Internationales Journals

CRTgeeDR: An R package for generalized estimating equations with missing data in cluster randomized trials.

Prague M., Wang R., DeGruttola V.

Published in :

Submitted in Journal of statistical softwares.

Abstract

Semi-parametric approaches based on generalized estimating equation (GEE) are widely used to analyse correlated outcomes. Most available softwares had been developed for longitudinal settings. In this paper, we present a R package CRTgeeDR for estimating parameters in marginal regression in cluster randomized trials (CRTs). Theory for adjusting for missing at random outcomes by inverse-probability weighting methods (IPW) based on the use of a propensity score had been largely studied and implemented. We exhibit that in CRTs most of the available softwares use an implementation of weights that lead to a bias in estimation if a non-independence working correlation structure is chosen. In CRTgeeDR, we solve this problem by using a different implementation while keeping the consistency properties of the IPW. Moreover, in CRTs using an augmented GEE (AUG) allow to improve efficiency by adjusting for treatment-covariate interactions and imbalance in baseline covariates between treatment groups using an outcome model. In CRTgeeDR, we extend the abilities of existing packages such as geepack and geeM to allow such data augmentation. Finally, one may want to combine IPW and AUG in a Doubly Robust (DR) estimator, which lead to consistent estimation when either the propensity score or the outcome model corresponds to the true data generation process Prague et al. (2016). The DR approach is implemented in CRTgeeDR. Simulations studies demonstrate the consistency of IPW implemented in CRTgeeDR and the gains associated with the use of the DR for analyzing a binary outcome using a logit regression. Finally, we reanalyzed data from a sanitation CRT in developing countries Guiteras et al. (2015) with the DR approach compared to classical GEE and demonstrated a significant intervention effect.

Bibtex

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Key words :

Augmentation, Cluster randomized trial, Correlated data, CRTgeeDR, Doubly Robust, geeM, Generalized Estimating Equation, geepack, inverse probability weighting (IPW), MAR, marginal e ect, missing data, R.

Modeling CD4+ T cells dynamics in HIV-infected patients receiving repeated cycles of exogenous interleukin 7.

A. Jarne, D. Commenges, M. Prague, Y. Levy, R. Thiébaut and the Inspire 2&3 group

Published in :

Submitted in Annals of Applied statistics.

Abstract

Combination Antiretroviral Therapy (cART) succeeds to control viral replication in most HIV infected patients. This is normally followed by a reconstitution of the CD4+ T cells pool; however, this does not happen for a substantial proportion of patients. For these patients, an immunotherapy based on injections of Interleukin 7 (IL-7) has been recently proposed as a co-adjutant treatment in the hope of obtaining long-term reconstitution of the T cells pool. Several questions arise as to the long-term efficiency of this treatment and the best protocol to apply. Mathematical and statistical models can help answering these questions. We develop a model based on a system of ordinary differential equations and a statistical model of variability and measurement. We can estimate key parameters of this model using the data from the main studies for this treatment, the INSPIRE, INSPIRE 2 & INSPIRE 3 trials. In all three studies, cycles of three injections have been administered; in the last two studies, for the first time, repeated cycles of exogenous IL-7 have been administered. Repeated measures of total CD4+ T cells count in 128 patients as well as CD4+Ki67+ T cells count (the number of cells expressing the proliferation marker Ki67) in some of them were available. Our aim was to estimate the possible different effects of successive injections in a cycle, to estimate the effect of repeated cycles and to assess different protocols. The use of dynamical models together with our complex statistical approach allow us to analyze major biological questions. We found a strong effect of IL-7 injections on the proliferation rate; however, the effect of the third injection of the cycle appears to be much weaker than the first ones. Also, despite a slightly weaker effect of repeated cycles with respect to the initial one, our simulations show the ability of this treatment of maintaining adequate CD4+ T cells count for years. We were also able to compare different protocols, showing that cycles of two injections should be sufficient in most cases.

Bibtex

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Key words :

Mechanistic models, Interleukin 7, HIV, Modeling, CD4.

Accounting for interference variables using semi-parametric augmentation for improving efficiency in clustered randomized trials with missing at random outcomes.

Prague M., Wang R., Stephens A., Tchetgen Tchetgen E, DeGruttola V.

Published in :

Accepted with minor edits in Biometrics.

