Latent Outcomes Research Articles

Abstract PR-12: The circadian rhythm gene Dec2 regulates multiple components of the antigen presentation pathway to promote pancreatic cancer dormancy by immune evasion

Abstract Metastatic recurrence following curative-intent pancreatic surgery is a major clinical problem as nearly all die of this at both early and latent periods following surgery. While latent recurrences are due to the reactivation of disseminated dormant tumor cells, the mechanisms that regulate dormancy remain unclear. Using a novel model of early-stage resectable pancreatic cancer that mimics early and latent outcomes in patients, we identified Dec2 (bhlh41) a transcriptional repressor and circadian regulator, as upregulated in dormant tumor cells harvested from the liver. Disseminated tumor cells harvest from the livers of latent recurrent mice were Dec2 hi/Ki67lo indicating that these cells are dormant. We confirmed the relationship of Dec2 and Ki67 in human pancreatic cancer using liver tissue specimens from patients with metastatic disease whereby small clusters of metastatic cells were Dec2hi/Ki67lo and larger metastatic tumors were Dec2lo/Ki67hi. To understand the mechanism of Dec2 in dormancy we deleted Dec2 from the tumor cells evaluated them in the resection model and observed a substantial increase in survival after resection due to an immune mediated mechanism as the survival benefit was abrogated in immunodeficient conditions indicating that Dec2 promotes tumor progression through immune mediated mechanism. Dec2 promotes immune evasion by downregulating multiple components of the antigen presentation pathway including proteosome and immunoproteosome genes, MHCI and MHCI chaperones. As Dec2 is a circadian regulator, we evaluated several components of the antigen presentation pathway including proteosome and surface MHCI and found these to display oscillation that was not only anti-phase with Dec2 but resulted in differential T-cell mediated tumor cell killing depending on the time of day. These studies elucidate a new mechanism by which Dec2 regulates cancer dormancy through immune evasion and identifies several novel therapeutic strategies to restore anti-tumor immunity. They also provide the first evidence that antigen presentation is under circadian regulation in pancreatic cancer. Citation Format: Chris Harris, Crissy Dudgeon, Lan Wang, Orjola Prela, Juliana Cazarin de Menezes, Brian Altman, Brian Haab, Chongfeng Gao, Zachary Klamer, Anna Repesh, Subhajyoti De, Darren R Carpizo. The circadian rhythm gene Dec2 regulates multiple components of the antigen presentation pathway to promote pancreatic cancer dormancy by immune evasion [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr PR-12.

Causal diagrams for disease latency bias.

Disease latency is defined as the time from disease initiation to disease diagnosis. Disease latency bias (DLB) can arise in epidemiological studies that examine latent outcomes, since the exact timing of the disease inception is unknown and might occur before exposure initiation, potentially leading to bias. Although DLB can affect epidemiological studies that examine different types of chronic disease (e.g. Alzheimer's disease, cancer etc), the manner by which DLB can introduce bias into these studies has not been previously elucidated. Information on the specific types of bias, and their structure, that can arise secondary to DLB is critical for researchers, to enable better understanding and control for DLB. Here we describe four scenarios by which DLB can introduce bias (through different structures) into epidemiological studies that address latent outcomes, using directed acyclic graphs (DAGs). We also discuss potential strategies to better understand, examine and control for DLB in these studies. Using causal diagrams, we show that disease latency bias can affect results of epidemiological studies through: (i) unmeasured confounding; (ii) reverse causality; (iii) selection bias; (iv) bias through a mediator. Disease latency bias is an important bias that can affect a number of epidemiological studies that address latent outcomes. Causal diagrams can assist researchers better identify and control for this bias.

Causal inference with latent outcomes

AbstractWhile causal inference has become front and center in empirical political science, we know little about how to analyze causality with latent outcomes, such as political values, beliefs, and attitudes. In this article, we develop a framework for defining, identifying, and estimating the causal effect of an observed treatment on a latent outcome, which we call the latent treatment effect (LTE). We describe a set of assumptions that allow us to identify the LTE and propose a hierarchical item response model to estimate it. We highlight an often overlooked exclusion restriction assumption, which states that treatment status should not affect the observed indicators other than through the latent outcome. A simulation study shows that the hierarchical approach offers unbiased estimates of the LTE under the identification and modeling assumptions, whereas conventional two‐step approaches are biased. We illustrate our proposed methodology using data from two published experimental studies.

