeated measures, allows for proper accounting for association structures and can improve our understanding and interpretation of longitudinal immuno-epidemiological data.

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Adjusting for measurement error in baseline prognostic biomarkers included in a time-to-event analysis: a joint modelling approach

BMC Medical Research Methodology ◽

10.1186/1471-2288-13-146 ◽

2013 ◽

Vol 13 (1) ◽

Cited By ~ 11

Author(s):

Michael J Crowther ◽

Paul C Lambert ◽

Keith R Abrams

Keyword(s):

Measurement Error ◽

Prognostic Biomarkers ◽

Event Analysis ◽

Time To Event ◽

Joint Modelling ◽

Modelling Approach

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Joint modelling of individual trajectories, within-individual variability and a later outcome: systolic blood pressure through childhood and left ventricular mass in early adulthood

10.1101/19008474 ◽

2019 ◽

Author(s):

Richard M.A. Parker ◽

George Leckie ◽

Harvey Goldstein ◽

Laura D. Howe ◽

Jon Heron ◽

...

Keyword(s):

Blood Pressure ◽

Left Ventricular Mass ◽

Random Effect ◽

Early Adulthood ◽

Credible Interval ◽

Left Ventricular ◽

Individual Variability ◽

Joint Modelling ◽

Ventricular Mass ◽

Modelling Approach

ABSTRACTWithin-individual variability of repeatedly-measured exposures may predict later outcomes: e.g. blood pressure (BP) variability (BPV) is an independent cardiovascular risk factor above and beyond mean BP. Since two-stage methods, known to introduce bias, are typically used to investigate such associations, we introduce a joint modelling approach, examining associations of both mean BP and BPV across childhood to left ventricular mass (indexed to height; LVMI) in early adulthood with data from the UK’s Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Using multilevel models, we allow BPV to vary between individuals (a “random effect”) as well as to depend on covariates (allowing for heteroscedasticity). We further distinguish within-clinic variability (“measurement error”) from visit-to-visit BPV. BPV was predicted to be greater at older ages, at higher bodyweights, and in females, and was positively correlated with mean BP. BPV had a positive association with LVMI (10% increase in SD(BP) was predicted to increase LVMI by mean = 0.42% (95% credible interval: −0.47%, 1.38%)), but this association became negative (mean = −1.56%, 95% credible interval: −5.01%, 0.44%)) once the effect of mean BP on LVMI was adjusted for. This joint modelling approach offers a flexible method of relating repeatedly-measured exposures to later outcomes.

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