Joint modeling of longitudinal outcomes and survival using latent growth modeling approach in a mesothelioma trial

Trajectory patterns and cumulative burden of CEA during follow-up with non-small cell lung cancer outcomes: A retrospective longitudinal cohort study

BACKGROUND

Previous studies of non-small cell lung cancer (NSCLC) focused on CEA measured at a single time point, ignoring serial CEA measurements.

METHODS

This retrospective cohort included 2959 patients underwent surgery for stage I-III NSCLC. CEA trajectory patterns and long-term cumulative CEA burden were evaluated using the latent class growth mixture model.

RESULTS

Four CEA trajectory groups were identified, named as low-stable, decreasing, early-rising and later-rising. Compared with the low-stable group, the adjusted hazard ratios associated with death were 1.27, 4.50, and 3.68 for the other groups. Cumulative CEA burden were positively associated with the risk of death in patients not belonging to the low-stable group. The 5-year overall survival (OS) rates decreased from 62.3% to 33.0% for the first and fourth quantile groups of cumulative CEA burden. Jointly, patients with decreasing CEA trajectory could be further divided into the decreasing & low and decreasing & high group, with 5-year OS rates to be 77.9% and 47.1%. Patients with rising CEA trajectory and high cumulative CEA were found to be more likely to develop bone metastasis.

CONCLUSIONS

Longitudinal trajectory patterns and long-term cumulative burden of CEA were independent prognostic factors of NSCLC. We recommend CEA in postoperative surveillance of NSCLC.

Association of Lung Function Decline with All-Cause and Cancer-Cause Mortality after World Trade Center Dust Exposure

Rationale: In numerous cohorts, lung function decline is associated with all-cause and cardiovascular-cause mortality, but the association between the decrease in forced expiratory volume in 1 second (FEV1) and cancer-cause mortality, particularly after occupational/environmental exposure(s), is unclear. Exposure to dust/smoke from the World Trade Center (WTC) disaster caused inflammation and lung injury in Fire Department of the City of New York rescue/recovery workers. In addition, prior research found that >10% of the cohort experienced greater than twice the age-related decrease in FEV1 (⩾64 ml/yr). Objectives: To evaluate the association of longitudinal lung function with all-cause and cancer-cause mortality after exposure to the WTC disaster. Methods: We conducted a prospective cohort study using longitudinal prebronchodilator FEV1 data for 12,264 WTC-exposed firefighters and emergency medical service providers. All-cause and cancer-cause mortality were ascertained using National Death Index data from September 12, 2001, through December 31, 2021. Joint longitudinal survival models evaluated the association of baseline FEV1 and change in FEV1 from baseline with all-cause and cancer-cause mortality adjusted for age, race/ethnicity, height, smoking, work assignment (firefighters vs. emergency medical service providers), and WTC exposure. Results: By December 31, 2021, 607 of the 12,264 individuals in the cohort (4.9%) had died (crude rate = 259.5 per 100,000 person-years), and 190 of 12,264 (1.5%) had died from cancer (crude rate = 81.2 per 100,000 person-years). Baseline FEV1 was ⩾80% predicted in 10,970 of the 12,264 (89.4%); final FEV1 was ⩾80% in 9,996 (81.5%). Lower FEV1 at baseline was associated with greater risk for all-cause mortality (hazard ratio [HR] per liter = 2.32; 95% confidence interval [95% CI] = 1.98-2.72) and cancer-cause mortality (HR per liter = 1.99; 95% CI = 1.49-2.66). Longitudinally, each 100-ml/yr decrease in FEV1 was associated with an 11% increase in all-cause mortality (HR = 1.11; 95% CI = 1.06-1.15) and a 7% increase in cancer-cause mortality (HR = 1.07; 95% CI = 1.00-1.15). Compared with FEV1 decrease <64 ml/yr, those with FEV1 decrease ⩾64 ml/yr had higher all-cause (HR = 2.91; 95% CI = 2.37-3.56) and cancer-cause mortality (HR = 2.68; 95% CI = 1.90-3.79). Conclusions: Baseline FEV1 and longitudinal FEV1 decrease are associated with increased risk of all-cause and cancer-cause mortality in a previously healthy occupational cohort, the majority of whom had normal lung function, after intense exposure to dust/smoke. Further investigation is needed to define pathways by which lung function impacts mortality after an irritant exposure.

