A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies.

Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson-Gamma recruitment model, a Bayesian technique built on a doubly stochastic Poisson process. This approach is based on the modeling of enrollments as a Poisson process where the recruitment rates are assumed to be constant over time and to follow a common Gamma prior distribution. However, the constant-rate assumption is a restrictive limitation that is rarely appropriate for applications in real studies. In this paper, we illustrate a flexible generalization of this methodology which allows the enrollment rates to vary over time by modeling them through B-splines. We show the suitability of this approach for a wide range of recruitment behaviors in a simulation study and by estimating the recruitment progression of the Canadian Co-infection Cohort.

Economic impact of generic antiretrovirals in France for HIV patients' care: a simulation between 2019 and 2023.

BACKGROUND: In a context where the economic burden of HIV is increasing as HIV patients now have a close to normal lifespan, the availability of generic antiretrovirals commonly prescribed in 2017 and the imminence of patent expiration are expected to provide substantial savings in the coming years. This article aims to assess the economic impact of these generic antiretrovirals in France and specifically over a five-year period. METHODS: An agent-based model was developed to simulate patient trajectories and treatment use over a five-year period. By comparing the results of costs for trajectories simulated under different predefined scenarios, a budget impact model can be created and sensitivity analyses performed on several parameters of importance. RESULTS: The potential economic savings from 2019 to 2023 generated by generic antiretrovirals range from €309 million when the penetration rate of generics is set at 10% to €1.5 billion at 70%. These savings range from €984 million to €993 million as the delay between patent and generic marketing authorisation varies from 10 to 15 years, and from €965 million to €993 million as the Negotiated Price per Unit (NPU) of generics at market-entry varies from 40 to 50% of the NPU for patents. DISCUSSION: This economic savings simulation could help decision makers to anticipate resource allocations for further innovation in antiretrovirals therapies as well as prevention, especially by funding the Pre-Exposure Prophylaxis (PrEP) or HIV screening.

Using Poisson-gamma model to evaluate the duration of recruitment process when historical trials are available.

At the design of clinical trial operation, a question of a paramount interest is how long it takes to recruit a given number of patients. Modelling the recruitment dynamics is the necessary step to answer this question. Poisson-gamma model provides very convenient, flexible and realistic approach. This model allows predicting the trial duration using data collected at an interim time with very good accuracy. A natural question arises: how to evaluate the parameters of recruitment model before the trial begins? The question is harder to handle as there are no recruitment data available for this trial. However, if there exist similar completed trials, it is appealing to use data from these trials to investigate feasibility of the recruitment process. In this paper, the authors explore the recruitment data of two similar clinical trials (Intergroupe Francais du Myélome 2005 and 2009). It is shown that the natural idea of plugging the historical rates estimated from the completed trial in the same centres of the new trial for predicting recruitment is not a relevant strategy. In contrast, using the parameters of a gamma distribution of the rates estimated from the completed trial in the recruitment dynamic model of the new trial provides reasonable predictive properties with relevant confidence intervals. Copyright © 2017 John Wiley & Sons, Ltd.

Interest of the trajectory method for the evaluation of outcomes after in utero drug exposure: example of anxiolytics and hypnotics.

PURPOSE: The aim of this study was to examine the potential benefit to take into account duration and intensity of drug exposure using the recently published method based on individual drug trajectories. This approach was used to define profiles of exposure to anxiolytics/hypnotics during pregnancy and to evaluate the potential effect on newborn health. METHODS: The study was performed in EFEMERIS database (54 918 mother-children pairs). An estimation of adaptation to extrauterine life was assessed using several criteria especially cardio-respiratory symptoms. A proxy variable called "neonatal pathology" was created. The occurrence of this event was studied using two approaches: The Standard Method comparing exposed and unexposed newborns, The Trajectory Method comparing the different profiles of exposure. RESULTS: Around 5% of newborns (n = 2768) were identified to be exposed to anxiolytics or hypnotics during pregnancy. Using the Standard Method, 6.2% of exposed newborns developed a "neonatal pathology" against 4.8% of unexposed newborns (odds ratios [OR] = 0.9[0.8-1.2], p = 0.7). With the Trajectory Method taking into account evolution of exposure during pregnancy and treatment intensity, four profiles of pregnant women were identified. A significant difference in the rates of "neonatal pathologies" was observed between profiles (p = 0.0002). Newborns of the two profiles exposed in utero to high constant level of anxiolytics or hypnotics were more at risk of developing "neonatal pathology" than unexposed newborns (OR1  = 2.0 [1.0-3.9] and OR2  = 7.6 [2.8-20.5]). CONCLUSIONS: The present study demonstrates the interest of this method based on individual drug trajectories for the evaluation of outcomes in pharmaco-epidemiological studies and more specifically during pregnancy. Copyright © 2017 John Wiley & Sons, Ltd.

