Galina Velikova

Anxiety Disorders in Cancer Patients: Their Nature, Associations, and Relation to Quality of Life

PURPOSE We aimed to estimate the prevalence and types of anxiety disorders diagnosed according to standardized criteria in cancer patients, to compare screening tools in detecting them, and to examine their demographic, oncologic, and psychosocial associations. METHODS In this cross-sectional observational study of 178 subjects with lymphoma, renal cell carcinoma, malignant melanoma, or plasma cell dyscrasia, we related responses to questionnaires (administered by computer touch-screen) measuring psychological symptoms, quality of life (QOL), and social support to standardized psychiatric interviews and cancer management. RESULTS Forty-eight percent of subjects reported sufficient anxiety for anxiety disorder to be considered. At subsequent diagnostic interview, 18% fulfilled International Classification of Disorders, 10th Revision criteria for anxiety disorder, including 6% of patients who reported low levels of anxiety by questionnaire. When subjects reported anxiety by questionnaire, if disruptive somatic anxiety was present, this increased the probability of diagnosable anxiety disorder from.31 to.7. The most accurate screening questionnaires were the trait scale of the State-Trait Anxiety Inventory and the Hospital Anxiety and Depression scale. Female sex and negative aspects of social support were associated with anxiety disorder in multivariate analyses. Anxiety disorder was independently associated with a deficit in QOL, particularly insomnia. CONCLUSION Anxiety symptoms are common in cancer patients. Screening by questionnaire seems to assess anxiety symptoms adequately but discriminates abnormal anxiety inadequately. To improve this, we may need to use criteria such as disruption from anxiety, as illustrated by the impact of anxiety disorders on QOL. There seem to be few oncologic variables that could target screening for anxiety disorders.

Evidence-Based Guidelines for Determination of Sample Size and Interpretation of the European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire Core 30

UNLABELLED PURPOSE; To use published literature to estimate large, medium, and small differences in quality of life (QOL) data from the European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30). METHODS An innovative method combining systematic review of published studies, expert opinions, and meta-analysis was used to estimate large, medium, and small differences for QLQ-C30 scores. Published mean data were identified from the literature. Differences (contrasts) between groups (eg, between treatment groups, age groups, and performance status groups) were reviewed by 34 experts in QOL measurement and cancer treatment. The experts, blinded to actual QOL results, were asked to predict these differences. A large difference was defined as one representing unequivocal clinical relevance. A medium difference was defined as likely to be clinically relevant but to a lesser extent. A small difference was one believed to be subtle but nevertheless clinically relevant. A trivial difference was used to describe circumstances unlikely to have any clinical relevance. Actual QOL results were combined using meta-analytic techniques to estimate differences corresponding to small, medium, or large effects. RESULTS Nine hundred eleven articles were identified, leading to 152 relevant articles (2,217 contrasts) being reviewed by at least two experts. Resulting estimates from the meta-analysis varied depending on the subscale. Thus, the recommended minimum to detect medium differences ranges from 9 (cognitive functioning) to 19 points (role functioning). CONCLUSION Guidelines for the size of effects are provided for the QLQ-C30 subscales. These guidelines can be used for sample size calculations for clinical trials and can also be used to aid interpretation of differences in QLQ-C30 scores.

Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer

IntroductionBreast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice.MethodsMore than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer ‘stem’ cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account.ResultsThe 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working.ConclusionsWith resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years.

Rasch fit statistics and sample size considerations for polytomous data

BackgroundPrevious research on educational data has demonstrated that Rasch fit statistics (mean squares and t-statistics) are highly susceptible to sample size variation for dichotomously scored rating data, although little is known about this relationship for polytomous data. These statistics help inform researchers about how well items fit to a unidimensional latent trait, and are an important adjunct to modern psychometrics. Given the increasing use of Rasch models in health research the purpose of this study was therefore to explore the relationship between fit statistics and sample size for polytomous data.MethodsData were collated from a heterogeneous sample of cancer patients (n = 4072) who had completed both the Patient Health Questionnaire – 9 and the Hospital Anxiety and Depression Scale. Ten samples were drawn with replacement for each of eight sample sizes (n = 25 to n = 3200). The Rating and Partial Credit Models were applied and the mean square and t-fit statistics (infit/outfit) derived for each model.ResultsThe results demonstrated that t-statistics were highly sensitive to sample size, whereas mean square statistics remained relatively stable for polytomous data.ConclusionIt was concluded that mean square statistics were relatively independent of sample size for polytomous data and that misfit to the model could be identified using published recommended ranges.