Semantic item-level metrics relate to future memory decline beyond existing cognitive tests in older adults without dementia

In normal aging, the cognitive domain of semantic memory remains preserved, while the domain of episodic memory declines to some extent. In Alzheimer's disease dementia, both semantic and episodic memory become impaired early in the disease process. Given the need to develop sensitive and accessible cognitive markers for early detection of dementia, we investigated among older adults without dementia whether item-level metrics of semantic fluency related to episodic memory decline above and beyond existing neuropsychological measures and total fluency score. Participants were drawn from the community-based Washington Heights-Inwood Columbia Aging Project cohort (N = 583 English speakers, Mage = 76.3 ± 6.8) followed up to five visits across up to 11 years. We examined the association of semantic fluency metrics with subsequent declines in memory performance using latent growth curve models covaried for age and recruitment wave. Results showed that item-level metrics (e.g., lexical frequency, age of acquisition, and semantic neighborhood density) were associated with a decline in episodic memory-even when covarying for other cognitive tests-while the standard total score was not. Moderation analyses showed that the relationship of semantic fluency metrics with memory decline did not differ across race, sex/gender, or education. In conclusion, item-level data hold a wealth of information with potential to reveal subtle semantic memory impairment, which tracks with episodic memory impairment, among older adults without dementia beyond existing neuropsychological measures. Implementation of psycholinguistic metrics may point to cognitive tools that have better prognostic value or are more sensitive to cognitive change in the context of clinical trials or observational studies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Using joint models to study the association between CD4 count and the risk of death in TB/HIV data

Background

The association structure linking the longitudinal and survival sub-models is of fundamental importance in the joint modeling framework and the choice of this structure should be made based on the clinical background of the study. However, this information may not always be accessible and rationale for selecting this association structure has received relatively little attention in the literature. To this end, we aim to explore four alternative functional forms of the association structure between the CD4 count and the risk of death and provide rationale for selecting the optimal association structure for our data. We also aim to compare the results obtained from the joint model to those obtained from the time-varying Cox model.

Methods

We used data from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) AIDS Treatment programme, the Starting Antiretroviral Therapy at Three Points in Tuberculosis (SAPiT) study, an open-label, three armed randomised, controlled trial between June 2005 and July 2010 (N=642). In our analysis, we combined the early and late integrated arms and compared results to the sequential arm. We utilized the Deviance Information Criterion (DIC) to select the final model with the best structure, with smaller values indicating better model adjustments to the data.

Results

Patient characteristics were similar across the study arms. Combined integrated therapy arms had a reduction of 55% in mortality (HR:0.45, 95% CI:0.28-0.72) compared to the sequential therapy arm. The joint model with a cumulative effects functional form was chosen as the best association structure. In particular, our joint model found that the area under the longitudinal profile of CD4 count was strongly associated with a 21% reduction in mortality (HR:0.79, 95% CI:0.72-0.86). Where as results from the time-varying Cox model showed a 19% reduction in mortality (HR:0.81, 95% CI:0.77-0.84).

Conclusions

In this paper we have shown that the “current value” association structure is not always the best structure that expresses the correct relationship between the outcomes in all settings, which is why it is crucial to explore alternative clinically meaningful association structures that links the longitudinal and survival processes.

Quality of survey responses at older ages predicts cognitive decline and mortality risk

Background and Objectives

It is widely recognized that survey satisficing, inattentive, or careless responding in questionnaires reduces the quality of self-report data. In this study, we propose that such low-quality responding (LQR) can carry substantive meaning at older ages. Completing questionnaires is a cognitively demanding task and LQR among older adults may reflect early signals of cognitive deficits and pathological aging. We hypothesized that older people displaying greater LQR would show faster cognitive decline and greater mortality risk.

