Analysis Of Longitudinal Data Research Articles

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data.

As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed. To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time. Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models. The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants. Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes.

Head Impact Exposure in Hawaiian High School Football: Influence of Adherence Rates on a Helmetless Tackling and Blocking Training Intervention.

High school football remains a popular, physically demanding sport despite the known risks for acute brain and neck injury. Impacts to the head also raise concerns about their cumulative effects and long-term health consequences. To examine the effectiveness of a helmetless tackling training program to reduce head impact exposure in football participants. A three-year, quasi-experimental, prospective cohort (clinicaltrials.gov #NCTXXX) study. Honolulu (XXX, XXX) area public and private secondary schools with varsity and junior varsity football. Football participants (n=496) ages 14 to 18 years old. Intervention(s) Participants wore new football helmets furnished with head impact sensor technology. Teams employed a season-long helmetless tackling and blocking intervention in Years 2 and 3 consisting of a 3-phase, systematic progression of 10 instructional drills. Head impact frequency per athlete exposure (ImpAE), location, and impact magnitude per participant intervention adherence levels (60% and 80%). An overall regression analysis revealed a significant negative association between ImpAE and adherence (p=0.003, beta=-1.21, SE=0.41). In year 3, a longitudinal data analysis of weekly ImpAE data resulted in an overall difference between the adherent and non-adherent groups (p=0.040 at 80%; p=0.004 at 60%), mainly due to decreases in top and side impacts. Mean cumulative impact burden for the adherent group (n=131: 2,105.84g ± 219.76,) was significantly (p=0.020) less than the non-adherent group (n=90: 3,158.25g ± 434.80) at the 60% adherence level. Participants adhering to the intervention on at least a 60% level experienced a 34% to 37% significant reduction in the number of head impacts (per exposure) through the season. These results provide additional evidence that a helmetless tackling and blocking training intervention (utilizing the HuTT® program) reduces head impact exposure in high school football players. Adherence to an intervention is crucial for achieving intended outcomes.

Does the Use of Media and Other Information Sources Cause the Perceived Experience of Climate Change or is it the Other Way Around?

ABSTRACT The perceived experience of climate change can be an important motivator of individual climate action. Recent scholarship argued that the use of media and other information sources is a basis of perceived experience. The current study analyzed a cross-lagged panel model to know if the use of information causes perceived experience or vice versa. Analysis of longitudinal data from a sample of Singapore residents (N = 466) showed that the use of traditional media, but not social media or interpersonal sources, explained change in perceived experience. In contrast, perceived experience explained change in the use of traditional media and interpersonal communication. We discuss these findings vis-à-vis the reinforcing spirals model of media effects and offer practical insights for climate change communicators, especially journalists.

Estimating dynamic treatment regimes for ordinal outcomes with household interference: Application in household smoking cessation.

The focus of precision medicine is on decision support, often in the form of dynamic treatment regimes, which are sequences of decision rules. At each decision point, the decision rules determine the next treatment according to the patient's baseline characteristics, the information on treatments and responses accrued by that point, and the patient's current health status, including symptom severity and other measures. However, dynamic treatment regime estimation with ordinal outcomes is rarely studied, and rarer still in the context of interference - where one patient's treatment may affect another's outcome. In this paper, we introduce the weighted proportional odds model: a regression based, approximate doubly-robust approach to single-stage dynamic treatment regime estimation for ordinal outcomes. This method also accounts for the possibility of interference between individuals sharing a household through the use of covariate balancing weights derived from joint propensity scores. Examining different types of balancing weights, we verify the approximate double robustness of weighted proportional odds model with our adjusted weights via simulation studies. We further extend weighted proportional odds model to multi-stage dynamic treatment regime estimation with household interference, namely dynamic weighted proportional odds model. Lastly, we demonstrate our proposed methodology in the analysis of longitudinal survey data from the Population Assessment of Tobacco and Health study, which motivates this work. Furthermore, considering interference, we provide optimal treatment strategies for households to achieve smoking cessation of the pair in the household.

Negative religious coping versus spiritual struggles: Moderator or main effect?