Quality of life instruments in oncology

The objective of this article is to aid clinicians in understanding the current state of the development and application of quality of life (QOL) instruments as outcome measures in cancer clinical research and practice. As a result of the achievements of the past two decades, the concept of QOL has been defined and many reliable and valid measurement tools have been developed. The two main approaches to QOL assessment, psychometric-based and utility-based, are discussed together with a brief description of the strategies for meaningful interpretation of QOL profiles. QOL measures in oncology have the potential to be used to study populations in randomised clinical trials, to aid patient-clinician interactions in routine practice and to support policy decision making and economic evaluation of healthcare provision.

Phase III Open-Label Randomized Study of Eribulin Mesylate Versus Capecitabine in Patients With Locally Advanced or Metastatic Breast Cancer Previously Treated With an Anthracycline and a Taxane

Purpose This phase III randomized trial (ClinicalTrials.gov identifier: NCT00337103) compared eribulin with capecitabine in patients with locally advanced or metastatic breast cancer (MBC). Patients and Methods Women with MBC who had received prior anthracycline- and taxane-based therapy were randomly assigned to receive eribulin or capecitabine as their first-, second-, or third-line chemotherapy for advanced/metastatic disease. Stratification factors were human epidermal growth factor receptor-2 (HER2) status and geographic region. Coprimary end points were overall survival (OS) and progression-free survival (PFS). Results Median OS times for eribulin (n = 554) and capecitabine (n = 548) were 15.9 and 14.5 months, respectively (hazard ratio [HR], 0.88; 95% CI, 0.77 to 1.00; P = .056). Median PFS times for eribulin and capecitabine were 4.1 and 4.2 months, respectively (HR, 1.08; 95% CI, 0.93 to 1.25; P = .30). Objective response rates were 11.0% for eribulin and 11.5% for capecitabine. Global health status and overall quality-of-life scores over time were similar in the treatment arms. Both treatments had manageable safety profiles consistent with their known adverse effects; most adverse events were grade 1 or 2. Conclusion In this phase III study, eribulin was not shown to be superior to capecitabine with regard to OS or PFS.

Self-Reported Quality of Life of Individual Cancer Patients: Concordance of Results With Disease Course and Medical Records

PURPOSE To investigate the applicability of a standard quality of life (QL) questionnaire to individual cancer patients and to explore the potential for impact of QL information on the process of care by comparing at group level the QL results with the medical records. PATIENTS AND METHODS One hundred fourteen consecutive patients at the oncology clinics at St Jamess Hospital, Leeds, United Kingdom, completed the European Organization for the Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ)-C30 on a touch-screen computer over a 6-month period. The QL results were compared with the corresponding medical records at individual and group level. RESULTS For individual patients, the serial measurement of QL allowed recognition of patterns over time corresponding to disease course. At group level, a higher proportion of patients reported problems on EORTC QLQ-C30 than were mentioned in the medical records (McNemar paired test, P <.01). Most often clinicians mentioned pain (22% to 39%), and at the initial visit role (66%), and social issues (77%). For the rest of the symptoms and functions, the problems were recorded in between 1% and 25% of the notes, but 20% to 76% of the patients reported QL impairment. Problems that were not recorded in the medical notes tended to be of low severity, with a significant trend observed for pain, fatigue, nausea/vomiting, dyspnea, loss of appetite, and physical function scale (chi(2) test, 11.55 to 34.42, df = 1, P <.001). CONCLUSION The QL data on individual patients was consistent with the clinical records, thus providing evidence for the validity of these measures in assessment of the individual. The QL profiles had more information on symptoms and particularly on functional issues, such as emotional distress and physical performance.