Research Design and Methods

We analyzed data from 9,288 adults 65 years or older in the Health and Retirement Study. Indicators of LQR were derived from participants’ response patterns in 102 psychosocial questionnaire items administered in 2006-2008. Latent growth models examined whether LQR predicted initial status and change in cognitive functioning, assessed with the modified Telephone Interview for Cognitive Status, over the subsequent 10 years. Discrete-time survival models examined whether LQR was associated with mortality risk over the 10 years. We also examined evidence for indirect (mediated) effects in which LQR predicts mortality via cognitive trajectories.

Results

After adjusting for age, gender, race, marital status, education, health conditions, smoking status, physical activity, and depressive symptoms, greater LQR was cross-sectionally associated with poorer cognitive functioning, and prospectively associated with faster cognitive decline over the follow-up period. Furthermore, greater LQR was associated with increased mortality risk during follow-up, and this effect was partially accounted for by the associations between LQR and cognitive functioning.

Discussion and Implications

Self-report questionnaires are not formally designed as cognitive tasks but this study shows that LQR indicators derived from self-report measures provide objective, performance-based information about individuals’ cognitive functioning and survival. Self-report surveys are ubiquitous in social science, and indicators of LQR may be of broad relevance as predictors of cognitive and health trajectories in older people.

A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior

Joint models of longitudinal and survival outcomes have gained much popularity in recent years, both in applications and in methodological development. This type of modelling is usually characterised by two submodels, one longitudinal (e.g., mixed-effects model) and one survival (e.g., Cox model), which are connected by some common term. Naturally, sharing information makes the inferential process highly time-consuming. In particular, the Bayesian framework requires even more time for Markov chains to reach stationarity. Hence, in order to reduce the modelling complexity while maintaining the accuracy of the estimates, we propose a two-stage strategy that first fits the longitudinal submodel and then plug the shared information into the survival submodel. Unlike a standard two-stage approach, we apply a correction by incorporating an individual and multiplicative fixed-effect with informative prior into the survival submodel. Based on simulation studies and sensitivity analyses, we empirically compare our proposal with joint specification and standard two-stage approaches. The results show that our methodology is very promising, since it reduces the estimation bias compared to the other two-stage method and requires less processing time than the joint specification approach.

Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data

The statistical analysis of the information generated by medical follow-up is a very important challenge in the field of personalized medicine. As the evolutionary course of a patient's disease progresses, his/her medical follow-up generates more and more information that should be processed immediately in order to review and update his/her prognosis and treatment. Hence, we focus on this update process through sequential inference methods for joint models of longitudinal and time-to-event data from a Bayesian perspective. More specifically, we propose the use of sequential Monte Carlo (SMC) methods for static parameter joint models with the intention of reducing computational time in each update of the full Bayesian inferential process. Our proposal is very general and can be easily applied to most popular joint models approaches. We illustrate the use of the presented sequential methodology in a joint model with competing risk events for a real scenario involving patients on mechanical ventilation in intensive care units (ICUs).

Considerations in testing treatment effects on transient event driven health status changes measured by patient reported outcomes

Many treatments and drugs are intended to reduce the occurrence of negative events of interest, control the severity of the events, accelerate recovery from the events, or a combination of these effects. While assessing the clinical effect is typically the primary objective of a trial, testing the treatment effect on the health status of patients based on patient reported outcome (PRO) can be a useful component in determining the value of a treatment. Analysis of PROs in this setting, however, face the following challenges: the PRO value immediately after the event occurrence is often not captured, and the effect of the event on health status measured by the PRO is transient as subjects recover over time. Therefore, traditional statistical methods used to assess treatment effects suffer from low power for PROs. In this manuscript, we apply a kernel smoothing technique to estimate before- and after-event PRO values. We also propose new test outcomes based on observed and estimated PRO values and evaluate tests that focus on the tail distributions. We demonstrate that the tail distribution tests using the new outcomes can achieve high power under certain conditions.

Longitudinal body weight and sputum conversion in patients with tuberculosis, Southwest Ethiopia: a retrospective follow-up study

OBJECTIVES

To describe the association between change in body weight and sputum smear conversion and to identify factors linked with body weight and sputum smear conversion in Jimma University Specialized Hospital, Southwest Ethiopia.

DESIGN

A retrospective follow-up study.

SETTING

Teaching hospital in Southwest Ethiopia.

