Nurturing Longitudinal Samples 2.0.

BACKGROUND: While longitudinal designs can provide significant advantages compared to single measurement/cross sectional designs, they require careful attention to study infrastructure and the risk of attrition among the sample over multiple time points. OBJECTIVE: The strategies used to design and manage an appropriate infrastructure for a longitudinal study and approaches to retain samples are explored using examples from 2 studies, a 25-year study of persons living with multiple sclerosis and a 10-year longitudinal follow-up of breast cancer survivors. RESULTS: Key strategies (developing appropriate infrastructure, minimizing costs to participants, and maximizing rewards of study participation) have helped address the serious threat of attrition in these longitudinal samples. CONCLUSION: Implementation of these strategies can help mitigate some of the disadvantages and leverage the strengths of longitudinal research to produce reliable, insightful, and impactful outcomes.

Resilience in the Face of Cancer: On the Importance of Defining and Studying Resilience as a Dynamic Process of Adaptation.

Resilience is defined as the maintenance or relatively quick recovery of mental health during and after adversity. Rather than focusing on psychopathology and its causes, resilience research aims to understand what protective mechanisms shield individuals against developing such disorders and translate these insights to improve psychosocial care. This resilience approach seems especially promising for the field of oncology because patients face stressor after stressor from diagnosis to survivorship. Helping patients to learn how they can best use the resources and abilities available to them can empower patients to handle subsequent stressors. In the past few decades, resilience has increasingly been considered as a dynamic process of adaptation. While researchers use this definition, resilience has not yet been studied as a dynamic process in the field of oncology. As a result, the potential of resilience research to gain insight into what helps protect cancer patients from developing psychopathology is limited. We discuss conceptual and methodological proposals to advance resilience research in oncology. Most importantly, we propose applying prospective longitudinal designs to capture the dynamic resilience process. By gaining insight in how cancer patients engage in protective factors, resilience research can come to its full potential and help prevent psychopathology.

Enabling analytical power calculations for multilevel models with autocorrelated errors through deriving and approximating the precision matrix.

To unravel how within-person psychological processes fluctuate in daily life, and how these processes differ between persons, intensive longitudinal (IL) designs in which participants are repeatedly measured, have become popular. Commonly used statistical models for those designs are multilevel models with autocorrelated errors. Substantive hypotheses of interest are then typically investigated via statistical hypotheses tests for model parameters of interest. An important question in the design of such IL studies concerns the determination of the number of participants and the number of measurements per person needed to achieve sufficient statistical power for those statistical tests. Recent advances in computational methods and software have enabled the computation of statistical power using Monte Carlo simulations. However, this approach is computationally intensive and therefore quite restrictive. To ease power computations, we derive simple-to-use analytical formulas for multilevel models with AR(1) within-person errors. Analytic expressions for a model family are obtained via asymptotic approximations of all sample statistics in the precision matrix of the fixed effects. To validate this analytical approach to power computation, we compare it to the simulation-based approach via a series of Monte Carlo simulations. We find comparable performances making the analytic approach a useful tool for researchers that can drastically save them time and resources.

A new sample-size planning approach for person-specific VAR(1) studies: Predictive accuracy analysis.

Researchers increasingly study short-term dynamic processes that evolve within single individuals using N = 1 studies. The processes of interest are typically captured by fitting a VAR(1) model to the resulting data. A crucial question is how to perform sample-size planning and thus decide on the number of measurement occasions that are needed. The most popular approach is to perform a power analysis, which focuses on detecting the effects of interest. We argue that performing sample-size planning based on out-of-sample predictive accuracy yields additional important information regarding potential overfitting of the model. Predictive accuracy quantifies how well the estimated VAR(1) model will allow predicting unseen data from the same individual. We propose a new simulation-based sample-size planning method called predictive accuracy analysis (PAA), and an associated Shiny app. This approach makes use of a novel predictive accuracy metric that accounts for the multivariate nature of the prediction problem. We showcase how the values of the different VAR(1) model parameters impact power and predictive accuracy-based sample-size recommendations using simulated data sets and real data applications. The range of recommended sample sizes is smaller for predictive accuracy analysis than for power analysis.

