Abstract Hypnotizability, one’s ability to experience cognitive, emotional, behavioral and physical changes in response to suggestions in the context of hypnosis, is a stable neurobehavioral trait associated with improved treatment outcomes from hypnosis-based therapy. Increasing hypnotizability in people who are low-to-medium hypnotizable individuals could improve both the efficacy and effectiveness of therapeutic hypnosis as a clinical intervention. Hypnotizability is associated with dorsolateral prefrontal cortex (DLPFC) functions and connectivity with the salience network, yet there is conflicting evidence as to whether unilateral inhibition of the DLPFC changes hypnotizability. We hypothesized that using personalized neuroimaging-guided targeting to non-invasively stimulate the left DLPFC with transcranial magnetic stimulation (TMS) would temporarily increase hypnotizability. In a preregistered, double-blinded, randomized controlled trial, we recruited a sample of 80 patients with fibromyalgia syndrome, a functional pain disorder for which hypnosis has been a demonstrated beneficial non-pharmacological treatment option. All participants were TMS-naive. Participants were randomly assigned to active or sham continuous theta-burst stimulation over a personalized neuroimaging-derived left-DLPFC target, a technique termed SHIFT (Stanford Hypnosis Integrated with Functional Connectivity-targeted Transcranial Stimulation). We tested our hypothesis using the hypnotic induction profile scores, a standardized measure of hypnotizability. Pre-to-post SHIFT change in the hypnotic induction profile scores was significantly greater in the active versus sham group after 92 s of stimulation ( P = 0.046). Only the active SHIFT group showed a significant increase in hypnotizability following stimulation (active: P < 0.001; sham: P = 0.607). As such, modulation of trait hypnotizability is possible in humans using non-invasive neuromodulation. Our findings support a relationship between the inhibition of the left DLPFC and an increase in hypnotizability. Dose–response optimization of spaced SHIFT should be explored to understand the optimal dose–response relationship. ClinicalTrials.gov registration: NCT02969707 .
Cluster randomized trials (CRTs) have been widely used in field experiments treating a cluster of individuals as the unit of randomization. This study focused particularly on situations where CRTs are accompanied by a common complication, namely, treatment noncompliance or, more generally, intervention nonadherence. In CRTs, compliance may be related not only to individual characteristics but also to the environment of clusters individuals belong to. Therefore, analyses ignoring the connection between compliance and clustering may not provide valid results. Although randomized field experiments often suffer from both noncompliance and clustering of the data, these features have been studied as separate rather than concurrent problems. On the basis of Monte Carlo simulations, this study demonstrated how clustering and noncompliance may affect statistical inferences and how these two complications can be accounted for simultaneously. In particular, the effect of the intervention on individuals who not only were assigned to active intervention but also abided by this intervention assignment (complier average causal effect) was the focus. For estimation of intervention effects considering noncompliance and data clustering, an ML-EM estimation method was employed.
In longitudinal studies, outcome trajectories can provide important information about substantively and clinically meaningful underlying subpopulations who may also respond differently to treatments or interventions.Growth mixture analysis is an efficient way of identifying heterogeneous trajectory classes.However, given its exploratory nature, it is unclear how involvement of latent classes should be handled in the analysis when estimating causal treatment effects.In this paper, we propose a 2-step approach, where formulation of trajectory strata and identification of causal effects are separated.In Step 1, we stratify individuals in one of the assignment conditions (reference condition) into trajectory strata on the basis of growth mixture analysis.InStep 2, we estimate treatment effects for different trajectory strata, treating the stratum membership as partly known (known for individuals assigned to the reference condition and missing for the rest).The results can be interpreted as how subpopulations that differ in terms of outcome prognosis under one treatment condition would change their prognosis differently when exposed to another treatment condition.Causal effect estimation in Step 2 is consistent with that in the principal stratification approach (Frangakis and Rubin, 2002) in the sense that clarified identifying assumptions can be employed and therefore systematic sensitivity analyses are possible.Longitudinal development of attention deficit among children from the Johns Hopkins School Intervention Trial (Ialongo et al., 1999) will be presented as an example.
An analytical approach was employed to compare sensitivity of causal effect estimates with different assumptions on treatment noncompliance and non-response behaviors. The core of this approach is to fully clarify bias mechanisms of considered models and to connect these models based on common parameters. Focusing on intention-to-treat analysis, systematic model comparisons are performed on the basis of explicit bias mechanisms and connectivity between models. The method is applied to the Johns Hopkins school intervention trial, where assessment of the intention-to-treat effect on school children's mental health is likely to be affected by assumptions about intervention noncompliance and nonresponse at follow-up assessments. The example calls attention to the importance of focusing on each case in investigating relative sensitivity of causal effect estimates with different identifying assumptions, instead of pursuing a general conclusion that applies to every occasion.
