Emmanuel Lesaffre

Suggestive evidence that pelvic endometriosis is a progressive disease, whereas deeply infiltrating endometriosis is associated with pelvic pain.

In a 3-year prospective study of 643 consecutive laparoscopies for infertility, pelvic pain, or infertility and pain, the pelvic area, the depth of infiltration, and the volume of endometriotic lesions were evaluated. The incidence, area, and volume of subtle lesions decreased with age, whereas for typical lesions these parameters and the depth of infiltration increased with age. Deeply infiltrating endometriosis was strongly associated with pelvic pain, women with pain having larger and deeper lesions. Because deep endometriosis has little emphasis in the revised American Fertility Society classification and after analyzing the diagnoses made in each class, considerations for a simplifying revision with inclusion of deep lesions are suggested. In conclusion, suggestive evidence is presented to support the concept that endometriosis is a progressive disorder, and it is demonstrated that deep endometriosis is strongly associated with pelvic pain.

A Linear Mixed-Effects Model with Heterogeneity in the Random-Effects Population

Abstract This article investigates the impact of the normality assumption for random effects on their estimates in the linear mixed-effects model. It shows that if the distribution of random effects is a finite mixture of normal distributions, then the random effects may be badly estimated if normality is assumed, and the current methods for inspecting the appropriateness of the model assumptions are not sound. Further, it is argued that a better way to detect the components of the mixture is to build this assumption in the model and then “compare” the fitted model with the Gaussian model. All of this is illustrated on two practical examples.

Estimating Disease Prevalence in a Bayesian Framework Using Probabilistic Constraints

Studies sometimes estimate the prevalence of a disease from the results of one or more diagnostic tests that are applied to individuals of unknown disease status. This approach invariably means that, in the absence of a gold standard and without external constraints, more parameters must be estimated than the data permit. Two assumptions are regularly made in the literature, namely that the test characteristics (sensitivity and specificity) are constant over populations and the tests are conditionally independent given the true disease status. These assumptions have been criticized recently as being unrealistic. Nevertheless, to estimate the prevalence, some restrictions on the parameter estimates need to be imposed. We consider 2 types of restrictions: deterministic and probabilistic restrictions, the latter arising in a Bayesian framework when expert knowledge is available. Furthermore, we consider 2 possible parameterizations allowing incorporation of these restrictions. The first is an extension of the approach of Gardner et al and Dendukuri and Joseph to more than 2 diagnostic tests and assuming conditional dependence. We argue that this system of equations is difficult to combine with expert opinions. The second approach, based on conditional probabilities, looks more promising, and we develop this approach in a Bayesian context. To evaluate the combination of data with the (deterministic and probabilistic) constraints, we apply the recently developed Deviance Information Criterion and effective number of parameters estimated (pD) together with an appropriate Bayesian P value. We illustrate our approach using data collected in a study on the prevalence of porcine cysticercosis with verification from external data.

Proteolytic roles of matrix metalloproteinase (MMP)‐13 during progression of chronic periodontitis: initial evidence for MMP‐13/MMP‐9 activation cascade

AIM Matrix metalloproteinases (MMP)-13 can initiate bone resorption and activate proMMP-9 in vitro, and both these MMPs have been widely implicated in tissue destruction associated with chronic periodontitis. We studied whether MMP-13 activity and TIMP-1 levels in gingival crevicular fluid (GCF) associated with progression of chronic periodontitis assessed clinically and by measuring carboxy-terminal telopeptide of collagen I (ICTP) levels. We additionally addressed whether MMP-13 could potentiate gelatinase activation in diseased gingival tissue. MATERIALS AND METHODS In this prospective study, GCF samples from subjects undergoing clinical progression of chronic periodontitis and healthy controls were screened for ICTP levels, MMP-13 activity and TIMP-1. Diseased gingival explants were cultured, treated or not with MMP-13 with or without adding CL-82198, a synthetic MMP-13 selective inhibitor, and assayed by gelatin zymography and densitometric analysis. RESULTS Active sites demonstrated increased ICTP levels and MMP-13 activity (p<0.05) in progression subjects. The MMP-9 activation rate was elevated in MMP-13-treated explants (p<0.05) and MMP-13 inhibitor prevented MMP-9 activation. CONCLUSIONS MMP-13 could be implicated in the degradation of soft and hard supporting tissues and proMMP-9 activation during progression of chronic periodontitis. MMP-13 and -9 can potentially form an activation cascade overcoming the protective TIMP-1 shield, which may become useful for diagnostic aims and a target for drug development.

A Semi-Parametric Shared Parameter Model to Handle Nonmonotone Nonignorable Missingness

Longitudinal studies often generate incomplete response patterns according to a missing not at random mechanism. Shared parameter models provide an appealing framework for the joint modelling of the measurement and missingness processes, especially in the nonmonotone missingness case, and assume a set of random effects to induce the interdependence. Parametric assumptions are typically made for the random effects distribution, violation of which leads to model misspecification with a potential effect on the parameter estimates and standard errors. In this article we avoid any parametric assumption for the random effects distribution and leave it completely unspecified. The estimation of the model is then made using a semi-parametric maximum likelihood method. Our proposal is illustrated on a randomized longitudinal study on patients with rheumatoid arthritis exhibiting nonmonotone missingness.

