Defended theses

Analysis of cluster randomized trials with a time-to-event outcome

Cluster randomised trials (CRTs) are trials in which groups of individuals, called clusters, are randomised, rather than individuals themselves. In such study design, the responses of the individuals within the same cluster are correlated and statistical analysis must take into account this clustering. Time-to-event outcomes are not
uncommon in CRTs, but there is no recommendation on the optimal analysis.
In randomised clinical trials with a time-to-event outcome, the intervention effect could be quantified by a difference in restricted mean survival time (RMST) between the intervention and control groups up to time 𝑡∗. This measure is an alternative to the hazard ratio that does not rely on the proportional hazards assumption. It is
easily interpretable as the expected survival gain until 𝑡∗ due to the intervention. The main objective of this work was to study the difference in RMST to analyse CRTs with time-to-event outcomes.
In the first part of this work, we proposed and compared through a simulation study, two approaches to estimate a difference in RSMT in CRTs (the integration of the Kaplan-Meier curves and the pseudo-values regression). The methods showed good performance when there was a sufficient number of clusters (≥50). For CRTs with a limited number of clusters, a permutation test for pseudo-values regression was implemented and controlled the type I error, which was over 5% otherwise. In the second part of the work, we proposed the use of small samples bias-corrections of the sandwich variance estimator, as an alternative to the permutation test. These methods, especially the Fay and Graubard correction associated with a Student distribution, showed type I errors close to
5%.
This work opens the way for better analysing time-to-event outcomes and for quantifying the intervention effect by a difference in RMST in CRTs.

Satisfaction and care needs in cancer patients: validation of two French assessment tools

Patient satisfaction is an indicator of quality of care. It is a subjective concept that reflects the adequacy between what a patient expects from the healthcare system and care providers and his or her perception of quality of care received. Its assessment is based on patient-reported outcome measures. Its improvement depends on the identification of patients’ expectations and care needs. In oncology care, and particularly in palliative situation, patient satisfaction is a sign of global and personalized care. The main objective of the present thesis was to develop and validate two French assessment tools of satisfaction with care and needs for care in cancer patients.
We performed a translation and a cross-cultural adaptation of the FAMCARE-Patient questionnaire to obtain a French version (FFP-16). Content validity and face validity were good, as the psychometric properties. We also develop a French tool (ACCOmPAgNE) for care needs assessment in cancer patients. It combines four approaches: domain importance, problem intensity and burden, and care needs. Items were generated from literature analysis. Content and face validities were verified and were considered as good. Using these tools could facilitate communication between patients and caregivers, implementation of personalized care and improvement of satisfaction with care.

Development and evaluation of methods to examine sources of heterogeneity in DIF and response shift when analyzing patient-reported outcome data

Subjective concepts such as fatigue, anxiety, or quality of life are increasingly being used in biomedical research. These unobservable subjective concepts are measured by means of questionnaires, usually self-reported by patients. However, the analysis of these data can be challenging. Indeed, the direct comparison of questionnaire scores between different groups of individuals or over time assumes that all individuals interpret the questions in the same way and that this interpretation does not change over time. Yet, this assumption may be unmet. For example, some individuals may interpret the measured concept (and the associated items) differently because of their cultural, environmental, and personal characteristics, but also because of the experiences they went through.

This phenomenon is known as differential item functioning (DIF). In addition, following a major health event, some individuals may also change their interpretation of the items composing the questionnaire (response shift, RS). Both DIF and RS can threaten the accurate interpretation of patient-reported outcome measures. In addition, they are also phenomena that deserve to be studied.  This thesis aims to propose and evaluate, by simulations, methodological developments to study the sources of heterogeneity of these two phenomena.  This manuscript is composed of three main works: (i) the development of a method aiming at individualizing RS detection, (ii) the development of a method for detecting DIF in the presence of two binary covariates, and (iii) the study of the psychometric properties of the post-traumatic growth inventory (a phenomenon potentially related to RS)

Application of Rasch Measurement Theory to clinical research: Practical and theoretical aspects of a metrological approach for the evaluation of clinical trial endpoints

Metrology benefited to physical measures, on which most clinical trial endpoints are based on. As Patient-Reported Outcomes (PROs) measures are more and more used in clinical trials, it is urgent that they reach the same level of credibility and interpretability than traditional trial endpoints. The objective of this work was thus to explore how metrology can benefit to the evaluation of treatments as per the PRO endpoints from clinical trials. The Rasch model has previously shown numerous advantages in fulfilling metrological requirements. A first step of this work was to conceptualize the trial in the case of a PRO endpoint through a scientific model, based on metrological vocabulary: the clinical trial as a measurement system. From this, we list and categorize measurement uncertainty sources in clinical trials, in order to improve its expression with the trial PRO results. Finally, we present the results from a simulation study which suggest that calibration of PRO measures does not negatively impact the results from the trial. Proposals to improve measurement of patient experience in clinical trials are then made based on this work.

Analysis of the place of phase IV studies for drug risk assessment in vulnerable populations

During clinical development, the risk to the patient is monitored. However, when a health product is placed on the market, knowledge of its benefit-risk balance is only fragmentary. The post-marketing surveillance system, which makes it possible to update this balance, is based essentially on spontaneous reporting of adverse effects. What is the need for phase IV studies, in addition to clinical trial data and pharmacovigilance evaluation?  The objective of our research is to study the complementary approach of phase IV studies and their interest in relation to phase III studies and pharmacovigilance data, by focusing on an example of a population that is little studied in clinical trials, the elderly, and old drugs, benzodiazepines and an effect occurring during long-term use, drug dependence.  Our work shows that post-marketing studies are essential to know the risk and dependence profile of a drug, which evolves throughout its "life".  Predicting possible situations of dependence, detour, etc. requires a good evaluation of the benefit-risk balance not only for medical use in the MA, but also in all off-label use situations, including non-medical use. These data are essential to understand the obstacles to the application of recommendations or regulations on proper use.

Applicability of randomised trials results in general medicine

Many clinical practice guidelines are based on randomised controlled trials performed in secondary or tertiary care settings and general practitioners questioned their relevance for primary care patients.

The first objective was to compare the intervention effect estimates between randomised controlled trials performed in primary care and randomised controlled trials performed in secondary or tertiary care settings by using a meta-epidemiological approach. There was no difference in intervention effect estimates between the two types of randomised trials with a ratio of odds ratio of 0.98 (95% confidence interval 0.88 to 1.08). Nevertheless, the main medical fields encountered in this study were not fully representative of the medical conditions encountered in primary care with many studies on psychiatry or addictology (38.2%) or pneumology (13.2%) and very few in endocrinology or cardiovascular diseases.

The second objective was to compare the characteristics of type 2 diabetes patients of general practices to those included in the randomised trials on which clinical practice guidelines are based. Primary care patients differed from patients included in randomised trials in many important aspects. They were older (mean±standard deviation 68.8 ±1.1 years vs 59.9 years [standardised difference 0.8]), had higher BMI (31.5 (6.93) kg/m² vs 28.2kg/m² [standardised difference 0.48]). They also had more hypoglycemic (80.7% vs 45.4% [standardised difference 0.89] than randomised trials patients, but less cardiovascular history (myocardial infarction: 7.6% vs 23.1% [standardised difference -1.14]).

Our results show, that there is a need for study including a wider range of patients to ensure a better generalisability of the results and improve the use of study findings to daily practice.