Minimal important differences for interpreting health-related quality of life scores from the EORTC QLQ-C30 in lung cancer patients participating in randomized controlled trials.

BACKGROUND: The aim of this study was to determine the smallest changes in health-related quality of life (HRQOL) scores in a subset of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire core 30 (EORTC QLQ-C30) scales, which could be considered as clinically meaningful in patients with non-small-cell lung cancer (NSCLC). METHODS: WHO performance status (PS) and weight change were used as clinical anchors to determine minimal important differences (MIDs) in HRQOL change scores (range, 0-100) in the EORTC QLQ-C30 scales. Selected distribution-based methods were used for comparison. FINDINGS: In a pooled dataset of 812 NSCLC patients undergoing treatment, the values determined to represent the MID depended on whether patients were improving or deteriorating. MID estimates for improvement (based on a one-category change in PS, 5 - <20% weight gain) were physical functioning (9, 5); role functioning (14, 7); social functioning (5, 7); global health status (9, 4); fatigue (14, 5); and pain (16, 2). The respective MID estimates for deterioration (based on PS, weight loss) were physical (4, 6); role (5, 5); social (7, 9); global health status (4, 4); fatigue (6, 11); and pain (3, 7). INTERPRETATION: Based on the selected QLQ-C30 scales, the MID may depend upon whether the patients' PS is improving or worsening, but our results are not definitive. The MID estimates for the specified scales can help clinicians and researchers evaluate the significance of changes in HRQOL and assess the value of a health care intervention or compare treatments. The estimates also can be useful in determining sample sizes in the design of future clinical trials.

Investigating association between behavior, corticosterone, heart rate, and blood pressure in rats using surrogate marker evaluation methodology.

The drug development process involves identifying a compound and assessing its merit through rigorous pre-clinical and clinical trials. The pre-clinical stage is designed to assess the chemical properties of the new drug, as well as to determine the steps for synthesis and purification. In this stage of drug development, circumstances might dictate the use of alternative endpoints than the originally anticipated clinically relevant endpoint. In this regard, identification and evaluation of surrogate endpoints is of paramount importance. The validation methods make it possible to quantify degrees of association between the clinically relevant endpoint, also termed the true endpoint, and the alternative, surrogate endpoint. In this paper, we adapt the surrogate marker evaluation methodology of Alonso et al. (2003); (2006), developed for the case of two longitudinal outcomes, to the situation where either a longitudinal surrogate and cross sectional true endpoint is recorded, or vice versa. The work is motivated by a preclinical experiment conducted to assess association between corticosterone (CORT), heart rate, and blood pressure in rats, the data from which are then subjected to analysis. It was found that there is a weak relationship between CORT and behavior, and between CORT on the one hand and heart rate and blood pressure on the other hand, but a reasonably high degree of association was registered between heart rate and behavior.

Application of penalized splines in analyzing neuronal data.

Neuron experiments produce high-dimensional data structures. Therefore, application of smoothing techniques in the analysis of neuronal data from electrophysiological experiments has received considerable attention of late. We investigate the use of penalized splines in the analysis of neuronal data. This is first illustrated when interested in the temporal trend of a single neuron. An approach to investigate the maximal firing rate, based on the penalizedspline model is proposed. Determination of the time of maximal firing rate is based on non-linear optimization of the objective function with the corresponding confidence intervals constructed based on the first-order derivative function. To distinguish between the curves from different experimental conditions in a moment-by-moment sense, bias adjusted simulation-based simultaneous confidence bands leading to global inference in the time domain are constructed. The bands are an extension of the approach proposed by Ruppert et al. (2003). These methods are in a second step extended towards the analysis of a population of neurons via a marginal or population-averaged model.

Application of semiparametric mixed models and simultaneous confidence bands in a cardiovascular safety experiment with longitudinal data.

Several pharmacological studies involve experiments aimed at testing for a difference between experimental groups wherein the data are longitudinal in nature, frequently with long sequences per subject. Oftentimes, treatment effect, if present, is not constant over time. In such situations, imposing a parametric mean structure can be too complicated and/or restrictive. A more flexible approach is to model the mean using a semiparametric smooth function, estimated using, for example, penalized smoothing splines. We formulate a series of models exhibiting how the group-specific mean profiles could possibly differ. Once an appropriate model is chosen, interest lies in identifying specific time points where the groups differ. For this purpose, we propose the use of simultaneous confidence bands around the fitted models wherein the bands take into account within and between-subject variability, as well as variability arising from smoothing.

Examining the relationships among health-related quality-of-life indicators in cancer patients participating in clinical trials: a pooled study of baseline EORTC QLQ-C30 data.

AIMS: Cancer patients experience multiple and concurrent health-related problems and symptoms due to their illness and therapies. The first objective of this analysis was to identify how health-related quality-of-life (HRQoL) indicators cluster among cancer patients and how possible clusters change across patients with different sociodemographic and clinical characteristics. The second objective of this study was to identify which HRQoL indicators are linked to patients' perception of overall quality of life. METHODS: Retrospective pooling of 30 closed randomized European Organisation for Research and Treatment of Cancer (EORTC) clinical trials yielded baseline EORTC Quality of Life Core Questionnaire (QLQ-C30) HRQoL data for a total of 7417 patients. A cluster analysis was performed to determine how the 15 HRQoL indicators obtained with the QLQ-C30 cluster overall and by patient characteristics. RESULTS: Three main clusters emerged from the overall dataset: a physical cluster, a psychological cluster and a gastrointestinal cluster. The same clusters were found in subgroups defined according to sociodemographic and clinical characteristics, while some differences emerged among cancer sites. The Global Health scale was found to be part of the physical cluster in the overall dataset. This result was consistent across different levels of disease severity, while divergent results were seen across some cancer sites. CONCLUSION: Our findings suggest that HRQoL indicators are interrelated. Understanding these relationships may aid clinicians in managing the symptom burden experienced by patients, as well as policy-makers, in defining psychosocial support plans.

Analysis of cross-over designs with serial correlation within periods using semi-parametric mixed models.

The use of semi-parametric mixed models has proven useful in a wide variety of settings. Here, we focus on the application of the methodology in the particular case of a cross-over design with relatively long sequences of repeated measurements within each treatment period and for each subject. Other than an overall measure of the difference between each one of the experimental groups and the control group, specific time point comparisons may also be of interest. To that effect, we propose the use of flexible semi-parametric mixed models, enabling the construction of simulation-based simultaneous confidence bands. The bands take into account both between- and within-subject variabilities, while simultaneously correcting for multiple time point comparisons. Owing to the relatively long sequences of measurements per subject, the presence of serially correlated errors is anticipated and investigated. We illustrate how several formulations of semi-parametric mixed models can be fitted and the construction of simulation-based simultaneous confidence bands using SAS PROC MIXED.