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Search Results: 1 - 10 of 331897 matches for " Hélène Jacqmin-Gadda "
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Copy Mean: A New Method to Impute Intermittent Missing Values in Longitudinal Studies  [PDF]
Christophe Genolini, René écochard, Hélène Jacqmin-Gadda
Open Journal of Statistics (OJS) , 2013, DOI: 10.4236/ojs.2013.34A004
Abstract:

Longitudinal studies are those in which the same variable is repeatedly measured at different times. These studies are more likely than others to suffer from missing values. Since the presence of missing values may have an important impact on statistical analyses, it is important that they should be dealt with properly. In this paper, we present “Copy Mean”, a new method to impute intermittent missing values. We compared its efficiency in eleven imputation methods dedicated to the treatment of missing values in longitudinal data. All these methods were tested on three markedly different real datasets (stationary, increasing, and sinusoidal pattern) with complete data. For each of them, we generated nine types of incomplete datasets that include 10%, 30%, or 50% of missing data using either a Missing Completely at Random, a Missing at Random, or a Missing Not at Random missingness mechanism. Our results show that Copy Mean has a great effectiveness, exceeding or equaling the performance of other methods in almost all configurations. The effectiveness of linear interpolation is highly data-dependent. The Last Occurrence Carried Forward method is strongly discouraged.

Mixed models for longitudinal left-censored repeated measures
Rodolphe Thiébaut,Hélène Jacqmin-Gadda
Statistics , 2007, DOI: 10.1016/j.cmpb.2003.08.004
Abstract: Longitudinal studies could be complicated by left-censored repeated measures. For example, in Human Immunodeficiency Virus infection, there is a detection limit of the assay used to quantify the plasma viral load. Simple imputation of the limit of the detection or of half of this limit for left-censored measures biases estimations and their standard errors. In this paper, we review two likelihood-based methods proposed to handle left-censoring of the outcome in linear mixed model. We show how to fit these models using SAS Proc NLMIXED and we compare this tool with other programs. Indications and limitations of the programs are discussed and an example in the field of HIV infection is shown.
Joint modelling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach
Cécile Proust-Lima,Jean-Fran?ois Dartigues,Hélène Jacqmin-Gadda
Statistics , 2014,
Abstract: Joint models initially dedicated to a single longitudinal marker and a single time-to-event need to be extended to account for the rich longitudinal data of cohort studies. Multiple causes of clinical progression are indeed usually observed, and multiple longitudinal markers are collected when the true latent trait of interest is hard to capture (e.g. quality of life, functional dependency, cognitive level). These multivariate and longitudinal data also usually have nonstandard distributions (discrete, asymmetric, bounded,...). We propose a joint model based on a latent process and latent classes to analyze simultaneously such multiple longitudinal markers of different natures, and multiple causes of progression. A latent process model describes the latent trait of interest and links it to the observed longitudinal outcomes using flexible measurement models adapted to different types of data, and a latent class structure links the longitudinal and the cause-specific survival models. The joint model is estimated in the maximum likelihood framework. A score test is developed to evaluate the assumption of conditional independence of the longitudinal markers and each cause of progression given the latent classes. In addition, individual dynamic cumulative incidences of each cause of progression based on the repeated marker data are derived. The methodology is validated in a simulation study and applied on real data about cognitive aging coming from a large population-based study. The aim is to predict the risk of dementia by accounting for the competing death according to the profiles of semantic memory measured by two asymmetric psychometric tests.
Bivariate linear mixed models using SAS proc MIXED
Rodolphe Thiébaut,Hélène Jacqmin-Gadda,Geneviève Chêne,Catherine Leport,Daniel Commenges
Statistics , 2007,
Abstract: Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers. In this paper, we present a bivariate linear mixed model including random effects or first-order auto-regressive process and independent measurement error for both markers. Codes and tricks to fit these models using SAS Proc MIXED are provided. Limitations of this program are discussed and an example in the field of HIV infection is shown. Despite some limitations, SAS Proc MIXED is a useful tool that may be easily extendable to multivariate response in longitudinal studies.
