Biomedical subjects

H C Van Houwelingen

Publications and source records attributed to H C Van Houwelingen.

14 recordsLinked to original sources

PubMed2003

Predictors of lung transplant survival in eurotransplant.

This study was undertaken to assess the influence of patient/donor and center factors on lung transplantation outcome. Outcomes of all consecutive first cadaveric lung transplants performed at 21 Eurotransplant centers in 1997-99 were analyzed. The risk-adjusted center effect on mortality was estimated. A Cox model was built including donor and recipient age and gender, primary disease, HLA mismatches, patient's residence, cold ischemic time, donor's cause of death, serum creatinine, type of lung transplant, respiratory support status, clinical condition and percentage predicted FEV1. The center effect was calculated (expressed as the standardized difference between the observed and expected survival rates), and empirical and full Bayes methods were applied to evaluate between-center differences. A total of 590 adults underwent lung transplantation. The primary disease (p=0.01), HLA-mismatches (p = 0.02), clinical condition(p < 0.0001) and the patient's respiratory support status (p = 0.05) were significantly associated with survival. After adjusting for case-mix, no between-center differences could be found. An in-depth empirical Bayes analysis showed the between-center variation to be zero. Similar results were obtained from the full Bayes analysis. Based on these data, there is no scientific basis to support a hypothesis of possible association between center volume and lung survival rates.

Adult↗

PubMed2000

On meta-analytic assessment of surrogate outcomes.

We discuss the strengths and weaknesses of the meta-analytic approach to estimating the effect of a new treatment on a true clinical outcome measure, T, from the effect of treatment on a surrogate response, S. The meta-analytic approach (see Daniels and Hughes (1997) 16, 1965-1982) uses data from a series of previous studies of interventions similar to the new treatment. The data are used to estimate relationships between summary measures of treatment effects on T and S that can be used to infer the magnitude of the effect of the new treatment on T from its effects on S. We extend the class of models to cover a broad range of applications in which the parameters define features of the marginal distribution of (T, S). We present a new bootstrap procedure to allow for the variability in estimating the distribution that governs the between-study variation. Ignoring this variability can lead to confidence intervals that are much too narrow. The meta-analytic approach relies on quite different data and assumptions than procedures that depend, for example, on the conditional independence, at the individual level, of treatment and T, given S (see Prentice (1989) 8, 431-440). Meta-analytic calculations in this paper can be used to determine whether a new study, based only on S, will yield estimates of the treatment effect on T that are precise enough to be useful. Compared to direct measurement on T, the meta-analytic approach has a number of limitations, including likely serious loss of precision and difficulties in defining the class of previous studies to be used to predict the effects on T for a new intervention.

Journal Article↗

PubMed1998

Incorporation of family history in logistic regression models.

For diseases with a genetic component, logistic regression models are presented that incorporate family history in a quantitative way. In the largest model, every type of relative has their own regression coefficient. The other two models are submodels, which incorporate family history either by the number of cases in the family minus its expectation or by a weighted number of cases in the family minus its expectation. For various genetic effects, namely polygenic and autosomal dominant effects, the performance of these simple logistic models is studied. First, the predictive values of the logistic and true genetic models are computed and compared. Secondly, a simulation study is carried out to investigate the effects of estimation of the parameters in a small data set. Thirdly, the logistic models are fitted to a data set of Von Willebrand Factor responses of target individuals and their families; in these models, family history has a significant effect. The conclusion is that for the genetic effects considered the logistic models perform well.

Female↗

PubMed1998

Modelling the cause of dependency with application to filaria infection.

A preliminary data set is analysed containing filaria specific IgG4 and IgE levels and the presence of microfilariae of 196 people from families of a village in Indonesia. Since filaria infected people may not be microfilaria positive, a filaria infection can easily be missed. First, the probabilities of a filaria infection are estimated from the IgG4 levels and the presence of microfilariae using the EM algorithm. By dichotomizing these probabilities, infection status is estimated for each person. Then for IgG4, IgE and infection status, the correlations between observations are modelled. Three causes for a correlation are considered, namely genetic, intra-uterine or environmental effects. The correlation structure of the genetic and the intra-uterine effects are quite similar and consequently it may be difficult to disentangle them. Empirical variograms are plotted and the various variance components are estimated by maximizing the log-likelihood. For infection status an environmental effect is found and for IgG4 and IgE levels genetic effects are found.

Family Health↗

PubMed1996

The outcome of kidney grafts from multiorgan donors and kidney only donors.