Detecting Cohort Effects in Accelerated Longitudinal Designs Using Multilevel Models.

Accelerated longitudinal designs allow researchers to efficiently collect longitudinal data covering a time span much longer than the study duration. One important assumption of these designs is that each cohort (a group defined by their age of entry into the study) shares the same longitudinal trajectory. Although previous research has examined the impact of violating this assumption when each cohort is defined by a single age of entry, it is possible that each cohort is instead defined by a range of ages, such as groups that experience a particular historical event. In this paper we examined how including cohort membership in linear and quadratic multilevel models performed in detecting and controlling for cohort effects in this scenario. Using a Monte Carlo simulation study, we assessed the performance of this approach under conditions related to the number of cohorts, the overlap between cohorts, the strength of the cohort effect, the number of affected parameters, and the sample size. Our results indicate that models including a proxy variable for cohort membership based on age at study entry performed comparably to using true cohort membership in detecting cohort effects accurately and returning unbiased parameter estimates. This indicates that researchers can control for cohort effects even when true cohort membership is unknown.

The future is dynamic: A call for intensive longitudinal data in immunopsychiatry.

The long-term value of immunopsychiatry will be based on its ability to translate basic science into effective clinical interventions. In this article, we discuss a key obstacle to achieving this important translational goal-namely, the preponderance of studies that are cross-sectional, or that have months-to-years long follow-up periods. Immunopsychiatric processes such as stress, inflammation, and depression symptoms are inherently dynamic and fluctuate over hours, days, and weeks. This fact suggests that higher-density data collection with only days between measurements is necessary to capture-with adequate resolution-the actual dynamics of these systems, determine optimal time lags with which to observe associations between variables of interest, and maximize the translational potential of these data. To illustrate these points, we use pilot data from our own intensive longitudinal immunopsychiatric study. We then conclude by making several recommendations for future research. By learning how to better use existing data for dynamically informative studies as well as collecting intensive longitudinal data, we believe immunopsychiatry will be much better positioned to advance our causal understanding of the interplay between the immune system and health.

Improved health status of severe COPD patients after being included in an integrated primary care service: A prospective cohort study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a prevalent lung disease. It is assumed that severe patients will receive better treatment in specialised care centres but the prevalence of severe COPD in primary care is high. Integrated primary care services combine input from several sources and advice from pulmonologists to provide general practitioners with support needed to improve diagnosis and treatment of patients with COPD. OBJECTIVES: To evaluate patient-reported outcomes and costs of managing patients classified as GOLD D in an integrated primary care service over 12 months. METHODS: Patients were included in this 1-year prospective cohort study if they met the 2014 GOLD D criteria, were aged ≥ 40 years and gave written informed consent for this study. Recruitment took place through the patients' general practitioners. The primary outcome was health status, assessed with the Clinical COPD Questionnaire (CCQ) and COPD Assessment Test (CAT). Secondary outcomes included self-reported exacerbations, quality-adjusted life years and health(care)-related costs. RESULTS: Forty-nine patients were included. At baseline, the mean CAT score was 15.9 and the median CCQ score was 1.7. After 12 months, scores had improved by 2.3 (95% confidence interval, 0.8-3.7) and 0.4 (95% confidence interval, 0.2-0.7), respectively. Percentage of patients with ≥2 exacerbations in the past 12 months also decreased from baseline (77.6%) to 12 months (16.7%). Changes in mean quarterly costs were small. CONCLUSION: An integrated service for COPD based in primary care may improve the health status of patients with a large burden of disease while not increasing health care costs.

A multi-methods and longitudinal study of patients' perceptions in injectable opioid agonist treatment: Implications for advancing patient-centered methodologies in substance use research.

