Regression Approaches to Assess Effect of Treatments That Arrest Progression of Symptoms.

Motivated by a small sample example in neonatal onset multisystem inflammatory disease (NOMID), we propose a method that can be used when the interest is testing for an association between a changes in disease progression with start of treatment compared to historical disease progression prior to treatment. Our method estimates the longitudinal trajectory of the outcome variable and adds an interaction term between an intervention indicator variable and the time since initiation of the intervention. This method is appropriate for a situation in which the intervention slows or arrests the effect of the disease on the outcome, as is the case in our motivating example. By simulation in small samples and restricted sets of treatment initiation times, we show that the generalized estimating equations (GEE) formulation with small sample adjustments can bound the Type I error rate better than GEE and linear mixed models without small sample adjustments. Permutation tests (permuting the time of treatment initiation) is another valid approach that can also be useful. We illustrate the methodology through an application to a prospective cohort of NOMID patients enrolled at the NIH clinical center.

Sensitive periods and other timing hypotheses in developmental psychopathology: A tutorial.

Researchers often aim to assess whether repeated measures of an exposure are associated with repeated measures of an outcome. A question of particular interest is how associations between exposures and outcomes may differ over time. In other words, researchers may seek the best form of a temporal model. While several models are possible, researchers often consider a few key models. For example, researchers may hypothesize that an exposure measured during a sensitive period may be associated with repeated measures of the outcome over time. Alternatively, they may hypothesize that the exposure measured immediately before the current time period may be most strongly associated with the outcome at the current time. Finally, they may hypothesize that all prior exposures are important. Many analytic methods cannot compare and evaluate these alternative temporal models, perhaps because they make the restrictive assumption that the associations between exposures and outcomes remains constant over time. Instead, we provide a tutorial describing four temporal models that allow the associations between repeated measures of exposures and outcomes to vary, and showing how to test which temporal model is best supported by the data. By finding the best temporal model, developmental psychopathology researchers can find optimal windows for intervention.

Using Conditional Entropy Networks of Ordinal Measures to Examine Changes in Self-Worth Among Adolescent Students in High School.

We implement an analytic approach for ordinal measures and we use it to investigate the structure and the changes over time of self-worth in a sample of adolescents students in high school. We represent the variations in self-worth and its various sub-domains using entropy-based measures that capture the observed uncertainty. We then study the evolution of the entropy across four time points throughout a semester of high school. Our analytic approach yields information about the configuration of the various dimensions of the self together with time-related changes and associations among these dimensions. We represent the results using a network that depicts self-worth changes over time. This approach also identifies groups of adolescent students who show different patterns of associations, thus emphasizing the need to consider heterogeneity in the data.

Common Cause Versus Dynamic Mutualism: An Empirical Comparison of Two Theories of Psychopathology in Two Large Longitudinal Cohorts.

Mental disorders are among the leading causes of global disease burden. To respond effectively, a strong understanding of the structure of psychopathology is critical. We empirically compared two competing frameworks, dynamic-mutualism theory and common-cause theory, that vie to explain the development of psychopathology. We formalized these theories in statistical models and applied them to explain change in the general factor of psychopathology (p factor) from early to late adolescence (N = 1,482) and major depression in middle adulthood and old age (N = 6,443). Change in the p factor was better explained by mutualism according to model-fit indices. However, a core prediction of mutualism was not supported (i.e., predominantly positive causal interactions among distinct domains). The evidence for change in depression was more ambiguous. Our results support a multicausal approach to understanding psychopathology and showcase the value of translating theories into testable statistical models for understanding developmental processes in clinical sciences.

Normalized Clinical Severity Scores Reveal a Correlation between X Chromosome Inactivation and Disease Severity in Rett Syndrome.

Rett Syndrome (RTT) is a severe neurodevelopmental disorder predominately diagnosed in females and primarily caused by pathogenic variants in the X-linked gene Methyl-CpG Binding Protein 2 (MECP2). Most often, the disease causing the MECP2 allele resides on the paternal X chromosome while a healthy copy is maintained on the maternal X chromosome with inactivation (XCI), resulting in mosaic expression of one allele in each cell. Preferential inactivation of the paternal X chromosome is theorized to result in reduced disease severity; however, establishing such a correlation is complicated by known MECP2 genotype effects and an age-dependent increase in severity. To mitigate these confounding factors, we developed an age- and genotype-normalized measure of RTT severity by modeling longitudinal data collected in the US Rett Syndrome Natural History Study. This model accurately reflected individual increase in severity with age and preserved group-level genotype specific differences in severity, allowing for the creation of a normalized clinical severity score. Applying this normalized score to a RTT XCI dataset revealed that XCI influence on disease severity depends on MECP2 genotype with a correlation between XCI and severity observed only in individuals with MECP2 variants associated with increased clinical severity. This normalized measure of RTT severity provides the opportunity for future discovery of additional factors contributing to disease severity that may be masked by age and genotype effects.

Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications.

BACKGROUND AND HYPOTHESIS: Substantive inquiry into the predictive power of eye movement (EM) features for clinical high-risk (CHR) conversion and their longitudinal trajectories is currently sparse. This study aimed to investigate the efficiency of machine learning predictive models relying on EM indices and examine the longitudinal alterations of these indices across the temporal continuum. STUDY DESIGN: EM assessments (fixation stability, free-viewing, and smooth pursuit tasks) were performed on 140 CHR and 98 healthy control participants at baseline, followed by a 1-year longitudinal observational study. We adopted Cox regression analysis and constructed random forest prediction models. We also employed linear mixed-effects models (LMMs) to analyze longitudinal changes of indices while stratifying by group and time. STUDY RESULTS: Of the 123 CHR participants who underwent a 1-year clinical follow-up, 25 progressed to full-blown psychosis, while 98 remained non-converters. Compared with the non-converters, the converters exhibited prolonged fixation durations, decreased saccade amplitudes during the free-viewing task; larger saccades, and reduced velocity gain during the smooth pursuit task. Furthermore, based on 4 baseline EM measures, a random forest model classified converters and non-converters with an accuracy of 0.776 (95% CI: 0.633, 0.882). Finally, LMMs demonstrated no significant longitudinal alterations in the aforementioned indices among converters after 1 year. CONCLUSIONS: Aberrant EMs may precede psychosis onset and remain stable after 1 year, and applying eye-tracking technology combined with a modeling approach could potentially aid in predicting CHRs evolution into overt psychosis.

Bayesian model averaging of longitudinal dose-response models.

Selecting a safe and clinically beneficial dose can be difficult in drug development. Dose justification often relies on dose-response modeling where parametric assumptions are made in advance which may not adequately fit the data. This is especially problematic in longitudinal dose-response models, where additional parametric assumptions must be made. This paper proposes a class of longitudinal dose-response models to be used in the Bayesian model averaging paradigm which improve trial operating characteristics while maintaining flexibility a priori. A new longitudinal model for non-monotonic longitudinal profiles is proposed. The benefits and trade-offs of the proposed approach are demonstrated through a case study and simulation.

Human milk oligosaccharide composition and associations with growth: results from an observational study in the US.

Background: Breast milk is the recommended source of nutrients for newborns and infants. Human milk oligosaccharides (HMO) are the third most abundant solid component in human milk and their composition varies during lactation. Objectives: Our objective was to investigate longitudinal and cross-sectional changes in HMO composition and whether these changes were associated with infant growth up to 24 months of age. Associations with maternal characteristics were also investigated. Methods: 24 HMOs were quantified in samples taken at 2 weeks (n = 107), 6 weeks (n = 97) and 3 months (n = 76), using high performance liquid chromatography. Body length, weight, and head circumference were measured at 8 timepoints, until 24 months. Clusters of breast milk samples, reflecting different HMO profiles, were found through a data-driven approach. Longitudinal associations were investigated using functional principal component analysis (FPCA) and used to characterize patterns in the growth trajectories. Results: Four clusters of samples with similar HMO composition were derived. Two patterns of growth were identified for length, body weight and head circumference via the FPCA approach, explaining more than 90% of the variance. The first pattern measured general growth while the second corresponded to an initial reduced velocity followed by an increased velocity ("higher velocity"). Higher velocity for weight and height was significantly associated with negative Lewis status. Concentrations of 3'GL, 3FL, 6'GL, DSNLT, LNFP-II, LNFP-III, LNT, LSTb were negatively associated with higher velocity for length. Conclusion: We introduced novel statistical approaches to establish longitudinal associations between HMOs evolution and growth. Based on our approach we propose that HMOs may act synergistically on children growth. A possible causal relationship should be further tested in pre-clinical and clinical setting.

