Heterogeneity in suicide risk: Evidence from personalized dynamic models.

Most theories of suicide propose within-person changes in psychological states cause suicidal thoughts/behaviors; however, most studies use between-person analyses. Thus, there are little empirical data exploring current theories in the way they are hypothesized to occur. We used a form of statistical modeling called group iterative multiple model estimation (GIMME) to explore one theory of suicide: The Interpersonal Theory of Suicide (IPTS). GIMME estimates personalized statistical models for each individual and associations shared across individuals. Data were from a real-time monitoring study of individuals with a history of suicidal thoughts/behavior (adult sample: participants = 111, observations = 25,242; adolescent sample: participants = 145, observations = 26,182). Across both samples, none of theorized IPTS effects (i.e., contemporaneous effect from hopeless to suicidal thinking) were shared at the group level. There was significant heterogeneity in the personalized models, suggesting there are different pathways through which different people come to experience suicidal thoughts/behaviors. These findings highlight the complexity of suicide risk and the need for more personalized approaches to assessment and prediction.

How do patients interpret and respond to a novel patient-reported eastern cooperative oncology group performance status (ECOG)?

PURPOSE: Performance status is an important concept in oncology, but is typically clinician-reported. Efforts are underway to include patient-reported measures in cancer care, which may improve patient symptoms, quality of life and overall survival. The purpose of this study was to gain a preliminary understanding of how patients determined their physical performance status based on a novel patient-reported version of the Eastern Cooperative Oncology Group Performance Status (ECOG) scale. METHODS: We conducted qualitative interviews, including concept elicitation and cognitive interviewing as part of the Patient Reports of Physical Functioning Study (PROPS) to investigate how participants selected their answers to a novel patient-reported ECOG. Participants were administered the patient-reported ECOG and asked to describe devices and modifications used to keep up with daily activities. RESULTS: Participants generally understood the ECOG as intended. Participants with recent changes in status had some difficulty selecting an answer. Most participants used modifications and assistive devices in their daily lives but did not incorporate these into their rational for the ECOG. CONCLUSION: The potential benefits of a patient-reported ECOG are numerous and this study demonstrates that participants were able to understand and answer the patient-reported ECOG as intended. We recommend future evaluation for the most-appropriate recall period, whether to include modifications in the ECOG instructions, and if increasing the number of response options to the patient-reported ECOG may improve confidence when providing an answer.

Facets of physical function assessed by patient-reported outcome measures in oncology research.

PURPOSE: The U.S. Food & Drug Administration has identified physical functioning (PF) as a core patient-reported outcome (PRO) in cancer clinical trials. The purpose of this study was to identify PF PRO measures (PROMs) in adult cancer populations and classify the PROMs by content covered (facets of PF) in each measure. METHODS: As part of the Patient Reports of Physical Functioning Study (PROPS) research program, we conducted a targeted literature review to identify PROMs that could be used in clinical trials to evaluate PF from the patient perspective. Next, we convened an advisory panel to conduct a modified, reactive, Delphi study to reach consensus on which PF facets are assessed by PROMs identified in the review. The panel engaged in a "card sort" activity to classify PROM items by PF facets. Consensus was reached when 80% of panel members agreed that at least one facet was being measured by each PROM item. RESULTS: The literature review identified 13 PROMs that met inclusion criteria. Eight facets of PF were identified for classification in the Delphi study: ability, completion, difficulty, limitation, quality, frequency, bother, and satisfaction. Through two rounds, the panel documented and classified conceptual approaches for each PRO item presented. The most prevalent PF facets were ability, difficulty, and limitation. CONCLUSION: Classifying PF PROMs by PF facets will promote more consistent communication regarding the aspects of PF represented in each PROM, helping researchers prioritize measures for inclusion in cancer clinical trials.

What facets of physical function are most important to adults diagnosed with cancer?

