Longitudinal trajectory analysis of sepsis after laparoscopic surgery

Many issues of interest to counseling psychologists involve questions regarding how individuals change over time. Although counseling psychologists often examine average levels of change, statistical methods can also identify patterns of change over time by empirically grouping together individuals with similar patterns of change (e.g., group-based trajectory modeling and latent growth mixture modeling). The purpose of this article is to provide an overview of these methods for counseling psychologists. We discuss the conceptual frameworks and assumptions of average-level and person-centered techniques such as group-based trajectory modeling and latent growth mixture modeling. We provide a nontechnical guide for conducting these analyses using data from a study of psychotherapy outcomes in a sample of mental health center clients ( N = 1,050). We discuss caveats associated with these methods, including the potential for overinterpreting nongeneralizable results. Last, we suggest best practices for reporting and interpreting results.

Trajectories refer to the motion paths followed by objects in space. Disease trajectories, which depict the evolution of disease processes over time, are significantly important for assessing diseases, formulating treatment strategies, and predicting prognosis. Critical illness is one of the leading causes of death. With advances in critical care medicine, there is increasing focus on the occurrence and development of critical illnesses. Understanding the development trajectory of critical illness is helpful to promote the early identification, intervention, and treatment of high-risk patients, avoid prolongation of the course of disease, reduce the risk of multiple organ failure, and provide important reference for the development of targeted prevention and intervention strategies, thereby reducing the incidence and mortality of critical illness. In recent years, various trajectory modeling methods have been applied to the study of critical illness. These include, but are not limited to, latent growth curve modeling (LGCM), growth mixture modeling (GMM), group-based trajectory modeling (GBTM), latent transition analysis (LTA), and latent class analysis (LCA). The aim of this article is to review the definition of disease trajectories, the methods used in trajectory modeling, and their applications and future prospects in critical illness research.

Introduction to the special issue on "Statistical approaches and methodological issues in longitudinal analysis of substance use and related behaviors"

Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers’ efficiency when it comes to choosing the most appropriate technique that best suits their research questions.

Several studies have analyzed longitudinal data on posttraumatic stress symptoms (PTSS) from individuals who were proximal to the September 11, 2001, terrorist attacks (9/11) in an attempt to identify different trajectories of mental health in the years following mass trauma. The results of these studies have been heterogeneous, with researchers who used latent growth mixture modeling (LGMM) tending to identify four trajectories and those who used group-based trajectory modeling (GBTM) identifying five to seven trajectories. Given that no study has applied both GBTM and LGMM to their data, it remains unknown which modeling approach and what number of trajectories best fit post-9/11 PTSS data. The present study aimed to address that question by applying both LGMM and GBTM to data from the largest sample of survivors to date, comprising 37,545 New York City community members. When analyzing four waves of PTSS, reflecting participants' mental health up to 15 years post-9/11, LGMM fit the data better than GBTM. Our optimal solution consisted of four trajectories: low-stable (72.2% of the sample), decreasing (12.8%), increasing (9.5%), and high-stable (5.5%) symptoms. Covariate analyses indicated that economic factors (i.e., having a household income less than $25,000 and experiencing job loss due to 9/11) increased the odds of belonging to the high-stable symptom trajectory group to the greatest degree, ORs=4.93-6.08. The results suggest that providing financial support, including affordable mental health care, could be an important intervention in the wake of future mass traumatic events.

Group-based trajectory models are increasingly being applied in clinical research to map the developmental course of symptoms and assess heterogeneity in response to clinical interventions. In this review, we provide a nontechnical overview of group-based trajectory and growth mixture modeling alongside a sampling of how these models have been applied in clinical research. We discuss the challenges associated with the application of both types of group-based models and propose a set of preliminary guidelines for applied researchers to follow when reporting model results. Future directions in group-based modeling applications are discussed, including the use of trajectory models to facilitate causal inference when random assignment to treatment condition is not possible.