Perceived Relative Deprivation Across the Adult Lifespan: An Examination of Aging and Cohort Effects.

Despite being a core psychological construct for over 70 years, research has yet to examine how perceptions of deprivation relative to other individuals and/or groups develop across adulthood. As such, this preregistered study uses cohort-sequential latent growth modeling to examine changes in individual- and group-based relative deprivation (IRD and GRD, respectively) across the adult lifespan. Across 10 annual assessments of a nationwide random sample of adults (Ntotal = 58,878; ethnic minority n = 11,927; 62.7% women; ages 21-80), mean levels of IRD trended downward across the lifespan, whereas mean levels of GRD generally increased from young-to-middle adulthood before declining across late adulthood. Subtle cohort effects emerged for both constructs, although both IRD and GRD largely followed a normative aging process. Critically, the development of GRD-but not IRD-differed between ethnic groups, providing insights into how one's objective status may shape subjective (dis)advantage over time.

Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients.

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.

The effect of cardiovascular risk on disease progression in de novo Parkinson's disease patients: An observational analysis.

Background: Currently available treatment options for Parkinson's disease are symptomatic and do not alter the course of the disease. Recent studies have raised the possibility that cardiovascular risk management may slow the progression of the disease. Objectives: We estimated the effect of baseline cardiovascular risk factors on the progression of Parkinson's disease, using measures for PD-specific motor signs and cognitive functions. Methods: We used data from 424 de novo Parkinson's disease patients and 199 age-matched controls from the observational, multicenter Parkinson's Progression Markers Initiative (PPMI) study, which included follow-up of up to 9 years. The primary outcome was the severity of PD-specific motor signs, assessed with the MDS-UPDRS part III in the "OFF"-state. The secondary outcome was cognitive function, measured with the Montreal Cognitive Assessment, Symbol Digit Modalities Test, and Letter-Number Sequencing task. Exposures of interest were diabetes mellitus, hypertension, body mass index, cardiovascular event history and hypercholesterolemia, and a modified Framingham risk score, measured at baseline. The effect of each of these exposures on disease progression was modeled using linear mixed models, including adjustment for identified confounders. A secondary analysis on the Tracking Parkinson's cohort including 1,841 patients was performed to validate our findings in an independent patient cohort. Results: Mean age was 61.4 years, and the average follow-up was 5.5 years. We found no statistically significant effect of any individual cardiovascular risk factor on the MDS-UPDRS part III progression (all 95% confidence intervals (CIs) included zero), with one exception: in the PD group, the estimated effect of a one-point increase in body mass index was 0.059 points on the MDS-UPDRS part III per year (95% CI: 0.017 to 0.102). We found no evidence for an effect of any of the exposures on the rate of change in cognitive functioning in the PD group. Similar results were observed for the Tracking Parkinson's cohort (all 95% CIs overlapped with PPMI), but the 95% CI of the effect of body mass index on the MDS-UPDRS part III progression included zero. Conclusions: Based on this analysis of two large cohorts of de novo PD patients, we found no evidence to support clinically relevant effects of cardiovascular risk factors on the clinical progression of Parkinson's disease.

Sleep efficiency predicts improvements in fear extinction and PTSD symptoms during prolonged exposure for veterans with comorbid insomnia.

Prolonged exposure (PE) is an evidenced-based psychotherapy for PTSD, but many Veterans fail to achieve a clinically meaningful response. Sleep issues are prevalent in Veterans and may interfere with PE by disrupting the learning and consolidation of fear extinction memories during PE exposures. Here, we examined whether changes in fear extinction across imaginal exposures and PTSD symptoms during PE were predicted by diary-assessed levels of nightly sleep efficiency (SE; i.e., percent of time in bed spent sleeping), which may indirectly index sleep fragmentation and sleep-facilitated memory processes. Participants were Veterans with PTSD and comorbid insomnia (N = 40) participating in a clinical trial of cognitive-behavioral therapy for insomnia plus PE. SE was measured via nightly sleep diaries, fear extinction was operationalized as a reduction in peak distress between weekly imaginal exposures, and PTSD symptoms were assessed bi-weekly. Cross-lagged panel models revealed that higher sleep efficiency during the week predicted lower peak distress at the subsequent imaginal exposure and lower PTSD symptoms at the subsequent assessment, whereas PTSD symptoms and peak distress did not predict subsequent sleep efficiency. Efficient sleep may facilitate fear extinction and PTSD reduction during PE. Targeting sleep efficiency could improve PE effectiveness for Veterans with comorbid insomnia.