Abstract

Semi-parametric methods are often used for the estimation of intervention effects on correlated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random (MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be used to deal with informative missingness. Also, augmented generalized estimating equations (AUG) correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However, in the presence of interactions between treatment and baseline covariates, neither method alone produces consistent estimates for the marginal treatment effect if the model for interaction is not correctly specified. We propose an AUG-IPW estimator that weights by the inverse of the probability of being a complete case and allows different outcome models in each intervention arm. This estimator is doubly robust (DR), it gives correct estimates whether the missing data process or the outcome model is correctly specified. We consider the problem of covariate interference which arises when the outcome of an individual may depend on covariates of other individuals. When interfering covariates are not modeled, the DR property prevents bias as long as covariate interference is not present simultaneously for the outcome and the missingness. An R package is developed implementing the proposed method. An extensive simulation study and an application to a CRT of HIV risk reduction-intervention in South Africa illustrate the method.

Bibtex

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Key words :

Augmentation; Cluster-randomized trials; GEE; Interactions; Interference; Inverse probability weighting (IPW); Missing at random (MAR); Propensity score; R package; Semi-parametric methods.

Dynamic versus marginal structural models for estimating the effect of HAART on CD4 in observational studies : application to the Aquitaine Cohort study and the Swiss HIV Cohort Study.

Prague M., Commenges D., Gran JM., Ledergerber B., Young J., Furrer H. and Thiébaut R.

Published in :

In revision in Biometrics.

Abstract

Highly active antiretroviral therapy (HAART) has proved efficient in increasing CD4 counts in many randomized clinical trials. Because randomized trials have some limitations (e.g. short du- ration, highly selected subjects), it is still interesting to assess the effect of HAART using observa- tional studies. This is however challenging because treatment is started preferentially in subjects with severe conditions, in particular in subjects with low CD4 counts. The difficulty of assessing treatment effect in observational studies is a general problem arising for other diseases which has been treated using Marginal Structural Models (MSM) relying on the counterfactual formalism. Another approach to causality is based on dynamical models. We present first discrete-time dy- namic models, based on Linear Increment Models (LIM), which the simplest models consist in one difference equation for CD4 counts while a system of two difference equations allows modeling jointly CD4 counts and viral load. Then we further extend in continuous time with mechanistic models based on ordinary differential equations with random effects (ODE-NLME). Mechanistic models allow incorporating biological knowledge when available which leads to increased power for detecting a treatment effect. Inference in ODE-NLME however is challenging from a numeri- cal point of view, and requests specific methods and softwares. LIM are a valuable intermediary option in terms of consistency, precision and complexity. The different approaches are compared using a simulation study and then applied to two HIV cohorts studies (the ANRS CO3 Aquitaine Cohort and the Swiss HIV Cohort Study).

Bibtex

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Key words :

Causality; Dynamic models; Dynamic treatment regimen; HAART; Linear Increment Models (LIM); Marginal Structural Models (MSM); Mechanistic models; Non Linear Mixed Effect Models (NLME); Observational study; ODE-NLME; Ordinary Differential Equation (ODE); Treatment effect.

Superior efficacy of a microbicide and vaccine combination over single prevention approaches against vaginal SHIV challenge in cynomolgus monkeys.

Le Grand R., Bosquet N., Dispinseri S., Hopewell N., Gosse L., Desjardins D., Shen X., Tomaras G., Saidi H., Prague M., Barnett S., Thiebaut R., Cope A., Scarlatti G., Shattock R.J.

Published in :

Submitted to Plos Pathogens.

Abstract

Although vaccines and microbicides have demonstrated partial success against HIV infection in clinical trials, their combined introduction could provide more potent protection. We used a non-human primate model to determine potential interactions of combining a partially effective microbicide with an envelope based vaccine. The vaccine-microbicide combination provided an 88% reduction in per exposure risk of infection, following 12 consecutive low dose intravaginal challenges with SHIVSF162P3 relative to vaccine alone, which provided no protection compared to controls. The microbicide alone provided a modest reduction against naïve controls (45%). Protected animals in the vaccine-microbicide group were challenged a further 12 times in the absence of microbicide and demonstrated a 98% reduction in risk of infection. Taken together a total risk reduction of 91% was observed in this group over 24 exposures (P=0.004). These important findings suggest that combined implementation of new biomedical prevention strategies may provide significant gains in HIV prevention.

Bibtex

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Key words :

Microbicide; Vaccine; HIV; Monkeys; Animal model; survival analysis; mixed effect models .

Quantifying and Predicting the Effect of Exogenous Interleukin-7 on CD4+T Cells in HIV-1 Infection

Thiébaut R., Drylewicz J., Prague M., Lacabaratz C., Beq S., Crough T., Sekaly R.P., Lederman M.M., Sereti I., Commenges D., Levy Y.

Published in :

PLOS Computational Biology. 10(5): DOI: 10.1371/journal.pcbi.1003630 (May 2014).

