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Journal of Biomedical Informatics Jul 2022Causal inference for observational longitudinal studies often requires the accurate estimation of treatment effects on time-to-event outcomes in the presence of...
OBJECTIVE
Causal inference for observational longitudinal studies often requires the accurate estimation of treatment effects on time-to-event outcomes in the presence of time-dependent patient history and time-dependent covariates.
MATERIALS AND METHODS
To tackle this longitudinal treatment effect estimation problem, we have developed a time-variant causal survival (TCS) model that uses the potential outcomes framework with an ensemble of recurrent subnetworks to estimate the difference in survival probabilities and its confidence interval over time as a function of time-dependent covariates and treatments.
RESULTS
Using simulated survival datasets, the TCS model showed good causal effect estimation performance across scenarios of varying sample dimensions, event rates, confounding and overlapping. However, increasing the sample size was not effective in alleviating the adverse impact of a high level of confounding. In a large clinical cohort study, TCS identified the expected conditional average treatment effect and detected individual treatment effect heterogeneity over time. TCS provides an efficient way to estimate and update individualized treatment effects over time, in order to improve clinical decisions.
DISCUSSION
The use of a propensity score layer and potential outcome subnetworks helps correcting for selection bias. However, the proposed model is limited in its ability to correct the bias from unmeasured confounding, and more extensive testing of TCS under extreme scenarios such as low overlapping and the presence of unmeasured confounders is desired and left for future work.
CONCLUSION
TCS fills the gap in causal inference using deep learning techniques in survival analysis. It considers time-varying confounders and treatment options. Its treatment effect estimation can be easily compared with the conventional literature, which uses relative measures of treatment effect. We expect TCS will be particularly useful for identifying and quantifying treatment effect heterogeneity over time under the ever complex observational health care environment.
Topics: Bias; Causality; Cohort Studies; Humans; Longitudinal Studies; Survival Analysis
PubMed: 35714819
DOI: 10.1016/j.jbi.2022.104119 -
Inquiry : a Journal of Medical Care... 2023The association between retirement and functioning remains still poorly known. This scoping review examines physical, social, cognitive, and mental functioning after... (Review)
Review
The association between retirement and functioning remains still poorly known. This scoping review examines physical, social, cognitive, and mental functioning after retirement, describes the changes in them, determines the different aspects that affect functioning, and documents the main characteristics of the phenomenon. We systematically scoped the relevant studies on functioning after retirement using CINAHL, MEDLINE, Medic, and PubMed databases. This scoping review included both qualitative and quantitative studies. The studies were analysed with inductive content analysis. After retirement, functioning was found to decline but also improve, and additionally, inequalities in functioning emerged. Functioning after retirement changed in ways which were: declining functioning, improving functioning, and inequalities in functioning. Only a few qualitative studies were found. This scoping review shows that functioning after retirement changes in varying ways. The results show that more qualitative research is needed to help us gain a more profound understanding on, for example, individuals' motives to improve leisure, physical, and social activities after retirement, which are likely to contribute to changes in functioning. Additionally, further longitudinal studies would offer knowledge about the long-term effects of retirement on the different dimensions of functioning.
Topics: Humans; Retirement; Longitudinal Studies; Surveys and Questionnaires
PubMed: 36604784
DOI: 10.1177/00469580221142477 -
Nutrients Jul 2022The present systematic review and meta-analysis investigated the cross-sectional and longitudinal associations between protein intake and frailty in older adults. (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The present systematic review and meta-analysis investigated the cross-sectional and longitudinal associations between protein intake and frailty in older adults.
METHODS
We conducted a systematic review and meta-analysis of cross-sectional and longitudinal studies that investigated the association between protein intake and frailty in older adults. Cross-sectional, case-control, and longitudinal cohort studies that investigated the association between protein intake and frailty as a primary or secondary outcome in people aged 60+ years were included. Studies published in languages other than English, Italian, Portuguese, or Spanish were excluded. Studies were retrieved on 31 January 2022.
RESULTS
Twelve cross-sectional and five longitudinal studies that investigated 46,469 community-dwelling older adults were included. The meta-analysis indicated that absolute, bodyweight-adjusted, and percentage of protein relative to total energy consumption were not cross-sectionally associated with frailty. However, frail older adults consumed significantly less animal-derived protein than robust people. Finally, high protein consumption was associated with a significantly lower risk of frailty.