How to take into account exposure to drugs over time in pharmacoepidemiology studies of pregnant women?

PURPOSE: The aim of this study was to develop a new pharmacoepidemiological method to take into account intensity and evolution of drug exposure, applied to pregnant women. METHODS: Pregnant women were classified according to their drug exposure, in three steps: Conversion of prescription data into exposure variables (using ATC-DDD) Construction of individual trajectories of exposure Clustering of individual trajectories of exposure (using the R package Kml) We applied this method to psychotropic drugs prescribed during pregnancy. The present study involved women, included in the EFEMERIS database, who gave birth in Haute-Garonne (France) between 2004 and 2010 (N = 54 918). RESULTS: Exposure to psychotropic drugs of 3708 pregnant women was studied (6.7%). The pregnant women could be classified into four groups with homogeneous trajectories of exposure: low constant exposure during pregnancy (Cluster A: 70.8% of women); decreasing exposure during the first trimester of pregnancy and low constant exposure thereafter (Cluster B: 19.6%); moderate constant exposure (Cluster C: 8.2%); and high albeit decreasing exposure (Cluster D: 1.4%). CONCLUSIONS: The proposed new method enabled us to describe more precisely women's exposure to drugs during pregnancy, and to distinguish different profiles of exposure. This method could be used to investigate specific outcomes related to duration and intensity of drug exposure during pregnancy, and also to study adverse drug reactions throughout life. Copyright © 2016 John Wiley & Sons, Ltd.

Estimating the causal effect of an exposure on change from baseline using directed acyclic graphs and path analysis.

When estimating the causal effect of an exposure of interest on change in an outcome from baseline, the choice between a linear regression of change adjusted or unadjusted for the baseline outcome level is regularly debated. This choice mainly depends on the design of the study and the regression-to-the-mean phenomena. Moreover, it might be necessary to consider additional variables in the models (such as factors influencing both the baseline value of the outcome and change from baseline). The possible combinations of these elements make the choice of an appropriate statistical analysis difficult. We used directed acyclic graphs (DAGs) to represent these elements and to guide the choice of an appropriate linear model for the analysis of change. Combined with DAGs, we applied path analysis principles to show that, under some functional assumptions, estimations from the appropriate model could be unbiased. In the situation of randomized studies, DAG interpretation and path analysis indicate that unbiased results could be expected with both models. In the case of confounding, additional (and sometimes untestable) assumptions, such as the presence of unmeasured confounders, or effect modification over time should be considered. When the observed baseline value influences the exposure ("cutoff designs"), linear regressions adjusted for baseline level should be preferred to unadjusted linear regression analyses. If the exposure starts before the beginning of the study, linear regression unadjusted for baseline level may be more appropriate than adjusted analyses.

An omnibus test for several hazard alternatives in prevention randomized controlled clinical trials.

The logrank test is optimal for testing the equality of survival distributions against a proportional hazards alternative. Under a late effects alternative, it is no longer appropriate, and one may turn to Fleming-Harrington's class of weighted logrank tests instead. In some settings, such as in preventive clinical trials where the statistical analysis has to be designed before the trial begins, it can be difficult to choose a priori between the logrank and Fleming-Harrington tests. A solution to this issue is provided. A decision rule is constructed for the problem of testing the equality of two survival distributions when the expected alternative may be one of the proportional hazards and late effects. A formula for computing the necessary sample size is obtained for this decision rule. A comprehensive simulation study is conducted to assess finite sample properties of the proposed test statistic. The proposed test improves both the logrank test and Fleming-Harrington's test for late effects. Finally, the methodology is illustrated on a data set in the field of prevention of Alzheimer's disease.

Deleterious impact of feto-maternal MHC compatibility on the success of pregnancy in a macaque model.