Abstract

Exogenous Interleukin-7 (IL-7), in supplement to antiretroviral therapy, leads to a substantial increase of all CD4+ T cell subsets in HIV-1 infected patients. However, the quantitative contribution of the several potential mechanisms of action of IL-7 is unknown. We have performed a mathematical analysis of repeated measurements of total and naive CD4+ T cells and their Ki67 expression from HIV-1 infected patients involved in three phase I/II studies (N = 53 patients). We show that, besides a transient increase of peripheral proliferation, IL-7 exerts additional effects that play a significant role in CD4+ T cell dynamics up to 52 weeks. A decrease of the loss rate of the total CD4+ T cell is the most probable explanation. If this effect could be maintained during repeated administration of IL-7, our simulation study shows that such a strategy may allow maintaining CD4+ T cell counts above 500 cells/µL with 4 cycles or fewer over a period of two years. This in-depth analysis of clinical data revealed the potential for IL-7 to achieve sustained CD4+ T cell restoration with limited IL-7 exposure in HIV-1 infected patients with immune failure despite antiretroviral therapy.

Bibtex

 @article{thiebaut2014quantifying,title={Quantifying and predicting the effect of exogenous interleukin-7 on CD4+ T cells in HIV-1 infection}, author={Thi{\'e}baut, Rodolphe and Drylewicz, Julia and Prague, M{\'e}lanie and Lacabaratz, Christine and Beq, St{\'e}phanie and Jarne, Ana and Croughs, Th{\'e}r{\`e}se and Sekaly, Rafick-Pierre and Lederman, Michael M and Sereti, Irini and others}, journal={PLoS computational biology}, volume={10}, number={5}, pages={e1003630}, year={2014}, publisher={Public Library of Science}}
                    

Key words :

T cells; HIV infections; Homeostasis; Antiretroviral therapy; T helper cells; HIV-1; Mathematical models.

Dynamical models of biomarkers and clinical progression for personalized medicine: The HIV context

M. Prague,D. Commenges and R. Thiébaut

Published in :

Advanced Drug Delivery reviews. 65(7): 954--965 (June 2013).

Abstract

Mechanistic models, based on ordinary differential equation systems, can exhibit very good predictive abilities that will be useful to build treatment monitoring strategies. In this review, we present the potential and the limitations of such models for guiding treatment (monitoring and optimizing) in HIV-infected patients. In the context of antiretroviral therapy, several biological processes should be considered in addition to the interaction between viruses and the host immune system: the mechanisms of action of the drugs, their pharmacokinetics and pharmacodynamics, as well as the viral and host characteristics. Another important aspect to take into account is clinical progression, although its implementation in such modelling approaches is not easy. Finally, the control theory and the use of intrinsic properties of mechanistic models make them very relevant for dynamic treatment adaptation. Their implementation would nevertheless require their evaluation through clinical trials.

Bibtex

 @article{prague2013dynamical,title={Dynamical models of biomarkers and clinical progression for personalized medicine: The HIV context},author={Prague, M{\'e}lanie and Commenges, Daniel and Thi{\'e}baut, Rodolphe},journal={Advanced drug delivery reviews},volume={65},number={7},pages={954--965},year={2013},publisher={Elsevier}}

                    

Key words :

Mechanistic models; Dynamic models; Personalized medicine; HIV infection; Biomarkers.

NIMROD: A Program for Inference via Normal Approximation of the Posterior in Models with Random effects based on Ordinary Differential Equations.

- Prague M., Commenges D., Guedj J., Drylewicz J., Thiébaut R.

Published in :

Computer methods and Programs in Biomedecine. 111(2): 447--458 (Aug. 2013).

Abstract

Models based on ordinary differential equations (ODE) are widespread tools for describing dynamical systems. In biomedical sciences, data from each subject can be sparse making difficult to precisely estimate individual parameters by standard non-linear regression but information can often be gained from between-subjects variability. This makes natural the use of mixed-effects models to estimate population parameters. Although the maximum likelihood approach is a valuable option, identifiability issues favour Bayesian approaches which can incorporate prior knowledge in a flexible way. However, the combination of difficulties coming from the ODE system and from the presence of random effects raises a major numerical challenge. Computations can be simplified by making a normal approximation of the posterior to find the maximum of the posterior distribution (MAP). Here we present the NIMROD program (normal approximation inference in models with random effects based on ordinary differential equations) devoted to the MAP estimation in ODE models. We describe the specific implemented features such as convergence criteria and an approximation of the leave-one-out cross-validation to assess the model quality of fit. In pharmacokinetics models, first, we evaluate the properties of this algorithm and compare it with FOCE and MCMC algorithms in simulations. Then, we illustrate NIMROD use on Amprenavir pharmacokinetics data from the PUZZLE clinical trial in HIV infected patients.