CONCLUSIONS
Our pooled analysis indicates that protein intake, whether absolute, adjusted, or relative to total energy intake, is not significantly associated with frailty in older adults. However, we observed that frail older adults consumed significantly less animal protein than their robust counterparts.
Topics: Aged; Energy Intake; Frail Elderly; Frailty; Humans; Independent Living; Longitudinal Studies
PubMed: 35807947
DOI: 10.3390/nu14132767 -
BMC Bioinformatics Mar 2023One of the main challenges of microbiome analysis is its compositional nature that if ignored can lead to spurious results. Addressing the compositional structure of...
BACKGROUND
One of the main challenges of microbiome analysis is its compositional nature that if ignored can lead to spurious results. Addressing the compositional structure of microbiome data is particularly critical in longitudinal studies where abundances measured at different times can correspond to different sub-compositions.
RESULTS
We developed coda4microbiome, a new R package for analyzing microbiome data within the Compositional Data Analysis (CoDA) framework in both, cross-sectional and longitudinal studies. The aim of coda4microbiome is prediction, more specifically, the method is designed to identify a model (microbial signature) containing the minimum number of features with the maximum predictive power. The algorithm relies on the analysis of log-ratios between pairs of components and variable selection is addressed through penalized regression on the "all-pairs log-ratio model", the model containing all possible pairwise log-ratios. For longitudinal data, the algorithm infers dynamic microbial signatures by performing penalized regression over the summary of the log-ratio trajectories (the area under these trajectories). In both, cross-sectional and longitudinal studies, the inferred microbial signature is expressed as the (weighted) balance between two groups of taxa, those that contribute positively to the microbial signature and those that contribute negatively. The package provides several graphical representations that facilitate the interpretation of the analysis and the identified microbial signatures. We illustrate the new method with data from a Crohn's disease study (cross-sectional data) and on the developing microbiome of infants (longitudinal data).
CONCLUSIONS
coda4microbiome is a new algorithm for identification of microbial signatures in both, cross-sectional and longitudinal studies. The algorithm is implemented as an R package that is available at CRAN ( https://cran.r-project.org/web/packages/coda4microbiome/ ) and is accompanied with a vignette with a detailed description of the functions. The website of the project contains several tutorials: https://malucalle.github.io/coda4microbiome/.
Topics: Infant; Humans; Cross-Sectional Studies; Algorithms; Data Analysis; Longitudinal Studies; Microbiota
PubMed: 36879227
DOI: 10.1186/s12859-023-05205-3 -
Journal of Personality and Social... Mar 2022Personality traits and physical health both change over the life span. Theoretical models and empirical evidence suggest that these changes are related. The current...
Personality traits and physical health both change over the life span. Theoretical models and empirical evidence suggest that these changes are related. The current study investigated the dynamic relations between personality traits and physical health at both the between-person and the within-person levels. Data were drawn from three longitudinal studies: the Veterans Affairs Normative Aging Study (NAS; N = 1,734), the Longitudinal Internet Studies for the Social Sciences (LISS; N = 13,559), and the Swedish Adoption/Twin Study of Aging (SATSA, N = 2,209). Using random intercept cross-lagged panel models (RI-CLPMs) and the continuous time (CT) models, after controlling the between-person variance, generally, evidence was found for bidirectional associations between changes in neuroticism and extraversion and changes in self-rated health and general disease level. Bidirectional associations between changes in neuroticism and change in cardiovascular diseases and central nervous system diseases were observed only when time was modeled as continuous. We also found within-person associations between changes in neuroticism and extraversion and changes in performance-based ratings of motor functioning impairment. According to the current findings, the dynamic within-person relations between personality traits and health outcomes were largely in the direction consistent with their between-person connections, although the within-person relationships were substantially smaller in strength when compared their between-person counterparts. Findings from the current study highlight the importance of distinguishing between-person and within-person effects when examining the longitudinal relationship between personality traits and health. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Topics: Aging; Humans; Longitudinal Studies; Neuroticism; Personality; Personality Disorders
PubMed: 35157486
DOI: 10.1037/pspp0000399 -
Bioinformatics (Oxford, England) Aug 2022Longitudinal studies increasingly collect rich 'omics' data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease...