BACKGROUND: Patients' perceptions are vital to the delivery and evaluation of substance use treatment. They are most frequently collected at one time-point and measured using patient satisfaction questionnaires or qualitative methodologies. Interestingly, the findings of these studies often diverge, as satisfaction scores tend to be highly positive, while qualitative findings suggest dissatisfaction and areas for improvement. This divergence limits current understandings of patients' perceptions and their potential change over time in treatment. OBJECTIVE: This study explores the relationship between open-ended positive and negative perceptions of treatment and patient satisfaction scores over time. METHODS: The RUTH (Research on the Utilization of Therapeutic Hydromorphone) prospective cohort study included 131 participants receiving injectable diacetylmorphine or hydromorphone in Canada's first injectable opioid agonist treatment (iOAT) program. The study collected the Client Satisfaction Questionnaire (CSQ-8) at eight time-points over an 18-month period. Following a multi-methods approach, the study complemented the CSQ-8 with open-ended positive and negative comments of iOAT. The research team analyzed these comments thematically at each time-point to develop positive and negative perception themes. We then used growth curve modeling to explore the relationship between positive and negative perception themes and patient satisfaction over time. FINDINGS: Over the eight time-points, six positive and eight negative perception themes emerged, broadly reflecting structural (e.g., expansion of iOAT), process (e.g., schedules), relational (e.g., interactions with providers), and outcome-related (e.g., met/unmet needs) perceptions of iOAT. On average, participants reported high satisfaction (grand mean = 29.2 out of 32), and scores did not significantly change over time. However, we did find significant unexplained variation within participants in their satisfaction trajectories and between participants in their initial satisfaction scores. In conditional growth curve models, the theme "unfavorable interactions with providers" had the strongest independent effect on overall satisfaction trajectories. CONCLUSIONS: This study provides an example of how open-ended comments can be integrated with patient satisfaction questionnaire data to gather a comprehensive and patient-centered evaluation of substance use treatment. Considering the iOAT context specifically, relational dynamics and daily treatment access were significant predictors of patient satisfaction over time and may be attributes of iOAT that require further investigation.

Measuring motivational relationship processes in experience sampling: A reliability model for moments, days, and persons nested in couples.

The investigation of within-person process models, often done in experience sampling designs, requires a reliable assessment of within-person change. In this paper, we focus on dyadic intensive longitudinal designs where both partners of a couple are assessed multiple times each day across several days. We introduce a statistical model for variance decomposition based on generalizability theory (extending P. E. Shrout & S. P. Lane, 2012), which can estimate the relative proportion of variability on four hierarchical levels: moments within a day, days, persons, and couples. Based on these variance estimates, four reliability coefficients are derived: between-couples, between-persons, within-persons/between-days, and within-persons/between-moments. We apply the model to two dyadic intensive experience sampling studies (n1 = 130 persons, 5 surveys each day for 14 days, ≥ 7508 unique surveys; n2 = 508 persons, 5 surveys each day for 28 days, ≥ 47764 unique surveys). Five different scales in the domain of motivational processes and relationship quality were assessed with 2 to 5 items: State relationship satisfaction, communal motivation, and agentic motivation; the latter consists of two subscales, namely power and independence motivation. Largest variance components were on the level of persons, moments, couples, and days, where within-day variance was generally larger than between-day variance. Reliabilities ranged from .32 to .76 (couple level), .93 to .98 (person level), .61 to .88 (day level), and .28 to .72 (moment level). Scale intercorrelations reveal differential structures between and within persons, which has consequences for theory building and statistical modeling.

Applying planned missingness designs to longitudinal panel studies in developmental science: An overview.

Longitudinal panel studies are widely used in developmental science to address important research questions on human development across the lifespan. These studies, however, are often challenging to implement. They can be costly, time-consuming, and vulnerable to test-retest effects or high attrition over time. Planned missingness designs (PMDs), in which partial data are intentionally collected from all or some of the participants, are viable solutions to these challenges. This article provides an overview of several PMDs with potential utilities in longitudinal studies, including the multi-form designs, multi-method designs, varying lag designs, accelerated longitudinal designs, and efficient designs for analysis of change. For each of the designs, the basic rationale, design considerations, data analysis, advantages, and limitations are discussed. The article is concluded with some general recommendations to developmental researchers and promising directions for future research.

Change in the Mindset of a Paediatric Exercise Physiologist: A Review of Fifty Years Research.

