References of "Statistics in Medicine"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailSmooth estimation of survival functions and hazard ratios from interval-censored data using Bayesian penalized B-splines
Cetinyürek, Aysun ULg; Lambert, Philippe ULg

in Statistics in Medicine (2011), 30(1), 75-90

We discuss the use of Bayesian P-spline and of the composite link model to estimate survival functions and hazard-ratios from interval-censored data. If one further assumes proportionality of the hazards ... [more ▼]

We discuss the use of Bayesian P-spline and of the composite link model to estimate survival functions and hazard-ratios from interval-censored data. If one further assumes proportionality of the hazards, the proposed strategy provides a smoothed estimate of the baseline hazard along with estimates of global covariate effects. The frequentist properties of our Bayesian estimators are assessed by an extensive simulation study. We further illustrate the methodology by two examples showing that the proportionality of the hazards might also be found inappropriate from interval-censored data. [less ▲]

Detailed reference viewed: 29 (12 ULg)
Full Text
Peer Reviewed
See detailPerspectives on genome-wide multi-stage family-based association studies.
Van Steen, Kristel ULg

in Statistics in Medicine (2011), 30(18), 2201-2221

With the establishment of large consortiums of researchers, genome-wide association (GWA) studies have become increasingly popular and feasible. Although most of these association studies focus on ... [more ▼]

With the establishment of large consortiums of researchers, genome-wide association (GWA) studies have become increasingly popular and feasible. Although most of these association studies focus on unrelated individuals, a lot of advantages can be exploited by including families in the analysis as well. To overcome the additional genotyping cost, multi-stage designs are particularly useful. In this article, I offer a perspective view on genome-wide family-based association analyses, both within a model-based and model-free paradigm. I highlight how multi-stage designs and analysis techniques, which are quite popular in clinical epidemiology, can enter GWA settings. I furthermore discuss how they have proven successful in reducing analysis complexity, and in overcoming one of the most cumbersome statistical hurdles in the genome-wide context, namely controlling increased false positives due to multiple testing. [less ▲]

Detailed reference viewed: 15 (5 ULg)
Full Text
Peer Reviewed
See detailImproving strategies for detecting genetic patterns of disease susceptibility in association studies
Calle, M. L.; Urrea, V.; Malats, N. et al

in Statistics in Medicine (2008), 27(30), 6532-6546

Detailed reference viewed: 21 (6 ULg)
Full Text
Peer Reviewed
See detailFunctional ANOVA with random functional effects: an application to event-related potentials modelling for electroencephalograms analysis
Bugli, Céline; Lambert, Philippe ULg

in Statistics in Medicine (2006), 25

The di erential e ects of basic visual or auditory stimuli on electroencephalograms (EEG), named event related potentials (ERPs), are often used to evaluate the impact of treatments on brain performances ... [more ▼]

The di erential e ects of basic visual or auditory stimuli on electroencephalograms (EEG), named event related potentials (ERPs), are often used to evaluate the impact of treatments on brain performances. In the present paper, we propose a P-splines based model that can be used to evaluate treatment e ect on the timing and the amplitude of some peaks of the ERPs curves. Functional ANOVA is an adaptation of linear model or analysis of variance to analyse functional observations. The changes in the functional of interest e ects are generally described using smoothing splines. Eilers and Marx proposed to work with P-splines, a combination of B-splines and di erence penalties on coe cients. We de ne a Psplines model for ERPs curves combined with random e ects. In particular, we show that it is a useful alternative to classical strategies requiring the visual and usually imprecise localization of speci c ERP peaks from curves with a low signal-to-noise ratio. [less ▲]

Detailed reference viewed: 22 (2 ULg)
Full Text
Peer Reviewed
See detailBayesian proportional hazards model with time varying regression coefficients: a penalized Poisson regression approach
Lambert, Philippe ULg; Eilers, Paul H.C.

in Statistics in Medicine (2005), 24

One can fruitfully approach survival problems without covariates in an actuarial way. In narrow time bins, the number of people at risk is counted together with the number of events. The relationship ... [more ▼]

One can fruitfully approach survival problems without covariates in an actuarial way. In narrow time bins, the number of people at risk is counted together with the number of events. The relationship between time and probability of an event can then be estimated with a parametric or semi-parametric model. The number of events observed in each bin is described using a Poisson distribution with the log mean speci ed using a exible penalized B-splines model with a large number of equidistant knots. Regression on pertinent covariates can easily be performed using the same log-linear model, leading to the classical proportional hazard model. We propose to extend that model by allowing the regression coe cients to vary in a smooth way with time. Penalized B-splines models will be proposed for each of these coe cients. We show how the regression parameters and the penalty weights can be estimated e ciently using Bayesian inference tools based on the Metropolis-adjusted Langevin algorithm. [less ▲]

