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Biomedical subjects

Geert Molenberghs

Publications and source records attributed to Geert Molenberghs.

At least 19 recordsLinked to original sources
PubMed2006

Comparing onset of antidepressant action using a repeated measures approach and a traditional assessment schedule.

BACKGROUND: It has been recommended that onset of antidepressant action be assessed using survival analyses with assessments taken at least twice per week. However, such an assessment schedule is problematic to implement. The present study assessed the feasibility of comparing onset of action between treatments using a categorical repeated measures approach with a traditional assessment schedule. METHOD: Four scenarios representative of antidepressant clinical trials were created by varying mean improvements over time. Two assessment schedules were compared within the simulated 8-week studies: (i) 'frequent' assessment--16 postbaseline visits (twice-weekly for 8 weeks); (ii) 'traditional' assessment--5 postbaseline visits (Weeks 1, 2, 4, 6, and 8). Onset was defined as a 20 per cent improvement from baseline, and had to be sustained at all subsequent assessments. Differences between treatments were analysed with a survival analysis (KM = Kaplan-Meier product limit method) and a categorical mixed-effects model repeated measures analysis (MMRM-CAT). RESULTS: More frequent assessments resulted in small reductions in empirical standard errors compared with traditional assessments for both analytic methods. More frequent assessments altered estimates of treatment group differences in KM such that power was increased when the difference between treatments was increasing over time, but power decreased when the treatment difference decreased over time. More frequent assessments had a minimal effect on estimates of treatment group differences in MMRM-CAT. The MMRM-CAT analysis of data from a traditional assessment schedule provided adequate control of type I error, and had power comparable to or greater than that with KM analyses of data from either a frequent or a traditional assessment schedule. CONCLUSION: In the scenarios tested in this study it was reasonable to assess treatment group differences in onset of action with MMRM-CAT and a traditional assessment schedule. Additional research is needed to assess whether these findings hold in data with drop-out and across definitions of onset.

Antidepressive Agents↗
PubMed2006

A unifying approach for surrogate marker validation based on Prentice's criteria.

Part of the recent literature on the evaluation of surrogate endpoints starts from a multi-trial approach which leads to a definition of validity in terms of the quality of both trial-level and individual-level association between a potential surrogate and a true endpoint, Buyse et al. These authors proposed their methodology based on the simplest cross-sectional case in which both the surrogate and the true endpoint are continuous and normally distributed. Different variations to this theme have been implemented for binary responses, times to event, combinations of binary and continuous endpoints, etc. However, a drawback of this methodology is that different settings have led to different definitions to quantify the association at the individual-level. In the longitudinal setting; Alonso et al. defined a class of canonical correlation functions that can be used to study surrogacy at the trial and individual-level. In the present work, we propose a new approach to evaluate surrogacy in the repeated measurements framework, we also show the connection between this proposal and the previous ones reported in the literature. Finally, we extend this concept to the non-normal case using the so-called 'likelihood reduction factor' (LRF) a new validation measure based on some of the Prentice's criteria. We apply the previous methodology using data from two clinical studies in psychiatry and ophthalmology.

Antipsychotic Agents↗
PubMed2005

Perinatal outcome of 12,021 singleton and 3108 twin births after non-IVF-assisted reproduction: a cohort study.

Perinatal outcome of pregnancies caused by assisted reproduction technique (ART) is substantially worse when compared with pregnancies following natural conception. We investigated the possible risks of non-IVF ART on perinatal health. We conducted a retrospective cohort study with two exposure groups: a study group of pregnancies after controlled ovarian stimulation (COS), with or without artificial insemination (AI), and a naturally conceived comparison group. We used the data from the regional registry of all hospital deliveries in the Dutch-speaking part of Belgium during the period from January 1993 until December 2003 to investigate differences in perinatal outcome of singleton and twin pregnancies. 12 021 singleton and 3108 twin births could be selected. Naturally conceived subjects were matched for maternal age, parity, fetal sex and year of birth. The main outcome measures were duration of pregnancy, birth weight, perinatal morbidity and perinatal mortality. Our overall results showed a significantly higher incidence of prematurity (<32 and <37 weeks), low and very low birth weight, transfer to the neonatal intensive care unit and most neonatal morbidity parameters for COS/AI singletons. Twin pregnancies resulting from COS/AI showed an increased rate of neonatal mortality, assisted ventilation and respiratory distress syndrome. After excluding same-sex twin sets, COS/AI twin pregnancies were at increased risk for extreme prematurity and very low birth weight. In conclusion, COS/AI singleton and twin pregnancies are significantly disadvantaged compared to naturally conceived children.

