Nicolas Savy

General Model, Based on Two Mixed Weibull Distributions of Bacterial Resistance, for Describing Various Shapes of Inactivation Curves

ABSTRACT Cells of Listeria monocytogenes or Salmonella enterica serovar Typhimurium taken from six characteristic stages of growth were subjected to an acidic stress (pH 3.3). As expected, the bacterial resistance increased from the end of the exponential phase to the late stationary phase. Moreover, the shapes of the survival curves gradually evolved as the physiological states of the cells changed. A new primary model, based on two mixed Weibull distributions of cell resistance, is proposed to describe the survival curves and the change in the pattern with the modifications of resistance of two assumed subpopulations. This model resulted from simplification of the first model proposed. These models were compared to the Whitings model. The parameters of the proposed model were stable and showed consistent evolution according to the initial physiological state of the bacterial population. Compared to the Whitings model, the proposed model allowed a better fit and more accurate estimation of the parameters. Finally, the parameters of the simplified model had biological significance, which facilitated their interpretation.

Sharp large deviations for the fractional Ornstein-Uhlenbeck process

We investigate the sharp large deviation properties of the energy and the maximum likelihood estimator for the Ornstein-Uhlenbeck process driven by a fractional Brownian motion with Hurst index greater than one half.

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

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.

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-Harringtons 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-Harringtons test for late effects. Finally, the methodology is illustrated on a data set in the field of prevention of Alzheimers disease.

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

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.

Estimating controlled direct effects in the presence of intermediate confounding of the mediator-outcome relationship: Comparison of five different methods.

In mediation analysis between an exposure X and an outcome Y, estimation of the direct effect of X on Y by usual regression after adjustment for the mediator M may be biased if Z is a confounder between M and Y, and is also affected by X. Alternative methods have been described to avoid such a bias: inverse probability of treatment weighting with and without weight truncation, the sequential g-estimator and g-computation. Our aim was to compare the usual linear regression adjusted for M to these methods when estimating the controlled direct effect between X and Y in the causal structure and to explore the size of the potential bias. Estimations were computed in several simulated data sets as well as real data. We observed an increased bias of the controlled direct effect estimation using linear regression adjusted for M for larger effects of X on M and larger effects of Z on M. The sequential g-estimator and g-computation gave unbiased estimations with adequate coverage values in every situation studied. With continuous exposure X and mediator M, inverse probability of treatment weighting resulted in some bias and less satisfactory coverage for large effects of X on M and Z on M.

A comparison of the constant piecewise weighted logrank and Fleming-Harrington tests

The Fleming-Harrington and constant piecewise wei- ghted logrank tests for late effects in clinical trials are considered. Both tests depend on a parameter (q for the Fleming-Harrington and t∗ for the constant piecewise weighted logrank) that has to be chosen before the trial analysis. The problem of choosing the most appropriate test and associated parameter value for a given trial is addressed. For this purpose, the tests are compared in terms of their sensitivity to q and t∗ and of their asymptotic relative efficiency and necessary sample size. Some guidelines for choosing the most appropriate weight for testing late effects are provided. The methodology is illustrated on a medical dataset.

How to deal with the Poisson-gamma model to forecast patients' recruitment in clinical trials when there are pauses in recruitment dynamic?

Recruiting patients is a crucial step of a clinical trial. Estimation of the trial duration is a question of paramount interest. Most techniques are based on deterministic models and various ad hoc methods neglecting the variability in the recruitment process. To overpass this difficulty the so-called Poisson-gamma model has been introduced involving, for each centre, a recruitment process modelled by a Poisson process whose rate is assumed constant in time and gamma-distributed. The relevancy of this model has been widely investigated. In practice, rates are rarely constant in time, there are breaks in recruitment (for instance week-ends or holidays). Such information can be collected and included in a model considering piecewise constant rate functions yielding to an inhomogeneous Cox model. The estimation of the trial duration is much more difficult. Three strategies of computation of the expected trial duration are proposed considering all the breaks, considering only large breaks and without considering breaks. The bias of these estimations procedure are assessed by means of simulation studies considering three scenarios of breaks simulation. These strategies yield to estimations with a very small bias. Moreover, the strategy with the best performances in terms of prediction and with the smallest bias is the one which does not take into account of breaks. This result is important as, in practice, collecting breaks data is pretty hard to manage.

Additional costs of polymyalgia rheumatica with giant-cell arteritis

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.

Incremental Costs in Giant Cell Arteritis

To assess and compare direct costs between giant cell arteritis (GCA) patients and matched controls and to identify incremental cost drivers.