Bibtex

 @article{prague2013nimrod,title={NIMROD: A program for inference via a normal approximation of the posterior in models with random effects based on ordinary differential equations},author={Prague, M{\'e}lanie and Commenges, Daniel and Guedj, J{\'e}r{\'e}mie and Drylewicz, Julia and Thi{\'e}baut, Rodolphe},journal={Computer methods and programs in biomedicine},volume={111},number={2},pages={447--458},year={2013},publisher={Elsevier}}
                    

Key words :

Maximum likelihood; Maximum a posteriori; HIV; Non-linear mixed-effects model; Ordinary differential equations; Pharmacokinetics.

Treatment monitoring of HIV infected patients based on mechanistic models.

Prague M., Commenges D., Drylewicz J., Thiébaut R.

Published in :

Biometrics. 68(3): 902--911 (sept. 2012).

Abstract

For most patients, the HIV viral load can be made undetectable by highly active retroviral treat-ments (HAART); the virus however cannot be eradicated. Thus, the major problem is to try to reduce the sideeffects of the treatment that patients have to take during their life time. We tackle the problem of monitoring the treatment dose, with the aim of giving the minimum dose that yields an undetectable viral load. The approach is based on mechanistic models of the interaction between virus and the immune system. It is shown that the “activated cells model”, allows making good predictions of the effect of dose changes and thus could be a good basis for treatment monitoring. Then, we use the fact that in dynamical models there is a non-trivial equilibrium point, that is with a virus load larger than zero, only if the reproductive number R0 is larger than one. For reducing side effects we may give a dose just above the critical dose corresponding to R0 = 1. A prior distribution of the parameters of the model can be taken as the posterior arising from the analysis of previous clinical trials. Then the observations for a given patient can be used to dynamically tune the dose so that there is a high probability that the reproductive number is below one. The advantage of the approach is that it does not depend on a cost function, weighing side effects and efficiency of the drug. It is shown that it is possible to approach the critical dose if the model is correct. A sensitivity analysis assesses the robustness of the approach.

Bibtex

 @article{prague2012treatment,title={Treatment Monitoring of HIV-Infected Patients based on Mechanistic Models},author={Prague, M. and Commenges, D. and Drylewicz, J. and Thi{\'e}baut, R},journal={Biometrics},volume={68},number={3},pages={902-911},year={2012},publisher={Wiley Online Library}}

Key words :

Bayes; Differential equations; Epidemiology; HIV; Metropolis-Hastings algorithm; Monitoring; Optimal control.

Acute versus chronic partial sleep deprivation in middle-aged people: differential effect on performance and sleepiness.

Philip P, Sagaspe P, Prague M, Tassi P, Capelli A, Bioulac B, Commenges D, Taillard J.

Published in :

Sleep. 35(7): 997--1002 (aug. 2012)

Abstract

Study Objective: To evaluate the effects of acute sleep deprivation and chronic sleep restriction on vigilance, performance, and self-perception of sleepiness.
Design: Habitual night followed by 1 night of total sleep loss (acute sleep deprivation) or 5 consecutive nights of 4 hr of sleep (chronic sleep restriction) and recovery night.
Participants: Eighteen healthy middle-aged male participants (age [(± standard deviation] = 49.7 ± 2.6 yr, range 46-55 yr).
Measurment: Multiple sleep latency test trials, Karolinska Sleepiness Scale scores, simple reaction time test (lapses and 10% fastest reaction times), and nocturnal polysomnography data were recorded.
Results: Objective and subjective sleepiness increased immediately in response to sleep restriction. Sleep latencies after the second and third nights of sleep restriction reached levels equivalent to those observed after acute sleep deprivation, whereas Karolinska Sleepiness Scale scores did not reach these levels. Lapse occurrence increased after the second day of sleep restriction and reached levels equivalent to those observed after acute sleep deprivation. A statistical model revealed that sleepiness and lapses did not progressively worsen across days of sleep restriction. Ten percent fastest reaction times (i.e., optimal alertness) were not affected by acute or chronic sleep deprivation. Recovery to baseline levels of alertness and performance occurred after 8-hr recovery night.
Conclusions: In middle-aged study participants, sleep restriction induced a high increase in sleep propensity but adaptation to chronic sleep restriction occurred beyond day 3 of restriction. This sleepiness attenuation was underestimated by the participants. One recovery night restores daytime sleepiness and cognitive performance deficits induced by acute or chronic sleep deprivation.

Bibtex

@article{philip2012acute, title={Acute versus chronic partial sleep deprivation in middle-aged people: differential effect on performance and sleepiness.}, author={Philip, P. and Sagaspe, P. and Prague, M. and Tassi, P. and Capelli, A. and Bioulac, B. and Commenges, D. and Taillard, J.}, journal={Sleep},volume={35},number={7}, pages={997},year={2012}}

Key words :

Acute and chronic sleep deprivation; cognitive performance; sleepiness.