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A range of anxiety measures is used in oncology but their comparability is unknown. We examined variations in measurement across three commonly used instruments: Hospital Anxiety and Depression-Anxiety subscale (HADS-A); Depression, Anxiety, Stress Scale - Anxiety subscale (DASS-A); and Generalised Anxiety Disorder scale (GAD-7). Participants (n = 164) completed the self-report measures and the Generalised Anxiety Disorder module of the Structured Clinical Interview for DSM-IV (SCID). We performed Rasch analysis and calculated diagnostic accuracy statistics. Instruments measured similar constructs of anxiety, but had different ranges of measurement, with the HADS-A including lower severity symptoms than the other two measures. Anxiety severity was similar for GAD-7 'mild' and HADS-A 'possible' categories, but 'mild' anxiety on the DASS-A represented more severe symptoms. Conversely, DASS-A 'severe' anxiety represented less intense symptoms than GAD-7 'severe' anxiety. Co-calibration indicated a score of eight on the HADS-A was equivalent in anxiety severity to scores of three on the DASS-A and six on the GAD-7. Area under the curve (AUC) was just acceptable for HADS-A and GAD-7 but not DASS-A. The HADS-A, DASS-A and GAD-7 displayed important differences in how they measured anxiety. In particular, categorical classifications of anxiety severity (mild/moderate/severe) were not equivalent across instruments. Thus, prevalence estimates of anxiety symptoms will vary as a consequence of the instrument used. The GAD-7 and HADS-A obtained more similar results and better AUC than the DASS-A. Our co-calibration could be used in future studies and meta-analyses of individual participant data to set cut-off points that provide more consistent classification of anxiety severity., Author(s): Kerrie Clover [sup.1] [sup.2] , Sylvie D. Lambert [sup.3] , Christopher Oldmeadow [sup.4] , Benjamin Britton [sup.2] , Madeleine T. King [sup.5] , Alex J. Mitchell [sup.6] , Gregory [...]

An individualised treatment rule (ITR) is a decision rule that aims to improve individuals' health outcomes by recommending treatments according to subject-specific information. In observational studies, collected data may contain many variables that are irrelevant to treatment decisions. Including all variables in an ITR could yield low efficiency and a complicated treatment rule that is difficult to implement. Thus, selecting variables to improve the treatment rule is crucial. We propose a doubly robust variable selection method for ITRs, and show that it compares favourably with competing approaches. We illustrate the proposed method on data from an adaptive, web-based stress management tool. [ABSTRACT FROM AUTHOR]

Objectives: In a sample of primary care participants with chronic physical conditions and comorbid depressive symptoms: to describe the cross-sectional and longitudinal associations of activation and self-efficacy with demographic, physical and mental health status, health behaviors, depression self-care, health care utilization, and use of self-care tools; and to examine the effects of a depression self-care coaching intervention on these two outcomes. Design/Study Setting: A secondary analysis of activation and self-efficacy data collected as part of a randomized trial to compare the effects of a telephone-based coached depression self-care intervention with a noncoached intervention. Activation (Patient Activation Measure) was measured at baseline and 6 months. Depression self-care self-efficacy was assessed at baseline, at 3 months, and at 6 months. Principal Findings: In multivariable cross-sectional analyses (n = 215), activation and/or self-efficacy were associated with language, birthplace, better physical and mental health, individual exercise, specialist visits, and antidepressant nonuse. In longitudinal analyses (n = 158), an increase in activation was associated with increased medication adherence; an increase in self-efficacy was associated with use of cognitive self-care strategies and increases in social and solitary activities. There were significant improvements from baseline to 6 months in activation and self-efficacy scores both among coached and noncoached groups. The self-care coaching intervention did not affect 6-month activation or self-efficacy but was associated with quicker improvement in self-efficacy. Conclusions: Overall, the results for activation and self-efficacy were similar, although self-efficacy correlated more consistently than activation with depression-specific behaviors and was responsive to a depression self-care coaching intervention.

Cancer survivors often rely on the internet for health information, which has varying levels of readability, suitability, and quality. There is a need for high-quality online self-management resources for cancer survivors with fear of cancer recurrence (FCR). This study evaluated the readability, suitability, and quality of publicly available online FCR self-management resources. A Google search using FCR-related keywords identified freely available FCR self-management resources for cancer survivors in English. Resource readability (reading grade level), suitability, and quality were evaluated using relevant assessment tools. Descriptive statistics and cluster analysis identified resources with higher suitability and quality scores. Mean resource (n = 23) readability score was grade 11 (SD = 1.6, Range = 9–14). The mean suitability score was 56.0% (SD = 11.4%, Range = 31.0–76.3%), indicating average suitability and the mean quality score was 53% (SD = 11.7%, Range = 27–80%), indicating fair quality. A cluster of 15 (65%) resources with higher suitability and quality scores was identified. There were no significant associations between suitability or quality scores and the type of organisation that published the resources. Online FCR self-management resources varied in readability, suitability and quality. Resources with higher quality and suitability scores relative to other resources are identified for use by healthcare professionals and cancer survivors. Resources that are more culturally appropriate, with lower reading grade levels and detailed self-management strategies are needed. [ABSTRACT FROM AUTHOR]

In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have grown in popularity as they offer a more individualized approach. As a result, sequential multiple assignment randomized trials (SMARTs) have gained attention as the most suitable clinical trial design to formalize the study of these strategies. While the number of SMARTs has increased in recent years, sample size and design considerations have generally been carried out in frequentist settings. However, standard frequentist formulae require assumptions on interim response rates and variance components. Misspecifying these can lead to incorrect sample size calculations and correspondingly inadequate levels of power. The Bayesian framework offers a straightforward path to alleviate some of these concerns. In this paper, we provide calculations in a Bayesian setting to allow more realistic and robust estimates that account for uncertainty in inputs through the 'two priors' approach. Additionally, compared to the standard frequentist formulae, this methodology allows us to rely on fewer assumptions, integrate pre‐trial knowledge, and switch the focus from the standardized effect size to the MDD. The proposed methodology is evaluated in a thorough simulation study and is implemented to estimate the sample size for a full‐scale SMART of an internet‐based adaptive stress management intervention on cardiovascular disease patients using data from its pilot study conducted in two Canadian provinces. [ABSTRACT FROM AUTHOR]