Fiber-reinforced dental composites in beam testing

OBJECTIVES The purpose of this study was to systematically review current literature on in vitro tests of fiber-reinforced composite (FRC) beams, with regard to studies that followed criteria described in an International Standard. The reported reinforcing effects of various fibers on the flexural strength and elastic modulus of composite resin beams were analyzed. SOURCES Original, peer reviewed papers, selected using Medline from 1950 to 2007, on in vitro testing of FRC beams in comparison to non-reinforced composite beams. Also information from conference abstracts (IADR) was included. DATA With the keywords (fiber or fibre) and (resin or composite) and (fixed partial denture or FPD), the literature search revealed 1427 titles. Using this strategy a broad view of the clinical and non-clinical literature on fiber-reinforced FPDs was obtained. Restricting to three-point bending tests, 7 articles and 1 abstract (out of 126) were included. Finally, the data of 363 composite beams were analyzed. The differences in mean flexural strength and/or modulus between reinforced and unreinforced beams were set out in a forest plot. Meta-regression analyses were performed (single and multiple regression models). CONCLUSIONS Under specific conditions we have been able to show that fibers do reinforce resin composite beams. The flexural modulus not always seems to increase with polyethylene-reinforcement, even when fibers are located at the tensile side. Besides, fiber architecture (woven vs. unidirectional) seems to be more important than the type of fiber for flexural strength and flexural modulus.

Dose-escalation models for combination phase I trials in oncology

Designing combination drug phase I trials has become increasingly complex, due to the increasing diversity in classes of agents, mechanisms of action, safety profiles and drug-administration schedules. With approximately 850 agents currently in development for cancer treatment, it is evident that combination development must be prioritised, as based on a specific hypothesis, as well as a projected development path for the involved combination. In this manuscript the most relevant issues and pitfalls for combination drug phase I trial design are discussed. Several phase I study designs that incorporate controls to circumvent bias due to imbalances in observed background toxicity are discussed.

Bayesian latent class models with conditionally dependent diagnostic tests: A case study

In the assessment of the accuracy of diagnostic tests for infectious diseases, the true disease status of the subjects is often unknown due to the lack of a gold standard test. Latent class models with two latent classes, representing diseased and non-diseased subjects, are often used to analyze this type of data. In its basic format, latent class analysis requires the observed outcomes to be statistically independent conditional on the disease status. In most diagnostic settings, this assumption is highly questionable. During the last decade, several methods have been proposed to estimate latent class models with conditional dependence between the test results. A class of flexible fixed and random effects models were described by Dendukuri and Joseph in a Bayesian framework. We illustrate these models using the analysis of a diagnostic study of three field tests and an imperfect reference test for the diagnosis of visceral leishmaniasis. We show that, as observed earlier by Albert and Dodd, different dependence models may result in similar fits to the data while resulting in different inferences. Given this problem, selection of appropriate latent class models should be based on substantive subject matter knowledge. If several clinically plausible models are supported by the data, a sensitivity analysis should be performed by describing the results obtained from different models and using different priors.

BAYESIAN SEMIPARAMETRIC INFERENCE FOR MULTIVARIATE DOUBLY-INTERVAL-CENSORED DATA

Based on a data set obtained in a dental longitudinal study, conducted in Flanders (Belgium), the joint time to caries distribution of permanent first molars was modeled as a function of covariates. This involves an analysis of multivariate continuous doubly-interval-censored data since: (i) the emergence time of a tooth and the time it experiences caries were recorded yearly, and (ii) events on teeth of the same child are dependent. To model the joint distribution of the emergence times and the times to caries, we propose a dependent Bayesian semiparametric model. A major feature of the proposed approach is that survival curves can be estimated without imposing assumptions such as proportional hazards, additive hazards, proportional odds or accelerated failure time.

Prediction of the response to peg-interferon-alfa in patients with HBeAg positive chronic hepatitis B using decline of HBV DNA during treatment

Peginterferon (PEG‐IFN) results in HBeAg loss combined with virologic response in only a minority of patients with HBeAg positive chronic hepatitis B. Baseline predictors of response to PEG‐IFN include HBV‐genotype, pre‐treatment HBV DNA levels, and ALT. The aims of this study were to develop a model, which improves the baseline prediction of response to PEG‐IFN for individual patients by including early HBV DNA measurements during treatment and to establish an early indication for cessation of treatment. One hundred thirty‐six patients treated with PEG‐IFN were included in the study. Response was defined as loss of HBeAg and HBV DNA <10,000 copies/ml at 26 weeks post‐treatment. Logistic regression analysis techniques were used to develop a dynamic prediction model with HBV DNA during the first 32 weeks of therapy. An early clinically useful rule for dis(continuation) of treatment was identified with a grid of cut‐off values of HBV DNA decline during treatment. Adding HBV DNA decline to baseline prediction increased c‐statistics from 0.846 to 0.857, 0.855 to 0.866 at weeks 4, 12, and 24. A HBV DNA decline of at least 2 log10 within 24 weeks was strongly associated with response when added to the baseline prediction model: OR 5.7 (95% CI: 1.70–20.0; P = 0.004). A dynamic model including HBV DNA decline during treatment provides more accurate predictions of response to PEG‐IFN. The model strongly supports individual decision making on treatment (dis)continuation in patients with HBeAg positive chronic hepatitis B. It is recommended that PEG‐IFN treatment is stopped by 24 weeks if HBV DNA declined <2 log10. J. Med. Virol. 82: 1135–1142, 2010.