Defended theses

Characterization of social cognition abilities in behavioral addictions through the prism of gaming and gambling disorders

This thesis focused on social cognition (SC) in the two behavioral addictions (BA) included in international classifications: gaming (GmD) and gambling (GbD) disorders. Two systematic reviews exposed the scarcity of studies linking SC and BA. Nevertheless, studies included in the reviews suggested the presence of an alteration of some of the SC’s components. Additionally, patient-reported outcomes confirmed the presence of interpersonal difficulties. These elements demonstrated the necessity to explore the SC abilities of patients with Gmd or GbD, in order to improve knowledge on addictive processes and propose alternative treatments focused on these difficulties. This thesis presented three studies on gamblers or gamers, whether or not they have an addiction. The first study showed specific allocation of attention toward social information in poker players compared to controls. The second study demonstrated a link between difficulties in identifying facial emotion and GbD and specificities in social metacognition in GmD. Finally, preliminary results of a study regarding GbD patients at the beginning of their treatment showed the importance of taking into account patient-reported outcomes in SC. Those results were discussed in light of clinical et scientific aspects, and put in perspective with future possible research.​

Causal inference from observational data: development and applications for critical care

The increasing amount of observational data, especially in critical care, leads to the consideration of causal inference statistical methods. This thesis presents two works that highlight the current challenges of these statistical methods used on observational health data. The first analysis evaluates the impact of barbiturates in a population of trauma brain injured patients prospectively included in the open critical care cohort AtlanRéa. The evaluation of the impact of barbiturates was possible by respecting the assumptions of causal inference and by using a method based on propensity scores: inverse probability weighting.

Beyond the results of this analysis, which showed an increase in mortality in the group treated with barbiturates, we were faced with the problem of the violation of the positivity assumption. We then compared different statistical methods of causal inference in a context of violation of the positivity assumption, which can be associated with an extrapolation issue. The methods predicting the occurrence of the outcome are the most robust in these situations. In this context of accumulation of health data, a perspective of optimization of the use of statistical methods in the framework of causal inference will reside in the use of machine learning algorithms to avoid the problems of model specification.

Multilevel joint modelling of target lesions dynamics and survival : application to the prediction of the response to immunotherapy in bladder cancer

Treatment evaluation in oncology relies on time-to-death and longitudinal measurements of the Sum of Longest Diameters (SLD) of target lesions. Both processes and their association can be analyzed together using a nonlinear joint model. However, using a composite marker such as SLD neglects the heterogeneity in lesion dynamics, which might be exacerbated under immunotherapy.

The main objective of this PhD was to develop multilevel nonlinear joint models of tumor dynamics and their impact on survival, to better characterize all the source of variability in the response to treatment. We relied on data from a phase 2 (IMvigor210) and a phase 3 (IMvigor211) clinical trials of 300 and 900 advanced or metastatic Urothelial Carcinoma (UC) patients, treated with atezolizumab immune checkpoint inhibitor. In a first nonlinear joint model, we showed the impact of tumor location on its dynamics and association with survival. In particular, the liver lesions dynamics was strongly associated with the risk of death as compared to other location. Then, we showed the ability of HMC Bayesian algorithm implemented in Stan software to provide unbiased and precise estimation of the parameters of a nonlinear joint model of SLD and survival, with reasonable sensitivity to prior information. Finally, we developed a Bayesian hierarchical joint model of individual lesions and survival. An additional level of random effect was integrated, specific to the lesion, to quantify the inter-lesions variability under immunotherapy. Using individual dynamic prediction approaches, we showed the benefit of the individual lesions follow-up to identify most at risk patient as compared to SLD follow-up.

This work paves the way for a better understanding of the inter and intra-patient variability in response to new immunotherapy treatments.

Management of complex patients in Pediatric Odontology: Benefit / Risk of practices and improvement pathways, focus on EMONO

The widespread use of MEOPA in dental practices is relatively recent. A review of practices and the legal framework related to the use of nitrous oxide in pediatric odontology seems so particularly relevant. In addition, the emergence of derived uses, outside the context of care or during, leads to increased vigilance towards it. It therefore seems appropriate to identify the effects felt and sought by children during inhalation of MEOPA in the context of dental care. During this thesis, we recalled the context of French use of nitrous oxide and then analyzed it against the benefit / risk of the various other sedations used in pediatric odontology. We then developed a presentation of the different works carried out, their materials and methods and their results. The whole was then discussed and future prospects considered

Expression the cluster effect for binary outcomes

In cluster randomized trials, it is recommended to report a measure of intracluster correlation, such as the intraclass correlation coefficient (ICC), for each primary outcome.

Providing intracluster correlation estimates, which we can also call clustering estimates, may help in sample size calculation of future cluster randomized trials but also in interpreting the results of a trial. For instance, a lower intracluster correlation in the intervention arm as compared to the control one may reveal a better standardization in practices among clusters of the intervention arm, leading to a lower between-cluster heterogeneity in outcomes. Yet, when the outcome is binary, the ICC is known to be associated with the prevalence of the outcome. This may raise issues when using ICC estimates to plan a new study, because expected outcome prevalences may di er from those observed in the study from which the ICC estimates were derived. This association also challenges the interpretation of the ICC because ICC values no longer just depend on clustering level. The aim of this PhD thesis was to study several intracluster correlation measures to identify whether they depend on the outcome prevalence as the ICC does or not. We first focused on the R coefficient, a coefficient initially proposed by Rosner for ophthalmologic data and later extended by Crespi et al. who asserted that the R coefficient may be less influenced by the outcome prevalence than is the ICC. We showed by a simulation study that this assertion is false and that the R coefficient is probably even worse than the ICC as an intracluster correlation measure. We further studied other measures such as the variance partition coefficient, the median odds ratio or the tetrachoric correlation coefficient. We also proposed to consider the relative deviation of an ICC estimate to its theoretical maximum possible value. All these measures were studied in an extensive simulation study, whose conclusion was that all of them depend in some way on the outcome prevalence. Although some measures may be preferred in some situations, none outperforms the others in every situation, and none can be considered independent from the outcome prevalence. Assessing intracluster correlation independently from the outcome prevalence remains an open  eld of research.

Which place for shared decision making in the context of kidney transplantation? A mixed-methods research exploring patient experience.

Although kidney transplantation provides a significant benefit over dialysis, question regarding the eligibility for transplantation, the impact of replacement treatment on their lives, make the mode of renal replacement therapy a difficult decision. Therefore, Health Authority suggests shared decision-making to help patients make timely treatment modality decision. Little is known about how patient perceive their participation in the shared decision-making process. This research aims to explore the experience of patients and the factors that influence them indecision-making situations, as well as to evaluate the impact of this experience on their future. This research is based on a mixed methods research (QUANTI > quali).

It combines an interpretive phenomenological analysis and an observational study design to measure decisional conflict perceived by patients on the waiting list and to explore the factors that influence decision regret, quality of life and adherence among transplant recipients. This study reports that the experience of waiting list was identified as a necessary step in their pathway. They experienced as an implicit decision that shapes patients' attitudes towards other decisions and influences their ability to cope with the uncertainty of living with chronic kidney disease. The challenge of considering all stages of shared medical decision-making is major in the context of kidney transplantation to support patient participation decision.