While previous research indicates that negative religious coping relates with distress, it is unclear if this represents generalized spiritual struggles or a specific vulnerability activated by negative events. Moreover, past research treats coping as a stable phenomenon, although it likely fluctuates. This research simultaneously tested both models longitudinally and examined the relationship between coping and distress. The current study is a secondary analysis of longitudinal data collected online over 3 years from 397 participants. Life events, negative religious coping, and depressive symptoms were assessed on six occasions, and linear mixed-effect models were used to analyze data. Negative religious coping was largely stable with increases at times of stress. Participants with higher mean negative coping reported more depressive symptoms regardless of life circumstances (i.e., main effect), likely reflecting generalized spiritual struggles. In addition, those with higher mean coping or with particularly high levels at a given time reported even higher levels of symptoms when they experienced more negative life events (i.e., moderation effect). Results suggest that previous research merges two distinct phenomena-spiritual struggles and negative coping. This has important theoretical and research implications and suggests that integrating spirituality and religion into treatment can be warranted.

Multivariate mixed-effects ordinal logistic regression models with difference-in-differences estimator of the impact of WORTH Yetu on household hunger and socioeconomic status among OVC caregivers in Tanzania.

Although most of the livelihood programmes target women, those that involve women and men have been evaluated as though men and women were a single homogenous population, with a mere inclusion of gender as an explanatory variable. This study evaluated the impact of WORTH Yetu (an economic empowerment intervention to improve livelihood outcomes) on household hunger, and household socioeconomic status (SES) among caregivers (both women and men) of orphaned and vulnerable children (OVC) in Tanzania. The study hypothesized that women and men respond to livelihood interventions differently, hence a need for gender-disaggregated impact evaluation of such interventions. This is a secondary analysis of longitudinal data, involving caregivers' baseline (2016-2019) and follow-up (2019-2020) data from the USAID Kizazi Kipya project in 25 regions of Tanzania. Two dependent variables (ie, outcomes) were assessed; household hunger which was measured using the Household Hunger Scale (HHS), and Socioeconomic Status (SES) using the Principal Component Analysis (PCA). WORTH Yetu, a livelihood intervention implemented by the USAID Kizazi Kipya project was the main independent variable whose impact on the two outcomes was evaluated using multivariate analysis with a multilevel mixed-effects, ordinal logistic regression model with difference-in-differences (DiD) estimator for impact estimation. The analysis was based on 497,293 observations from 249,655 caregivers of OVC at baseline, and 247,638 of them at the follow-up survey. In both surveys, 70% were women and 30% were men. Their mean age was 49.3 (±14.5) years at baseline and 52.7 (±14.8) years at the follow-up survey. Caregivers' membership in WORTH Yetu was 10.1% at the follow-up. After adjusting for important confounders there was a significant decline in the severity of household hunger by 46.4% among WORTH Yetu members at the follow-up compared to the situation at the baseline (adjusted Odds Ratio (aOR) = 0.536, 95% Confidence Interval (CI) [0.521, 0.553]). The decline was 45.7% among women (aOR = 0.543 [0.524, 0.563]) and 47.5% among men (aOR = 0.525 [0.497, 0.556]). Regarding SES, WORTH Yetu members were 15.9% more likely to be in higher wealth quintiles at the follow-up compared to the situation at the baseline (aOR = 1.159 [1.128, 1.190]). This impact was 20.8% among women (aOR = 1.208 [1.170, 1.247]) and 4.6% among men (aOR = 1.046 [0.995, 1.101]). WORTH Yetu was associated with a significant reduction in household hunger, and a significant increase in household SES among OVC caregivers in Tanzania within an average follow-up period of 1.6 years. The estimated impacts differed significantly by gender, suggesting that women and men responded to the WORTH Yetu intervention differently. This implied that the design, delivery, and evaluation of such programmes should happen in a gender responsive manner, recognising that women and men are not the same with respect to the programmes.