Is adolescent internet use a risk factor for the development of depression symptoms or vice-versa?

BACKGROUND: The extent to which digital media use by adolescents contributes to poor mental health, or vice-versa, remains unclear. The purpose of the present study is to clarify the strength and direction of associations between adolescent internet use and the development of depression symptoms using a longitudinal modeling approach. We also examine whether associations differ for boys and girls. METHODS: Data are drawn from (N = 1547) participants followed for the Quebec longitudinal Study of Child Development (QLSCD 1998-2020). Youth self-reported internet use in terms of the average hours of use per week at the ages of 13, 15, and 17. Youth also self-reported depression symptoms at the same ages. RESULTS: After testing sex-invariance, random intercepts cross-lagged panel models stratified by sex, revealed that internet use by girls was associated with significant within-person (time-varying) change in depression symptoms. Girl's internet use at age 13 was associated with increased depression symptoms at age 15 (ß = 0.12) and internet use at age 15 increased depression at age 17 (ß = 0.10). For boys, internet use was not associated with significant time varying change in depression symptoms. CONCLUSIONS: The present findings support the hypothesis that internet use by adolescents can represent a significant risk factor for the development of depressive symptoms, particularly in girls.

Childhood mental health difficulties mediate the long-term association between early-life adversity at age 3 and poorer cognitive functioning at ages 11 and 14.

BACKGROUND: Early-life adversity is associated with adverse mental health outcomes and poorer cognitive functioning in later development. However, little is known about how early-life adversity, mental health, and cognition affect one another or how the effects unfold over time. Here, we test the hypothesis that early-life adversity may lead to mental health challenges which in turn have adverse consequences for the development of cognitive abilities. METHODS: In a large (N = 13,287) longitudinal (5 wave) sample assessed at ages 3, 5, 7, 11 and 14, we use both path analysis approach and latent growth curve mediation model to study whether poorer mental health in childhood may mediate the effects of early-life adversity on later working memory and vocabulary outcomes. RESULTS: We found a significant total association between early-life adversity and poorer performance on working memory (ß = .123, p < .001, [95% CI 0.106, 0.141]) and vocabulary scores (ß = -.111, p < .001, [95% CI -0.129, -0.093]). Notably, current and previous mental health mediated a substantial proportion (working memory: 59%; vocabulary: 70%) of these effects. Further longitudinal modeling showed that early-life adversity has an enduring adverse effect on mental health, and that poorer mental health is associated with poorer cognitive performance later on in development. In a complementary analysis using latent growth curve mediation model, we found indirect associations between early-life adversity and working memory through baseline mental health at age 3 (intercept: ß = .083, p < .001, [95% CI 0.072, 0.094]) and change in mental health across ages 3-11 (slope: ß = -.012, p = .001, [95% CI -0.019, -0.005]). Likewise, baseline mental health at age 3 (intercept: ß = -.095, p < .001, [95% CI -0.107, -0.083]) and change in mental health across ages 3-14 (slope: ß = .007, p = .001, [95% CI 0.003, 0.011]) significantly and completely mediated the relation between early-life adversity and vocabulary outcome. CONCLUSIONS: These findings have important potential clinical and educational implications, because they suggest that academic and cognitive resilience may be supported through early mental health interventions in vulnerable children.

Cognitive subgroups and their longitudinal trajectories in bipolar disorder.

INTRODUCTION: Cognitive functioning in bipolar disorder is heterogeneous with evidence for multiple subgroups. However, cognitive subgroup change patterns over time remains unknown. While prior work suggests minimal differences in cognitive functioning patterns over time between those with bipolar disorder and controls, group-based analyses may obscure unique subgroup-based changes. MATERIAL AND METHODS: Participants diagnosed with bipolar disorder (I, II, NOS; n = 568) and unaffected controls (n = 234) completed baseline, one- and five-year neuropsychological assessments. Data reduction techniques were used to limit the number of neuropsychological variables. Bipolar disorder participant baseline neuropsychological data were entered into hierarchical cluster analyses and resultant clusters were entered in multilevel models, which tested for differences in baseline and longitudinal cognitive changes in cognition among the cluster groups and with controls. RESULTS: Results were consistent with bipolar disorder participants forming three subgroups with high (n = 209), mid (n = 259), and low (n = 100) cognition. These groups were associated with unique clinical characteristics. Multilevel models demonstrated that over a five-year period, the low group improved, relative to the high and mid groups, and with controls, in auditory memory. Over the five-year period, the mid group, in comparison with the high group, improved in visual memory; additionally, the high group remained stable, in comparison with a slight decline in the control group, in inhibitory control. CONCLUSION: These results demonstrate that cognition-based subgroups of bipolar disorder participants have minimal differences in their longitudinal course in relation to each other and with unaffected controls.

