The importance of using ordinal scores for patient classification based on health-related quality of life trajectories.

Changes in health-related quality of life (HRQoL) over time are not necessarily homogeneous within a population of interest. Our study aim was twofold: to determine homogeneous patient subpopulations distinguished by HRQoL trajectories, and to identify the particular patient profile associated with each subpopulation. To classify patients according to HRQoL dimension scores, we compared mixtures of linear mixed models (LMMs) classically applied to scores defined by the EORTC procedure, and mixtures of random effect cumulative models (CMs) applied to scores treated as ordinal variables. A simulation study showed that the mixture of LMMs overestimated the number of subpopulations and was less able to correctly classify patients than the mixture of CMs. Considering HRQoL scores as ordinal rather than continuous variables is relevant when classifying patients. The mixture of CMs for ordinal scores is able to identify homogeneous subpopulations and their associated trajectories. The application focused on changes over time in HRQoL data (collected using the EORTC QLQ-C30 questionnaire) from 132 breast cancer patients from the Moral study. Once the classification is obtained only from HRQoL scores, class membership was then explained through a logistic regression model, given a large panel of variables collected at baseline. Analysis of data revealed that deterioration over time of role functioning and insomnia was closely related to patient anxiety: anxiety at baseline is a prognostic factor for a poor level and/or a deterioration over time of HRQoL. For functional dimensions, large tumor size and high education level were associated with worse HRQoL scores.

Impact of Model Choice When Studying the Relationship Between Blood Pressure Variability and Risk of Stroke Recurrence.

[Figure: see text].

Longitudinal analysis of health-related quality of life in cancer clinical trials: methods and interpretation of results.

PURPOSE: Health-related quality of life (HRQoL) is assessed by self-administered questionnaires throughout the care process. Classically, two longitudinal statistical approaches were mainly used to study HRQoL: linear mixed models (LMM) or time-to-event models for time to deterioration/time until definitive deterioration (TTD/TUDD). Recently, an alternative strategy based on generalized linear mixed models for categorical data has also been proposed: the longitudinal partial credit model (LPCM). The objective of this article is to evaluate these methods and to propose recommendations to standardize longitudinal analysis of HRQoL data in cancer clinical trials. METHODS: The three methods are first described and compared through statistical, methodological, and practical arguments, then applied on real HRQoL data from clinical cancer trials or published prospective databases. In total, seven French studies from a collaborating group were selected with longitudinal collection of QLQ-C30. Longitudinal analyses were performed with the three approaches using SAS, Stata and R software. RESULTS: We observed concordant results between LMM and LPCM. However, discordant results were observed when we considered the TTD/TUDD approach compared to the two previous methods. According to methodological and practical arguments discussed, the approaches seem to provide additional information and complementary interpretations. LMM and LPCM are the most powerful methods on simulated data, while the TTD/TUDD approach gives more clinically understandable results. Finally, for single-item scales, LPCM is more appropriate. CONCLUSION: These results pledge for the recommendation to use of both the LMM and TTD/TUDD longitudinal methods, except for single-item scales, establishing them as the consensual methods for publications reporting HRQoL.

Joint longitudinal and time-to-event cure models for the assessment of being cured.

Medical time-to-event studies frequently include two groups of patients: those who will not experience the event of interest and are said to be "cured" and those who will develop the event and are said to be "susceptible". However, the cure status is unobserved in (right-)censored patients. While most of the work on cure models focuses on the time-to-event for the uncured patients (latency) or on the baseline probability of being cured or not (incidence), we focus in this research on the conditional probability of being cured after a medical intervention given survival until a certain time. Assuming the availability of longitudinal measurements collected over time and being informative on the risk to develop the event, we consider joint models for longitudinal and survival data given a cure fraction. These models include a linear mixed model to fit the trajectory of longitudinal measurements and a mixture cure model. In simulation studies, different shared latent structures linking both submodels are compared in order to assess their predictive performance. Finally, an illustration on HIV patient data completes the comparison.

Treatment of class III multiple gingival recessions: Prognostic factors for achieving a complete root coverage.