De Casteljau's algorithm in geometric data analysis: Theory and application

For decades, de Casteljau's algorithm has been used as a fundamental building block in curve and surface design and has found a wide range of applications in fields such as scientific computing and discrete geometry, to name but a few. With increasing interest in nonlinear data science, its constructive approach has been shown to provide a principled way to generalize parametric smooth curves to manifolds. These curves have found remarkable new applications in the analysis of parameter-dependent, geometric data. This article provides a survey of the recent theoretical developments in this exciting area as well as its applications in fields such as geometric morphometrics and longitudinal data analysis in medicine, archaeology, and meteorology.

Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis.

During the height of the COVID-19 pandemic, the Korean government temporarily allowed full scale telehealth care for safety and usability. However, limited studies have evaluated the impact of telehealth by analyzing the physical and/or mental health data of patients with COVID-19 diagnosis collected through telehealth targeting Korean population. This study aimed to identify subgroup of depressive symptom trajectories in patients with clinically mild COVID-19 using collected longitudinal data from a telehealth-based contactless clinical trial. A total of 199 patients with COVID-19 were accrued for contactless clinical trial using telehealth from March 23 to July 20, 2022. Depressive symptoms were measured using the patient health questionnaire-9 on the start day of quarantine, on the final day of quarantine, and 1 month after release from quarantine. Additionally, acute COVID-19 symptoms were assessed every day during quarantine. This study used a latent class mixed model to differentiate subgroups of depressive symptom trajectories and a logistic regression model with Firth's correction to identify associations between acute COVID-19 symptoms and the subgroups. Two latent classes were identified: class 1 with declining linearity at a slow rate and class 2 with increasing linearity. Among COVID-19 symptoms, fever, chest pain, and brain fog 1 month after release from quarantine showed strong associations with class 2 (fever: OR, 19.43, 95% CI, 2.30-165.42; chest pain: OR, 6.55, 95% CI, 1.15-34.61; brain fog: OR, 7.03, 95% CI 2.57-20.95). Sleeping difficulty and gastrointestinal symptoms were also associated with class 2 (gastrointestinal symptoms: OR, 4.76, 95% CI, 1.71-14.21; sleeping difficulty: OR, 3.12, 95% CI, 1.71-14.21). These findings emphasize the need for the early detection of depressive symptoms in patients in the acute phase of COVID-19 using telemedicine. Active intervention, including digital therapeutics, may help patients with aggravated depressive symptoms.

Daily spiritual experiences and allostatic load trajectories: a longitudinal study of midlife African American women.

This study aimed to evaluate the association between daily spiritual experiences and allostatic load (AL) trajectories in midlife African American women. A longitudinal analysis of public-use data from 727 African American women in the Study of Women's Health Across the Nation (SWAN) was performed. We included African American women who completed the Daily Spiritual Experiences Scale at SWAN visit 4 (2000-2001) and had AL data at three or more study visits over 7 years. AL was calculated at each visit using 10 biomarkers: systolic and diastolic blood pressure, body mass index, C-reactive protein, high-density lipoprotein cholesterol, total cholesterol, waist-to-hip ratio, fasting serum glucose, triglycerides, and dehydroepiandrosterone. Group-based trajectory modeling identified women with similar patterns of AL. We used multinomial logistic regression to estimate associations between daily spiritual experiences (some days or less, most days, daily, many times a day) and AL trajectories. Our sample had a mean ± SD age of 49.9 ± 2.66 years, 47% were early perimenopausal, and 17% earned <$19,999 annually. The mean ± SD AL score was 2.52 ± 1.68. Three AL trajectories were identified: low (35.1%), moderate (44.7%), and high (20.2%). In age-adjusted models, women who reported daily comfort in religion and spirituality were less likely to follow a high AL trajectory (odds ratio, 0.41; 95% CI, 0.18-0.93); the association was attenuated when controlling for depressive symptoms (odds ratio, 0.48; 95% CI, 0.19-1.21). Findings from this study do not support an independent association between spirituality in African American women and AL trajectories in midlife. Studies with a larger sample and additional measures of spirituality are warranted in this population.