Language development as a mechanism linking socioeconomic status to executive functioning development in preschool.

Childhood socioeconomic status (SES) is related to disparities in the development of both language and executive functioning (EF) skills. Emerging evidence suggests that language development may precede and provide necessary scaffolding for EF development in early childhood. The present preregistered study investigates how these skills co-develop longitudinally in early childhood and whether language development explains the relationship between SES and EF development. A socioeconomically diverse sample of 305 children completed repeated assessments of language (sentence comprehension) and EF (cognitive flexibility, behavioral inhibition, and cognitive inhibition) at four waves spaced 9 months apart from ages 3 to 5 years. Bivariate latent curve models with structured residuals were estimated to disaggregate between-person and within-person components of stability and change. Results revealed bidirectional relationships between language and EF across all waves. However, at 3 years, language comprehension more strongly predicted EF than the reverse; yet by 5 years, the bidirectional effects across domains did not significantly differ. Children from higher-SES backgrounds exhibited higher initial language and EF skills than children from lower-SES families, though SES was not associated with either rate of growth. Finally, early language-mediated the association between SES and early EF skills, and this model outperformed a reverse direction mediation. Together, results suggest that EF development is driven by early language development, and that SES disparities in EF are explained, at least in part, by early differences in language comprehension. These findings have implications for early interventions to support children's language skills as a potential pathway to improving early EF development.

How nonshared environmental factors come to correlate with heredity.

Conventional longitudinal behavioral genetic models estimate the relative contribution of genetic and environmental factors to stability and change of traits and behaviors. Longitudinal models rarely explain the processes that generate observed differences between genetically and socially related individuals. We propose that exchanges between individuals and their environments (i.e., phenotype-environment effects) can explain the emergence of observed differences over time. Phenotype-environment models, however, would require violation of the independence assumption of standard behavioral genetic models; that is, uncorrelated genetic and environmental factors. We review how specification of phenotype-environment effects contributes to understanding observed changes in genetic variability over time and longitudinal correlations among nonshared environmental factors. We then provide an example using 30 days of positive and negative affect scores from an all-female sample of twins. Results demonstrate that the phenotype-environment effects explain how heritability estimates fluctuate as well as how nonshared environmental factors persist over time. We discuss possible mechanisms underlying change in gene-environment correlation over time, the advantages and challenges of including gene-environment correlation in longitudinal twin models, and recommendations for future research.

Differential return on investment: Academic growth in mathematics and reading based on initial performance.

