Pourquoi les politiques publiques sont-elles si peu suivies d’effets ?:Quelques interrogations

L’insertion des femmes sur le marché du travail a connu à la fois des avancées et des reculs. Si davantage de femmes accèdent à l’éducation supérieure et aux emplois qualifiés, d’autres sont touchées par la précarité et connaissent une dégradation de leurs conditions de travail et de vie. Face à ce constat ambivalent, on peut questionner la mise en œuvre et l’efficacité des politiques qui visent à promouvoir l’égalité entre les femmes et les hommes. Cet article a pour objectif de soulever quelques débats. Le plus souvent, les politiques publiques au sens large (y compris la protection sociale) sont définies en termes de compensation et de correction des inégalités et des discriminations. Mais elles ne concernent pas les causes effectives de l’extension du sous-emploi des femmes, qui relèvent du fonctionnement même du marché du travail. C’est donc la définition des politiques publiques qu’il faut interroger, en dépassant une vision binaire qui oppose d’une part un champ économique extérieur, d’autre part un champ social, juridique et culturel qui, seul, pourrait être l’objet d’inflexions. En réalité, le champ économique est aussi le produit des politiques publiques : la libre-concurrence et la prééminence du marché sont le résultat d’une action volontaire des États. Il faut donc réintégrer les politiques économiques dans le champ de la réflexion sur les moyens de combattre les discriminations à l’encontre des femmes.The integration of women into the labour market has gone through both upswings and downturns. In view of this ambivalent result, we can question the efficiency of public policies set up to overcome gender inequality and fight gender discrimination. Does a real will exist, and if so why is it so inefficient or so poorly implemented? What forms do individual and collective resistance take? Most of the time, public policies are defined in terms of compensation and correction. But they don’t deal with the actual causes of women’s underemployment resulting from labour market adjustments. It is therefore the definition of the public policies that we need to examine, going beyond a binary view that opposes economic issues, on the one hand, to social, juridical and cultural concerns on the other

Modeling and simulation of count data

Count data, or number of events per time interval, are discrete data arising from repeated time to event observations. Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family. Clinical trial data characterization often involves population count analysis. This tutorial presents the basics and diagnostics of count modeling and simulation in the context of pharmacometrics. Consideration is given to overdispersion, underdispersion, autocorrelation, and inhomogeneity

Pharmacometric Methods and Novel Models for Discrete Data

Pharmacodynamic processes and disease progression are increasingly characterized with pharmacometric models. However, modelling options for discrete-type responses remain limited, although these response variables are commonly encountered clinical endpoints. Types of data defined as discrete data are generally ordinal, e.g. symptom severity, count, i.e. event frequency, and time-to-event, i.e. event occurrence. Underlying assumptions accompanying discrete data models need investigation and possibly adaptations in order to expand their use. Moreover, because these models are highly non-linear, estimation with linearization-based maximum likelihood methods may be biased. The aim of this thesis was to explore pharmacometric methods and novel models for discrete data through (i) the investigation of benefits of treating discrete data with different modelling approaches, (ii) evaluations of the performance of several estimation methods for discrete models, and (iii) the development of novel models for the handling of complex discrete data recorded during (pre-)clinical studies. A simulation study indicated that approaches such as a truncated Poisson model and a logit-transformed continuous model were adequate for treating ordinal data ranked on a 0-10 scale. Features that handled serial correlation and underdispersion were developed for the models to subsequently fit real pain scores. The performance of nine estimation methods was studied for dose-response continuous models. Other types of serially correlated count models were studied for the analysis of overdispersed data represented by the number of epilepsy seizures per day. For these types of models, the commonly used Laplace estimation method presented a bias, whereas the adaptive Gaussian quadrature method did not. Count models were also compared to repeated time-to-event models when the exact time of gastroesophageal symptom occurrence was known. Two new model structures handling repeated time-to-categorical events, i.e. events with an ordinal severity aspect, were introduced. Laplace and two expectation-maximisation estimation methods were found to be performing well for frequent repeated time-to-event models. In conclusion, this thesis presents approaches, estimation methods, and diagnostics adapted for treating discrete data. Novel models and diagnostics were developed when lacking and applied to biological observations

Modeling a Composite Score in Parkinson's Disease Using Item Response Theory

In the current work, we present the methodology for development of an Item Response Theory model within a non-linear mixed effects framework to characterize the longitudinal changes of the Movement Disorder Society (sponsored revision) of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) endpoint in Parkinson's disease (PD). The data were obtained from Parkinson's Progression Markers Initiative database and included 163,070 observations up to 48 months from 430 subjects belonging to De Novo PD cohort. The probability of obtaining a score, reported for each of the items in the questionnaire, was modeled as a function of the subject's disability. Initially, a single latent variable model was explored to characterize the disease progression over time. However, based on the understanding of the questionnaire set-up and the results of a residuals-based diagnostic tool, a three latent variable model with a mixture implementation was able to adequately describe longitudinal changes not only at the total score level but also at each individual item level. The linear progression rates obtained for the patient-reported items and the non-sided items were similar, each of which roughly take about 50 months for a typical subject to progress linearly from the baseline by one standard deviation. However for the sided items, it was found that the better side deteriorates quicker than the disabled side. This study presents a framework for analyzing MDS-UPDRS data, which can be adapted to more traditional UPDRS data collected in PD clinical trials and result in more efficient designs and analyses of such studies