PURPOSE: Successful patient-focused drug development involves selecting and measuring outcomes in clinical trials that are important to patients. The U.S. Food & Drug Administration's definition of clinical benefit includes how patients feel, function, or survive. Patients are considered the experts in describing how they feel and function. In cancer trials, patient-reported measures of physical function provide insight into how patients function at baseline, benefit from the interventions being studied, and the impact of treatment side effects. We conducted a qualitative study with adults diagnosed with cancer to describe facets of physical function from their perspective and to identify which facets are most important to this patient population. METHODS: Using concept elicitation and cognitive interviewing techniques, we conducted semi-structured interviews with 72 adults ≥ 22 years of age with cancer who received treatment with an anticancer drug or biologic within six months of the interview. We selected participants using purposive sampling with the aim to elicit diverse experiences regarding how they may interpret and respond to questions related to physical function. Participants were presented with patient-reported outcome (PRO) items representative of PRO measures used in cancer and general populations. RESULTS: Five facets of how physical function relates to activities were defined from the patient perspective: ability, difficulty, limitation, satisfaction, and completion. More than half of the participants indicated that ability was the most important facet of physical function. The next most important were satisfaction (18.3%), limitation (14.1%), difficulty (5.6%), and completion (2.8%). CONCLUSION: This study demonstrates that we must be more specific about the facets of physical function that we set out to assess when we use PRO measures to describe the patient experience. These results have implications for the specificity of physical function facets when measured in cancer clinical trials.

Evaluating Recall Periods for Patient-Reported Outcome Measures: A Systematic Review of Quantitative Methods.

OBJECTIVES: The current guidance for selection of recall periods recommends considering the design of the study, nature of the condition, patient's burden and ability to recall, and intent of the outcome measure. Empirical study of the accuracy of recall periods is recommended; however, there is not consensus on how to quantitatively evaluate the consistency of results from patient-reported outcome measures (PROMs) with different recall periods. We conducted a systematic review to describe quantitative methods for evaluating results obtained from PROMs with differing recall periods to lay the groundwork for establishing consensus. METHODS: We searched MEDLINE, Embase, Scopus, and American Psychological Association PsycINFO for studies where participants are given the same health-related measure (eg, quality of life, well-being, functioning, and pain) with differing recall periods. RESULTS: A total of 7174 abstracts were screened. The 30 included studies reflected a wide range of domains, including pain, fatigue, and sexual behavior and function. The recall periods ranged from momentary to 6 months. The analytic approaches varied, including different methods for assessing relative agreement, absolute agreement, and for assessing combined relative and absolute agreement. CONCLUSIONS: We found variability in how PROM recall periods were evaluated, suggesting an opportunity for greater consensus on methodological approach. As a starting point, we provide recommendations for which methods are preferred for which contexts.

Health-related quality of life profiles in adolescents and young adults with chronic conditions.

PURPOSE: To assess health-related quality of life (HRQOL) among adolescents and young adults (AYAs) with chronic conditions. METHODS: AYAs (N = 872) aged 14-20 years completed NIH's Patient-Reported Outcomes Measurement Information System® (PROMIS®) measures of physical function, pain interference, fatigue, social health, depression, anxiety, and anger. Latent profile analysis (LPA) was used to group AYAs into HRQOL profiles using PROMIS T-scores. The optimal number of profiles was determined by model fit statistics, likelihood ratio test, and entropy. Multinomial logistic regression models were used to examine how LPA's HRQOL profile membership was associated with patient demographic and chronic conditions. The model prediction accuracy on profile membership was evaluated using Huberty's I index with a threshold of 0.35 for good effect. RESULTS: A 4-profile LPA model was selected. A total of 161 (18.5%), 256 (29.4%), 364 (41.7%), and 91 (10.4%) AYAs were classified into Minimal, Mild, Moderate, and Severe HRQOL Impact profiles. AYAs in each profile had distinctive mean scores with over a half standard deviation (5-points in PROMIS T-scores) of difference between profiles across most HRQOL domains. AYAs who were female or had conditions such as mental health condition, hypertension, and self-reported chronic pain were more likely to be in the Severe HRQOL Impact profile. The Huberty's I index was 0.36. CONCLUSIONS: Approximately half of AYAs with a chronic condition experience moderate to severe HRQOL impact. The availability of risk prediction models for HRQOL impact will help to identify AYAs who are in greatest need of closer clinical care follow-up.