Estimation of dynamical model parameters taking into account undetectable marker values
Rodolphe Thiébaut, Jérémie Guedj, Hélène Jacqmin-Gadda, Geneviève Chêne, Pascale Trimoulet, Didier Neau, Daniel Commenges
BMC Medical Research Methodology , 2006, DOI: 10.1186/1471-2288-6-38
Abstract: The method consists in a full likelihood approach distinguishing the contribution of observed and left-censored measurements assuming a lognormal distribution of the outcome. Parameters of analytical solution of system of differential equations taking into account left-censoring are estimated using standard software.A simulation study with only 14% of measurements being left-censored showed that model parameters were largely biased (from -55% to +133% according to the parameter) with the exception of the estimate of initial outcome value when left-censored viral load values are replaced by the value of the threshold. When left-censoring was taken into account, the relative bias on fixed effects was equal or less than 2%. Then, parameters were estimated using the 100 measurements of HCV RNA available (with 12% of left-censored values) during the first 4 weeks following treatment initiation in the 17 patients included in the trial. Differences between estimates according to the method used were clinically significant, particularly on the death rate of infected cells. With the crude approach the estimate was 0.13 day-1 (95% confidence interval [CI]: 0.11; 0.17) compared to 0.19 day-1 (CI: 0.14; 0.26) when taking into account left-censoring. The relative differences between estimates of individual treatment efficacy according to the method used varied from 0.001% to 37%.We proposed a method that gives unbiased estimates if the assumed distribution is correct (e.g. lognormal) and that is easy to use with standard software.Dynamical models based on system of differential equations have been successfully used for a better understanding of the pathogenesis of infectious diseases [1,2]. Two landmark papers appeared in 1995 demonstrating the high turnover of the human immunodeficiency virus (HIV) and infected CD4+ T lymphocytes cells [3,4]. Using such dynamical models, Neumann et al. [5] gave some insight in the effect of interferon based therapy used to treat patients infect
Joint modelling of longitudinal and multi-state processes: application to clinical progressions in prostate cancer
Lo?c Ferrer,Virginie Rondeau,James J. Dignam,Tom Pickles,Hélène Jacqmin-Gadda,Cécile Proust-Lima
Statistics , 2015,
Abstract: Joint modelling of longitudinal and survival data is increasingly used in clinical trials on cancer. In prostate cancer for example, these models permit to account for the link between longitudinal measures of prostate-specific antigen (PSA) and the time of clinical recurrence when studying the risk of relapse. In practice, multiple types of relapse may occur successively. Distinguishing these transitions between health states would allow to evaluate, for example, how PSA trajectory and classical covariates impact the risk of dying after a distant recurrence post-radiotherapy, or to predict the risk of one specific type of clinical recurrence post-radiotherapy, from the PSA history. In this context, we present a joint model for a longitudinal process and a multi-state process which is divided into two sub-models: a linear mixed sub-model for longitudinal data, and a multi-state sub-model with proportional hazards for transition times, both linked by shared random effects. Parameters of this joint multi-state model are estimated within the maximum likelihood framework using an EM algorithm coupled to a quasi-Newton algorithm in case of slow convergence. It is implemented under R, by combining and extending the mstate and JM packages. The estimation program is validated by simulations and applied on pooled data from two cohorts of men with localized prostate cancer and treated by radiotherapy. Thanks to the classical covariates available at baseline and the PSA measurements collected repeatedly during the follow-up, we are able to assess the biomarker's trajectory, define the risks of transitions between health states, and quantify the impact of the PSA dynamics on each transition intensity.
Joint latent class model for longitudinal data and interval-censored semi-competing events: Application to dementia
Ana?s Rouanet,Pierre Joly,Jean-Fran?ois Dartigues,Cécile Proust-Lima,Hélène Jacqmin-Gadda
Statistics , 2015,
Abstract: Joint models are used in ageing studies to investigate the association between longitudinal markers and a time-to-event, and have been extended to multiple markers and/or competing risks. The competing risk of death must be considered in the elderly because death and dementia have common risk factors. Moreover, in cohort studies, time-to-dementia is interval-censored because dementia is only assessed intermittently. So subjects can become demented and die between two follow-up visits without being diagnosed. To study pre-dementia cognitive decline, we propose a joint latent class model combining a (possibly multivariate) mixed model and an illness-death model handling both interval censoring (by accounting for a possible unobserved transition to dementia) and semi-competing risks. Parameters are estimated by maximum likelihood handling interval censoring. The correlation between the marker and the times-to-events is captured by latent classes, homogeneous groups with specific risks of death and dementia and profiles of cognitive decline. We propose markovian and semi-markovian versions. Both approaches are compared to a joint latent class model for standard competing risks through a simulation study, and then applied in a prospective cohort study of cerebral and functional ageing to distinguish different profiles of cognitive decline associated with risks of dementia and death. The comparison highlights that among demented subjects, mortality depends more on age than duration of dementia. This model distinguishes the so-called terminal pre-death decline (among non-demented subjects) from the pre-dementia decline.