Handling underlying discrete variables with bivariate mixed hidden Markov models in NONMEM

Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data. Hidden Markov models (HMMs) characterize the relationship between observed and hidden variables where the hidden variables can represent an underlying and unmeasurable disease status for example. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. The two hidden states included in the model were remission and exacerbation and two observation sources were considered, patient reported outcomes (PROs) and forced expiratory volume (FEV1). Estimation properties in the software NONMEM of model parameters were investigated with and without random and covariate effect parameters. The influence of including random and covariate effects of varying magnitudes on the parameters in the model was quantified and a power analysis was performed to compare the power of a single bivariate MHMM with two separate univariate MHMMs. A bivariate MHMM was developed for simulating and analysing hypothetical COPD data consisting of PRO and FEV1 measurements collected every week for 60 weeks. Parameter precision was high for all parameters with the exception of the variance of the transition rate dictating the transition from remission to exacerbation (relative root mean squared error [RRMSE] > 150%). Parameter precision was better with higher magnitudes of the transition probability parameters. A drug effect was included on the transition rate probability and the precision of the drug effect parameter improved with increasing magnitude of the parameter. The power to detect the drug effect was improved by utilizing a bivariate MHMM model over the univariate MHMM models where the number of subject required for 80% power was 25 with the bivariate MHMM model versus 63 in the univariate MHMM FEV1 model and > 100 in the univariate MHMM PRO model. The results advocates for the use of bivariate MHMM models when implementation is possible

Detecting placebo and drug effects on Parkinson's disease symptoms by longitudinal item-score models

This study tested the hypothesis that analyzing longitudinal item scores of the Unified Parkinson's Disease Rating Scale could allow a smaller trial size and describe a drug's effect on symptom progression. Two historical studies of the dopaminergic drug ropinirole were analyzed: a cross-over formulation comparison trial in 161 patients with early-stage Parkinson's disease, and a 24-week, parallel-group, placebo-controlled efficacy trial in 393 patients with advanced-stage Parkinson's disease. We applied item response theory to estimate the patients' symptom severity and developed a longitudinal model using the symptom severity to describe the time course of the placebo response and the drug effect on the time course. Similarly, we developed a longitudinal model using the total score. We then compared sample size needs for drug effect detection using these two different models. Total score modeling estimated median changes from baseline at 24 weeks (90% confidence interval) of -3.7 (-5.4 to -2.0) and -9.3 (-11 to -7.3) points by placebo and ropinirole. Comparable changes were estimated (with slightly higher precision) by item-score modeling as -2.0 (-4.0 to -1.0) and -9.0 (-11 to -8.0) points. The treatment duration was insufficient to estimate the symptom progression rate; hence the drug effect on the progression could not be assessed. The trial sizes to detect a drug effect with 80% power on total score and on symptom severity were estimated (at the type I error level of 0.05) as 88 and 58, respectively. Longitudinal item response analysis could markedly reduce sample size; it also has the potential for assessing drug effects on disease progression in longer trials

Maximum Likelihood Methods for Dose-Response Models

Estimation methods for nonlinear mixed-effects modelling have considerably improved over the last decades. Nowadays several algorithms implemented in different softwares are used. The present study aimed at comparing their performance for dose-response models. inserm-00709829, version 1- 19 Jun 2012 Eight scenarios were considered using a sigmoid Emax model, with varying sigmoidicity factors and residual error models. 100 simulated datasets for each scenario were generated. 100 individuals with observations at 4 doses constituted the rich design and at 2 doses for the sparse design. Nine parametric approaches for maximum likelihood estimation were studied

Bounded integer model‐based analysis of psoriasis area and severity index in patients with moderate‐to‐severe plaque psoriasis receiving BI 730357

Abstract BI 730357 is investigated as an oral treatment of plaque psoriasis. We analyzed the impact of three dosage regimens on the Psoriasis Area and Severity Index (PASI) response with modeling based on phase I and II data from 109 healthy subjects and 274 patients with moderate‐to‐severe plaque psoriasis. The pharmacokinetics (PK) was characterized by a two‐compartment model with dual absorption paths and a first‐order elimination. Higher baseline C‐reactive protein was associated with lower clearance and patients generally had lower clearance compared with healthy subjects. A bounded integer PK/pharmacodynamic model characterized the effect on the observed PASI. The maximum drug effect was largest for patients with no prior biologic use, smaller for patients with prior use of non‐interleukin‐17 inhibitors, and smallest for patients with prior interleukin‐17 inhibitor use. The models allowed robust simulation of large patient populations, predicting a plateau in PASI outcomes for BI 730357 exposure above 2000 nmol/L