The impact of feto-maternal histocompatibility on reproduction has inspired long-lasting debates. However, after the review of numerous articles, the impact of HLA allele sharing within couples on fecundity remains questionable. We decided to explore the impact of major histocompatibility complex (MHC) feto-maternal compatibility on reproduction in a cynomolgus macaque facility composed of animals of Mauritian descent. The Mauritian-derived macaque population presents a very restricted MHC polymorphism (only seven founding haplotypes) due to a strong founding bottleneck effect. The MHC polymorphism was investigated in 237 trios (male, female and offspring) using 17 microsatellite markers distributed across the MHC. Haplotypes were confirmed by segregation analysis. We evaluated the relative frequencies of MHC-compatible and MHC-semi-compatible offspring with the mothers. Among the 237 trios, we selected 42 trios for which the identity of the father is certain and for which the theoretical probabilities of fully compatible and semi-compatible offspring were equal. We found 11 offspring fully compatible and 31 offspring semi-compatible with their respective mother. The observed proportions were clearly outside the interval of confidence of 99 % and therefore most probably resulted from a selection of the semi-compatible offspring during pregnancy. We concluded that MHC fully compatible cynomolgus macaque offspring have a selective survival disadvantage in comparison with offspring inheriting a paternal MHC haplotype differing from maternal haplotypes.

The time-dependent Poisson-gamma model in practice: Recruitment forecasting in HIV trials.

Despite a growing body of literature in the area of recruitment modeling for multicenter studies, in practice, statistical models to predict enrollments are rarely used and when they are, they often rely on unrealistic assumptions. The time-dependent Poisson-Gamma model (tPG) is a recently developed flexible methodology which allows analysts to predict recruitments in an ongoing multicenter trial, and its performance has been validated on data from a cohort study. In this article, we illustrate and further validate the tPG model on recruitment data from randomized controlled trials. Additionally, in the appendix, we provide a practical and easy to follow guide to its implementation via the tPG R package. To validate the model, we show the predictive performance of the proposed methodology in forecasting the recruitment process of two HIV vaccine trials conducted by the HIV Vaccine Trials Network in multiple Sub-Saharan countries.

Identification and Economic Evaluation of Differentiated Thyroid Cancer Care Consumption Patterns Using Sequence Analysis.

Objectives: This study aims to assess the impact of care consumption patterns and individual characteristics on the cost of treating differentiated thyroid carcinoma (DTC), in France, with a specific emphasis on socioeconomic position. Methods: The methodology involved a net cost approach utilising cases from the EVATHYR cohort and controls from the French National Health Insurance database. Care consumption patterns were created using Optimal Matching and clustering techniques. The individual characteristics influence on patterns was assessed using multinomial logistic regression. The individual characteristics and patterns influence on care costs was assessed using generalised estimating equations. Results: The findings revealed an average cost of €13,753 per patient during the initial 3 years. Regression models suggested the main predictors of high DTC specific care consumption tended to include having a high risk of cancer recurrence (OR = 4.97), being a woman (OR = 2.00), and experiencing socio-economic deprivation (OR = 1.26), though not reaching statistical significance. Finally, high DTC-specific care consumers also incurred higher general care costs (RR = 1.35). Conclusion: The study underscores the increased costs of managing DTC, shaped by consumption habits and socioeconomic position, emphasising the need for more nuanced DTC management strategies.

Agent-based modeling in medical research, virtual baseline generator and change in patients' profile issue.

Simulation studies are promising in medical research in particular to improve drug development. For instance, one can aim to develop In Silico Clinical Trial in order to challenge trial's design parameters in terms of feasibility and probability of success of the trial. Approaches based on agent-based models draw on a particularly useful framework to simulate patients evolution. In this paper, an approach based on agent-based modeling is described and discussed in the context of medical research. An R-vine copula model is used to represent the multivariate distribution of the data. A baseline data cohort can then be simulated and execution models can be developed to simulate the evolution of patients. R-vine copula models are very flexible tools which allow researchers to consider different marginal distributions than the ones observed in the data. It is then possible to perform data augmentation to explore a new population by simulating baseline data which are slightly different than those of the original population. A simulation study illustrates the efficiency of copula modeling to generate data according to specific marginal distributions but also highlights difficulties inherent to data augmentation.

Agent based modeling in health care economics: examples in the field of thyroid cancer.

Although they remain little used in the field of Health Care Economics, Agent Based Models (ABM) are potentially powerful decision-making tools that open up great prospects. The reasons for this lack of popularity are essentially to be found in a methodology that should be further clarified. This article hence aims to illustrate the methodology by means of two applications to medical examples. The first example of ABM illustrates the construction of a Baseline Data Cohort by means of a Virtual Baseline Generator. The aim is to describe the prevalence of thyroid cancer in the French population over the long term according to different scenarios of evolution of this population. The second study considers a setting where the Baseline Data Cohort is an established cohort of (real) patients: the EVATHYR cohort. The aim of the ABM is to describe the long-term costs associated with different scenarios of thyroid cancer management. The results are evaluated using several simulation runs in order to observe the variability of simulations and to derive prediction intervals. The ABM approach is very flexible since several sources of data can be involved and a large variety of simulation models can be calibrated to generate observations according to different evolution scenarios.