Genetic associations with dementia-related proteinopathy: Application of item response theory.

Although dementia-related proteinopathy has a strong negative impact on public health, and is highly heritable, understanding of the related genetic architecture is incomplete. We applied multidimensional generalized partial credit modeling (GPCM) to test genetic associations with dementia-related proteinopathies. Data were analyzed to identify candidate single nucleotide variants for the following proteinopathies: Aβ, tau, α-synuclein, and TDP-43. Final included data comprised 966 participants with neuropathologic and WGS data. Three continuous latent outcomes were constructed, corresponding to TDP-43-, Aβ/Tau-, and α-synuclein-related neuropathology endophenotype scores. This approach helped validate known genotype/phenotype associations: for example, TMEM106B and GRN were risk alleles for TDP-43 pathology; and GBA for α-synuclein/Lewy bodies. Novel suggestive proteinopathy-linked alleles were also discovered, including several (SDHAF1, TMEM68, and ARHGEF28) with colocalization analyses and/or high degrees of biologic credibility. A novel methodology using GPCM enabled insights into gene candidates for driving misfolded proteinopathies. Latent factor scores for proteinopathies were estimated using a generalized partial credit model. The three latent continuous scores corresponded well with proteinopathy severity. Novel genes associated with proteinopathies were identified. Several genes had high degrees of biologic credibility for dementia risk factors.

Caregiving Outcomes of Sub/Urban and Rural Caregivers: The Powerful Tools for Caregivers Program

ABSTRACT Objectives The research aimed to investigate the effectiveness of the Powerful Tools for Caregivers (PTC) program for rural caregivers. Methods A total of 160 caregivers participated in the program. Exploratory factor analysis was performed to identify latent caregiving outcomes. Paired samples t-tests were conducted to investigate changes in caregiving outcomes before and after the program. To examine geographical differences in the caregiving outcomes, independent samples t-tests were performed. Qualitative responses of the participants were also analyzed. Results Self-care and positive coping strategies were identified as latent caregiving outcomes. When comparing these two outcome scores before and after the program, the scores significantly improved. The changes in the caregiving outcomes of rural caregivers were greater than those of sub/urban caregivers. Although rural and sub/urban caregivers qualitatively reported similar themes, there were geographical differences in the percentages of caregivers reporting each theme. Conclusions The PTC program was effective in improving self-care and positive coping strategies, but the effect of the program was more pronounced in rural areas. Our findings suggest that interventions focusing on self-care and positive coping strategies may beneficially impact rural caregivers. Clinical Implications The PTC program appears promising to improve self-care strategies and enhance self-efficacy of rural caregivers.

Tuberculosis infection and stillbirth in Ethiopia-A prospective cohort study.

BackgroundTuberculosis is among the leading causes of death among infectious diseases. Regions with a high incidence of tuberculosis, such as sub-Saharan Africa, are disproportionately burdened by stillbirth and other pregnancy complications. Active tuberculosis increases the risk of pregnancy complications, but the association between latent tuberculosis infection (LTBI) and pregnancy outcomes is unknown. We explored the effect of latent tuberculosis infection on the risk of stillbirth in women attending antenatal care clinics in Ethiopia, a country with >170 000 annual cases of active tuberculosis.MethodPregnant women were enrolled from antenatal care at three health facilities in Adama, Ethiopia, during 2015–2018, with assessment for previous and current active tuberculosis and testing for LTBI using QuantiFERON-TB-GOLD-PLUS. Proportions of stillbirth (≥ 20 weeks of gestation) and neonatal death (< 29 days of birth) were compared with respect to categories of maternal tuberculosis infection (tuberculosis-uninfected, LTBI, previous-, and current active tuberculosis). Multivariable logistic regression was performed for stillbirth.ResultsAmong 1463 participants enrolled, the median age was 25 years, 10.2% were HIV-positive, 34.6% were primigravidae, and the median gestational age at inclusion was 18 weeks. Four (0.3%) were diagnosed with active tuberculosis during pregnancy, 68 (4.6%) reported previous treatment for active tuberculosis, 470 (32.1%) had LTBI, and 921 (63.0%) were tuberculosis-uninfected. Stillbirth was more frequent in participants with LTBI compared to tuberculosis-uninfected participants, although not reaching statistical significance (19/470, 4.0% vs 25/921, 2.7%, adjusted [for age, gravidity and HIV serostatus] odds ratio 1.38, 95% confidence interval 0.73–2.57, p = 0.30). Rates of neonatal death (5/470, 1.1% vs 10/921, 1.1%) were similar between these categories.ConclusionLatent tuberculosis infection was not significantly associated with stillbirth or neonatal death in this cohort. Studies based on larger cohorts and with details on causes of stillbirth, as well as other pregnancy outcomes, are needed to further investigate this issue.