Applying latent profile analysis to identify adolescents and young adults with chronic conditions at risk for poor health-related quality of life.

The impact of chronic diseases on health-related quality of life (HRQOL) in adolescents and young adults (AYAs) is understudied. Latent profile analysis (LPA) can identify profiles of AYAs based on their HRQOL scores reflecting physical, mental, and social well-being. This paper will (1) demonstrate how to use LPA to identify profiles of AYAs based on their scores on multiple HRQOL indicators; (2) explore associations of demographic and clinical factors with LPA-identified HRQOL profiles of AYAs; and (3) provide guidance on the selection of adult or pediatric versions of Patient-Reported Outcomes Measurement Information System® (PROMIS®) in AYAs. A total of 872 AYAs with chronic conditions completed the adult and pediatric versions of PROMIS measures of anger, anxiety, depression, fatigue, pain interference, social health, and physical function. The optimal number of LPA profiles was determined by model fit statistics and clinical interpretability. Multinomial regression models examined clinical and demographic factors associated with profile membership. As a result of the LPA, AYAs were categorized into 3 profiles: Minimal, Moderate, and Severe HRQOL Impact profiles. Comparing LPA results using either the pediatric or adult PROMIS T-scores found approximately 71% of patients were placed in the same HRQOL profiles. AYAs who were female, had hypertension, mental health conditions, chronic pain, and those on medication were more likely to be placed in the Severe HRQOL Impact Profile. Our findings may facilitate clinicians to screen AYAs who may have low HRQOL due to diseases or treatments with the identified risk factors without implementing the HRQOL assessment.

Estimating both directed and undirected contemporaneous relations in time series data using hybrid-group iterative multiple model estimation.

Researchers across varied fields increasingly are collecting and analyzing intensive longitudinal data (ILD) to examine processes across time at the individual level. Two types of relations are typically examined: lagged and contemporaneous. Lagged relations capture how variables at a prior time point can be used to explain variance in variables at a later time point. These are always modeled using auto- and cross-regressions by means of vector autoregression (VAR). By contrast, there are two types of relations commonly used to model the contemporaneous relations, which model how variables relate instantaneously. Until now, researchers must opt to either model contemporaneous relations as undirected relations among residuals (e.g., partial or full correlations) or as directed relations among the variables (e.g., paths or regressions). The choice for how to model contemporaneous relations has implications for inferences as well as the potential to introduce bias in the VAR lagged relations if the wrong type of relation is used. This article introduces a novel data-driven method, hybrid-group iterative multiple model estimation (GIMME), that provides a solution to the problem of having to choose one or the other type of contemporaneous relation to model. The modeling framework utilized in hybrid-GIMME allows for both types of contemporaneous relations in addition to the standard VAR relations. Both simulated and empirical data were used to test the performance of hybrid-GIMME. Results suggest this is a robust method for recovering contemporaneous relations in an exploratory manner, particularly with an ample number of time points per person. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Predicting student outcomes using digital logs of learning behaviors: Review, current standards, and suggestions for future work.

Using traces of behaviors to predict outcomes is useful in varied contexts ranging from buyer behaviors to behaviors collected from smart-home devices. Increasingly, higher education systems have been using Learning Management System (LMS) digital data to capture and understand students' learning and well-being. Researchers in the social sciences are increasingly interested in the potential of using digital log data to predict outcomes and design interventions. Using LMS data for predicting the likelihood of students' success in for-credit college courses provides a useful example of how social scientists can use these techniques on a variety of data types. Here, we provide a primer on how LMS data can be feature-mapped and analyzed to accomplish these goals. We begin with a literature review summarizing current approaches to analyzing LMS data, then discuss ethical issues of privacy when using demographic data and equitable model building. In the second part of the paper, we provide an overview of popular machine learning algorithms and review analytic considerations such as feature generation, assessment of model performance, and sampling techniques. Finally, we conclude with an empirical example demonstrating the ability of LMS data to predict student success, summarizing important features and assessing model performance across different model specifications.