BACKGROUND: Students vary in their initial achievement when they enter school and their rate of academic growth as they move through school. These differences have implications for classroom instruction and educational policy. Although previous research has examined initial achievement and growth differences, a gap remains in understanding how initial level of achievement interacts with subsequent growth as children move through school. AIM: Using Vygotsky's zone of proximal development (ZPD) and return on investment as theoretical grounding, this registered report examined how students' initial academic performance relative to their school predicts their subsequent academic achievement. The stage 1 accepted registered report is available at https://osf.io/9zmak/. Specifically, we tracked the achievement of a cohort of students who started at or above their school's mean at the beginning of third grade and tested a range of hypotheses regarding their achievement and growth as well as which students showed the greatest gains from their time in school. SAMPLE: Using a large database of student academic achievement in the United States, this registered report included de-identified data from all students from fall 2014 to spring 2017 in grades three through five from the ten US states with the highest participation for the Northwest Evaluation Association's Measures of Academic Progress (MAP®) - a computer adaptive test of academic achievement in mathematics and reading. Because the MAP is taken at least twice per school year, up to six scores were included on mathematics and reading achievement for effective samples of approximately 220,000 students. METHOD: We built separate reading and mathematics three-level piecewise longitudinal hierarchical linear models (student repeated measures, nested within students, nested within schools) to model student growth from the beginning of third grade to the end of fifth grade (i.e., three academic years and two summers). RESULTS: For both mathematics and reading, average student achievement growth slowed as they progressed from third through fifth grade. From there, the findings diverged. In mathematics, student growth was mostly similar across achievement levels and grades from third through fifth. However, in reading, above-average students demonstrated slower growth than average students during the school year but faster growth during the summer. Also of note, at the beginning of third grade, the highest achieving students outscored average students in their school by more than 2 years in mathematics and 3 years in reading. CONCLUSIONS: Our results may be able to be explained via a ZPD model, which posits development only occurs when students are placed in appropriately challenging environments. In mathematics, the observed pattern of relatively consistent growth across achievement levels suggests average students were just as likely to be in their ZPD as higher achieving students. In reading, as initial achievement increased, student reading growth slowed, which suggests the higher the initial achievement, the less likely students were to be in their ZPD. If a goal of education is for students to learn new things, our results suggest existing school offerings in reading are not meeting that goal equitably for students across the performance spectrum. Differential growth patterns should be considered when designing learning experiences for students who enter with a wide range of prior mastery.

Identifying trajectories of radiographic spinal disease in ankylosing spondylitis: a 15-year follow-up study of the PSOAS cohort.

OBJECTIVES: Little is known with certainty about the natural history of spinal disease progression in ankylosing spondylitis (AS). Our objective was to discover if there were distinct patterns of change in vertebral involvement over time and to study associated clinical factors. METHODS: Data were analysed from the Prospective Study of Outcomes in Ankylosing Spondylitis (PSOAS) observational cohort. All patients met modified New York Criteria for AS and had ≥2 sets of radiographs scored by modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) by two independent readers between 2002 and 2017. Group-based trajectory modelling (GBTM) was used to classify patients into distinct groups of longitudinal mSASSS considering sociodemographic and clinical covariables. The optimal trajectory model and number of trajectories was selected using Nagin's Bayesian information criterion (BIC). RESULTS: A total of 561 patients with 1618 radiographs were analysed. The optimum number of trajectory groups identified was four (BIC -4062). These groups were subsequently categorized as: non-progressors (204 patients), late-progressors (147 patients), early-progressors (107 patients) and rapid-progressors (103 patients). Baseline predictors associated with higher spinal disease burden groups included: baseline mSASSS, male gender, longer disease duration, elevated CRP and smoking history. In addition, time-varying anti-TNF use per year was associated with decreased mSASSS progression only in the rapid-progressor group. CONCLUSIONS: GBTM identified four distinct patterns of spinal disease progression in the PSOAS cohort. Male gender, longer disease duration, elevated CRP and smoking were associated with higher spinal disease groups. Independent confirmation in other AS cohorts is needed to confirm these radiographic patterns.

Trajectory of treatment response in the child and adolescent migraine prevention (CHAMP) study: A randomized clinical trial.

OBJECTIVE: Identify preventive medication treatment response trajectories among youth participating in the Childhood and Adolescent Migraine Prevention study. METHODS: Data were evaluated from 328 youth (ages 8-17). Childhood and Adolescent Migraine Prevention study participants completed headache diaries during a 28-day baseline period and a 168-day active treatment period during which youth took amitriptyline, topiramate, or placebo. Daily headache occurrence trajectories were established across baseline and active treatment periods using longitudinal hierarchical linear modeling. We tested potential treatment group differences. We also compared final models to trajectory findings from a clinical trial of cognitive behavioral therapy plus amitriptyline for youth with chronic migraine to test for reproducibility. RESULTS: Daily headache occurrence showed stability across baseline. Active treatment models revealed decreases in headache frequency that were most notable early in the trial period. Baseline and active treatment models did not differ by treatment group and replicated trajectory cognitive behavioral therapy plus amitriptyline trial findings. CONCLUSIONS: Replicating headache frequency trajectories across clinical trials provides strong evidence that youth can improve quickly. Given no effect for medication, we need to better understand what drives this clinically meaningful improvement. Results also suggest an expected trajectory of treatment response for use in designing and determining endpoints for future clinical trials.Trial Registration. ClinicalTrials.gov Identifier: NCT01581281.