Patients with Multiple Sclerosis in Year 5 After Initiation of Treatment with Cladribine Tablets: Analysis of Longitudinal Prescription Data from Germany (P8-6.017)

Patients with Multiple Sclerosis in Year 5 After Initiation of Treatment with Cladribine Tablets: Analysis of Longitudinal Prescription Data from Germany (P8-6.017)

Relationship between staff and quality of care in care homes: StaRQ mixed methods study.

Quality of life and care varies between and within the care homes in which almost half a million older people live and over half a million direct care staff (registered nurses and care assistants) work. The reasons are complex, understudied and sometimes oversimplified, but staff and their work are a significant influence. To explore variations in the care home nursing and support workforce; how resident and relatives' needs in care homes are linked to care home staffing; how different staffing models impact on care quality, outcomes and costs; how workforce numbers, skill mix and stability meet residents' needs; the contributions of the care home workforce to enhancing quality of care; staff relationships as a platform for implementation by providers. Mixed-method (QUAL-QUANT) parallel design with five work packages. WP1 - two evidence syntheses (one realist); WP2 - cross-sectional survey of routine staffing and rated quality from care home regulator; WP3 - analysis of longitudinal data from a corporate provider of staffing characteristics and quality indicators, including safety; WP4 - secondary analysis of care home regulator reports; WP5 - social network analysis of networks likely to influence quality innovation. We expressed our synthesised findings as a logic model. English care homes, with and without nursing, with various ownership structures, size and location, with varying quality ratings. Managers, residents, families and care home staff. Staffing's contribution to quality and personalised care requires: managerial and staff stability and consistency; sufficient staff to develop 'familial' relationships between staff and residents, and staff-staff reciprocity, 'knowing' residents, and skills and competence training beyond induction; supported, well-led staff seeing modelled behaviours from supervisors; autonomy to act. Outcome measures that capture the relationship between staffing and quality include: the extent to which resident needs and preferences are met and culturally appropriate; resident and family satisfaction; extent of residents living with purpose; safe care (including clinical outcomes); staff well-being and job satisfaction were important, but underacknowledged. Many of our findings stem from self-reported and routine data with known biases - such as under reporting of adverse incidents; our analysis may reflect these biases. COVID-19 required adapting our original protocol to make it feasible. Consequently, the effects of the pandemic are reflected in our research methods and findings. Our findings are based on data from a single care home operator and so may not be generalised to the wider population of care homes. Innovative and multiple methods and theory can successfully highlight the nuanced relationship between staffing and quality in care homes. Modifiable characteristics such as visible philosophies of care and high-quality training, reinforced by behavioural and relational role modelling by leaders can make the difference when sufficient amounts of consistent staff are employed. Greater staffing capacity alone is unlikely to enhance quality in a cost-effective manner. Social network analysis can help identify the right people to aid adoption and spread of quality and innovation. Future research should focus on richer, iterative, evaluative testing and development of our logic model using theoretically and empirically defensible - rather than available - inputs and outcomes. This study is registered as PROSPERO CRD42021241066 and Research Registry registration: 1062. This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 15/144/29) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 8. See the NIHR Funding and Awards website for further award information.

Tibial Slope Is Not Associated With Implant Migration Following Cemented Total Knee Arthroplasty With a Single Implant Design

BackgroundThe influence of tibial slope on tibial component migration following total knee arthroplasty has not been widely studied, although excessive posterior slope has been implicated in some failures. As implant micromotion measured with radiostereometric analysis can indicate successful fixation, the purpose of this study was to determine the associations between tibial slope, tibial component migration, and inducible displacement. MethodsRadiostereometric analyses at 6 visits over 2 years quantified implant migration for 200 cemented total knee arthroplasties. Longitudinal data analysis examined the influence of postoperative tibial slope on implant migration (overall maximum total point motion (MTPM) migration and anterior–posterior tilt migration), accounting for age, sex, and body mass index. The correlations of tibial slope with 1-year migration, continuous migration, and inducible displacements were also examined. Additionally, the amount of change in slope was compared to migration. ResultsThe mean posterior tibial slope was 8.0° (standard deviation [SD] 3.8°) preoperatively and 3.8° (SD 3.1°) postoperatively, with a mean reduction in slope of 4.2° (SD 4.7°). Postoperative tibial slope (range 14.0° posterior slope to 3.4° anterior slope) was not associated with longitudinal overall migration (P = .671) or anterior–posterior tilt migration (P = .704). There was no association between postoperative tibial slope and migration at 1 year postoperatively (P = .441 for MTPM migration, P = .570 for tilt migration), change in migration from 1 to 2 years (P = .951), or inducible displacement (P = .970 MTPM, P = .730 tilt). The amount of change in tibial slope was also not associated with migration or inducible displacement. ConclusionsResidual and change in postoperative tibial slope were not associated with implant migration into tilt or overall migration, or inducible displacement for a single implant design. These findings support positioning tibial implants in a range of slopes, which may support patient-specific approaches to implant alignment.