BACKGROUND: This report is intended to present a supplemental analysis of data from a prior report (Aroca et al., ) to investigate factors associated with a complete root coverage at 1 year. The purpose of the prior report was to investigate at 1 year the adjunction effect of EMD for the treatment of Miller's class III recession defects using a coronally advanced modified tunnel/CTG technique with (test group) or without (control group). The purpose of this report was to investigate additional factors associated with root coverage in the same data set. MATERIALS AND METHODS: On the 138 observations collected from 20 patients, a regression model was used to highlight the relationship between the percentages of root coverage (RC) and three following covariates: the distance from the tip of the papilla and the contact point (DCP) at baseline, the group membership (control vs. test) and tooth position in the mouth (maxillary vs. mandibular). RESULTS: The statistical analysis showed that there was a significant effect of the DCP at baseline (p = 0.01) and of the tooth type (p < .001) on the percentage of RC at 1 year, whereas no significant difference between the two techniques (group membership effect) was shown (p = 0.69). CONCLUSION: The probability to obtain a complete root coverage decreases when the DCP at baseline increases. Moreover, maxillary teeth are more likely to give better RC than mandibular teeth. However, in this analysis similar to the last, there was no group effect.

Longitudinal health-related quality of life analysis in oncology with time to event approaches, the STATA command qlqc30_TTD.

BACKGROUND AND OBJECTIVE: Health-related quality of life (HRQoL) has become one relevant and available alternative endpoint of clinical trials in cancer research to evaluate efficiency of care both for the patient and health system. HRQoL in oncology is mainly assessed using the 30-item European Organisation for Research and Treatment of Cancer Quality of Life-Questionnaire Core 30 (EORTC QLQ-C30). The EORTC QLQ-C30 questionnaire is usually assessed at different times along the clinical trials in order to analyze the kinetics of HRQoL evolution and to fully assess the impact of the treatment on the patient's HRQoL level. In this perspective, the realization of a longitudinal HRQoL analysis is essential and the time to HRQoL score deterioration approach is a method which is more and more used in clinical trials. METHOD: Using the Stata software, we developed a QLQ-C30 specific command, qlqc30_TTD, which implements longitudinal strategies based on the time to event methods by considering the time to HRQoL score deterioration. This user-written command providing automatic execution of the Time To Deterioration (TTD) and Time Until Definitive Deterioration (TUDD) methods. RESULT: The program implements all published definitions and provides the Kaplan-Meier curves for each dimension (by group) and a table with the Hazard Ratio and Log-Rank test. CONCLUSION: The longitudinal analysis of HRQoL data in cancer clinical trials remains complex with only few programs like ours computed. This program will be of great help and will allow a more systematic and quicker analysis of the HRQoL data in clinical trials in oncology.

EM algorithm estimation of a structural equation model for the longitudinal study of the quality of life.

Health-related quality of life (HRQoL) data are measured via patient questionnaires, completed by the patients themselves at different time points. We focused on oncology data gathered through the use of European Organization for Research and Treatment of Cancer questionnaires, which decompose HRQoL into several functional dimensions, several symptomatic dimensions, and the global health status (GHS). We aimed to perform a global analysis of HRQoL and reduce the number of analyses required by using a two-step approach. First, a structural equation model (SEM) was used for each time point; in these models, the GHS is explained by two latent variables. Each latent variable is a factor that summarizes, respectively, the functional dimensions and the symptomatic dimensions to the global measurement. This is achieved through the maximization of the likelihood of each SEM using the EM algorithm, which has the advantage of giving an estimation of the subject-specific factors and the influence of additional explanatory variables. Then, to consider the longitudinal aspect, the GHS variable and the two factors were concatenated for each patient visit at which the questionnaire was completed. The GHS and the two factors estimated in the first step can then be explained by additional explanatory variables using a linear mixed model.

Item response models for the longitudinal analysis of health-related quality of life in cancer clinical trials.

