scholarly journals Joint Modeling of Longitudinal Change and Survival

GeroPsych ◽  
2011 ◽  
Vol 24 (4) ◽  
pp. 177-185 ◽  
Author(s):  
Graciela Muniz Terrera ◽  
Andrea M. Piccinin ◽  
Fiona Matthews ◽  
Scott M. Hofer
Keyword(s):  
Repeated Measures ◽  
Memory Performance ◽  
Joint Modeling ◽  
Disease Diagnosis ◽  
Careful Consideration ◽  
Survival Model ◽  
Longitudinal Change ◽  
Survival Models ◽  
Time Data ◽  
Aging Research

Joint longitudinal-survival models are useful when repeated measures and event time data are available and possibly associated. The application of this joint model in aging research is relatively rare, albeit particularly useful, when there is the potential for nonrandom dropout. In this article we illustrate the method and discuss some issues that may arise when fitting joint models of this type. Using prose recall scores from the Swedish OCTO-Twin Longitudinal Study of Aging, we fitted a joint longitudinal-survival model to investigate the association between risk of mortality and individual differences in rates of change in memory. A model describing change in memory scores as following an accelerating decline trajectory and a Weibull survival model was identified as the best fitting. This model adjusted for random effects representing individual variation in initial memory performance and change in rate of decline as linking terms between the longitudinal and survival models. Memory performance and change in rate of memory decline were significant predictors of proximity to death. Joint longitudinal-survival models permit researchers to gain a better understanding of the association between change functions and risk of particular events, such as disease diagnosis or death. Careful consideration of computational issues may be required because of the complexities of joint modeling methodologies.

scholarly journals Cognition-Mortality Associations Are More Pronounced When Estimated Jointly in Longitudinal and Time-to-Event Models

2021 ◽  
Vol 12 ◽  
Author(s):  
Stephen Aichele ◽  
Sezen Cekic ◽  
Patrick Rabbitt ◽  
Paolo Ghisletta
Keyword(s):  
Cognitive Decline ◽  
Mortality Risk ◽  
Repeated Measures ◽  
Later Life ◽  
Joint Estimation ◽  
Longitudinal Change ◽  
Survival Models ◽  
Joint Models ◽  
Modeling Framework ◽  
Crystallized Abilities

With aging populations worldwide, there is growing interest in links between cognitive decline and elevated mortality risk—and, by extension, analytic approaches to further clarify these associations. Toward this end, some researchers have compared cognitive trajectories of survivors vs. decedents while others have examined longitudinal changes in cognition as predictive of mortality risk. A two-stage modeling framework is typically used in this latter approach; however, several recent studies have used joint longitudinal-survival modeling (i.e., estimating longitudinal change in cognition conditionally on mortality risk, and vice versa). Methodological differences inherent to these approaches may influence estimates of cognitive decline and cognition-mortality associations. These effects may vary across cognitive domains insofar as changes in broad fluid and crystallized abilities are differentially sensitive to aging and mortality risk. We compared these analytic approaches as applied to data from a large-sample, repeated-measures study of older adults (N = 5,954; ages 50–87 years at assessment; 4,453 deceased at last census). Cognitive trajectories indicated worse performance in decedents and when estimated jointly with mortality risk, but this was attenuated after adjustment for health-related covariates. Better cognitive performance predicted lower mortality risk, and, importantly, cognition-mortality associations were more pronounced when estimated in joint models. Associations between mortality risk and crystallized abilities only emerged under joint estimation. This may have important implications for cognitive reserve, which posits that knowledge and skills considered well-preserved in later life (i.e., crystallized abilities) may compensate for declines in abilities more prone to neurodegeneration, such as recall memory and problem solving. Joint longitudinal-survival models thus appear to be important (and currently underutilized) for research in cognitive epidemiology.

scholarly journals Longitudinal Change of Mental Health among Active Social Media Users in China during the COVID-19 Outbreak