PARTICIPANTS

A total of 450 patients with tuberculosis (TB) were included in the follow-up between 2011 and 2013.

MAIN OUTCOME MEASURES

The association between body weight and sputum conversion was measured using joint modelling.

RESULTS

The association between change in body weight and change in sputum conversion was -0.698 (p<0.001). A strong inverse association between change in body weight and change in sputum conversion was observed. The study variables sex, age, type of TB, HIV status, dose of anti-TB drug and length of enrolment to TB treatment were significantly associated with change in body weight of patients with TB. The study variables age, type of TB, dose of anti-TB drug and length of enrolment were significantly associated with change in sputum status of patients with TB.

CONCLUSIONS

Among patients with TB who were on anti-TB treatment, increase in body weight and positive sputum status were inversely related over time. TB prevention and control strategies should give emphasis on factors such as female sex, older age, non-pulmonary positive type of TB, HIV-positive, lower dose of anti-TB drug and length of enrolment to TB treatment during monitoring of trends in body weight and sputum status.

Psychosocial Factors and Sport Injuries: Meta-analyses for Prediction and Prevention

BACKGROUND

Several studies have suggested that psychosocial variables can increase the risk of becoming injured during sport participation.

OBJECTIVES

The main objectives of these meta-analyses were to examine (i) the effect sizes of relationships between the psychosocial variables (suggested as injury predictors in the model of stress and athletic injury) and injury rates, and (ii) the effects of psychological interventions aimed at reducing injury occurrence (prevention).

METHODS

Electronic databases as well as specific sport and exercise psychology journals were searched. The literature review resulted in 48 published studies containing 161 effect sizes for injury prediction and seven effect sizes for injury prevention.

RESULTS

The results showed that stress responses (r = 0.27, 80 % CI [0.20, 0.33]) and history of stressors (r = 0.13, 80 % CI [0.11, 0.15]) had the strongest associations with injury rates. Also, the results from the path analysis showed that the stress response mediated the relationship between history of stressors and injury rates. For injury prevention studies, all studies included (N = 7) showed decreased injury rates in the treatment groups compared to control groups.

CONCLUSION

The results support the model's suggestion that psychosocial variables, as well as psychologically, based interventions, can influence injury risk among athletes.

joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes

BACKGROUND

Joint modelling of longitudinal and time-to-event outcomes has received considerable attention over recent years. Commensurate with this has been a rise in statistical software options for fitting these models. However, these tools have generally been limited to a single longitudinal outcome. Here, we describe the classical joint model to the case of multiple longitudinal outcomes, propose a practical algorithm for fitting the models, and demonstrate how to fit the models using a new package for the statistical software platform R, joineRML.

RESULTS

A multivariate linear mixed sub-model is specified for the longitudinal outcomes, and a Cox proportional hazards regression model with time-varying covariates is specified for the event time sub-model. The association between models is captured through a zero-mean multivariate latent Gaussian process. The models are fitted using a Monte Carlo Expectation-Maximisation algorithm, and inferences are based on approximate standard errors from the empirical profile information matrix, which are contrasted to an alternative bootstrap estimation approach. We illustrate the model and software on a real data example for patients with primary biliary cirrhosis with three repeatedly measured biomarkers.

CONCLUSIONS

An open-source software package capable of fitting multivariate joint models is available. The underlying algorithm and source code makes use of several methods to increase computational speed.

Joint Models of Longitudinal and Time-to-Event Data with More Than One Event Time Outcome: A Review

Methodological development and clinical application of joint models of longitudinal and time-to-event outcomes have grown substantially over the past two decades. However, much of this research has concentrated on a single longitudinal outcome and a single event time outcome. In clinical and public health research, patients who are followed up over time may often experience multiple, recurrent, or a succession of clinical events. Models that utilise such multivariate event time outcomes are quite valuable in clinical decision-making. We comprehensively review the literature for implementation of joint models involving more than a single event time per subject. We consider the distributional and modelling assumptions, including the association structure, estimation approaches, software implementations, and clinical applications. Research into this area is proving highly promising, but to-date remains in its infancy.