From 1988 to 1994, 15356 renal cadaveric transplantations have been performed within the Eurotransplant area (Austria, Belgium, Germany, Luxembourg and The Netherlands); 8746 kidneys were obtained from multiorgan donors and 6610 from kidney only donors. To evaluate the impact of the procurement policy, multiorgan donor (MOD) versus kidney only donor (KOD), on renal graft survival, an observational study has been performed. Multivariate analysis using Cox's proportional hazards model served to quantify the role of the procurement policy on renal graft survival after adjustment for other prognostic factors. The kidneys obtained from MODs had a significantly better graft survival at 1, 3, and 5 years after transplantation than the kidneys obtained from KODs (85%, 75%, and 58% versus 78%, 68%, and 46% (P=0.0001). In the Cox model, patients transplanted with a KOD kidney had a 1.28 times higher risk of losing their graft than patients transplanted with a MOD kidney. This benefit in graft survival for MOD kidneys could not be explained by the fact that the MODs were younger and male, and that UW was used as preservation solution. A plausible explanation is that MODs, on average, because of the nonrenal transplants, are better supervised. We expect that optimal donor management will contribute to a better outcome of all renal grafts.

Adenosine↗

PubMed1995

Construction, validation and updating of a prognostic model for kidney graft survival.

The construction, validation and updating of a prognostic model for kidney graft survival is reported using data from the Eurotransplant database. First, a model is constructed for data from transplantations in the period 1984 to 1987. The model is later updated for the 1988 1990 data. The first data set was randomly split into a training set (two-thirds of the data) and a validation set (one-third). To prevent overfitting empirical Bayes estimation of the transplantation centre effect was employed. After that, the validation set was used for fine-tuning by shrinkage. For updating with the 1988 1990 data parametric models were used after suitable transformation of the time axis; it appeared that survival had slightly improved. This necessitated a correction of the parameters in the exponential model. Correctness of the model was checked by extension to a Weibull model. The lack of fit was statistically significant, but practically ignorable. Recommendations are made to place less emphasis on the selection of variables and cut-off points, and more emphasis on the fine-tuning of the prognostic model by means of low-dimensional parametric models in independent data sets.

Female↗

PubMed1993

A bivariate approach to meta-analysis.

The usual meta-analysis of a sequence of randomized clinical trials only considers the difference between two treatments and produces a point estimate and a confidence interval for a parameter that measures this difference. The usual parameter is the log(odds ratio) linked to Mantel-Haenszel methodology. Inference is made either under the assumption of homogeneity or in a random effects model that takes account of heterogeneity between trials. This paper has two goals. The first is to present a likelihood based method for the estimation of the parameters in the random effects model, which avoids the use of approximating Normal distributions. The second goal is to extend this method to a bivariate random effects model, in which the effects in both groups are supposed random. In this way inference can be made about the relationship between improvement and baseline effect. The method is demonstrated by a meta-analysis dataset of Collins and Langman.

Analysis of Variance↗

PubMed1993

Relative risk, risk difference and rate difference models for sparse stratified data: a pseudo likelihood approach.

We consider a relative risk and a risk difference model for binomial data, and a rate difference model for Poisson (person year) data. It is assumed that the data are stratified in a large number of small strata. If each stratum has its own parameter in the model, then, due to the large number of parameters, straightforward maximum likelihood leads to inconsistent estimates of the relevant parameters. By contrast to the logistic model, conditioning on the number of events per stratum does not help in eliminating the stratum nuisance parameters. We propose a pseudo likelihood method to overcome these consistency problems. The resulting pseudo maximum likelihood estimates can easily be computed with standard statistical software. Our approach gives a more general framework for the Mantel-Haenszel type estimators proposed in the literature. In the special case of a series of 2 x 2 tables, for the risk and rate difference models, our approach yields exactly these ad hoc Mantel-Haenszel estimators, while for the relative risk model it gives a close approximation of the Mantel-Haenszel relative risk estimator. For the regression models corresponding to the association measures relative risk, risk difference and rate difference, our method provides analogues of conditional logistic regression, which were not previously available.

Analysis of Variance↗

PubMed1993

Cross-validation in survival analysis.

The predictive value of a statistical model is conceptually different from the explained variation. In this paper we construct a measure of the predictive value of the Cox proportional hazards model, computed from the leave-one-out regression coefficients. These coefficients can also be used to calculate a shrinkage factor which can be applied to improve the predictions and that can be used in R2-type measures of the proportion of explained variation. Our methods are illustrated by a study of chemotherapy for advanced ovarian cancer.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed1993

Risk ratio and rate ratio estimation in case-cohort designs: hypertension and cardiovascular mortality.