BACKGROUND: The use of health-related quality of life (HRQoL) as an endpoint in cancer clinical trials is growing rapidly. Hence, research into the statistical approaches used to analyze HRQoL data is of major importance, and could lead to a better understanding of the impact of treatments on the everyday life and care of patients. Amongst the models that are used for the longitudinal analysis of HRQoL, we focused on the mixed models from item response theory, to directly analyze raw data from questionnaires. METHODS: We reviewed the different item response models for ordinal responses, using a recent classification of generalized linear models for categorical data. Based on methodological and practical arguments, we then proposed a conceptual selection of these models for the longitudinal analysis of HRQoL in cancer clinical trials. RESULTS: To complete comparison studies already present in the literature, we performed a simulation study based on random part of the mixed models, so to compare the linear mixed model classically used to the selected item response models. As expected, the sensitivity of the item response models to detect random effects with lower variance is better than that of the linear mixed model. We then used a cumulative item response model to perform a longitudinal analysis of HRQoL data from a cancer clinical trial. CONCLUSIONS: Adjacent and cumulative item response models seem particularly suitable for HRQoL analysis. In the specific context of cancer clinical trials and the comparison between two groups of HRQoL data over time, the cumulative model seems to be the most suitable, given that it is able to generate a more complete set of results and gives an intuitive illustration of the data.

Applying the Longitudinal Model from Item Response Theory to Assess Health-Related Quality of Life in the PRODIGE 4/ACCORD 11 Randomized Trial.

INTRODUCTION: A new longitudinal statistical approach was compared to the classical methods currently used to analyze health-related quality-of-life (HRQoL) data. The comparison was made using data in patients with metastatic pancreatic cancer. METHODS: Three hundred forty-two patients from the PRODIGE4/ACCORD 11 study were randomly assigned to FOLFIRINOX versus gemcitabine regimens. HRQoL was evaluated using the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30. The classical analysis uses a linear mixed model (LMM), considering an HRQoL score as a good representation of the true value of the HRQoL, following EORTC recommendations. In contrast, built on the item response theory (IRT), our approach considered HRQoL as a latent variable directly estimated from the raw data. For polytomous items, we extended the partial credit model to a longitudinal analysis (longitudinal partial credit model [LPCM]), thereby modeling the latent trait as a function of time and other covariates. RESULTS: Both models gave the same conclusions on 11 of 15 HRQoL dimensions. HRQoL evolution was similar between the 2 treatment arms, except for the symptoms of pain. Indeed, regarding the LPCM, pain perception was significantly less important in the FOLFIRINOX arm than in the gemcitabine arm. For most of the scales, HRQoL changes over time, and no difference was found between treatments in terms of HRQoL. DISCUSSION: The use of LMM to study the HRQoL score does not seem appropriate. It is an easy-to-use model, but the basic statistical assumptions do not check. Our IRT model may be more complex but shows the same qualities and gives similar results. It has the additional advantage of being more precise and suitable because of its direct use of raw data.

Comparison of three longitudinal analysis models for the health-related quality of life in oncology: a simulation study.

BACKGROUND: Health-Related Quality of Life (HRQoL) is an important endpoint in oncology clinical trials aiming to investigate the clinical benefit of new therapeutic strategies for the patient. However, the longitudinal analysis of HRQoL remains complex and unstandardized. There is clearly a need to propose accessible statistical methods and meaningful results for clinicians. The objective of this study was to compare three strategies for longitudinal analyses of HRQoL data in oncology clinical trials through a simulation study. METHODS: The methods proposed were: the score and mixed model (SM); a survival analysis approach based on the time to HRQoL score deterioration (TTD); and the longitudinal partial credit model (LPCM). Simulations compared the methods in terms of type I error and statistical power of the test of an interaction effect between treatment arm and time. Several simulation scenarios were explored based on the EORTC HRQoL questionnaires and varying the number of patients (100, 200 or 300), items (1, 2 or 4) and response categories per item (4 or 7). Five or 10 measurement times were considered, with correlations ranging from low to high between each measure. The impact of informative missing data on these methods was also studied to reflect the reality of most clinical trials. RESULTS: With complete data, the type I error rate was close to the expected value (5%) for all methods, while the SM method was the most powerful method, followed by LPCM. The power of TTD is low for single-item dimensions, because only four possible values exist for the score. When the number of items increases, the power of the SM approach remained stable, those of the TTD method increases while the power of LPCM remained stable. With 10 measurement times, the LPCM was less efficient. With informative missing data, the statistical power of SM and TTD tended to decrease, while that of LPCM tended to increase. CONCLUSIONS: To conclude, the SM model was the most powerful model, irrespective of the scenario considered, and the presence or not of missing data. The TTD method should be avoided for single-item dimensions of the EORTC questionnaire. While the LPCM model was more adapted to this kind of data, it was less efficient than the SM model. These results warrant validation through comparisons on real data.