The Effect of MSM and CD4+ Count on the Development of Cancer AIDS (AIDS-defining Cancer) and Non-cancer AIDS in the HAART Era

BACKGROUND

The HIV epidemic is increasing among Men who have Sex with Men (MSM) and the risk for AIDS defining cancer (ADC) is higher among them.

OBJECTIVE

To examine the effect of MSM and CD4+ count on time to cancer AIDS (ADC) and noncancer AIDS in competing risks setting in the HAART era.

METHOD

Using Ontario HIV Treatment Network Cohort Study data, HIV-positive adults diagnosed between January 1997 and October 2012 having baseline CD4+ counts ≤ 500 cells/mm3 were evaluated. Two survival outcomes, cancer AIDS and non-cancer AIDS, were treated as competing risks. Kaplan-Meier analysis, Cox cause-specific hazards (CSH) model and joint modeling of longitudinal and survival outcomes were used.

RESULTS

Among the 822 participants, 657 (79.9%) were males; 686 (83.5%) received anti-retroviral (ARV) ever. Regarding risk category, the majority (58.5%) were men who have Sex with men (MSM). Mean age was 37.4 years (SD = 10.3). In the multivariate Cox CSH models, MSM were not associated with cancer AIDS but with non-cancer AIDS [HR = 2.92; P = 0.055, HR = 0.54; P = 0.0009, respectively]. However, in joint models of longitudinal and survival outcomes, MSM were associated with cancer AIDS but not with non-cancer AIDS [HR = 3.86; P = 0.013, HR = 0.73; P = 0.10]. CD4+ count, age, ARV ever were associated with both events in the joint models.

CONCLUSION

This study demonstrates the importance of considering competing risks, and timedependent biomarker in the survival model. MSM have higher hazard for cancer AIDS. CD4+ count is associated with both survival outcomes.

Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical Trials

Joint models for longitudinal and survival data are routinely used in clinical trials or other studies to assess a treatment effect while accounting for longitudinal measures such as patient-reported outcomes (PROs). In the Bayesian framework, the deviance information criterion (DIC) and the logarithm of the pseudo marginal likelihood (LPML) are two well-known Bayesian criteria for comparing joint models. However, these criteria do not provide separate assessments of each component of the joint model. In this paper, we develop a novel decomposition of DIC and LPML to assess the fit of the longitudinal and survival components of the joint model, separately. Based on this decomposition, we then propose new Bayesian model assessment criteria, namely, ΔDIC and ΔLPML, to determine the importance and contribution of the longitudinal (survival) data to the model fit of the survival (longitudinal) data. Moreover, we develop an efficient Monte Carlo method for computing the Conditional Predictive Ordinate (CPO) statistics in the joint modeling setting. A simulation study is conducted to examine the empirical performance of the proposed criteria and the proposed methodology is further applied to a case study in mesothelioma.

Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues

Background

Available methods for the joint modelling of longitudinal and time-to-event outcomes have typically only allowed for a single longitudinal outcome and a solitary event time. In practice, clinical studies are likely to record multiple longitudinal outcomes. Incorporating all sources of data will improve the predictive capability of any model and lead to more informative inferences for the purpose of medical decision-making.

Methods

We reviewed current methodologies of joint modelling for time-to-event data and multivariate longitudinal data including the distributional and modelling assumptions, the association structures, estimation approaches, software tools for implementation and clinical applications of the methodologies.

Results

We found that a large number of different models have recently been proposed. Most considered jointly modelling linear mixed models with proportional hazard models, with correlation between multiple longitudinal outcomes accounted for through multivariate normally distributed random effects. So-called current value and random effects parameterisations are commonly used to link the models. Despite developments, software is still lacking, which has translated into limited uptake by medical researchers.

Conclusion

Although, in an era of personalized medicine, the value of multivariate joint modelling has been established, researchers are currently limited in their ability to fit these models routinely. We make a series of recommendations for future research needs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-016-0212-5) contains supplementary material, which is available to authorized users.

Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group

Explicitly modeling underlying relationships between a survival endpoint and processes that generate longitudinal measured or reported outcomes potentially could improve the efficiency of clinical trials and provide greater insight into the various dimensions of the clinical effect of interventions included in the trials. Various strategies have been proposed for using longitudinal findings to elucidate intervention effects on clinical outcomes such as survival. The application of specifically Bayesian approaches for constructing models that address longitudinal and survival outcomes explicitly has been recently addressed in the literature. We review currently available methods for carrying out joint analyses, including issues of implementation and interpretation, identify software tools that can be used to carry out the necessary calculations, and review applications of the methodology.

Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival

Longitudinal data on aging, health, and longevity provide a wealth of information to investigate different aspects of the processes of aging and development of diseases leading to death. Statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements became known as the "joint models" (JM). An important point to consider in analyses of such data in the context of studies on aging, health, and longevity is how to incorporate knowledge and theories about mechanisms and regularities of aging-related changes that accumulate in the research field into respective analytic approaches. In the absence of specific observations of longitudinal dynamics of relevant biomarkers manifesting such mechanisms and regularities, traditional approaches have a rather limited utility to estimate respective parameters that can be meaningfully interpreted from the biological point of view. A conceptual analytic framework for these purposes, the stochastic process model of aging (SPM), has been recently developed in the biodemographic literature. It incorporates available knowledge about mechanisms of aging-related changes, which may be hidden in the individual longitudinal trajectories of physiological variables and this allows for analyzing their indirect impact on risks of diseases and death. Despite, essentially, serving similar purposes, JM and SPM developed in parallel in different disciplines with very limited cross-referencing. Although there were several publications separately reviewing these two approaches, there were no publications presenting both these approaches in some detail. Here, we overview both approaches jointly and provide some new modifications of SPM. We discuss the use of stochastic processes to capture biological variation and heterogeneity in longitudinal patterns and important and promising (but still largely underused) applications of JM and SPM to predictions of individual and population mortality and health-related outcomes.

Assessing model fit in joint models of longitudinal and survival data with applications to cancer clinical trials

Joint models for longitudinal and survival data now have a long history of being used in clinical trials or other studies in which the goal is to assess a treatment effect while accounting for longitudinal assessments such as patient-reported outcomes or tumor response. Compared to using survival data alone, the joint modeling of survival and longitudinal data allows for estimation of direct and indirect treatment effects, thereby resulting in improved efficacy assessment. Although global fit indices such as AIC or BIC can be used to rank joint models, these measures do not provide separate assessments of each component of the joint model. In this paper, we develop a novel decomposition of AIC and BIC (i.e., AIC = AICLong + AICSurv|Long and BIC = BICLong + BICSurv|Long) that allows us to assess the fit of each component of the joint model and in particular to assess the fit of the longitudinal component of the model and the survival component separately. Based on this decomposition, we then propose ΔAICSurv and ΔBICSurv to determine the importance and contribution of the longitudinal data to the model fit of the survival data. Moreover, this decomposition, along with ΔAICSurv and ΔBICSurv, is also quite useful in comparing, for example, trajectory-based joint models and shared parameter joint models and deciding which type of model best fits the survival data. We examine a detailed case study in mesothelioma to apply our proposed methodology along with an extensive set of simulation studies.

Longitudinal change of self-perceptions of aging and mortality

OBJECTIVE

To understand the association between self-perceptions of aging (SPA) and mortality in late life. Method. The sample (n = 1,507) was drawn from the Australian Longitudinal Study of Aging (baseline age = 65-103 years). We used joint growth curve and survival models on 5 waves of data for a period of 16 years to investigate the random intercept and slope of SPA for predicting all-cause mortality.

RESULTS

The unadjusted model revealed that poor SPA at baseline, as well as decline in SPA, increased the risk of mortality (SPA intercept hazard ratio [HR] = 1.21, 95% confidence interval [CI] = 1.13, 1.31; SPA slope HR = 1.17, 95% CI = 1.02, 1.33). This relationship remained significant for the SPA intercept after adjusting for other risk factors including demographics, physical health, cognitive functioning, and well-being.

CONCLUSION

These findings suggest that a single measurement of SPA in late life may be very informative of future long-term vulnerability to health decline and mortality. Furthermore, a dynamic measure of SPA may be indicative of adaptation to age-related changes. This supports a "self-fulfilling" hypothesis, whereby SPA is a lens through which age-related changes are interpreted, and these interpretations can affect future health and health behaviors.