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 833
Author(s):  
Tianli Liu ◽  
Sijia Li ◽  
Xiaochun Qiao ◽  
Xinming Song
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Repeated Measures ◽  
Mainland China ◽  
Longitudinal Change ◽  
Symptom Checklist ◽  
Learning Models ◽  
Online Data ◽  
Repeated Measures Analysis ◽  
Machine Learning Models

During the COVID-19 pandemic, every day, updated case numbers and the lasting time of the pandemic became major concerns of people. We collected the online data (28 January to 7 March 2020 during the COVID-19 outbreak) of 16,453 social media users living in mainland China. Computerized machine learning models were developed to estimate their daily scores of the nine dimensions of the Symptom Checklist—90 (SCL-90). Repeated measures analysis of variance (ANOVA) was used to compare the SCL-90 dimension scores between Wuhan and non-Wuhan residents. Fixed effect models were used to analyze the relation of the estimated SCL-90 scores with the daily reported cumulative case numbers and lasting time of the epidemic among Wuhan and non-Wuhan users. In non-Wuhan users, the estimated scores for all the SCL-90 dimensions significantly increased with the lasting time of the epidemic and the accumulation of cases, except for the interpersonal sensitivity dimension. In Wuhan users, although the estimated scores for all nine SCL-90 dimensions significantly increased with the cumulative case numbers, the magnitude of the changes was generally smaller than that in non-Wuhan users. The mental health of Chinese Weibo users was affected by the daily updated information on case numbers and the lasting time of the COVID-19 outbreak.

scholarly journals Psychosocial Telephone Counseling for Survivors of Cervical Cancer: Results of a Randomized Biobehavioral Trial

2015 ◽  
Vol 33 (10) ◽  
pp. 1171-1179 ◽  
Author(s):  
Lari Wenzel ◽  
Kathryn Osann ◽  
Susie Hsieh ◽  
Jo A. Tucker ◽  
Bradley J. Monk ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Repeated Measures ◽  
Statistical Significance ◽  
Multivariable Analysis ◽  
Telephone Counseling ◽  
Longitudinal Change ◽  
Categorical Variables ◽  
Patient Reported ◽  
Cytokine Levels ◽  
Cancer Experience

Purpose Survivors of cervical cancer experience quality-of-life (QOL) disruptions that persist years after treatment. This study examines the effect of a psychosocial telephone counseling (PTC) intervention on QOL domains and associations with biomarkers. Patients and Methods We conducted a randomized clinical trial in survivors of cervical cancer, who were ≥ 9 and less than 30 months from diagnosis (n = 204), to compare PTC to usual care (UC). PTC included five weekly sessions and a 1-month booster. Patient-reported outcomes (PROs) and biospecimens were collected at baseline and 4 and 9 months after enrollment. Changes in PROs over time and associations with longitudinal change in cytokines as categorical variables were analyzed using multivariable analysis of variance for repeated measures. Results Participant mean age was 43 years; 40% of women were Hispanic, and 51% were non-Hispanic white. Adjusting for age and baseline scores, participants receiving PTC had significantly improved depression and improved gynecologic and cancer-specific concerns at 4 months compared with UC participants (all P < .05); significant differences in gynecologic and cancer-specific concerns (P < .05) were sustained at 9 months. Longitudinal change in overall QOL and anxiety did not reach statistical significance. Participants with decreasing interleukin (IL) -4, IL-5, IL-10, and IL-13 had significantly greater improvement in QOL than those with increasing cytokine levels. Conclusion This trial confirms that PTC benefits mood and QOL cancer-specific and gynecologic concerns for a multiethnic underserved population of survivors of cancer. The improvement in PROs with decreases in T-helper type 2 and counter-regulatory cytokines supports a potential biobehavioral pathway relevant to cancer survivorship.

scholarly journals Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3392-3403 ◽  
Author(s):  
Jue Wang ◽  
Sheng Luo
Keyword(s):  
Survival Data ◽  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Proportional Hazards Model ◽  
Latent Trait ◽  
Joint Modeling ◽  
Fine Motor ◽  
Terminal Event ◽  
Longitudinal Outcomes