Detailed reference viewed: 22 (8 ULg)
Full Text
Peer Reviewed
See detailPermutation based methods for comparing quality of life between observed treatments
Moerkerke, Beatrijs; Goetghebeur, Els; Van Steen, Kristel ULg et al

in Statistics in Medicine (2005), 24(24), 4055-66

Quality of life is becoming an important outcome for the comparison of aggressive therapies. To measure quality of life (QOL), questionnaires have been designed that ask patients about symptoms and ... [more ▼]

Quality of life is becoming an important outcome for the comparison of aggressive therapies. To measure quality of life (QOL), questionnaires have been designed that ask patients about symptoms and functionality in several aspects of daily life. Primary analyses of such questionnaires typically focus on a summary statistic, such as a sum score or a single global question. This avoids inflated type I errors or loss of power due to multiple testing of individual items. In return, specific questions and answers that initially mattered to the patient may unfortunately get buried. To avoid reduced specificity and interpretability for both patients and physicians, we propose to also analyse all original questions. In this paper, we seek to detect items of the QOL questionnaire that differ significantly over observed treatments even in the face of multiple testing. We sequentially build a model that combines features which additionally discriminate between treatments. To achieve this, we draw on insights gained in the field of statistical genetics where one is often confronted with a vast amount of predictors, e.g. of a genotypic nature. Specifically, we adopt a permutation based approach to evaluate the null distribution of the maximum of many correlated test statistics and use it to build a regression model that explains QOL differences between treatment arms. We apply the new methodology to analyse QOL data in an observational study of four different treatments of breast cancer. We discover that a single question captures most of the observed treatment differences in this population. [less ▲]

Detailed reference viewed: 16 (9 ULg)
Full Text
Peer Reviewed
See detailParametric accelerated failure time models with random effects and an application to kidney transplant survival
Lambert, Philippe ULg; Collett, Dave; Kimber, Alan et al

in Statistics in Medicine (2004), 23

Accelerated failure time models with a shared random component are described, and are used to evaluate the effect of explanatory factors and different transplant centres on survival times following kidney ... [more ▼]

Accelerated failure time models with a shared random component are described, and are used to evaluate the effect of explanatory factors and different transplant centres on survival times following kidney transplantation. Different combinations of the distribution of the random effects and baseline hazard function are considered and the fit of such models to the transplant data is critically assessed. A mixture model that combines short- and long-term components of a hazard function is then developed, which provides a more flexible model for the hazard function. The model can incorporate different explanatory variables and random effects in each component. The model is straightforward to fit using standard statistical software, and is shown to be a good fit to the transplant data. [less ▲]

Detailed reference viewed: 39 (3 ULg)
Full Text
Peer Reviewed
See detailMulticollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection
Van Steen, Kristel ULg; Curran, D.; Kramer, J. et al

in Statistics in Medicine (2002), 21(24), 3865-3884

Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to ... [more ▼]

Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component. suggesting that it is redundant in the model, In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright (C) 2002 John Wiley Sons, Ltd. [less ▲]

Detailed reference viewed: 22 (4 ULg)
Full Text
Peer Reviewed
See detailA copula based model for multivariate non normal longitudinal data: analysis of a dose titration safety study on a new antidepressant
Lambert, Philippe ULg; Vandenhende, François

in Statistics in Medicine (2002), 21

A new model for multivariate non-normal longitudinal data is proposed. In a first step, each longitudinal series of data corresponding to a given response is modelled separately using a copula to relate ... [more ▼]

A new model for multivariate non-normal longitudinal data is proposed. In a first step, each longitudinal series of data corresponding to a given response is modelled separately using a copula to relate the marginal distributions of the response at each time of observation. In a second step, at each observation time, the conditional (on the past) distributions of each response are related using another copula describing the relationship between the corresponding variables. Note that there is no need to consider the same family of distributions for these response variables. The technique is illustrated in a dose titration safety study on a new antidepressant. The haemodynamic effect on diastolic blood pressure, systolic blood pressure and heart rate is studied. These three responses are measured repeatedly over time on ten healthy volunteers during the dose escalation. The available covariates are sex and the concentration of drug in the plasma at time of measurement. [less ▲]

Detailed reference viewed: 33 (3 ULg)
Peer Reviewed
See detailSensitivity analysis of longitudinal binary quality of life data with drop-out: an example using the EORTC QLQ-C30
Van Steen, Kristel ULg; Curran, D.; Molenberghs, G.

in Statistics in Medicine (2001), 20(24), 3901-20

Analysing quality of life data (QOL) may be complicated for several reasons. Quality of life data not only involves repeated measures but is also usually collected on ordered categorical responses. In ... [more ▼]