Introduction: Growth in early infancy is hypothesized to affect chronic disease risk factors later in life. To date, most reports draw on European ancestry cohorts with few observations of early growth. To determine if previous findings generalize to diverse study populations and to accommodate more detailed growth estimates, we investigated the association between monthly infant growth from birth to 5 months and dyslipidemia in adolescents in a Hispanic/Latino cohort. Methods: We characterized infant growth in males (n=345) and females (n=308) from the Santiago Longitudinal Study (SLS) using three metrics: weight (kg), length (cm) and weight-for-length (g/cm). Nonlinear mixed effects (SITAR) and latent growth mixture models (LGMM) were two approaches to estimate infant growth characteristics. Growth functioned as an exposure and lipid levels at 17 years were the outcome, including HDL-C, LDL-C, and TG. We used a false discovery rate of 0.05 to report findings. Results: Height trajectories presented the strongest evidence for an effect on dyslipidemia in adolescence in both the SITAR and LGMM models. SITAR analyses demonstrated an inverse relationship between height velocity before six months of age and HDL-C levels in adolescence. LGMM models offered more nuanced findings. A two-class height trajectory model offered the best fit: one group (n~268) had shorter length at birth accompanied by lower velocity/higher acceleration; the other group (n~406) had higher length at birth and higher velocity/lower acceleration. The lower velocity/higher acceleration group had lower mean HDL-C (mg/dL) (35.4, se=0.9) than the higher velocity/lower acceleration group (43.8, se=0.7), χ2(1)=36.4, p-value=<0.001. Similarly, the best-fitting 3-class weight trajectory model indicated the lower velocity/medium acceleration group had a higher mean LDL-C (mg/dL) (97.7, se=1.6, n~441) than the highest velocity/lowest acceleration group (88.8, se=2.0, n~202), χ2(1)=15.2, p-value=<0.001. A similar pattern for LDL-C emerged for the weight-for-length trajectories, χ2(1)=8.6, p-value=0.003. Summary: This study provides evidence of associations between infant growth from 0 and 5 months and blood lipid profiles during adolescence. Based on two different analytic approaches, characteristics of infant length trajectories were associated with HDL-C at mean age 17 years. These findings align with the well-established relationship between height and CVD in adulthood. Furthermore, groups with higher acceleration in all three trajectory types were more likely to have adverse lipid outcomes. Future research can inform the role of infant body size change in the context of downstream effects and CVD risk.

Classifying medication adherence is important for efficiently targeting adherence improvement interventions. The purpose of this study was to evaluate the use of a novel method, group-based trajectory models, for classifying patients by their long-term adherence. We identified patients who initiated a statin between June 1, 2006 and May 30, 2007 in prescription claims from CVS Caremark and evaluated adherence over the subsequent 15 months. We compared several adherence summary measures, including proportion of days covered (PDC) and trajectory models with 2-6 groups, with the observed adherence pattern, defined by monthly indicators of full adherence (defined as having ≥24 d covered of 30). We also compared the accuracy of adherence prediction based on patient characteristics when adherence was defined by either a trajectory model or PDC. In 264,789 statin initiators, the 6-group trajectory model summarized long-term adherence best (C=0.938), whereas PDC summarized less well (C=0.881). The accuracy of adherence predictions was similar whether adherence was classified by PDC or by trajectory model. Trajectory models summarized adherence patterns better than traditional approaches and were similarly predicted by covariates. Group-based trajectory models may facilitate targeting of interventions and may be useful to adjust for confounding by health-seeking behavior.

Pneumonia-associated acute respiratory distress syndrome (ARDS) is characterized by high mortality, yet current prognostic models often rely on static biomarkers that fail to capture dynamic immune responses. This protocol introduces a reproducible computational framework that employs Group-Based Trajectory Modeling (GBTM) to identify distinct lymphocyte count trajectories and assess their prognostic value for mortality risk in ARDS patients with pneumonia. Using data extracted from the MIMIC-IV v2.2 database, the protocol details each step from data curation and preprocessing to trajectory construction and model validation. The approach includes subgroup identification through GBTM, followed by multivariable logistic and Cox regression analyses to quantify associations between trajectory patterns and 28-day mortality, adjusting for key clinical covariates such as APS III score, ICU stay, and heart rate. Model performance is comprehensively evaluated using ROC curves, calibration plots, and decision curve analysis, ensuring both statistical robustness and clinical interpretability. By leveraging longitudinal immune data rather than single-timepoint measurements, this workflow provides clinicians with a methodologically transparent, data-driven strategy to improve risk stratification and explore immune heterogeneity in critical illness. The protocol is fully reproducible, adaptable to other longitudinal biomarkers, and designed for visualization and instructional demonstration, making it an accessible tool for researchers seeking to integrate temporal biomarker modeling into critical care prognostics.