In this review, the career of a pediatric exercise physiologist (HCGK) is given over a period of almost 50 years. His research was concentrated on the relationship of physical activity (physical education, sport, and daily physical activity) with health and fitness in teenagers in secondary schools. (1) His first experiment was an exercise test on a bicycle ergometer to measure aerobic fitness by estimating physical work capacity at a heart rate of 170 beats/minute (PWC170). (2) Secondly, a randomized control trial (RCT) was performed with an intervention of more intensive physical education (PE) with circuit interval training during three lessons per week over a period of six weeks. (3) Thereafter, a second RCT was performed with an intervention of two extra PE lessons per week over a whole school year. The results of these two RCTs appeared to be small or nonsignificant, probably because the effects were confounded by differences in maturation and the habitual physical activity of these teenagers. (4) Therefore, the scope of the research was changed into the direction of a long-term longitudinal study (the Amsterdam Growth And Health Longitudinal Study). This study included male and female teenagers that were followed over many years to get insight into the individual changes in biological factors (growth, fitness, obesity, hypercholesterolemia, and hypertension) and lifestyle parameters such as nutrition, smoking, alcohol usage, and daily physical activity. With the help of new advanced statistical methods (generalized estimating equations, random coefficient analysis, and autoregression analysis) suitable for longitudinal data, research questions regarding repeated measurements, tracking, or stability were answered. New measurement techniques such as mineral bone density by means of dual-energy X-ray absorptiometry (DEXA) showed that bone can also be influenced by short bursts of mechanical load. This changed his mind: In children and adolescents, not only can daily aerobic exercise of at least 30 to 60 minutes duration increase the aerobic power of muscles, but very short highly intensive bursts of less than one minute per day can also increase the strength of their bones.

Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion.

Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score (LCS) models provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.

Conducting sensitivity analyses to identify and buffer power vulnerabilities in studies examining substance use over time.

INTRODUCTION: A priori power analysis is increasingly being recognized as a useful tool for designing efficient research studies that improve the probability of robust and publishable results. However, power analyses for many empirical designs in the addiction sciences require consideration of numerous parameters. Identifying appropriate parameter estimates is challenging due to multiple sources of uncertainty, which can limit power analyses' utility. METHOD: We demonstrate a sensitivity analysis approach for systematically investigating the impact of various model parameters on power. We illustrate this approach using three design aspects of importance for substance use researchers conducting longitudinal studies base rates, individual differences (i.e., random slopes), and correlated predictors (e.g., co-use) and examine how sensitivity analyses can illuminate strategies for controlling power vulnerabilities in such parameters. RESULTS: Even large numbers of participants and/or repeated assessments can be insufficient to observe associations when substance use base rates are too low or too high. Large individual differences can adversely affect power, even with increased assessments. Collinear predictors are rarely detrimental unless the correlation is high. CONCLUSIONS: Increasing participants is usually more effective at buffering power than increasing assessments. Research designs can often enhance power by assessing participants twice as frequently as substance use occurs. Heterogeneity should be carefully estimated or empirically controlled, whereas collinearity infrequently impacts power significantly. Sensitivity analyses can identify regions of model parameter spaces that are vulnerable to bad guesses or sampling variability. These insights can be used to design robust studies that make optimal use of limited resources.

The Promise and Challenges of Intensive Longitudinal Designs for Imbalance Models of Adolescent Substance Use.

Imbalance models of adolescent brain development attribute the increasing engagement in substance use during adolescence to within-person changes in the functional balance between the neural systems underlying socio-emotional, incentive processing, and cognitive control. However, the experimental designs and analytic techniques used to date do not lend themselves to explicit tests of how within-person change and within-person variability in socio-emotional processing and cognitive control place individual adolescents at risk for substance use. For a more complete articulation and a more stringent test of these models, we highlight the promise and challenges of using intensive longitudinal designs and analysis techniques that encompass many (often >10) within-person measurement occasions. Use of intensive longitudinal designs will lend researchers the tools required to make within-person inferences in individual adolescents that will ultimately align imbalance models of adolescent substance use with the methodological frameworks used to test them.

Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study.

Dynamic causal modelling (DCM) for resting state fMRI - namely spectral DCM - is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most applications of spectral DCM have focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, and physiological states. Quantifying these variations may help understand functional brain architectures - and inform clinical applications. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns.

Toward biomarkers of the addicted human brain: Using neuroimaging to predict relapse and sustained abstinence in substance use disorder.