The Reciprocal Relationships between Self-Directed Learning Ability, Career Maturity, and Creativity: An Autoregressive Cross-Lagged Panel Analysis for Longitudinal Data

Objectives The purpose of this study was to investigate the longitudinal causal associations between self-directed learning ability, career maturity, and creativity over three annual waves.&#x0D; Methods The present study used panel datasets spanning three years from the Seoul Education Longitudinal Study (SELS). Three waves of panel data collected from Year 7 (first grade of high school), Year 8 (second grade), and Year 9 (third grade) from 2016 to 2018 were assessed by means of autoregressive cross-lagged modeling.&#x0D; Results The findings proved the autoregressive effects and cross-lagged effects between self-directed learning ability, career maturity, and creativity, The autoregressive effects of self-directed learning ability, career maturity, and creativity maintained moderately stable over time. Specifically, these results indicate that higher levels of self-directed learning ability at the previous time was associated with higher levels of self-directed learning ability at the subsequent time, and similarly for career maturity or creativity. Next, cross-lagged analysis revealed that self-directed learning ability was a significantly positive predictor of career maturity across time, and vice versa. In addition, career maturity was a significantly positive predictor of creativity indicating reciprocal relationships, and vice versa. However, self-directed learning ability positively predicted creativity, whereas creativity negatively predicted self-directed learning ability over time.&#x0D; Conclusions The current research is the first to address the reciprocal relationships between self-directed learning ability, career maturity, and creativity across time, using a three-wave cross-lagged panel model approach. The results supported the longitudinal causal model in which latent variables had a positive or negative cross-lagged effect on each other. The findings could be used to develop educational initiatives dedicated to students. The results were discussed in the light of the theoretical and practical implications.&#x0D;

Estimating Obesity Levels Using Decision Trees and K-Fold Cross-Validation: A Study on Eating Habits and Physical Conditions

This study harnesses the predictive capabilities of machine learning to explore the determinants of obesity within populations from Mexico, Peru, and Colombia, using a Decision Tree algorithm bolstered by 5-fold cross-validation. Our comprehensive analysis of 2111 individuals' lifestyle and physical condition data yielded accuracy, precision, recall, and F1-scores that notably peaked in the third and fifth folds. The findings affirmed the significance of dietary habits and physical activity as substantial predictors of obesity levels. The variability in model performance across the folds underscored the importance of robust cross-validation in enhancing the model's generalizability. This research contributes to the burgeoning field of data science in public health by providing a viable model for obesity prediction and laying the groundwork for targeted health interventions. Our study's insights are pivotal for public health officials and policymakers, serving as a stepping stone towards more sophisticated, data-driven approaches to combating obesity. The study, however, recognizes the inherent limitations of self-reported data and the need for broader datasets that encompass more diverse variables. Future research directions include the analysis of longitudinal data to establish causal relationships and the comparison of various machine learning models to optimize predictive performance

Bayesian relative composite quantile regression with ordinal longitudinal data and some case studies

