Application of Brownian motion theory to the analysis of membrane channel ionic trajectories calculated by molecular dynamics

This paper shows how Brownian motion theory can be used to analyze features of individual ion trajectories in channels as calculated by molecular dynamics, and that its use permits more precise determinations of diffusion coefficients than would otherwise be possible. We also show how a consideration of trajectories of single particles can distinguish between effects due to the magnitude of the diffusion coefficient and effects due to barriers and wells in the potential profile, effects which can not be distinguished by consideration of average fluxes

Risk factors of unmet needs among women with breast cancer in the post-treatment phase

OBJECTIVE: Unmet health care needs require additional care resources to achieve optimal patient well-being. In this nationwide study we examined associations between a number of risk factors and unmet needs after treatment among women with breast cancer, while taking into account their health care practices. We expected that more care use would be associated with lower levels of unmet needs. METHODS: A multicenter, prospective, observational design was employed. Women with primary breast cancer completed questionnaires 6 and 15 months post-diagnosis. Medical data were retrieved from medical records. Direct and indirect associations between sociodemographic and clinical risk factors, distress, care use, and unmet needs were investigated with structural equation modeling. RESULTS: Seven hundred forty-six participants completed both questionnaires (response rate 73.7%). The care services received were not negatively associated with the reported levels of unmet needs after treatment. Comorbidity was associated with higher physical and daily living needs. Higher age was associated with higher health system-related and informational needs. Having had chemotherapy and a mastectomy were associated with higher sexuality needs and breast cancer-specific issues, respectively. A higher level of distress was associated with higher levels of unmet need in all domains. CONCLUSIONS: Clinicians may use these results to timely identify which women are at risk of developing specific unmet needs after treatment. Evidence-based, cost-effective (online) interventions that target distress, the most influential risk factor, should be further implemented and disseminated among patients and clinicians

Risk factors of unmet needs among women with breast cancer in the post-treatment phase

Objective: Unmet health care needs require additional care resources to achieve optimal patient well-being. In this nationwide study we examined associations between a number of risk factors and unmet needs after treatment among women with breast cancer, while taking into account their health care practices. We expected that more care use would be associated with lower levels of unmet needs. Methods: A multicenter, prospective, observational design was employed. Women with primary breast cancer completed questionnaires 6 and 15 months post-diagnosis. Medical data were retrieved from medical records. Direct and indirect associations between sociodemographic and clinical risk factors, distress, care use, and unmet needs were investigated with structural equation modeling. Results: Seven hundred forty-six participants completed both questionnaires (response rate 73.7%). The care services received were not negatively associated with the reported levels of unmet needs after treatment. Comorbidity was associated with higher physical and daily living needs. Higher age was associated with higher health system-related and informational needs. Having had chemotherapy and a mastectomy were associated with higher sexuality needs and breast cancer-specific issues, respectively. A higher level of distress was associated with higher levels of unmet need in all domains. Conclusions: Clinicians may use these results to timely identify which women are at risk of developing specific unmet needs after treatment. Evidence-based, cost-effective (online) interventions that target distress, the most influential risk factor, should be further implemented and disseminated among patients and clinicians

Using structural equation modeling to detect response shifts and true change in discrete variables: an application to the items of the SF-36

The structural equation modeling (SEM) approach for detection of response shift (Oort in Qual Life Res 14:587-598, 2005. doi: 10.1007/s11136-004-0830-y ) is especially suited for continuous data, e.g., questionnaire scales. The present objective is to explain how the SEM approach can be applied to discrete data and to illustrate response shift detection in items measuring health-related quality of life (HRQL) of cancer patients. The SEM approach for discrete data includes two stages: (1) establishing a model of underlying continuous variables that represent the observed discrete variables, (2) using these underlying continuous variables to establish a common factor model for the detection of response shift and to assess true change. The proposed SEM approach was illustrated with data of 485 cancer patients whose HRQL was measured with the SF-36, before and after start of antineoplastic treatment. Response shift effects were detected in items of the subscales mental health, physical functioning, role limitations due to physical health, and bodily pain. Recalibration response shifts indicated that patients experienced relatively fewer limitations with "bathing or dressing yourself" (effect size d = 0.51) and less "nervousness" (d = 0.30), but more "pain" (d = -0.23) and less "happiness" (d = -0.16) after antineoplastic treatment as compared to the other symptoms of the same subscale. Overall, patients' mental health improved, while their physical health, vitality, and social functioning deteriorated. No change was found for the other subscales of the SF-36. The proposed SEM approach to discrete data enables response shift detection at the item level. This will lead to a better understanding of the response shift phenomena at the item level and therefore enhances interpretation of change in the area of HRQ

Psychometric evaluation of the PROMIS® Depression Item Bank: an illustration of classical test theory methods

Background: Psychometric theory offers a range of tests that can be used as supportive evidence of both validity and reliability of instruments aimed at measuring patient-reported outcomes (PRO). The aim of this paper is to illustrate psychometric tests within the Classical Test Theory (CTT) framework, comprising indices that are frequently applied to assess item- and scale-level psychometric properties of PRO instruments. Methods: Using data on the PROMIS Depression Item Bank, typical CTT indices for the assessment of psychometric properties are illustrated, including content validity, item-level data exploration, reliability, and construct validity, particularly confirmatory factor analysis, to test the unidimensionality assumption underlying the item bank. Analyses are carried out on an original item set of 51 depression items, the final (official) PROMIS Depression Item Bank consisting of 28 items, and an 8-item short form. Results: The analyses reported provide an informative illustration on how item- and scale-level reliability and validity statistics can be used to assess the psychometric quality of a PRO instrument. The results illustrate how the reported statistics can be used for item selection from an item pool (here: 51 items). Both the (final) 28-item bank and the 8-item short form show good psychometric properties supporting the high quality of individual items and the unidimensionality assumption of the item bank. Conclusions: It is our hope that our illustration of CTT methods, in conjunction with two companion papers illustrating modern test theory methods, will help researchers to confidently apply a range of statistical tests to evaluate item- and scale-level psychometric performance of PRO instruments

Structural equation modeling-based effect-size indices were used to evaluate and interpret the impact of response shift effects

The investigation of response shift in patient-reported outcomes (PROs) is important in both clinical practice and research. Insight into the presence and strength of response shift effects is necessary for a valid interpretation of change. When response shift is investigated through structural equation modeling (SEM), observed change can be decomposed into change because of recalibration response shift, change because of reprioritization and/or reconceptualization response shift, and change because of change in the construct of interest. Subsequently, calculating effect-size indices of change enables evaluation and interpretation of the clinical significance of these different types of change. Change was investigated in health-related quality of life data from 170 cancer patients, assessed before surgery and 3 months after surgery. Results indicated that patients deteriorated on general physical health and general fitness and improved on general mental health. The decomposition of change showed that the impact of response shift on the assessment of change was small. SEM can be used to enable the evaluation and interpretation of the impact of response shift effects on the assessment of change, particularly through calculation of effect-size indices. Insight into the occurrence and clinical significance of possible response shift effects will help to better understand changes in PRO