The complicated connection between closeness and the quality of romantic relationships

Closeness is often considered synonymous with better quality romantic relationships. However, individual differences exist in the degree of closeness people desire in their relationships. This study examined the implications that discrepancies between actual and ideal closeness have for relationship quality in romantic couples. A questionnaire was administered to a sample of 103 cohabiting couples ( N = 206) in the United States, who were randomly selected from a nationally representative survey panel. Dyadic analysis using actor–partner interdependence models with latent outcomes revealed that internal discrepancies between actual and idealized closeness were associated with poorer relationship quality for both individuals and their partners. These associations persisted above and beyond the effects of actual closeness and dyad-level differences in actual and ideal closeness. The association between closeness and relationship quality may be more individual than dyadic in nature, warranting renewed attention to the idiographic experience of closeness and its association with relational well-being.

Ownership Variation in Violated Regulations and National Care Standards: Evidence From Social Care Providers

Social care services are increasingly provisioned in quasi-markets in which for-profit, public, and third sector providers compete for contracts. Existing research has investigated the implications of this development by analyzing ownership variation in latent outcomes such as quality, but little is known about whether ownership predicts variation in more concrete outcomes, such as violation types. To address this research gap, we coded publicly available inspection reports of social care providers regulated by the Care Inspectorate in Scotland and created a novel data set enabling analysis of ownership variation in violations of (a) regulations, and (b) national care standards over an entire inspection year ( n = 4,178). Using negative binomial and logistic regression models, we find that for-profit providers are more likely to violate non-enforceable outcomes (national care standards) relative to other ownership types. We did not identify a statistically significant difference between for-profit and third sector providers with regard to enforceable outcomes (regulations).

Digital phenotyping: towards replicable findings with comprehensive assessments and integrative models in bipolar disorders

BackgroundDigital phenotyping promises to unobtrusively obtaining a continuous and objective input of symptomatology from patients’ daily lives. The prime example are bipolar disorders, as smartphone parameters directly reflect bipolar symptomatology. Empirical studies, however, have yielded inconsistent findings. We believe that three main shortcomings have to be addressed to fully leverage the potential of digital phenotyping: short assessment periods, rare outcome assessments, and an extreme fragmentation of parameters without an integrative analytical strategy.MethodsTo demonstrate how to overcome these shortcomings, we conducted frequent (biweekly) dimensional and categorical expert ratings and daily self-ratings over an extensive assessment period (12 months) in 29 patients with bipolar disorder. Digital phenotypes were monitored continuously. As an integrative analytical strategy, we used structural equation modelling to build latent psychopathological outcomes (mania, depression) and latent digital phenotype predictors (sleep, activity, communicativeness).OutcomesCombining gold-standard categorical expert ratings with dimensional self and expert ratings resulted in two latent outcomes (mania and depression) with statistically meaningful factor loadings that dynamically varied over 299 days. Latent digital phenotypes of sleep and activity were associated with same-day latent manic psychopathology, suggesting that psychopathological alterations in bipolar disorders relate to domains (latent variables of sleep and activity) and not only to specific behaviors (such as the number of declined incoming calls). The identification of latent psychopathological outcomes that dimensionally vary on a daily basis will enable to empirically determine which combination of digital phenotypes at which days prior to an upcoming episode are viable as digital prodromal predictors.