ABSTRACT In real applied fields such as clinical medicine, environmental sciences, psychology as well as economics, we often encounter the task of conducting statistical inference for longitudinal data with ordinal responses. The traditional methods of longitudinal data analysis are often inclined to model continuous responses, which are no longer suitable for such ordinal data. Logistic regression and probit regression are two considerable methods which are frequently used to model ordinal longitudinal responses. However, such modelling methods just depict the mean feature of latent outcome variable and may produce non-robust results when encountering nor-normal errors or outliers. As a proper alternative of mean regression models, composite quantile regression (CQR) method is usually employed to derive robust estimation. The target of this paper is to investigate the CQR estimation approach for ordinal latent longitudinal model. The joint Bayesian hierarchical model is established and a relative CQR estimation approach is suggested to conduct posterior inference for the considered model. Further, in longitudinal data modelling, excessive predictors may be brought into in the models which result in the decrease of the model prediction precision. Bayesian L 1 / 2 regularized prior is incorporated into ordinal longitudinal CQR model to conduct variable selection simultaneously. Finally, simulation studies and two ordinal longitudinal data analysis are hired to illustrate the considered method.

Quantitative analysis of air pollution levels and its health implications in Delhi-NCR: A longitudinal study

Air pollution is a pressing global issue, particularly acute in rapidly urbanizing regions like Delhi-NCR. This research presents a longitudinal study to quantitatively analyze air pollution levels and their health implications in Delhi-NCR. The research objectives include assessing air quality trends, examining the relationships between various pollutants, and investigating the impact of air pollution on respiratory admissions. Data was sourced from the Ambient Air Quality Monitoring Network, covering the years 2020 to 2022, focusing on key pollutants, including PM2.5, PM10, NO2, SO2, CO, and O3. A range of data analysis techniques, including correlation analysis, regression analysis, longitudinal data analysis, spatial analysis, and compliance with WHO guidelines, were applied. Key findings highlight persistently high levels of air pollutants, strong interrelations among pollutants, and consistent respiratory admissions, especially in winter. Regression analysis confirms a significant link between PM2.5 and respiratory admissions. Longitudinal analysis demonstrates the long-term health consequences of air pollution, and spatial analysis reveals district-wise variations in air quality. The study also emphasizes seasonal strategies, compliance with international guidelines, and socioeconomic disparities in health outcomes. This research has broader implications for evidence-based policymaking, emphasizing the urgency of addressing air quality concerns. It calls for targeted interventions, district-level strategies, season-specific measures, and equitable access to healthcare. These findings offer a comprehensive basis for addressing the air quality crisis in Delhi-NCR, ultimately promoting the well-being of its residents.

Joint analysis of multivariate longitudinal, imaging, and time-to-event data

Abstract Alzheimer’s (AD) is a progressive neurodegenerative disease frequently associated with memory deficits and cognitive decline. Despite its irreversible once onset, some discoveries revealed the existence of a certain percentage of people who are non-susceptible to AD. This study proposes a joint analysis of multivariate longitudinal data, survival data with a non-susceptible fraction, and ultrahigh-dimensional imaging data. The proposed model comprises three major components. The first component is a mixture proportional hazards cure model with images to examine the potential predictors of the non-susceptible probability and hazards of interest. The second component is a dynamic factor analysis model with images to characterize group-specific latent factors through multiple observed variables. The last component is a semiparametric trajectory model to reveal the change patterns of the dynamic latent factors in the ‘non-susceptible’ and ‘susceptible’ groups. A two-stage approach is developed for statistical inference. The first stage manages the imaging data through high-dimensional functional principal component analysis. The second stage develops a Bayesian approach coupled with penalized splines, data augmentation, and Markov chain Monte Carlo techniques to perform estimation. The application to the Alzheimer’s Disease Neuroimaging Initiative dataset sheds new insight into the pathology of AD.

Conceptualising the (dis)abling marketplace through value creation and destruction

ABSTRACT Drawing upon value theory, this study investigates both the enabling and disabling features of marketplaces for consumers in the disability market. Analysis of longitudinal qualitative data collected before and after the introduction of a new disability service provision scheme in Australia offer empirical evidence of such marketplaces that are both creating and destroying value for people with disability and their carers. In some instances, value creation and destruction can co-occur for individual consumers. These empirical insights lead to a theoretical conceptualisation of the marketplace as not being wholly disabling, but rather consisting of disabling and enabling factors. Understanding these factors allows service providers and policy makers to make adjustments in view of maximising value creation and minimising value destruction.

