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.

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.