A Newton-Like Algorithm for Likelihood Maximization: The Robust-Variance Scoring Algorithm
Daniel Commenges,Helene Jacqmin-Gadda,Cecile Proust,Jeremie Guedj
Mathematics , 2006,
Abstract: This article studies a Newton-like method already used by several authors but which has not been thouroughly studied yet. We call it the robust-variance scoring (RVS) algorithm because the main version of the algorithm that we consider replaces minus the Hessian of the loglikelihood used in the Newton-Raphson algorithm by a matrix $G$ which is an estimate of the variance of the score under the true probability, which uses only the individual scores. Thus an iteration of this algorithm requires much less computations than an iteration of the Newton-Raphson algorithm. Moreover this estimate of the variance of the score estimates the information matrix at maximum. We have also studied a convergence criterion which has the nice interpretation of estimating the ratio of the approximation error over the statistical error; thus it can be used for stopping the iterative process whatever the problem. A simulation study confirms that the RVS algorithm is faster than the Marquardt algorithm (a robust version of the Newton-Raphson algorithm); this happens because the number of iterations needed by the RVS algorithm is barely larger than that needed by the Marquardt algorithm while the computation time for each iteration is much shorter. Also the coverage rates using the matrix $G$ are satisfactory.
Work and family: associations with long-term sick-listing in Swedish women – a case-control study
Hélène Sandmark
BMC Public Health , 2007, DOI: 10.1186/1471-2458-7-287
Abstract: This case-control study included 283 women on long-term sick-listing ≥90 days, and 250 female referents, randomly chosen, living in five counties in Sweden. Bivariate and multivariate logistic regression analyses with odds ratios were calculated to estimate the associations between long-term sick-listing and factors related to occupational work and family life.Long-term sick-listing in women is associated with self-reported lack of competence for work tasks (OR 2.42 1.23–11.21 log reg), workplace dissatisfaction (OR 1.89 1.14–6.62 log reg), physical workload above capacity (1.78 1.50–5.94), too high mental strain in work tasks (1.61 1.08–5.01 log reg), number of employers during work life (OR 1.39 1.35–4.03 log reg), earlier part-time work (OR 1.39 1.18–4.03 log reg), and lack of influence on working hours (OR 1.35 1.47–3.86 log reg). A younger age at first child, number of children, and main responsibility for own children was also found to be associated with long-term sick-listing. Almost all of the sick-listed women (93%) wanted to return to working life, and 54% reported they could work immediately if adjustments at work or part-time work were possible.Factors in work and in family life could be important to consider to prevent women from being long-term sick-listed and promote their opportunities to remain in working life. Measures ought to be taken to improve their mobility in work life and control over decisions and actions regarding theirs lives.Since the end of the 1990s the number of people on long-term sick leave in Sweden has increased considerably, especially with regard to women. During the first years of the 21st century, women accounted for more than 60 percent of the days for which cash benefit was paid out from the national sickness insurance due to long-term sick-listing. This concerns sick leave 60 days or more. Sick-listing has increased most for the 20–39 age group, and least for the 60–64 age group. The periods of sickness absence have also on
La producción especializada de la cerámica doméstica y ritual Mochica
Bernier,Hélène;
Estudios atacame?os , 2009, DOI: 10.4067/S0718-10432009000100010
Abstract: from the third century ad, the mochica society became the first expansionist state to develop on the peruvian north coast. in the urban capital of huacas de moche, recent excavations revealed the existence of workshops dedicated to craft production. thousands of objects produced by craft specialists were also found in various domestic and funerary contexts. this article examines the organization of specialized production of domestic and ritual ceramic objects at huacas de moche. workshops will be described and the contexts of ceramic production will be discussed. the distribution and consumption of vessels and objects made by specialized ceramists will be examined as well. through the analysis of consumption patterns, we will discuss the social roles played by ceramists in the economic, political, and ritual spheres of the mochica society.
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