BackgroundWith the acceleration of population aging, cognitive impairment and depression have become serious public health challenges in countries around the world. The influencing factors of cognitive trajectory, depression trajectory, and dual trajectories in middle-aged and elderly adults have not been fully studied.MethodsThis study used data from the China Health and Retirement Longitudinal Study database spanning from 2011–2018. Group-based trajectory modelling and group-based dual trajectory modelling were employed to examine different trajectories. Restricted cubic spline and multivariate logistic regression analysis were used to elucidate the relationship between sleep duration and grip strength with these different trajectories. Mediation analysis was conducted to explore the mediating roles of sleep duration and grip strength in the activities of daily living (ADLs) and their impact on these trajectories.ResultsTrajectory analysis identified two longitudinal patterns of cognitive function and depression scores: low and high cognitive group, low and high depression group, respectively, and two states of the dual trajectories of cognition and depression: the stable state group and the state decline group. Sleep duration and grip strength were associated with the cognitive trajectory, depression trajectory and dual trajectories. Sleep duration has an inverted U-shaped relationship with cognitive trajectory. Grip strength was nonlinearly associated with the above trajectories. The mediation effects of sleep duration in the association between ADLs and cognitive, depression and dual trajectories were 3.14, 6.14, and 2.70%. While the mediation effects of grip strength were 7.21, 1.67 and 6.24%, respectively (P < 0.05).ConclusionsSleep duration and grip strength were not only associated with cognitive, depression, and dual trajectories, but also partially mediate the relationship between ADLs and these trajectories. This study will provide a basis for how to intervene in the cognitive and mental health of middle-aged and elderly adults.

Using Latent Profile Analysis and Related Approaches in Adolescent Health Research

Background: Different factors influence severity, progression, and outcomes in Parkinson's disease (PD). Lack of standardized clinical assessment limits comparison of outcomes and availability of well-characterized cohorts for collaborative studies.Methods: Structured clinical documentation support (SCDS) was developed within the DNA Predictions to Improve Neurological Health (DodoNA) project to standardize clinical assessment and identify molecular predictors of disease progression. The Longitudinal Clinical and Genetic Study of Parkinson's Disease (LONG-PD) was launched within the Genetic Epidemiology of Parkinson's disease (GEoPD) consortium using a Research Electronic Data Capture (REDCap) format mirroring the DodoNA SCDS. Demographics, education, exposures, age at onset (AAO), Unified Parkinson's Disease Rating Scale (UPDRS) parts I-VI or Movement Disorders Society (MDS)–UPDRS, Montreal Cognitive Assessment (MoCA)/Short Test of Mental Status (STMS)/Mini Mental State Examination (MMSE), Geriatric Depression Scale (GDS), Epworth Sleepiness Scale (ESS), dopaminergic therapy, family history, nursing home placement, death and blood samples were collected. DodoNA participants (396) with 6 years of follow-up and 346 LONG-PD participants with up to 3 years of follow-up were analyzed using group-based trajectory modeling (GBTM) focused on: AAO, education, family history, MMSE/MoCA/STMS, UPDRS II-II, UPDRS-III tremor and bradykinesia sub-scores, Hoehn and Yahr staging (H&Y) stage, disease subtype, dopaminergic therapy, and presence of autonomic symptoms. The analysis was performed with either cohort as the training/test set.Results: Patients are classified into slowly and rapidly progressing courses by AAO, MMSE score, H &Y stage, UPDRS-III tremor and bradykinesia sub-scores relatively early in the disease course. Late AAO and male sex assigned patients to the rapidly progressing group, whereas tremor to the slower progressing group. Classification is independent of which cohort serves as the training set. Frequencies of disease-causing variants in LRRK2 and GBA were 1.89 and 2.96%, respectively.Conclusions: Standardized clinical assessment provides accurate phenotypic characterization in pragmatic clinical settings. Trajectory analysis identified two different trajectories of disease progression and determinants of classification. Accurate phenotypic characterization is essential in interpreting genomic information that is generated within consortia, such as the GEoPD, formed to understand the genetic epidemiology of PD. Furthermore, the LONGPD study protocol has served as the prototype for collecting standardized phenotypic information at GEoPD sites. With genomic analysis, this will elucidate disease etiology and lead to targeted therapies that can improve disease outcomes.

