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

Toward a more patient-centered drug development process in clinical trials for patients with myelodysplastic syndromes/neoplasms (MDS): Practical considerations from the International Consortium for MDS (icMDS)

Notable treatment advances have been made in recent years for patients with myelodysplastic syndromes/neoplasms (MDS), and several new drugs are under development. For example, the emerging availability of oral MDS therapies holds the promise of improving patients' health-related quality of life (HRQoL). Within this rapidly evolving landscape, the inclusion of HRQoL and other patient-reported outcomes (PROs) is critical to inform the benefit/risk assessment of new therapies or to assess whether patients live longer and better, for what will likely remain a largely incurable disease. We provide practical considerations to support investigators in generating high-quality PRO data in future MDS trials. We first describe several challenges that are to be thoughtfully considered when designing an MDS-focused clinical trial with a PRO endpoint. We then discuss aspects related to the design of the study, including PRO assessment strategies. We also discuss statistical approaches illustrating the potential value of time-to-event analyses and their implications within the estimand framework. Finally, based on a literature review of MDS randomized controlled trials with a PRO endpoint, we note the PRO items that deserve special attention when reporting future MDS trial results. We hope these practical considerations will facilitate the generation of rigorous PRO data that can robustly inform MDS patient care and support treatment decision-making for this patient population.

Improvement of postoperative quality of life in patients with esophageal squamous cell carcinoma: does tea consumption have a role?

Background

To investigate the effect of tea consumption on the improvement of postoperative quality of life in male patients with esophageal squamous cell carcinoma (ESCC).

Methods

The quality of life information of 290 male patients with ESCC was collected. The time to deterioration and the number of events in each area of quality of life was calculated by time-to-deterioration (TTD) model. The association between postoperative tea drinking and postoperative quality of life in male ESCC patients was investigated using the Cox proportional risk model.

Results

Postoperative tea-drinking patients experienced delayed TTD in multiple domains, including general health, physical, role, emotional, and cognitive function, fatigue, nausea and vomiting, dyspnea, loss of appetite, constipation, diarrhea, eating problems, difficulty swallowing, choking while swallowing saliva, dry mouth, taste difficulties, coughing, and speech problems. The multivariate Cox regression analysis showed that drinking tea after surgery improved quality of life, including physical function (HR = 0.722, 95% CI: 0.559-0.933), role function (HR = 0.740, 95% CI: 0.557-0.983), eating problems (HR = 0.718, 95% CI: 0.537-0.960), odynophagia (HR = 0.682, 95% CI: 0.492-0.945), trouble swallowing saliva (HR = 0.624, 95% CI: 0.444-0.877), coughing (HR = 0.627, 95% CI: 0.442-0.889) and speech problems (HR = 0.631, 95% CI: 0.441-0.903). Furthermore, the improvement was more significant in patients who drank tea before surgery and continued to drink tea after surgery.

Conclusions

Postoperative tea drinking had a positive effect on delay in clinical deterioration and improvements in multiple functions and symptoms associated with ESCC in men.

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.

Prediction of quality of life in early breast cancer upon completion of adjuvant chemotherapy

Quality of life (QoL) is a complex, ordinal endpoint with multiple conditioning factors. A predictive model of QoL after adjuvant chemotherapy can support decision making or the communication of information about the range of treatment options available. Patients with localized breast cancer (n = 219) were prospectively recruited at 17 centers. Participants completed the EORTC QLQ-C30 questionnaire. The primary aim was to predict health status upon completion of adjuvant chemotherapy adjusted for multiple covariates. We developed a Bayesian model with six covariates (chemotherapy regimen, TNM stage, axillary lymph node dissection, perceived risk of recurrence, age, type of surgery, and baseline EORTC scores). This model allows both prediction and causal inference. The patients with mastectomy reported a discrete decline on all QoL scores. The effect of surgery depended on the interaction with age. Women with ages on either end of the range displayed worse scores, especially with mastectomy. The perceived risk of recurrence had a striking effect on health status. In conclusion, we have developed a predictive model of health status in patients with early breast cancer based on the individual's profile.

Quality of patient-reported outcome reporting according to the CONSORT statement in randomized controlled trials with glioblastoma patients

Background

Randomized controlled trials (RCTs) represent the best evidence in oncology research. Glioblastoma is the most frequent and deadly primary brain tumor, affecting health-related quality of life. An important end point is patient-reported outcomes (PROs). There are no data regarding how well publications of glioblastoma RCTs report PROs. A specific PRO extension of the Consolidated Standards of Reporting Trials (CONSORT) statement was created to improve the quality of reporting. The aim of this study was to evaluate adherence to the CONSORT-PRO statement in reporting RCTs addressing the treatment of patients with glioblastoma. PRO analysis methodology was explored and criteria associated with higher quality of reporting were investigated.

Methods

From PubMed/MEDLINE and the Cochrane Library databases, all phase 2 and 3 RCTs related to glioblastoma published between 1995 and 2018 were reviewed according to the CONSORT-PRO statements. An overall quality score on a 0 to 100 scale was defined based on these criteria and factors associated with this score were identified.