Abstract 2556: Longitudinal analyses of clinical sequencing data provide novel insights into the evolutionary dynamics of lung adenocarcinoma

Abstract Background: Tumor heterogeneity plays a crucial role in disease progression, metastasis and treatment response in lung adenocarcinoma (LUAD). Limited availability of multiple molecularly profiled samples from the same patients poses a challenge to validate models of tumor evolution using real-world data. We investigated genomic changes in a large cohort of patients with clinically directed DNA sequencing of multiple LUAD samples. Methods: We analyzed longitudinal genomic data from 524 patients with at least two LUAD samples sequenced with MSK-IMPACT- a targeted panel that identifies somatic mutations, copy number alterations and gene fusions in 341-505 genes. Our analysis of 1084 tumors (634 primaries [P] and 450 metastases [M]) included 189 matched P-P pairs, 99 M-M pairs and 271 P-M pairs. Pairs were required to share an identical somatic mutation to confirm relatedness. Additional criteria included: excision of primary tumor prior to excision of the metastasis in P-M pairs collected over 90 days apart and collection of M-M pairs from the same anatomic site. Intra-pulmonary metastases were removed from P-P pairs. Results: Tumor mutational burden was correlated in paired specimens from the same patient, irrespectively of P-P (R= 0.75, p&amp;lt;0.001; Spearman correlation), P-M (R=0.72, p&amp;lt;0.001) or M-M (R=0.6, p&amp;lt;0.001) status. Chromosomal instability, measured as the fraction of genome altered by copy number changes (FGA), was also correlated in P-P (R=0.69, p&amp;lt;0.001) and M-M (R=0.57, p&amp;lt;0.001) pairs, but tended to increase in metastases with respect to matched primaries (median 0.58 vs. 0.72, p&amp;lt;0.001; Wilcoxon test). In M-M pairs, changes in FGA differed by anatomic site. Liver pairs had the strongest correlation (n=17 pairs, R=0.86, p &amp;lt;0.01), whereas brain pairs had the weakest (n=35 pairs, R=0.085, p=0.71). Most somatic mutations were shared between paired samples (P-P: 639/1055, 60%, P-M: 1563/2877, 54%, M-M: 550/958, 57%), including those in LUAD drivers- TP53, KRAS, and EGFR. Conversely, most oncogenic copy number alterations - including CDKN2A deletions and MET amplifications - were private to one specimen (P-P: 183/276, 66%, P-M: 880/1148, 76%, M-M: 430/601, 71%), most often in the later sample (P-P: 113/183, 61%, P-M: 636/880, 72%, M-M: 212/430, 49%). Clinically targetable alterations (OncoKB Level 1) tended to be shared by tumor pairs from the same patient (93% shared vs. 6% private in P-P, p&amp;lt;0.01; 85% vs. 14% in P-M, p&amp;lt;0.01; 78% vs. 21% in M-M, p &amp;lt;0.01). Conclusions: In LUAD, metastatic specimens exhibit increased chromosomal instability in relation to their matched primaries. This translates into unique copy number alterations detected only in the metastasis. By contrast, driver mutations - which account for most of the clinically targetable alterations with currently approved FDA drugs - are more often shared between paired samples from the same patient. Citation Format: Brooke Mastrogiacomo, Elizabeth G. Dunne, Cameron N. Fick, John Nguyen, Jairam Hathwar, Manendra Lankadasari, Yuan Liu, Soo-Ryum Yang, Jason Chang, Walid K. Chatila, Nikolaus Schultz, David R. Jones, Francisco Sanchez-Vega. Longitudinal analyses of clinical sequencing data provide novel insights into the evolutionary dynamics of lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2556.

Advances in the analysis of intensive longitudinal pain data: a commentary on Leroux et al.

Advances in the analysis of intensive longitudinal pain data: a commentary on Leroux et al.