Learning Individualized Treatment Rules for Multiple-Domain Latent Outcomes

For many mental disorders, latent mental status from multiple-domain psychological or clinical symptoms may perform as a better characterization of the underlying disorder status than a simple summary score of the symptoms, and they may also serve as more reliable and representative features to differentiate treatment responses. Therefore, to address the complexity and heterogeneity of treatment responses for mental disorders, we provide a new paradigm for learning optimal individualized treatment rules (ITRs) by modeling patients’ latent mental status. We first learn the multi-domain latent states at baseline from the observed symptoms under a restricted Boltzmann machine (RBM) model, which encodes patients’ heterogeneous symptoms using an economical number of latent variables and yet remains flexible. We then optimize a value function defined by the latent states after treatment by exploiting a transformation of the observed symptoms based on the RBM without modeling the relationship between the latent mental states before and after treatment. The optimal treatment rules are derived using a weighted large margin classifier. We derive the convergence rate of the proposed estimator under the latent models. Simulation studies are conducted to test the performance of the proposed method. Finally, we apply the developed method to real world studies and we demonstrate the utility and advantage of our method in tailoring treatments for patients with major depression, and identify patient subgroups informative for treatment recommendations. Supplementary materials for this article are available online.

Past perspectives and new opportunities for the explanatory item response model.

Models of word reading that simultaneously take into account item-level and person-level fixed and random effects are broadly known as explanatory item response models (EIRM). Although many variants of the EIRM are available, the field has generally focused on the doubly explanatory model for modeling individual differences on item responses. Moreover, the historical application of the EIRM has been a Rasch version of the model where the item discrimination values are fixed at 1.0 and the random or fixed item effects only pertain to the item difficulties. The statistical literature has advanced to allow for more robust testing of observed or latent outcomes, as well as more flexible parameterizations of the EIRM. The purpose of the present study was to compare four types of Rasch-based EIRMs (i.e., doubly descriptive, person explanatory, item explanatory, doubly explanatory) and more broadly compare Rasch and 2PL EIRM when including person-level and item-level predictors. Results showed that not only was the error variance smaller in the unconditional 2PL EIRM compared to the Rasch EIRM due to including the item discrimination random effect, but that patterns of unique item-level explanatory variables differ between the two approaches. Results are interpreted within the context of what each statistical model affords to the opportunity for describing and explaining differences in word-level performance.

New Evidence on Outcomes of Primary Education Stipend Programme in Bangladesh

Primary Education Stipend Project, a conditional cash transfer programme, has been in operations since 2003 to increase attendance rate and stem the dropout rate of children from poor and vulnerable households in the rural areas of Bangladesh. This paper evaluates the behavioural impact of conditionality and gender targeting of transfer of fund on the direct and latent outcomes using propensity score estimation method based on a sample of 2,500 households with primary school going children from 125 primary education institutions. Results reveal that the transfer entails an income effect on the share of educational expenses and channeling the stipend through mothers of the students does not directly empower them as women but does empower them as mothers. It appears that two different but mutually reinforcing stimuli—income effect and women educational empowerment effect—lead to favourable educational outcomes of the recipient students.

Latent growth modeling of IRT versus CTT measured longitudinal latent variables.

Latent growth models are often used to measure individual trajectories representing change over time. The characteristics of the individual trajectories depend on the variability in the longitudinal outcomes. In many medical and epidemiological studies, the individual health outcomes cannot be observed directly and are indirectly observed through indicators (i.e. items of a questionnaire). An item response theory or a classical test theory measurement model is required, but the choice can influence the latent growth estimates. In this study, under various conditions, this influence is directly assessed by estimating latent growth parameters on a common scale for item response theory and classical test theory using a novel plausible value method in combination with Markov chain Monte Carlo. The latent outcomes are considered missing data and plausible values are generated from the corresponding posterior distribution, separately for item response theory and classical test theory. These plausible values are linearly transformed to a common scale. A Markov chain Monte Carlo method was developed to simultaneously estimate the latent growth and measurement model parameters using this plausible value technique. It is shown that estimated individual trajectories using item response theory, compared to classical test theory to measure outcomes, provide a more detailed description of individual change over time, since item response patterns (item response theory) are more informative about the health measurements than sum scores (classical test theory).

Pharmacological and residual effects in randomized placebo-controlled trials. A structural causal modelling approach.