Results

Forty-four RCTs were identified as relevant according to predefined criteria. The median overall quality score was 26. No difference was observed regarding reporting quality over the years. CONSORT-PRO items concerning data collection and analysis were poorly reported. Thirty-four trials (77%) used longitudinal data. The most frequent statistical method for PROs analysis was the mean change from baseline (63%). Factors associated with improved overall quality score were the presence of a secondary publication dedicated to PROs results, the statement of any targeted dimensions, and when trials reported results using multiple methods.

Conclusion

Despite the importance of measuring PROs in patients with glioblastoma, employment of the CONSORT-PRO statement is poor in RCTs.

Time to health-related quality of life improvement analysis was developed to enhance evaluation of modern anticancer therapies

OBJECTIVES

Major advances have recently been made in the treatments of cancer, which now also have the potential to improve patients' health-related quality of life (HRQOL). We propose the time to HRQOL improvement (TTI) and the time to sustained HRQOL improvement (TTSI) as potentially important cancer outcomes to be used in longitudinal HRQOL analyses.

STUDY DESIGN AND SETTING

As proof of principle, we defined TTI and TTSI, using the Fine-Gray model to include competing risks in estimates, in a case study in real life of a cohort of newly diagnosed patients with cancer receiving a targeted therapy. HRQOL was evaluated before and during therapy with six assessments over a 24-month period, using the well-validated European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30.

RESULTS

For each assessed HRQOL domain, we assessed TTI and TTSI and estimated the cumulative incidence of patients' clinically meaningful improvements, also accounting for the occurrence of competing events.

CONCLUSION

TTI and TTSI are potentially important outcomes in the era of modern anticancer therapies. The analysis of TTI and TTSI by competing risks approach will further add to the statistical methods that can be used to inform on the impact of cancer therapies on patients' HRQOL.

Severe gastrointestinal disease in very early systemic sclerosis is associated with early mortality

OBJECTIVES

To examine the incidence, predictors and outcomes associated with severe gastrointestinal (GI) disease in a large inception SSc cohort.

METHODS

SSc subjects with <2 years of disease duration were identified from two multicentre cohorts. Severe GI disease was defined as: malabsorption, hyperalimentation, pseudo-obstruction and/or ⩾10% weight loss in association with the use of antibiotics for bacterial overgrowth or oesophageal stricture. Kaplan-Meier, multivariate logistic regression and Cox proportional hazard analyses were performed to determine the cumulative incidence rate, independent clinical correlates and mortality rate associated with severe GI disease. A longitudinal mixed model was used to assess the impact of severe GI disease on the Short Form Health Survey.

RESULTS

In this inception SSc cohort, the probability of developing severe GI disease was estimated at 9.1% at 2 years and 16.0% at 4 years. In multivariate analysis, severe GI disease was associated with inflammatory myositis (odds ratio 4.68, 95% CI 1.65, 13.24), telangiectasias (odds ratio 2.45, 95% CI 1.19, 5.04) and modified Rodnan skin score (odds ratio 1.03, 95% CI 1.01, 1.07). Severe GI disease was associated with a >2-fold increase in the risk of death (hazard ratio 2.27, 95% CI 1.27, 4.09) and worse health-related quality of life [Short Form Health Survey physical (β = -2.37, P = 0.02) and mental (β = -2.86, P = 0.01) component summary scores].

CONCLUSION

Severe GI disease is common in early SSc and is associated with significant morbidity and increased mortality. More research is needed to understand, prevent and mitigate severe GI disease in SSc.

Validation of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 Summary Score in Patients With Hematologic Malignancies

OBJECTIVES

We investigated the validity of the recently developed European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) summary score in patients with hematologic malignancies. Specifically, we evaluated the adequacy of a single-factor measurement model for the QLQ-C30, and its known-groups validity and responsiveness to change over time.

METHODS

We used confirmatory factor analysis to test the single-factor model of the QLQ-C30, using baseline QLQ-C30 data (N = 2134). The QLQ-C30 summary score was compared to the original QLQ-C30 scales using general (age, sex, Eastern Cooperative Oncology Group performance status, comorbidity) and disease-specific (red blood cell transfusion dependency) groups. Repeated measurements allowed us to investigate responsiveness to change in a subgroup of patients with acute myeloid leukemia.

RESULTS

The single-factor model of the QLQ-C30 exhibited adequate fit in patients with hematologic malignancies. Known-group comparisons generally supported the construct validity of the summary score when using more general grouping variables (sociodemographics, broad clinical parameters). Nevertheless, when groups were formed on the basis of disease-specific variables (eg, transfusion dependency), the summary score performed less well the some of the original, separate scales of the QLQ-C30.

CONCLUSION

Our findings provide support for the validity of the single-factor model of the EORTC QLQ-C30 in patients with hematologic malignancies. Specifically, the results suggest that the summary score can be used as an endpoint in this population when symptom- or other health domain-specific hypotheses are not available.

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-017-0410-9) contains supplementary material, which is available to authorized users.

What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

Background

Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer.

Objective

Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]).

Methods

First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life.

Results

Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients’ concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus.

Conclusions

We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life.

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.