Multicolor flow cytometry in clinical samples for platelet signaling assessment.

Background: Availability of multichannel cytometers and specific commercial antibodies makes flow cytometry a new option to simultaneously assess multiple intracellular platelet signaling pathways for clinical purposes, in small volume of blood or low platelet count. Objectives: To describe a multicolor flow cytometry with fluorescent barcoding technique for screening signaling pathways downstream membrane receptors of major platelet agonists (adenosine diphosphate, thrombin, thromboxane, and collagen). Methods: By comparison with immunoblotting, we first selected the target phosphoproteins, AKT, P38MAPK, LIMK, and SPL76; the times of stimulation; and phosphoflow barcoding conditions. We then performed a clinical study on whole blood of patients without evidence of blood platelet disorder on standard biological screening, consulting for trivial or occasionally provoked bleeds without familial antecedent (bleeding of unknown origin, n = 23) or type-1 von Willebrand disease (n = 9). In addition, we included a small group of patients with definite platelet disorders (Glanzmann thrombasthenia, δ-storage pool deficiency, and immune glycoprotein VI-related disease with granule secretion defect). Results: The range, kinetics, and distribution of fluorescence intensity were established for each agonist-target protein combination. Principal component analysis indicates a correlation in response to a target phosphoprotein (AKT and P38MAPK) to different agonists but no correlation in the response of different target phosphoproteins to the same agonist. The heterogeneity of individual responses in the whole population displayed was analyzed using clustering algorithm. Patients with platelet storage pool deficiency were positioned as lowest responders on the heatmap. Conclusion: In complement of functional tests, this study introduces a new approach for rapid platelet signaling profiling in clinical practice.

Statistics, philosophy, and health: the SMAC 2021 webconference.

SMAC 2021 was a webconference organized in June 2021. The aim of this conference was to bring together data scientists, (bio)statisticians, philosophers, and any person interested in the questions of causality and Bayesian statistics, ranging from technical to philosophical aspects. This webconference consisted of keynote speakers and contributed speakers, and closed with a round-table organized in an unusual fashion. Indeed, organisers asked world renowned scientists to prepare two videos: a short video presenting a question of interest to them and a longer one presenting their point of view on the question. The first video served as a "teaser" for the conference and the second were presented during the conference as an introduction to the round-table. These videos and this round-table generated original scientific insights and discussion worthy of being shared with the community which we do by means of this paper.

Models for patients' recruitment in clinical trials and sensitivity analysis.

Taking a decision on the feasibility and estimating the duration of patients' recruitment in a clinical trial are very important but very hard questions to answer, mainly because of the huge variability of the system. The more elaborated works on this topic are those of Anisimov and co-authors, where they investigate modelling of the enrolment period by using Gamma-Poisson processes, which allows to develop statistical tools that can help the manager of the clinical trial to answer these questions and thus help him to plan the trial. The main idea is to consider an ongoing study at an intermediate time, denoted t(1). Data collected on [0,t(1)] allow to calibrate the parameters of the model, which are then used to make predictions on what will happen after t(1). This method allows us to estimate the probability of ending the trial on time and give possible corrective actions to the trial manager especially regarding how many centres have to be open to finish on time. In this paper, we investigate a Pareto-Poisson model, which we compare with the Gamma-Poisson one. We will discuss the accuracy of the estimation of the parameters and compare the models on a set of real case data. We make the comparison on various criteria : the expected recruitment duration, the quality of fitting to the data and its sensitivity to parameter errors. We discuss the influence of the centres opening dates on the estimation of the duration. This is a very important question to deal with in the setting of our data set. In fact, these dates are not known. For this discussion, we consider a uniformly distributed approach. Finally, we study the sensitivity of the expected duration of the trial with respect to the parameters of the model : we calculate to what extent an error on the estimation of the parameters generates an error in the prediction of the duration.

Real-world costs of illness of Hodgkin and the main B-Cell Non-Hodgkin lymphomas in France.