Adult↗
PubMed2005

Behavioral testing of antidepressant compounds: an analysis of crossover design for correlated binary data.

The differential reinforcement of low-rate 72 seconds schedule (DRL-72) is a standard behavioral test procedure for screening potential antidepressant compounds. The protocol for the DRL-72 experiment, proposed by Evenden et al. (1993), consists of using a crossover design for the experiment and one-way ANOVA for the statistical analysis. In this paper we discuss the choice of several crossover designs for the DRL-72 experiment and propose to estimate the treatment effects using either generalized linear mixed models (GLMM) or generalized estimating equation (GEE) models for clustered binary data.

Algorithms↗
PubMed2005

Perinatal outcome of ICSI pregnancies compared with a matched group of natural conception pregnancies in Flanders (Belgium): a cohort study.

A retrospective cohort study was conducted with an intracytoplasmic sperm injection (ICSI) group and a naturally conceived comparison group. A total of 1655 singleton and 1102 twin ICSI births were studied with regard to perinatal outcome. Control subjects (naturally conceived pregnancies) were selected from a regional registry and were matched for maternal age, parity, place of delivery, year of birth and fetal sex. The main outcome measures were duration of pregnancy, birth weight, Apgar score <5 after 5 min, neonatal complications, perinatal death and congenital malformations. Twin births, when compared with singletons, carry a much higher risk of poor perinatal outcome. For both ICSI singletons and ICSI twins, no significant difference was found between ICSI and naturally conceived pregnancies for all investigated parameters. After excluding like-sex twin pairs, ICSI twin pregnancies were at increased risk for perinatal mortality (OR = 2.74, CI = 1.26-5.98), prematurity (OR = 1.38, CI = 1.10-1.75) and low birth weight (OR = 1.34, CI = 1.06-1.69) compared with spontaneously conceived different-sex twin pairs. In conclusion, the perinatal outcome of ICSI singleton and twin pregnancies was very similar to that of spontaneously conceived pregnancies in this large cohort study. After excluding like-sex twin pairs, ICSI twins were at increased risk for prematurity, low birth weight and higher perinatal mortality compared with the natural conception comparison group.

Adult↗
PubMed2005

Obstetric and perinatal outcome of 1655 ICSI and 3974 IVF singleton and 1102 ICSI and 2901 IVF twin births: a comparative analysis.

A total of 3974 IVF and 1655 ICSI singleton births and 2901 IVF and 1102 ICSI twin births were evaluated. Pregnancies after both fresh and frozen transfers were included. IVF and ICSI singleton pregnancies were very similar for most obstetric and perinatal variables. The only significant difference was a higher risk for prematurity (< 37 weeks of amenorrhoea) in IVF pregnancies compared with ICSI pregnancies (12.4 versus 9.2%, OR = 1.39, 95% CI = 1.15-1.70). For twin pregnancies, differences were not statistically different except for a higher incidence of stillbirths in the ICSI group (2.08 versus 1.03%, OR = 2.04, 95% CI = 1.14-3.64). Intrauterine growth retardation with or without pregnancy-induced hypertension was observed more often in the ICSI group. Regression analysis of the data with correction for parity and female age showed similar results for twins. For singletons, this analysis showed similar results with the exception of low birth weight babies (< 2500 g), which were also observed more often in IVF pregnancies (9.6 versus 7.9%, OR = 0.79, CI = 0.65-0.98, P = 0.03). This large case-comparative retrospective analysis showed that the obstetric outcome and perinatal health of IVF and ICSI pregnancies is comparable.

Adult↗
PubMed2005

Applying concepts of generalizability theory on clinical trial data to investigate sources of variation and their impact on reliability.

This work aims at applying concepts of generalizability theory to data resulting from clinical trials. The focus is to study the sources of variance and their impact on the reliability and generalizability of a psychiatric measurement scale. The goal is to identify, measure, and thereby potentially find strategies to reduce the influence of these sources on the measurement in question for future trials. This approach was originally devised by Cronbach and his associates and is known as generalizability theory. This work shows how full modeling power in mixed models can be used to study generalizability using data from five double-blind randomized clinical trials, comparing the effects of risperidone to conventional antipsychotic agents for the treatment of chronic schizophrenia.

