Health sciences frequently deal with Patient Reported Outcomes (PRO) data for the evaluation of concepts, in particular health-related quality of life, which cannot be directly measured and are often called latent variables. Two approaches are commonly used for the analysis of such data: Classical Test Theory (CTT) and Item Response Theory (IRT). Longitudinal data are often collected to analyze the evolution of an outcome over time. The most adequate strategy to analyze longitudinal latent variables, which can be either based on CTT or IRT models, remains to be identified. This strategy must take into account the latent characteristic of what PROs are intended to measure as well as the specificity of longitudinal designs. A simple and widely used IRT model is the Rasch model. The purpose of our study was to compare CTT and Rasch-based approaches to analyze longitudinal PRO data regarding type I error, power, and time effect estimation bias. Four methods were compared: the Score and Mixed models (SM) method based on the CTT approach, the Rasch and Mixed models (RM), the Plausible Values (PV), and the Longitudinal Rasch model (LRM) methods all based on the Rasch model. All methods have shown comparable results in terms of type I error, all close to 5 per cent. LRM and SM methods presented comparable power and unbiased time effect estimations, whereas RM and PV methods showed low power and biased time effect estimations. This suggests that RM and PV methods should be avoided to analyze longitudinal latent variables.

BackgroundLiving with a chronic disease often means experiencing chronic treatments and regular multidisciplinary monitoring as well as a profound life-changing experience which may impact all aspects of a patients life. The patient experience of chronic disease is frequently assessed by patient reported measures (PRMs) which incorporate patients perspectives to better understand how illness, treatment and care impact the entirety of a patient’s life. The purpose of this review was to collect and review different kinds of available PRM instruments validated for chronic patients, to produce an inventory of explored concepts in these questionnaires and to identify and classify all dimensions assessing chronic patients experience.MethodsA systematic review of PRM instruments validated for chronic patients was conducted from three databases (Medline, the Cochrane library, and Psycinfo). Articles were selected after a double reading and questionnaires were classified according to their targeted concept. Then, all dimensions of the questionnaires were clustered into different categories.Results107 primary validation studies of PRM questionnaires were selected. Five kinds of instruments were recorded: 1) Questionnaires assessing health related quality of life or quality of life; 2) Instruments focusing on symptoms and functional status; 3) Instruments exploring patients’ feelings and attitude about illness; 4) Questionnaires related to patients’ experience of treatment or healthcare; 5) Instruments assessing patients attitudes about treatment or healthcare. Twelve categories of dimensions were obtained from these instruments.ConclusionsThis review provided an overview of some of the dimensions used to explore chronic patient experience. A large PRM diversity exists and none of the reviewed and selected questionnaires covered all identified categories of dimensions of patient experience of chronic disease. Furthermore, the definition of explored concepts varies widely among researchers and complex concepts often lack a clear definition in the reviewed articles. Before attempting to measure chronic patient experience, researchers should construct appropriate instruments focusing on well-defined concepts and dimensions encompassing patient’s personal experience, attitude and adaptation to illness, treatment or healthcare.Electronic supplementary materialThe online version of this article (10.1186/s12955-019-1084-2) contains supplementary material, which is available to authorized users.

Fatigue is the most prevalent symptom in breast cancer. It might be perceived differently among patients over time as a consequence of the differing patients’ adaptation and psychological adjustment to their cancer experience which can be related to response shift (RS). RS analyses can provide important insights on patients’ adaptation to cancer but it is usually assumed that RS occurs in the same way in all individuals which is unrealistic. This study aimed to identify patients’ subgroups in which different RS effects on self‐reported fatigue could occur over time using a combination of methods for manifest and latent variables. The FATSEIN study comprised 466 breast cancer patients followed over a 2‐year period. Fatigue was measured with the Multidimensional Fatigue Inventory questionnaire (MFI‐20) during 10 visits. A novel combination of Mixed Models, Growth Mixture Modeling, and Structural Equation Modeling was used to assess the occurrence of RS in fatigue changes to identify subgroups displaying different RS patterns over time. An increase in fatigue was evidenced over the 8‐month follow‐up, followed by a decrease between the 8‐ and 24‐month. Four latent classes of patients were identified. Different RS patterns were detected in all latent classes between the inclusion and 8 months (last cycle of chemotherapy). No RS was evidenced between 8‐ and 24‐month. Several RS effects were evidenced in different groups of patients. Women seemed to adapt differently to their treatment and breast cancer experience possibly indicating differing needs for medical/psychological support.

An algorithm has been developed for response shift analyses using IRT models and allows the investigation of non-uniform and uniform recalibration as well as reprioritization. Differences in RS detection between IRT and SEM may be due to differences between the two methods in handling missing data. However, one cannot conclude on the differences between IRT and SEM based on a single application on a dataset since the underlying truth is unknown. A next step would be to implement a simulation study to investigate those differences.

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This study reinforces the recommendations concerning the use of an MCID-Sc defined as a function of a range of baseline scores.

Patient-reported outcomes (PRO) have gained importance in clinical and epidemiological research and aim at assessing quality of life, anxiety or fatigue for instance. Item Response Theory (IRT) models are increasingly used to validate and analyse PRO. Such models relate observed variables to a latent variable (unobservable variable) which is commonly assumed to be normally distributed. A priori sample size determination is important to obtain adequately powered studies to determine clinically important changes in PRO. In previous developments, the Raschpower method has been proposed for the determination of the power of the test of group effect for the comparison of PRO in cross-sectional studies with an IRT model, the Rasch model. The objective of this work was to evaluate the robustness of this method (which assumes a normal distribution for the latent variable) to violations of distributional assumption. The statistical power of the test of group effect was estimated by the empirical rejection rate in data sets simulated using a non-normally distributed latent variable. It was compared to the power obtained with the Raschpower method. In both cases, the data were analyzed using a latent regression Rasch model including a binary covariate for group effect. For all situations, both methods gave comparable results whatever the deviations from the model assumptions. Given the results, the Raschpower method seems to be robust to the non-normality of the latent trait for determining the power of the test of group effect.

The results of the present study suggest that support in hospital units should be specific and depend on the cancer type.