Background: Lymphomas are costly diseases that suffer from a lack of detailed economic information, notably in a real-world setting. Decision-makers are increasing the search for Real-World Evidence (RWE) to assess the impact, in real-life, of healthcare management and to support their public decisions. Thus, we aimed to assess the real-world net costs of the active treatment phases of adult Hodgkin Lymphoma (HL), Follicular Lymphoma (FL) and Diffuse Large B Cell Lymphoma (DLBCL).Methods: We performed a retrospective cohort study using population-based data from a national representative sample of the French population covered by the health insurance system. Cost analysis was performed from the French health insurance perspective and took into account direct and sick leave compensation costs (€2,018). Healthcare costs were studied over the active treatment phase. We used multivariate modeling to adjust cost differences between lymphoma subtypes.Results: Analyses were performed on 224 lymphoma patients and 896 controls. The mean additional monthly costs due to HL, FL and DLBCL patients were respectively €5,188, €3,242 and €7,659 for the active treatment phase. The main additional cost driver was principally inpatient stay (hospitalization costs and costly cancer-related drugs), followed by outpatient medication and productivity loss. When adjusted, DLBCL remains significantly the most costly lymphoma subtype.Conclusion: This study provides an accurate assessment of the main lymphoma subtypes related cost with high magnitude of details in a real-world setting. We underline where potential cost saving could be realized via the use of biosimilar medication, and where lymphoma management could be improved with the early management of adverse events.KEY POINTSThis is one of the first studies which assess the additional cost of lymphoma in Europe, according the main sub-types of lymphoma and with real-world database.The additional monthly cost due to HL, FL and DLBCL patients were respectively €5,188, €3,242 and €7,659 for the active treatment phase and the main additional cost driver was principally inpatient stay (i.e. hospitalization costs and additional inpatient medicines, notably rituximab), followed by outpatient medication and productivity loss.This study provides an accurate and detailed lymphoma subtype cost description and comparison which supply data for efficiency evaluations and will allow French health policy to improve lymphoma management.

Patient blood management in elective bypass cardiac surgery: A 2-step single-centre interventional trial to analyse the impact of an educational programme and erythropoiesis stimulation on red blood cell transfusion.

Anaemia and iron deficiency are frequent in patients scheduled for cardiac surgery. Perioperative patient blood management (PBM) is widely recommended in current practice guidelines. The aim of this protocol is to analyse the effect of a global perioperative PBM programme on the red blood cell (RBC) transfusion ratio, morbidities and rehabilitation score in elective cardiac surgery.This study is a prospective, single-centre trial with a 2-step protocol, A and B, as follows: A: non-drug intervention: the caregiver is given a blood management educational programme; B: drug intervention: systematic correction of perioperative iron, vitamin deficiencies, and anaemia. This study was designed to enrol 900 patients (500 in group A and 400 in group B) in a rolling period starting at anaesthesia consultation and ending 3 months after surgery. The primary objective was a 20% reduction in RBC transfusion after implementation of PBM programmes (protocol A + B) when compared to our previous transfusion ratio in the first half of 2018 (30.4% vs 38%). The secondary objectives were to evaluate the impact for each step of the study on the RBC transfusion rate, morbidity and the quality of postoperative rehabilitation.The strength of this study is its evaluation of the effect of a global PBM programme on RBC transfusion in cardiac surgery through a 2-step protocol. We aim to assess for the first time the impact of non-drug and drug interventions on RBC transfusion, comorbidities and delayed rehabilitation parameters. TRIALS REGISTRATIONS: ClinicalTrials.gov, NCT04040023: registered 29 July 2019.

Additional Costs of Polymyalgia Rheumatica With Giant Cell Arteritis.

OBJECTIVE: To assess and compare direct costs between patients with giant cell arteritis (GCA) that is associated or not associated with polymyalgia rheumatic (PMR), and to identify the additional cost drivers due to PMR. METHODS: A population-based, retrospective cohort study using the French National Health Insurance System Database was conducted. Cost analysis was performed from the French health insurance perspective and direct medical and nonmedical costs were taken into account (based on 2014 costs [€]). Costs were analyzed according to different components and divided into 6-month periods to assess care consumption. Longitudinal multivariate analyses, using generalized estimating equations, were used to adjust the effect of PMR on the mean cost over time. RESULTS: Analyses were performed on 100 incident patients with GCA, 54 of whom had PMR. The cumulative additional cost due to PMR was €8,801 for 3 years, and €10,532 for 5 years. The significant additional costs occurred especially during the second and third years of follow-up, amounting to €1,769 between 12 and 18 months (P = 0.02), €1,924 between 18 and 24 months (P = 0.17), €1,458 between 24 and 30 months (P = 0.08), and €1,307 between 30 and 36 months (P = 0.07). The most important cost drivers were inpatient stays, paramedic procedures, and medications. Multivariate analyses showed a significant effect of PMR on mean cost during the first 3 years of follow-up (relative risk 1.76 [95% confidence interval 1.03-2.99], P = 0.038). CONCLUSION: To our knowledge, this study is the first to accurately assess the cost of PMR care in patients with GCA and to highlight that PMR is largely responsible for the high cost of GCA.