Antipsychotic Agents↗
PubMed2005

Biometry, biometrics, biostatistics, bioinformatics,..., bio-X.

Recent scientific evolutions force us to rethink our profession's position on the scientific map, in relation to our neighboring professions, the ones with which we traditionally have strong collaborative links as well as the newly emerging fields, but also within our own, diverse professional group. We will show that great inspiration can be drawn from our own history, in fact from the early days of the Society. A recent inspiring example has been set by the late Rob Kempton, who died suddenly just months before he was to become President of the International Biometric Society.

Biometry↗
PubMed2005

Direct likelihood analysis versus simple forms of imputation for missing data in randomized clinical trials.

BACKGROUND: In many clinical trials, data are collected longitudinally over time. In such studies, missingness, in particular dropout, is an often encountered phenomenon. METHODS: We discuss commonly used but often problematic methods such as complete case analysis and last observation carried forward and contrast them with broadly valid and easy to implement direct-likelihood methods. We comment on alternatives such as multiple imputation and the expectation-maximization algorithm. RESULTS: We apply these methods in particular to data from a study with continuous outcomes. The outcomes are modelled using a general linear mixed-effects model. The bias with CC and LOCF is established in the case study and the advantages of the direct-likelihood approach shown. CONCLUSIONS: We have established formal but easy to understand arguments for a shift towards a direct-likelihood paradigm when analysing incomplete data from longitudinal clinical trials, necessitating neither imputation nor deletion.

Data Collection↗
PubMed2005

Modeling anti-KLH ELISA data using two-stage and mixed effects models in support of immunotoxicological studies.

During preclinical drug development, the immune system is specifically evaluated after prolonged treatment with drug candidates, because the immune system may be an important target system. The response of antibodies against a T-cell-dependent antigen is recommenced by the FDA and EMEA for the evaluation of immunosuppression/enhancement. For that reason, we developed a semiquantitative enzyme-linked immunosorbent assay to measure antibodies against keyhole limpet hemocyanin. To our knowledge, the analysis of this kind of data is at this moment not yet fully explored. In this article, we describe two approaches for modeling immunotoxic data using nonlinear models. The first is a two-stage model in which we fit an individual nonlinear model for each animal in the first stage, and the second stage consists of testing possible treatment effects using the individual maximum likelihood estimates obtained in the first stage. In the second approach, the inference about treatment effects is based on a nonlinear mixed model, which accounts for heterogeneity between animals. In both approaches, we use a three-parameter logistic model for the mean structure.

Analysis of Variance↗
PubMed2005

A hierarchical Binomial-Poisson model for the analysis of a crossover design for correlated binary data when the number of trials is dose-dependent.

The differential reinforcement of a low-rate 72-seconds schedule (DRL-72) is a standard behavioral test procedure for screening a potential antidepressant compound. The data analyzed in the article are binary outcomes from a crossover design for such an experiment. Recently, Shkedy et al. (2004) proposed to estimate the treatments effect using either generalized linear mixed models (GLMM) or generalized estimating equations (GEE) for clustered binary data. The models proposed by Shkedy et al. (2004) assumed the number of responses at each binomial observation is fixed. This might be an unrealistic assumption for a behavioral experiment such as the DRL-72 because the number of responses (the number of trials in each binomial observation) is expected to be influenced by the administered dose level. In this article, we extend the model proposed by Shkedy et al. (2004) and propose a hierarchical Bayesian binomial-Poisson model, which assumes the number of responses to be a Poisson random variable. The results obtained from the GLMM and the binomial-Poisson models are comparable. However, the latter model allows estimating the correlation between the number of successes and number of trials.

Algorithms↗
PubMed2004

Modelling associations between time-to-event responses in pilot cancer clinical trials using a Plackett-Dale model.

This work was motivated by the need to find surrogate endpoints for survival of patients in oncology studies. The goal of this article is to determine associations between five time-to-event outcomes coming from three clinical trials for non-small cell lung cancer. To this end, we propose to use the multivariate Dale model for time-to-event data introduced by Tibaldi et al. (Stat. Med. 2003). We fit the model to these data, using a pseudo-likelihood approach to estimate the model parameters. We evaluate and compare the performance of different dimensional models and we relate the Dale model association parameter, i.e. the odds ratio, to well-known quantities such as Kendall's tau and Spearman's rho. Finally, the results are discussed with a perspective on surrogate marker validation. Some suggestions are made regarding further studies in this field.