Impairment caused by Amyotrophic lateral sclerosis (ALS) is multidimensional (e.g. bulbar, fine motor, gross motor) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of ALS use multiple longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we develop a joint model consisting of a multidimensional latent trait linear mixed model (MLTLMM) for the multiple longitudinal outcomes, and a proportional hazards model with piecewise constant baseline hazard for the event time data. Shared random effects are used to link together two models. The model inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation implemented in Stan language. Our proposed model is evaluated by simulation studies and is applied to the Ceftriaxone study, a motivating clinical trial assessing the effect of ceftriaxone on ALS patients.

scholarly journals An integrated system for modeling tree and stand survival

2017 ◽  
Vol 47 (10) ◽  
pp. 1405-1409 ◽  
Author(s):  
Quang V. Cao
Keyword(s):  
Survival Probability ◽  
Mathematical Structure ◽  
Survival Model ◽  
Integrated System ◽  
Survival Models ◽  
Tree Level ◽  
Individual Tree ◽  
Tree Survival ◽  
Different Levels ◽  
Model Approach

Traditionally, separate models have been used to predict the number of trees per unit area (stand-level survival) and the survival probability of an individual tree (tree-level survival) at a certain age. This study investigated the development of integrated systems in which survival models at different levels of resolution are related in a mathematical structure. Two approaches for modeling tree and stand survival were considered: deriving a stand-level survival model from a tree-level survival model (approach 1) and deriving a tree survival model from a stand survival model (approach 2). Both approaches rely on finding a tree diameter that yields a tree survival probability equal to the stand-level survival probability. The tree and stand survival models from either approach are conceptually compatible with each other but not numerically compatible. Parameters of these models can be estimated either sequentially or simultaneously. Results indicated that approach 2, with parameters estimated sequentially (first from the stand survival model and then from the derived tree survival model), performed best in predicting both tree- and stand-level survival. Although disaggregation did not help improve prediction of tree-level survival, this method can be used when numerical consistency between stand and tree survival is desired.

MO265LONGITUDINAL CHANGE IN PROTEINURIA AND KIDNEY OUTCOMES IN C3 GLOMERULOPATHY

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Fernando Caravaca-Fontán ◽  
Elena Goicoechea de Jorge ◽  
Manuel Praga
Keyword(s):  
Kidney Failure ◽  
Cox Model ◽  
Strong Association ◽  
Fold Increase ◽  
Joint Modeling ◽  
Longitudinal Change ◽  
C3 Glomerulopathy ◽  
Dynamic Prediction ◽  
Over Time

Abstract Background and Aims The association between a change in proteinuria over time and its impact in kidney prognosis has not been analyzed in C3 glomerulopathy. This study aims to investigate the association between the longitudinal change in proteinuria and the risk of kidney failure. Method Retrospective, multicenter observational cohort study in 35 nephrology departments belonging to the GLOSEN group. Patients diagnosed with C3 glomerulopathy between 1995 and 2020 were enrolled. A joint modeling of linear mixed-effects models was applied to assess the underlying trajectory of a repeatedly measured proteinuria, and a Cox model to evaluate the association of this trajectory with the risk of kidney failure. Results The study group consisted of 85 patients, 70 C3 glomerulonephritis and 15 dense deposit disease, with a median age of 26 years (range 13–41). During a median follow-up of 42 months, 25 patients reached kidney failure. The longitudinal change in proteinuria showed a strong association with the risk of this outcome, with a doubling of proteinuria levels resulting in a 2.5-fold increase of the risk. A second model showed that a ≥50% proteinuria reduction over time was significantly associated with a lower risk of kidney failure (HR: 0.79; 95%CI:0.56–0.97; p&lt;0.001). This association was also found when the ≥50% proteinuria reduction was observed within the first 6 and 12 months of follow-up. Conclusion The longitudinal change in proteinuria is strongly associated with the risk of kidney failure. The change in proteinuria over time can provide clinicians a dynamic prediction of kidney outcomes.