Shared and individual-specific daily stress-reactivity in a cross-diagnostic at-risk sample.

Altered stress-reactivity may represent a general risk factor for psychopathology. In a broad at-risk sample, we examined (a) how stress and mild, daily expressions of psychopathology were interrelated over time, (b) whether we could detect subgroups with similar dynamics between stress and daily expressions of psychopathology (i.e., stress-reactivity), and (c) whether stress-reactivity was associated with psychopathology and social functioning. One hundred twenty-two young adults (43.4% women, mean age 23.6) at risk for developing a wide range of psychopathology completed a 6-month daily diary study. We used group iterative multiple model estimation (GIMME) to identify temporal associations between event stress and 11 mild expressions of psychopathology (e.g., feeling down, restlessness) at group, subgroup, and individual levels. Stress was associated with feeling irritated during the same day for >70% of individuals, and with feeling down and worrying during the same day for >50% of individuals. No stable subgroups characterized by similar daily stress-reactivity were identified. Instead, we observed 71 different stress-reactivity patterns in 122 individuals. Average daily event stress, but not overall stress-reactivity (weighted stress-response), was associated with psychopathology severity and social dysfunction. This study showed important similarities, as well as many differences between individuals, in terms of the impact of stress on mild expressions of psychopathology in daily life. Clustering based on similar stress-reactivity did not lead to stable subgroups. Finally, average daily stress levels, but not daily stress-reactivity, were associated with psychopathologic severity and social dysfunction. Findings highlight the importance of considering heterogeneity in stress-reactivity, but also challenges for identifying generalizable processes in doing so. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Detecting Task-Dependent Functional Connectivity in Group Iterative Multiple Model Estimation with Person-Specific Hemodynamic Response Functions.

Introduction: Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at functional connectivity maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This article introduces HRF-GIMME, an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on functional magnetic resonance imaging data collected by using event-related designs. Critically, hemodynamic response function (HRF)-GIMME incorporates person-specific modeling of the HRF to accommodate known variability in onset delay and shape. Methods: After an introduction of the technical aspects of HRF-GIMME, the performance of HRF-GIMME is evaluated via both a simulation study and application to empirical data. The simulation study assesses the sensitivity and specificity of HRF-GIMME by using data simulated from one slow and two rapid event-related designs, and HRF-GIMME is then applied to two empirical data sets from similar designs to evaluate performance in recovering known neural circuitry. Results: HRF-GIMME showed high sensitivity and specificity across all simulated conditions, and it performed well in the recovery of expected relations between convolved task vectors and brain regions in both simulated and empirical data, particularly for the slow event-related design. Conclusion: Results from simulated and empirical data indicate that HRF-GIMME is a powerful new tool for obtaining directed functional connectivity maps of intrinsic and task-related connections that is able to uncover what is common across the sample as well as crucial individual-level path connections and estimates. Impact statement Group iterative multiple model estimation (GIMME) is a reliable method for creating functional connectivity maps of the connections between brain regions across time, and it is able to detect what is common across the sample and what is shared between subsets of participants, as well as individual-level path estimates. However, historically, GIMME does not model task-related effects. The novel HRF-GIMME algorithm enables the modeling of direct and modulatory task effects through individual-level estimation of the hemodynamic response function (HRF), presenting a powerful new tool for assessing task effects on functional connectivity networks in functional magnetic resonance imaging data.

Same-day, cross-day, and upward spiral relations between positive affect and positive health behaviours.