Distinguishing between pharmacological and residual effects, this paper considers the problem of causal assessment in the case of a particular model, namely a Sure Outcome of Random Events (SORE) model developed for the analysis of data from a randomized placebo-controlled double-blind trial of a drug. This model takes into account two kinds of observable effects, a therapeutic effect and a side-effect. For each observable effect, two latent factors are considered, i.e. a pharmacological (or explained) factor and a residual (or unexplained) one. The model presents a plausible mechanism generating the observed and latent outcomes, recursively decomposed into an ordered sequence of sub-mechanisms. The characteristics of this model leads to a novel assessment of causality that evaluates the effect of latent variables and of the bias resulting from ignoring the structural features of the data generating process. This approach is illustrated by a numerical example, along with a case study based on a secondary analysis of real data.

Testing Censoring Point Independence

Identification in censored regression analysis and hazard models of duration outcomes relies on the condition that censoring points are conditionally independent of latent outcomes, an assumption which may be questionable in many settings. This article proposes a test for this assumption based on a Cramer–von-Mises-like test statistic comparing two different nonparametric estimators for the latent outcome cdf: the Kaplan–Meier estimator, and the empirical cdf conditional on the censoring point exceeding (for right-censored data) the cdf evaluation point. The test is consistent and has power against a wide variety of alternatives. Applying the test to unemployment duration data from the NLSY, the SIPP, and the PSID suggests the assumption is frequently suspect.

Beyond the Balm of Communicative Planning: Can Actor -Network Theory Insights and a More Agonistic Practice Help Unlock Creative ‘Post-Conflict’ Potential?: Towards a Renewed Research Horizon in Northern Ireland

In a situation, where tribal tram lines seem stuck in place this commentary suggests that an imaginative jolt taking into account recent thinking in planning theory may be warranted and go some small way to subvert the current state of stasis on the still contested terrain of Northern Ireland/North of Ireland. The alliance of procedural agonism and analytical actor network theory (ANT) offers, it is suggested, the augmented potential of a more conceptually diverse approach to planning in terms of conjuring with spatial imaginaries and airing latent outcomes, where communicative planning on its own has exhibited imaginative limitations.

Patient Safety in Surgical Care

The 1999 report of the Institute of Medicine, To Err Is Human: Building a Safer Health System, made national headlines with its estimates of the frequency and severity of adverse events in health care, including that as many as 98,000 medical error–related deaths occur each year in the United States. The observation that the basic principles of human error are highly applicable to clinical practice has markedly advanced our understanding and willingness to address error in this setting. This review seeks to address the characteristics of systems in general and the system of surgical care in particular. It describes the growing knowledge of factors that affect human performance and how these factors contribute to adverse surgical outcomes. The review also outlines current obstacles to improving safety, identifies systems approaches to making improvements, and discusses ways in which surgeons can take the lead in overcoming these obstacles. An overall goal is that acceptance of error and a willingness to investigate its underlying causes will allow health care professionals to make use of the lessons learned from study of nonmedical systems. Tables include definitions of terms related to patient safety, the operation profile, handoff coordination and communication objectives and relevant strategies, nonmedical system techniques applicable to medical systems, Agency for Healthcare Quality and Research patient safety indicators, National Quality Forum list of health care facility–related serious reportable events, and examples of surgically relevant quality improvement practices appropriate for widespread implementation. Figures include the Swiss Cheese Model representing the relationship between latent and active errors and adverse outcomes, a schematic depiction of the process by which system failures may lead to injury, the Systems Engineering in Patient Safety Model of work system and patient safety, and a depiction of contrasting characteristics of medical practice in the 20th and 21st centuries. This review contains 4 figures, 7 tables, and 165 references.