Multivariate analysis in case-base designs depends on approximate methods. In the present study, new pseudo-likelihood methods are developed for this design. With these methods, the case-cohort risk ratio and rate ratio as well as their standard errors are easily estimated using logistic regression and Poisson regression, respectively. This is illustrated by the association between hypertension and cardiovascular mortality in a cohort, estimated by case-cohort analysis, using samples of several sizes. The estimates are compared with those obtaining in full-cohort and nested case-control designs. The results indicate that these methods, which require nothing but widely available computer software, are valid. The case-cohort design, therefore, is a good, sometimes even advantageous alternative to the nested case-control design, in studying a disease that is not very rare. Application of the risk ratio method to the full cohort, using a 'sample' of 100 per cent follows logically; whenever the true risk ratio is desired instead of the odds ratio, a multivariate model for its estimation is therefore available.

Adult↗

PubMed1993

Growth of airways and air spaces in teenagers is related to sex but not to symptoms.

To determine growth patterns of the lung and airways in adolescents, we analyzed maximal expiratory flow-volume curves, closing capacity, and residual volume. They were obtained every 6 mo for up to 7 yr in 430 boys and 125 girls (11-19 yr), of whom 143 boys and 36 girls were classified as symptomatic; symptoms were most often minor and limited to childhood. Development of flows vs. volumes was used to investigate growth of the airways relative to lung size. A model of isotropic growth of the airways and air spaces (J. Appl. Physiol. 65: 822-828, 1988) was modified for increasing elastic recoil pressure with growth. Growth of airways relative to volume occurred faster in teenage boys than in teenage girls and was compatible with isotropic growth in 92% of asymptomatic boys and in 44% of asymptomatic girls: dysanaptic growth in teenage girls seems to be a normal phenomenon and not a unique characteristic of symptomatic subjects. Subjects with respiratory symptoms in childhood and/or adolescence have lower flows for a given lung size and airway closure at a greater lung volume when they enter adulthood. However, no difference in patterns of lung growth was observed in association with the presence of respiratory symptoms.

Adolescent↗

PubMed1993

Large lungs after childhood asthma. A case-control study.

Several studies have suggested that the TLC after childhood asthma is increased compared wtih that in healthy subjects. The aim of this study was to assess whether TLC is increased after childhood asthma and whether this is associated with an increased growth of the lung during adolescence. During a mean period of 29 months we studied 53 patients and 106 healthy control subjects who were matched for sex, age, and standing height. The patients had had asthma for a mean period of 10 yr. We found that in asthmatics TLC was increased in both sexes by about 7% predicted compared with that in the matched control subjects. The growth of TLC in ml/yr during adolescence was less in patients; this can be accounted for by a delay in pubertal development. When corrected for the delay in growth of stature, growth of TLC in ml/cm in asthmatics was similar to that found in control subjects. These findings support the hypothesis of a developmental change of enhanced lung growth during childhood asthma; they do not support a mechanism with progressive loss of elastic recoil of the lung.

Adolescent↗

PubMed1987

Factors affecting the outcome in subdural empyema.

The case reports of 102 patients with subdural empyema, diagnosed in the years 1935-83, were reviewed to determine the factors affecting the outcome. Statistical analysis (likelihood ratio tests with chi square approximation and logistic regression) showed that year of diagnosis (p less than 0.01) and level of consciousness at the moment of diagnosis (p less than 0.01) had a significant bearing on the chance to survive and that these same two factors (each factor p less than 0.01) and extent of subdural pus accumulation at the moment of diagnosis (p less than 0.05) had a significant bearing on the chance of survival without severe disability. Among others the duration of the disease up to the moment of diagnosis and the mode of the first surgical procedure had no significant bearing on the outcome. These results together with those in the literature are discussed and it is concluded that diagnosis and treatment before the patient lapses into stupor or coma, increases the chance of survival and that with adequate management a mortality rate of 10% or lower is to be expected.

Anti-Bacterial Agents↗

PubMedDate not supplied

Penalized likelihood in Cox regression.

In a Cox regression model, instability of the estimated regression coefficients can be reduced by maximizing a penalized partial log-likelihood, where a penalty function of the regression coefficients is substracted from the partial log-likelihood. In this paper, we choose the optimal weight of the penalty function by maximizing the predictive value of the model, as measured by the crossvalidated partial log-likelihood. Our methods are illustrated by a study of ovarian cancer survival and by a study of centre-effects in kidney graft survival.

Clinical Trials as Topic↗