The rationale for choosing a final group-based trajectory modeling (GBTM) specification and evaluations of patient adherence patterns within groups are often omitted in the GBTM medication adherence literature. We aimed to (1) reveal the complexity of GBTM and (2) assess model discrimination of patient medication adherence patterns. Medicare administrative claims were used to measure statin medication adherence as a continuous value in the 6 months before an acute myocardial infarction (AMI) hospitalization. Different GBTM specifications beyond default settings were constructed and compared with the Bayesian information criterion. Spaghetti plots were used to compare individual adherence patterns with group averages. Overall, 113,296 prevalent statin users met eligibility criteria. Four adherence groups were identified: persistently adherent, moderately adherent, progressively nonadherent, and persistently nonadherent. Spaghetti plots showed the persistently adherent and persistently nonadherent groups had relatively homogeneous adherence patterns that matched predicted trajectories well. Spaghetti plots also showed that, while adherence patterns in the progressively nonadherent group were not as homogeneous, most patients in this group appeared to be discontinuing statin therapy pre-AMI. Subjective decisions are necessary to identify a final trajectory model. Greater transparency and disclosure of these decisions in the medication adherence literature are needed. Individual patient adherence patterns from spaghetti plots provided additional diagnostic information about trajectory models beyond standard model-fit assessments to determine if group-average adherence estimates represent homogeneous patterns of medication adherence.

Of 58 medication adherence group-based trajectory modeling (GBTM) published studies, 74% used binary and 26% used continuous GBTM. Few studies provided a rationale for this choice. No medication adherence studies have compared continuous and binary GBTM. The objective of this study was to assess whether continuous versus binary GBTM: (1) impacts adherence trajectory shapes; and (2) results in the differential classification of patients into adherence groups. Patients were prevalent statin users with myocardial infarction hospitalization, 66+ years old, and continuously enrolled in fee-for-service Medicare. Statin medication adherence was measured 6 months prehospitalization using administrative claims. Final GBTM specifications beyond default settings were selected using a previously defined standardized procedure and applied separately to continuous and binary (proportion of days covered ≥0.80) medication adherence measures. Assignment to adherence groups was compared between continuous and binary models using percent agreement of patient classification and the κ coefficient. Among 113,296 prevalent statin users, 4 adherence groups were identified in both models. Three groups were consistent: persistently adherent, progressively nonadherent, and persistently nonadherent. The fourth continuous group was moderately adherent (progressively adherent in the binary model). When comparing patient assignment into adherence groups between continuous and binary trajectory models, only 78.4% of patients were categorized into comparable groups (κ=0.641; 95% confidence interval: 0.638-0.645). The agreement was highest in the persistently adherent group (∼94%). Continuous and binary trajectory models are conceptually different measures of medication adherence. The choice between these approaches should be guided by study objectives and the role of medication adherence within the study-exposure, outcome, or confounder.

Group‐based trajectory modeling (GBTM) is applied to the citation curves of articles in six journals and to all citable items in a single field of science (virology, 24 journals) to distinguish among the developmental trajectories in subpopulations. Can citation patterns of highly‐cited papers be distinguished in an early phase as “fast‐breaking” papers? Can “late bloomers” or “sleeping beauties” be identified? Most interesting, we find differences between “sticky knowledge claims” that continue to be cited more than 10 years after publication and “transient knowledge claims” that show a decay pattern after reaching a peak within a few years. Only papers following the trajectory of a “sticky knowledge claim” can be expected to have a sustained impact. These findings raise questions about indicators of “excellence” that use aggregated citation rates after 2 or 3 years (e.g., impact factors). Because aggregated citation curves can also be composites of the two patterns, fifth‐order polynomials (with four bending points) are needed to capture citation curves precisely. For the journals under study, the most frequently cited groups were furthermore much smaller than 10%. Although GBTM has proved a useful method for investigating differences among citation trajectories, the methodology does not allow us to define a percentage of highly cited papers inductively across different fields and journals. Using multinomial logistic regression, we conclude that predictor variables such as journal names, number of authors, etc., do not affect the stickiness of knowledge claims in terms of citations but only the levels of aggregated citations (which are field‐specific).