Analysing quality of life data (QOL) may be complicated for several reasons. Quality of life data not only involves repeated measures but is also usually collected on ordered categorical responses. In addition, it is evident that not all patients provide the same number of assessments, due to attrition caused by death or other medical reasons. In the recent statistical literature, increasing attention is given to methods which can handle non-continuous outcomes in the presence of missing data. The aim of this paper is to investigate the effect on statistical conclusions of applying different modelling techniques to QOL data generated from an EORTC phase III trial. Treatment effects and treatment differences are of major concern. First, a random-effects model is fitted, relating a binary longitudinal response (derived from the physical functioning scale of the QLQ-C30) to several covariates. In a second approach, marginal models are fitted, retaining the response variable and the mean structure used before. The fitted marginal models only differ with respect to the considered estimation procedure: generalized estimating equations (GEE); weighted generalized estimating equations (WGEE), and maximum likelihood (ML). [less ▲]

Detailed reference viewed: 15 (3 ULg)
Full Text
Peer Reviewed
See detailOn the appropriateness of marginal models for repeated measurements in clinical trials
Lindsey, James ULg; Lambert, Philippe ULg

in Statistics in Medicine (1998), 17

Although models developed directly to describe marginal distributions have become widespread in the analysis of repeated measurements, some of their disadvantages are not well enough known. These include ... [more ▼]

Although models developed directly to describe marginal distributions have become widespread in the analysis of repeated measurements, some of their disadvantages are not well enough known. These include producing profile curves that correspond to no possible individual, possibly showing that a treatment is superior on average when it is poorer for each individual subject, implicitly generating complex and implausible physiological explanations, including underdispersion in subgroups, and sometimes corresponding to no possible probabilistic data generating mechanism. We conclude that such marginal models may sometimes be appropriate for descriptive observational studies, such as sample surveys in epidemiology, but should only be used with great care in causal experimental settings, such as clinical trials. [less ▲]

Detailed reference viewed: 33 (4 ULg)
Full Text
Peer Reviewed
See detailModelling irregularly sampled profiles of nonnegative dog triglyceride responses under different distributional assumptions
Lambert, Philippe ULg

in Statistics in Medicine (1996), 15

General methodology for modelling series of non-negative data observed at unequally spaced times is developed. The parameterization enables both the importance of the serial association, as well the order ... [more ▼]

General methodology for modelling series of non-negative data observed at unequally spaced times is developed. The parameterization enables both the importance of the serial association, as well the order of this dependence to be expressed. An example is given where the effects of three fibre based diets on dog triglyceride profiles are analysed and compared. Many different types of models based on common distributions such as the normal, exponential, gamma, Weibull and log-normal observations are presented. Comparison of possibly non-nested models fitted on the same data set is made using the Akaike criterion. [less ▲]

Detailed reference viewed: 14 (1 ULg)
Peer Reviewed
See detailAn uncertainty measure in logistic discrimination.
Lesaffre, E; Albert, Adelin ULg

in Statistics in Medicine (1988), 7(4), 525-33

The effect of sampling variation on individual decisions and error rates in logistic discriminant analysis is discussed. The concept of the beta-confidence allocation rule is introduced, which allows ... [more ▼]

The effect of sampling variation on individual decisions and error rates in logistic discriminant analysis is discussed. The concept of the beta-confidence allocation rule is introduced, which allows testing of whether observations are (in)correctly assigned at a given significance level. The procedure applied to sample data adds valuable information on the sharpness and the stability of the estimated classification rule. The method also suggests that individual posterior probabilities should be associated with a credibility measure. The potential of the approach is illustrated by an example from patients with liver disease. [less ▲]

Detailed reference viewed: 1 (0 ULg)
Peer Reviewed
See detailDiscriminant analysis based on multivariate response curves: a descriptive approach to dynamic allocation.
Albert, Adelin ULg

in Statistics in Medicine (1983), 2(1), 95-106

We examine the problem of discriminating between two groups in the context of multivariate response curves observed over a specified time interval. We propose a descriptive solution for the case where one ... [more ▼]

We examine the problem of discriminating between two groups in the context of multivariate response curves observed over a specified time interval. We propose a descriptive solution for the case where one can determine the response curves by linear interpolation between successive observations. Unlike most previously reported methods that use only the current multivariate observation, our approach accounts for the history of the process. Moreover the method has the potential advantage of being applicable dynamically, as one observes the multivariate response curve. Finally, the method demonstrates simplicity and flexibility, two important features for successful, routine, clinical application. [less ▲]

Detailed reference viewed: 4 (0 ULg)