Patient Safety in Surgical Care

The 1999 report of the Institute of Medicine, To Err Is Human: Building a Safer Health System, made national headlines with its estimates of the frequency and severity of adverse events in health care, including that as many as 98,000 medical error–related deaths occur each year in the United States. The observation that the basic principles of human error are highly applicable to clinical practice has markedly advanced our understanding and willingness to address error in this setting. This review seeks to address the characteristics of systems in general and the system of surgical care in particular. It describes the growing knowledge of factors that affect human performance and how these factors contribute to adverse surgical outcomes. The review also outlines current obstacles to improving safety, identifies systems approaches to making improvements, and discusses ways in which surgeons can take the lead in overcoming these obstacles. An overall goal is that acceptance of error and a willingness to investigate its underlying causes will allow health care professionals to make use of the lessons learned from study of nonmedical systems. Tables include definitions of terms related to patient safety, the operation profile, handoff coordination and communication objectives and relevant strategies, nonmedical system techniques applicable to medical systems, Agency for Healthcare Quality and Research patient safety indicators, National Quality Forum list of health care facility–related serious reportable events, and examples of surgically relevant quality improvement practices appropriate for widespread implementation. Figures include the Swiss Cheese Model representing the relationship between latent and active errors and adverse outcomes, a schematic depiction of the process by which system failures may lead to injury, the Systems Engineering in Patient Safety Model of work system and patient safety, and a depiction of contrasting characteristics of medical practice in the 20th and 21st centuries.This review contains 4 figures, 7 tables, and 165 references.

Patient Safety in Surgical Care

The 1999 report of the Institute of Medicine, To Err Is Human: Building a Safer Health System, made national headlines with its estimates of the frequency and severity of adverse events in health care, including that as many as 98,000 medical error–related deaths occur each year in the United States. The observation that the basic principles of human error are highly applicable to clinical practice has markedly advanced our understanding and willingness to address error in this setting. This review seeks to address the characteristics of systems in general and the system of surgical care in particular. It describes the growing knowledge of factors that affect human performance and how these factors contribute to adverse surgical outcomes. The review also outlines current obstacles to improving safety, identifies systems approaches to making improvements, and discusses ways in which surgeons can take the lead in overcoming these obstacles. An overall goal is that acceptance of error and a willingness to investigate its underlying causes will allow health care professionals to make use of the lessons learned from study of nonmedical systems. Tables include definitions of terms related to patient safety, the operation profile, handoff coordination and communication objectives and relevant strategies, nonmedical system techniques applicable to medical systems, Agency for Healthcare Quality and Research patient safety indicators, National Quality Forum list of health care facility–related serious reportable events, and examples of surgically relevant quality improvement practices appropriate for widespread implementation. Figures include the Swiss Cheese Model representing the relationship between latent and active errors and adverse outcomes, a schematic depiction of the process by which system failures may lead to injury, the Systems Engineering in Patient Safety Model of work system and patient safety, and a depiction of contrasting characteristics of medical practice in the 20th and 21st centuries. This review contains 4 figures, 7 tables, and 165 references.

Early Changes by 18 Fluorodeoxyglucose Positron Emission Tomography Coregistered with Computed Tomography Predict Outcome after Mycobacterium tuberculosis Infection in Cynomolgus Macaques

Cynomolgus macaques infected with low-dose Mycobacterium tuberculosis develop both active tuberculosis and latent infection similar to those of humans, providing an opportunity to study the clinically silent early events in infection. (18)Fluorodeoxyglucose radiotracer with positron emission tomography coregistered with computed tomography (FDG PET/CT) provides a noninvasive method to measure disease progression. We sought to determine temporal patterns of granuloma evolution that distinguished active-disease and latent outcomes. Macaques (n = 10) were infected with low-dose M. tuberculosis with FDG PET/CT performed during infection. At 24 weeks postinfection, animals were classified as having active disease (n = 3) or latent infection (n = 6), with one "percolator" monkey. Imaging characteristics (e.g., lesion number, metabolic activity, size, mineralization, and distribution of lesions) were compared among active and latent groups. As early as 3 weeks postinfection, more pulmonary granulomas were observed in animals that would later develop active disease than in those that would develop latent infection. Over time, new lesions developed in active-disease animals but not in latent animals. Granulomas and mediastinal lymph nodes from active-disease but not latent animals consistently increased in metabolic activity at early time points. The presence of fewer lesions at 3 weeks and the lack of new lesion development in animals with latent infection suggest that innate and rapid adaptive responses are critical to preventing active tuberculosis. A greater emphasis on innate responses and/or rapid recruitment of adaptive responses, especially in the airway, should be emphasized in newer vaccine strategies.