OBJECTIVE: This project investigated same-day and lagged (i.e., from one day to the next) associations between daily positive affect and three distinct positive health behaviours: physical activity, fruit and vegetable intake, and meditation. Cross-day analyses also examined the role of positive affect felt during the targeted health behaviours. DESIGN: Secondary data analyses used a 9-week daily diary study in which midlife adults (N = 217) were randomized to learn one of two contemplative practices (i.e., mindfulness meditation or loving-kindness meditation) while reporting nightly on their emotions and health behaviours. RESULTS: Results of same-day analyses revealed positive associations, both between-person and within-person, for the three positive health behaviours with daily positive affect. Results of lagged analyses revealed that positive affect experienced during fruit and vegetable intake on a given day predicted next-day fruit and vegetable intake, and that fruit and vegetable intake on a given day predicted next-day positive affect. CONCLUSION: The observed same-day relations between daily positive affect and engagement in positive health behaviours illuminate one path through which positive affect may contribute to health. The observed cross-day relations reveal a need for interdisciplinary research on mechanisms through which fruit and vegetable intake may shape next-day positive affect.

Specifying exogeneity and bilinear effects in data-driven model searches.

Data-driven model searches provide the opportunity to quantify person-specific processes using ambulatory assessment data. Here, the search space typically includes all potential relations among variables, meaning that all variables can potentially explain variability in all other variables. Oftentimes, this is unrealistic. For example, weather is unlikely to be predicted by someone's emotional state, whereas the reverse might be true. Allowing for specification of exogenous variables, or variables that are not predicted within the system, permits more realistic models and allows the researcher to model contextual change processes via the use of moderation variables. We use two sets of daily diary data to demonstrate the capabilities of allowing for the specification of exogenous variables in GIMME (Group Iterative Multiple Model Estimation), a model search algorithm that allows for models with idiographic, individual-level as well as subgroup- and group-level processes with intensive longitudinal data. First, using data collected from individuals diagnosed with personality disorders, we show results where weather-related and temporal basis variables are specified as exogenous, and reports on affect and behavior are endogenous. Next, we demonstrate the modeling of treatment effects in an intervention study, looking at data from a 6-week meditation workshop in midlife adults. Finally, we use the meditation intervention data to demonstrate modeling moderation effects, where relationships between two endogenous variables are dependent on the current stage of the study for a given participant (i.e., currently attending meditation classes or not). We end by presenting adaptive LASSO as a method for probing results obtained from GIMME.

Personalized models of psychopathology as contextualized dynamic processes: An example from individuals with borderline personality disorder.

OBJECTIVE: Psychopathology research has relied on discrete diagnoses, which neglects the unique manifestations of each individual's pathology. Borderline personality disorder combines interpersonal, affective, and behavioral regulation impairments making it particularly ill-suited to a "one size fits all" diagnosis. Clinical assessment and case formulation involve understanding and developing a personalized model for each patient's contextualized dynamic processes, and research would benefit from a similar focus on the individual. METHOD: We use group iterative multiple model estimation, which estimates a model for each individual and identifies general or shared features across individuals, in both a mixed-diagnosis sample (N = 78) and a subsample with a single diagnosis (n = 24). RESULTS: We found that individuals vary widely in their dynamic processes in affective and interpersonal domains both within and across diagnoses. However, there was some evidence that dynamic patterns relate to transdiagnostic baseline measures. We conclude with descriptions of 2 person-specific models as an example of the heterogeneity of dynamic processes. CONCLUSIONS: The idiographic models presented here join a growing literature showing that the individuals differ dramatically in the total patterning of these processes, even as key processes are shared across individuals. We argue that these processes are best estimated in the context of person-specific models, and that so doing may advance our understanding of the contextualized dynamic processes that could identify maintenance mechanisms and treatment targets. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Focusing personality assessment on the person: Modeling general, shared, and person specific processes in personality and psychopathology.