Breast cancer survivors often have persisting headache. In a secondary analysis of the Brief Behavioral Therapy for Cancer-Related Insomnia (BBT-CI) clinical trial (ClinicalTrials.gov identifier NCT02165839), the authors examined the effects of BBT-CI on headache outcomes in patients with breast cancer.Patients with breast cancer who were receiving chemotherapy were randomly assigned to receive either the BBT-CI intervention or the Healthy EAting Education Learning for healthy sleep (HEAL) control intervention, and both were delivered over 6 weeks by trained staff. Headache outcomes and heart rate variability (HRV) were measured at baseline, 6 weeks, 6 months, and 12 months. Mixed-effects models were used to examine longitudinal headache outcomes in the groups according to the intention to treat. Principal component analysis and agglomerative hierarchical clustering were conducted to reduce 16 variables for data-driven phenotyping.Patients in the BBT-CI arm (n = 73) exhibited a significant reduction in headache burden over time (P = .02; effect size [Cohen d] = 0.43), whereas the reduction was not significant among those in the HEAL arm (n = 66). The first principal component was positively loaded by headache, sleep, fatigue, and nausea/vomiting and was negatively loaded by cognitive, physical, and emotional functioning. Agglomerative hierarchical clustering revealed 3 natural clusters. Cluster I (n = 58) featured the highest burden of headache, insomnia, and nausea/vomiting; cluster II (n = 50) featured the lowest HRV despite a low burden of headache and insomnia; and cluster III (n = 31) showed an inverse relation between HRV and headache-insomnia, signifying autonomic dysfunction.BBT-CI is efficacious in reducing headache burden in breast cancer survivors. Patient phenotyping demonstrates a headache type featuring sleep disturbance, nausea/vomiting, and low physical functioning-revealing similarities to migraine.Breast cancer survivors often have persisting headache symptoms. In patients with cancer, treatment of chronic headache disorders using daily medications may be challenging because of drug interactions with chemotherapy and other cancer therapies as well as patients' reluctance to add more drugs to their medicine list. Headache and sleep disorders are closely related to each other. This study demonstrates that a sleep behavioral therapy reduced headache burden in breast cancer survivors. In addition, the majority of headache sufferers had a headache type with similarities to migraine-featuring sleep disturbance, nausea/vomiting, and low physical functioning.
9532 Background: Sleep disruption, prevalent in cancer patients and survivors, is associated with disrupted hormonal circadian rhythms and poor quality of life. Previous studies in cancer patients and survivors have pointed out the association between poor sleep and faster disease progression. However, until now these studies have been limited by their retrospective or correlational design, providing little resolution of the question of whether sleep disruption accelerates disease progression or whether disease progression dysregulates sleep, or whether a third factor might underlie the association between sleep dysregulation and disease progression. This study aimed to clarify this relationship by using a longitudinal research design to examine whether sleep disruption assessed at baseline predicts survival in women with metastatic breast cancer. Methods: We examined sleep quality and duration in 97 women diagnosed with metastatic breast cancer (mean age=54.6, SD=9.8) via wrist-worn actigraphy for 3 days and sleep diaries. Sleep quality was operationalized as poor sleep efficiency (the ratio of total asleep time to total time in bed * 100%). Results: As hypothesized, poor sleep efficiency was found to predict shorter survival (Hazard Ratio (HR), 0.96, 95% CI, 0.93 to 0.98, p<0.001) over 6 years. This relationship remained significant (HR, 0.94, CI, 0.91 to 0.97, p<.001) even after controlling for other known prognostic factors (age, ER status, cancer treatment, metastatic spread, cortisol levels, and depression). Conclusions: Our findings show that sleep dysregulation is a clear and significant independent prognostic factor for disease progression in metastatic breast cancer. Further research is needed to determine whether treating sleep disruption with cognitive behavioral therapy or medication can improve survival in metastatic breast cancer. Funded by P01AG018784, R01CA118867, K07CA132916.
Abstract Mediation analysis uses measures of hypothesized mediating variables to test theory for how a treatment achieves effects on outcomes and to improve subsequent treatments by identifying the most efficient treatment components. Most current mediation analysis methods rely on untested distributional and functional form assumptions for valid conclusions, especially regarding the relation between the mediator and outcome variables. Propensity score methods offer an alternative whereby the propensity score is used to compare individuals in the treatment and control groups who would have had the same value of the mediator had they been assigned to the same treatment condition. This article describes the use of propensity score weighting for mediation with a focus on explicating the underlying assumptions. Propensity scores have the potential to offer an alternative estimation procedure for mediation analysis with alternative assumptions from those of standard mediation analysis. The methods are illustrated investigating the mediational effects of an intervention to improve sense of mastery to reduce depression using data from the Job Search Intervention Study (JOBS II). We find significant treatment effects for those individuals who would have improved sense of mastery when in the treatment condition but no effects for those who would not have improved sense of mastery under treatment.