scholarly journals ALCOHOL CONSUMPTION AND MEMORY: A LONGITUDINAL ANALYSIS OF RECIPROCAL IMPACTS ACROSS OLD AGE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S651-S651
Author(s):  
Oliver K Schilling
Keyword(s):  
Alcohol Consumption ◽  
Episodic Memory ◽  
Old Age ◽  
Cognitive Aging ◽  
Memory Performance ◽  
Longitudinal Change ◽  
Moderate Alcohol Consumption ◽  
Age Related ◽  
Lagged Effects

Abstract Research on the association of alcohol consumption with cognitive aging revealed mixed evidence: Whereas a u-shaped relationship has been found in many studies, suggesting that low to moderate alcohol consumption predicts more favorable cognitive outcomes than abstinence, other findings suggest that alcohol is a more linearly related risk factor for cognitive decline. These inconsistencies may partly be due to methodological variation in the statistical modeling of intraindividual changes in both, alcohol consumption and cognition across old age. The present study analyzed longitudinal change in and the mutual effects between alcohol consumption habits and verbal episodic memory (word list recall), using vector autoregressive (VAR) mixed models with nonlinear cross-lagged effects. Data from the English Longitudinal Study of Ageing was examined, including N=13388 aged 50+ (M=67.6, SD=9.25; 54.7% female), assessed at up to eight occasions with two-year follow-up intervals (2002/3–2016/17). The self-reported one-year frequency of alcohol drinking days (ADD) served as indicator of alcohol consumption. Basically, ADD predicted follow-up memory performance in a reverse u-shaped fashion, indicating best memory performance after moderate ADD, compared with both ends of the ADD continuum (i.e., drinking never vs. every day). Considering moderators, most notably age did not interact with cross-lagged effects, suggesting that those observed across an older age-range were not more (or less) vulnerable to effects of alcohol consumption on memory performance. Thus, this study adds further support for non-detrimental, if not beneficial, effects of moderate alcohol consumption on cognitive aging – regarding in particular age-related loss of episodic memory.

scholarly journals Effect of LED Lighting Illuminance and Correlated Color Temperature on Working Memory

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Chung Won Lee ◽  
Jin Ho Kim
Keyword(s):  
Working Memory ◽  
Standard Deviation ◽  
Repeated Measures ◽  
Memory Performance ◽  
Light Condition ◽  
Bright Light ◽  
Color Temperature ◽  
Led Lighting ◽  
Temperature Conditions ◽  
Lighting Conditions

This study was conducted to verify how the illuminance and correlated color temperature of LED lighting affect working memory. For this study, an automatic LED lighting device based on a light sensor was developed and used, and the lighting conditions were treated with a total of six conditions (2 × 3): two illuminance conditions (dim: 400 lx, bright: 1,000 lx) and three correlated color temperature conditions (3,000 K, 5,000 K, and 7,000 K). There were 30 participants in the study, and the average age was 21.6 years (Standard deviation = 1.92). Participants were assigned to all six lighting conditions, and the placement order was randomized. For the measurement of working memory, 3-back task was used and the correct responses for 5 minutes were used as a dependent variable. As a result of repeated measures analysis of variance (ANOVA), both illuminance and correlated color temperature were found to be significant variables affecting working memory, and no interaction effect between illuminance and correlated color temperature was found. As a result of the post hoc verification conducted thereafter, the working memory performance in the bright light condition (1,000 lx) was 48.32 (Standard deviation = 15.63) on average, compared to 44.80 (Standard deviation = 15.29) in the relatively dim condition (400 lx). It was found that the condition of bright light was superior in performing working memory compared to relatively dim condition. The working memory performance in the correlated color temperature condition (5,000 K) was 48.32 (Standard deviation = 16.41) on average and higher than that of other color temperature conditions. As a result, working memory performance was the best in 1,000 lx, 5,000 K condition Mean = 53.43 (Standard deviation = 18.38), and 400 lx, 7,000 K condition Mean = 42.73 (Standard deviation = 17.68) showed the worst performance of working memory.

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