Adjuvants, Immunologic↗
PubMed2004

Pseudo-likelihood estimation for a marginal multivariate survival model.

In this paper, we propose a multivariate Plackett-Dale model for survival outcomes. A pseudo-likelihood method for the estimation of the parameters is proposed and these ideas are applied to two case studies. The modelling approach is similar in spirit but different from Parner's approach. The first study is in AIDS, where the overall survival time and different opportunistic infections in HIV-infected patients are studied. The second study is on adoption data where the association of the survival times within families is modelled, illustrating the use of the proposed methodology for the context of population genetics.

Acquired Immunodeficiency Syndrome↗
PubMed2004

Applying linear mixed models to estimate reliability in clinical trial data with repeated measurements.

Repeated measures are exploited to study reliability in the context of psychiatric health sciences. It is shown how test-retest reliability can be derived using linear mixed models when the scale is continuous or quasi-continuous. The advantage of this approach is that the full modeling power of mixed models can be used. Repeated measures with a different mean structure can be used to usefully study reliability, correction for covariate effects is possible, and a complicated variance-covariance structure between measurements is allowed. In case the variance structure reduces to a random intercept (compound symmetry), classical methods are recovered. With more complex variance structures (e.g., including random slopes of time and/or serial correlation), time-dependent reliability functions are obtained. The methodology is motivated by and applied to data from five double-blind randomized clinical trials comparing the effects of risperidone to conventional antipsychotic agents for the treatment of chronic schizophrenia. Model assumptions are investigated through residual plots and by investigating the effect of influential observations.

Analysis of Variance↗
PubMed2004

Sensitivity analysis for pattern mixture models.

Incomplete series of data is a common feature in quality-of-life studies, in particular in chronic diseases where attrition of patients is high. Two alternative approaches to modeling longitudinal data with incomplete measurements have frequently been proposed in the literature, selection models and pattern-mixture models. In this paper we focus on, by way of sensitivity analysis, extrapolating incomplete patterns using identifying restrictions. Perhaps the best known ones are so-called complete case missing value restrictions (CCMV), where for a given pattern, the conditional distribution of the missing data, given the observed data, is equated to its counterpart in the completers. Available case missing value (ACMV) restrictions equate this conditional density to the one calculated from the subgroup of all patterns for which all required components have been observed. Neighboring case missing value restrictions (NCMV) equate this conditional density to the one calculated from the the pattern with one additional measurement obtained. In this paper, these three identifying restriction strategies are used to multiply impute missing data in a study in metastatic prostate cancer. Multiple imputation is employed to reduce the uncertainty of single imputation. It is shown how hypothesis testing and sensitivity analyses are carried out in this setting.

Humans↗
PubMed2004

Analyzing incomplete longitudinal clinical trial data.

Using standard missing data taxonomy, due to Rubin and co-workers, and simple algebraic derivations, it is argued that some simple but commonly used methods to handle incomplete longitudinal clinical trial data, such as complete case analyses and methods based on last observation carried forward, require restrictive assumptions and stand on a weaker theoretical foundation than likelihood-based methods developed under the missing at random (MAR) framework. Given the availability of flexible software for analyzing longitudinal sequences of unequal length, implementation of likelihood-based MAR analyses is not limited by computational considerations. While such analyses are valid under the comparatively weak assumption of MAR, the possibility of data missing not at random (MNAR) is difficult to rule out. It is argued, however, that MNAR analyses are, themselves, surrounded with problems and therefore, rather than ignoring MNAR analyses altogether or blindly shifting to them, their optimal place is within sensitivity analysis. The concepts developed here are illustrated using data from three clinical trials, where it is shown that the analysis method may have an impact on the conclusions of the study.

Antidepressive Agents↗
PubMed2004

Prentice's approach and the meta-analytic paradigm: a reflection on the role of statistics in the evaluation of surrogate endpoints.

We put a perspective on the strengths and limitations of statistical methods for the evaluation of surrogate endpoints. Whereas using several trials overcomes some of the limitations of a single-trial framework (Prentice, 1989, Statistics in Medicine 8, 431-440), arguably the evaluation of surrogate endpoints can never be done using only statistical evidence but such evidence should be seen as but one component in a decision-making process that involves, among others, a number of clinical and biological considerations. We briefly present a hierarchical framework that incorporates ideas from Prentice's work and is uniformly applicable to different types of surrogate and true clinical outcomes.

Biometry↗