The ability to predict relapse is a major goal of drug addiction research. Clinical and diagnostic measures are useful in this regard, but these measures do not fully and consistently identify who will relapse and who will remain abstinent. Neuroimaging approaches have the potential to complement these standard clinical measures to optimize relapse prediction. The goal of this review was to survey the existing drug addiction literature that either used a baseline functional or structural neuroimaging phenotype to longitudinally predict a clinical outcome, or that examined test-retest of a neuroimaging phenotype during a course of abstinence or treatment. Results broadly suggested that, relative to individuals who sustained abstinence, individuals who relapsed had (1) enhanced activation to drug-related cues and rewards, but reduced activation to non-drug-related cues and rewards, in multiple corticolimbic and corticostriatal brain regions; (2) weakened functional connectivity of these same corticolimbic and corticostriatal regions; and (3) reduced gray and white matter volume and connectivity in prefrontal regions. Thus, beyond these regions showing baseline group differences, reviewed evidence indicates that function and structure of these regions can prospectively predict - and normalization of these regions can longitudinally track - important clinical outcomes including relapse and adherence to treatment. Future clinical studies can leverage this information to develop novel treatment strategies, and to tailor scarce therapeutic resources toward individuals most susceptible to relapse.

Why Causal Inference Matters to Nurses: The Case of Nurse Staffing and Patient Outcomes

Since the early 1990s researchers have steadily built a broad evidence base for the association between nurse staffing and patient outcomes. However, the majority of the studies in the literature employ designs that are unable to robustly examine causal pathways to meaningful improvement in patient outcomes. A focus on causal inference is essential to moving the field of nursing research forward, and as part of the essential skill-set for all nurses as consumers of research. In this article, we aim to describe the importance of causal inference in nursing research and discuss study designs that are more likely to produce causal findings. We first review the conceptual framework supporting this discussion and then use selected examples from the literature, typifying three key study designs ­ cross-sectional, longitudinal, and randomized control trials (RCTs). The discussion will illustrate strengths and limitation of existing evidence, focusing on the causal pathway between nurse staffing and outcomes. The article conclusion considers implications for future research.

Neuroscience of inhibition for addiction medicine: from prediction of initiation to prediction of relapse.

A core deficit in drug addiction is the inability to inhibit maladaptive drug-seeking behavior. Consistent with this deficit, drug-addicted individuals show reliable cross-sectional differences from healthy nonaddicted controls during tasks of response inhibition accompanied by brain activation abnormalities as revealed by functional neuroimaging. However, it is less clear whether inhibition-related deficits predate the transition to problematic use, and, in turn, whether these deficits predict the transition out of problematic substance use. Here, we review longitudinal studies of response inhibition in children/adolescents with little substance experience and longitudinal studies of already addicted individuals attempting to sustain abstinence. Results show that response inhibition and its underlying neural correlates predict both substance use outcomes (onset and abstinence). Neurally, key roles were observed for multiple regions of the frontal cortex (e.g., inferior frontal gyrus, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex). In general, less activation of these regions during response inhibition predicted not only the onset of substance use, but interestingly also better abstinence-related outcomes among individuals already addicted. The role of subcortical areas, although potentially important, is less clear because of inconsistent results and because these regions are less classically reported in studies of healthy response inhibition. Overall, this review indicates that response inhibition is not simply a manifestation of current drug addiction, but rather a core neurocognitive dimension that predicts key substance use outcomes. Early intervention in inhibitory deficits could have high clinical and public health relevance.

Hierarchical linear model: thinking outside the traditional repeated-measures analysis-of-variance box.

Longitudinal designs are common in the field of athletic training. For example, in the Journal of Athletic Training from 2005 through 2010, authors of 52 of the 218 original research articles used longitudinal designs. In 50 of the 52 studies, a repeated-measures analysis of variance was used to analyze the data. A possible alternative to this approach is the hierarchical linear model, which has been readily accepted in other medical fields. In this short report, we demonstrate the use of the hierarchical linear model for analyzing data from a longitudinal study in athletic training. We discuss the relevant hypotheses, model assumptions, analysis procedures, and output from the HLM 7.0 software. We also examine the advantages and disadvantages of using the hierarchical linear model with repeated measures and repeated-measures analysis of variance for longitudinal data.