MOTIVATION
Longitudinal studies increasingly collect rich 'omics' data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including non-uniform sampling intervals, missing data points, subject dropout and differing numbers of samples per subject.
RESULTS
In this work, we developed OmicsLonDA, a statistical method that provides robust identification of time intervals of temporal omics biomarkers. OmicsLonDA is based on a semi-parametric approach, in which we use smoothing splines to model longitudinal data and infer significant time intervals of omics features based on an empirical distribution constructed through a permutation procedure. We benchmarked OmicsLonDA on five simulated datasets with diverse temporal patterns, and the method showed specificity greater than 0.99 and sensitivity greater than 0.87. Applying OmicsLonDA to the iPOP cohort revealed temporal patterns of genes, proteins, metabolites and microbes that are differentially regulated in male versus female subjects following a respiratory infection. In addition, we applied OmicsLonDA to a longitudinal multi-omics dataset of pregnant women with and without preeclampsia, and OmicsLonDA identified potential lipid markers that are temporally significantly different between the two groups.
AVAILABILITY AND IMPLEMENTATION
We provide an open-source R package (https://bioconductor.org/packages/OmicsLonDA), to enable widespread use.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Pregnancy; Humans; Female; Male; Biomarkers; Longitudinal Studies; Proteins; Research Design
PubMed: 35762936
DOI: 10.1093/bioinformatics/btac403 -
Briefings in Bioinformatics Mar 2023In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction... (Review)
Review
In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often promising alternatives to standard statistical methods, especially in the context of high-dimensional data. In this paper, we review extensions of the standard RF method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate response longitudinal data and further categorize the repeated measurements according to whether the time effect is relevant. Even though most extensions are proposed for low-dimensional data, some can be applied to high-dimensional data. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions.
Topics: Random Forest; Longitudinal Studies; Software; Data Analysis
PubMed: 36653905
DOI: 10.1093/bib/bbad002 -
Cells, Tissues, Organs 2022
Topics: Bioartificial Organs; Biocompatible Materials; Longitudinal Studies; Tissue Engineering
PubMed: 34348257
DOI: 10.1159/000518251 -
Journal of Epidemiology and Community... Jan 2017Over the life course, we are invariably faced with some form of adversity. The process of positively adapting to adverse events is known as 'resilience'. Despite the... (Review)
Review
Over the life course, we are invariably faced with some form of adversity. The process of positively adapting to adverse events is known as 'resilience'. Despite the acknowledgement of 2 common components of resilience, that is, adversity and positive adaptation, no consensus operational definition has been agreed. Resilience operationalisations have been reviewed in a cross-sectional context; however, a review of longitudinal methods of operationalising resilience has not been conducted. The present study conducts a systematic review across Scopus and Web of Science capturing studies of ageing that posited operational definitions of resilience in longitudinal studies of ageing. Thirty-six studies met inclusion criteria. Non-acute events, for example, cancer, were the most common form of adversity identified and psychological components, for example, the absence of depression, the most common forms of positive adaptation. Of the included studies, 4 used psychometrically driven methods, that is, repeated administration of established resilience metrics, 9 used definition-driven methods, that is, a priori establishment of resilience components and criteria, and 23 used data-driven methods, that is, techniques that identify resilient individuals using latent variable models. Acknowledging the strengths and limitations of each operationalisation is integral to the appropriate application of these methods to life course and longitudinal resilience research.
Topics: Adaptation, Psychological; Aging; Humans; Longitudinal Studies; Psychometrics; Resilience, Psychological
PubMed: 27502781
DOI: 10.1136/jech-2015-206980 -
Statistics in Medicine Jul 2022We propose an inferential framework for fixed effects in longitudinal functional models and introduce tests for the correlation structures induced by the longitudinal...
We propose an inferential framework for fixed effects in longitudinal functional models and introduce tests for the correlation structures induced by the longitudinal sampling procedure. The framework provides a natural extension of standard longitudinal correlation models for scalar observations to functional observations. Using simulation studies, we compare fixed effects estimation under correctly and incorrectly specified correlation structures and also test the longitudinal correlation structure. Finally, we apply the proposed methods to a longitudinal functional dataset on physical activity. The computer code for the proposed method is available at https://github.com/rli20ST758/FILF.
Topics: Computer Simulation; Exercise; Humans; Longitudinal Studies; Research Design
PubMed: 35491388
DOI: 10.1002/sim.9421