Factor analysis of the Hospital Anxiety and Depression Scale from a large cancer population

The Hospital Anxiety and Depression Scale (HADS) is widely used as a tool for assessing psychological distress in patients and non-clinical groups. Previous studies have demonstrated conflicting results regarding the factor structure of the questionnaire for different groups of patients, and the general population. This study investigated the factor structure of the HADS in a large heterogeneous cancer population of 1474 patients. It also sought to investigate emerging evidence that the HADS conforms to the tripartite model of anxiety and depression (Clark & Watson, 1993), and to test the proposal that detection rates for clinical cases of anxiety and depression could be enhanced by partialling out the effects of higher order factors from the HADS (Dunbar et al., 2000). The results demonstrated a two-factor structure corresponding to the Anxiety and Depression subscales of the questionnaire. The factor structure remained stable for different subgroups of the sample, for males and females, as well as for different age groups, and a subgroup of metastatic cancer patients. The two factors were highly correlated (r =.52) and subsequent secondary factor analyses demonstrated a single higher order factor corresponding to psychological distress or negative affectivity. We concluded that the HADS comprises two factors corresponding to anhedonia and autonomic anxiety, which share a common variance with a primary factor namely psychological distress, and that the subscales of the HADS, rather than the residual scores (e.g. Dunbar et al., 2000) were more effective at detecting clinical cases of anxiety and depression.

Feasibility and Compliance of Automated Measurement of Quality of Life in Oncology Practice

PURPOSE Systematic quality-of-life (QOL) assessment may have value in oncology practice by increasing awareness of a wide range of issues, possibly increasing detection of psychologic morbidity, social problems, and changes in physical status, and improving care and its outcomes. However, logistic problems are substantial. Automated systems solve many of these problems. We field-tested the feasibility and compliance that can be achieved using a computer touchscreen system in two consecutive studies. PATIENTS AND METHODS In study 1, a prospective cohort of 272 patients was offered QOL assessment at each clinic appointment for 6 months. In study 2, all patients (N = 1,291) were offered QOL assessment as part of clinic routine during a 12-week period. RESULTS In study 1, 82% of patients agreed to take part, but over time, compliance was poor (median, 40%; mean, 43%) and deteriorated with longer follow-up. In study 2, the overall compliance was greatly increased (median, 100%; mean, 70%), and compliance was retained over multiple visits. In study 1, compliance was better in younger patients, males, and socially advantaged patients, but was not affected by the presence of depression or anxiety, or QOL. In the second study, building on experience in the first study, data collection and storage in the computer system was excellent, achieving 98% of collected data stored in one center. In general, patients were comfortable with the computers and the approach. Data collection on the wards was more difficult and less complete than in clinics, especially for patients undergoing acute admissions. CONCLUSION Feasibility with higher compliance was demonstrated in study 2, in which the data collection was integrated into routine care, and can be improved with further technical initiatives and education of staff.

Deriving a Preference-Based Measure for Cancer Using the EORTC QLQ-C30

OBJECTIVE The European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire (EORTC QLQ-C30) is one of the most commonly used measures in cancer care but in its current form cannot be used in economic evaluation because it does not incorporate preferences. We address this gap by estimating a preference-based measure for cancer from the EORTC QLQ-C30. METHODS Factor analysis, Rasch analysis, and other psychometric analyses were undertaken on a clinical trial dataset of 655 patients with multiple myeloma to derive a health state classification system amenable to valuation. Second a valuation study was conducted of 350 members of the UK general population using time trade-off. Mean and individual-level multivariate regression models were fitted to derive preference weights for the classification system. RESULTS The health state classification system has eight dimensions (physical functioning, role functioning, social functioning, emotional functioning, pain, fatigue and sleep disturbance, nausea, constipation, and diarrhea) with four or five levels each. Regression models have few inconsistencies (0 to 2) in estimated preference weights and small mean absolute error ranges (0.046 to 0.054). CONCLUSIONS It is feasible to derive a preference-based measure from the EORTC QLQ-C30 for use in economic evaluation. Future research will extend this to other countries and replicate across other patient groups.