Personality and psychopathology are composed of dynamic and interactive processes among diverse psychological systems, manifesting over time and in response to an individual's natural environment. Ambulatory assessment techniques promise to revolutionize assessment practices by allowing access to the dynamic data necessary to study these processes directly. Assessing manifestations of personality and psychopathology naturalistically in an individual's own ecology allows for dynamic modeling of key behavioral processes. However, advances in dynamic data collection have highlighted the challenges of both fully understanding an individual (via idiographic models) and how s/he compares with others (as seen in nomothetic models). Methods are needed that can simultaneously model idiographic (i.e., person-specific) processes and nomothetic (i.e., general) structure from intensive longitudinal personality assessments. Here we present a method, group iterative multiple model estimation (GIMME) for simultaneously studying general, shared (i.e., in subgroups), and person-specific processes in intensive longitudinal behavioral data. We first provide an introduction to the GIMME method, followed by a demonstration of its use in a sample of individuals diagnosed with personality disorder who completed daily diaries over 100 consecutive days. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Assessing the robustness of cluster solutions obtained from sparse count matrices.

Psychological researchers often seek to obtain cluster solutions from sparse count matrices (e.g., social networks; counts of symptoms that are in common for 2 given individuals; structural brain imaging). Increasingly, community detection methods are being used to subset the data in a data-driven manner. While many of these approaches perform well in simulation studies and thus offer some improvement upon traditional clustering approaches, there is no readily available approach for evaluating the robustness of these solutions in empirical data. Researchers have no way of knowing if their results are due to noise. We describe here 2 approaches novel to the field of psychology that enable evaluation of cluster solution robustness. This tutorial also explains the use of an associated R package, perturbR, which provides researchers with the ability to use the methods described herein. In the first approach, the cluster assignment from the original matrix is compared against cluster assignments obtained by randomly perturbing the edges in the matrix. Stable cluster solutions should not demonstrate large changes in the presence of small perturbations. For the second approach, Monte Carlo simulations of random matrices that have the same properties as the original matrix are generated. The distribution of quality scores ("modularity") obtained from the cluster solutions from these matrices are then compared with the score obtained from the original matrix results. From this, one can assess if the results are better than what would be expected by chance. perturbR automates these 2 methods, providing an easy-to-use resource for psychological researchers. We demonstrate the utility of this package using benchmark simulated data generated from a previous study and then apply the methods to publicly available empirical data obtained from social networks and structural neuroimaging. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Beta-adrenergic receptor mechanisms and pain sensitivity in women with menstrually related mood disorders.

UNLABELLED: Somatic symptoms experienced by women with a menstrually related mood disorder (MRMD) during their premenstrual luteal phase contribute to functional impairment. Yet, investigations on pathophysiological mechanisms contributing to heightened pain sensitivity in MRMD are sparse. During the luteal phase, 61 women with an MRMD and 61 non-MRMD controls were evaluated for ß-adrenergic receptor (ß-AR) responsivity using the isoproterenol sensitivity test. A subset (43 MRMD and 50 non-MRMD) then entered a double-blind, placebo-controlled, crossover protocol to examine the effect of ß-AR blockade with intravenous propranolol on sensitivity to experimental (cold pressor and ischemic) and clinical (McGill Pain Questionnaire score) pain. Women with an MRMD exhibited greater ß1- and ß2-AR responsivity, ischemic pain intensity, and affective clinical pain ratings than controls. Propranolol increased cold pressor pain tolerance in both groups, but it decreased cold pain intensity and ischemic pain unpleasantness ratings only in non-MRMD women. In contrast, propranolol decreased affective ratings of clinical pain in women with MRMD. Exploratory analyses indicated that only in MRMD women did greater ß-AR responsivity predict greater sensitivity to cold pressor and ischemic pain. This study provides the first evidence for a role of ß-AR mechanisms in the hyperalgesia and clinical pain experienced by women with MRMDs. PERSPECTIVE: This article describes the effects of ß-adrenergic receptor stimulation and blockade on experimental and clinical pain sensitivity in women with an MRMD. The results of this study may have implications for the management of the substantial somatic premenstrual symptomatology experienced by women with an MRMD.