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ABSTRACT: We propose an extension of the landmark model for ordinary survival data as a new approach to the problem of dynamic prediction in competing risks with time-dependent covariates. We fix a set of landmark time points t(LM) within the follow-up interval. For each of these landmark time points t(LM) , we create a landmark data set by selecting individuals at risk at t(LM) ; we fix the value of the time-dependent covariate in each landmark data set at t(LM) . We assume Cox proportional hazard models for the cause-specific hazards and consider smoothing the (possibly) time-dependent effect of the covariate for the different landmark data sets. Fitting this model is possible within the standard statistical software. We illustrate the features of the landmark modelling on a real data set on bone marrow transplantation. Copyright © 2012 John Wiley & Sons, Ltd.
Statistics in Medicine 10/2012; · 1.88 Impact Factor
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ABSTRACT: Research suggests that chronically ill patients and their partners perceive illness differently, and that these differences
have a negative impact on patients’ quality of life (QoL). This study assessed whether illness perceptions of patients with
Huntington’s disease (HD) differ from those of their partners, and examined whether spousal illness perceptions are important
for the QoL of the couples (n=51 couples). Partners reported that their HD-patient spouses suffered more symptoms and experienced less control than the
patients themselves reported. Illness perceptions of patients and partners correlated significantly with patient QoL. Partners’
beliefs in a long duration of the patients’ illness and less belief in cure, were associated with patient vitality scores.
Suggestions for future research emphasize the importance of qualitative research approaches in combination with cognitive-behavioural
approaches.
Quality of Life Research 04/2012; 16(5):793-801. · 2.30 Impact Factor
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ABSTRACT: Standard methods for linkage analysis ignore the phenotype of the parents when they are not genotyped. However, this information can be useful for gene mapping. In this paper we propose methods for age at onset genetic linkage analysis in sibling pairs, taking into account parental age at onset.
Two new score statistics are derived, one from an additive gamma frailty model and one from a log-normal frailty model. The score statistics are classical non-parametric linkage (NPL) statistics weighted by a function of the age at onset of the four family members. The weight depends on information from registries (age-specific incidences) and family studies (sib-sib and father-mother correlation).
In order to investigate how age at onset of sibs and their parents affect the information for linkage analysis the weight functions were studied for rare and common disease models, realistic models for breast cancer and human lifespan. We studied the performance of the weighted NPL methods by simulations. As illustration, the score statistics were applied to the GAW12 data. The results show that it is useful to include parental age at onset information in genetic linkage analysis.
Human Heredity 12/2009; 69(2):80-90. · 1.79 Impact Factor
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ABSTRACT: We address the problem of meta-analysis of pairs of survival curves under heterogeneity. Starting point for the meta-analysis is a set of studies, each comparing the same two treatments, containing information about multiple survival outcomes. Under heterogeneity, we model the number of events using an extension of the Poisson correlated gamma-frailty model with serial within-arm and positive between-arm correlations. The parameters of the models are estimated following a two-stage estimation procedure. In the first stage the underlying hazards and between-study variance are estimated using the marginals, while a second stage is used to estimate both within-arm and between-arm correlations. The methodology is illustrated with an observational study on breast cancer.
Statistics in Medicine 11/2009; 28(30):3782-97. · 1.88 Impact Factor
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ABSTRACT: We describe a new multivariate gamma distribution and discuss its implication in a Poisson-correlated gamma-frailty model. This model is introduced to account for between-subjects correlation occurring in longitudinal count data. For likelihood-based inference involving distributions in which high-dimensional dependencies are present, it may be useful to approximate likelihoods based on the univariate or bivariate marginal distributions. The merit of composite likelihood is to reduce the computational complexity of the full likelihood. A 2-stage composite-likelihood procedure is developed for estimating the model parameters. The suggested method is applied to a meta-analysis study for survival curves.
Biostatistics 10/2008; 10(2):245-57. · 2.14 Impact Factor
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ABSTRACT: Typically long-lived sibling pairs have been collected for linkage analysis of human longevity and information on life span of first-degree relatives is available to assess familial aggregation of life span. We propose a new weighted statistic for aggregation analysis, which tests for a relationship between a family history of excessive survival of the sibships of the long-lived pairs and the survival of their parents and their offspring. For linkage analysis, we derive a new weighted score statistic from a simple gamma frailty model, which assigns more weight to excessive long-lived pairs. We apply the methods to data from the Leiden Longevity Study, which consists of sibling pairs of age 90 years or above and their first-degree relatives. The pairs have been genotyped for microsatellite markers in a candidate region. Association was present between survival within the sibships and survival of the offspring, but not with the parental generation. For linkage analysis, weighting increased the value of the test statistic, but the result was not statistically significant. About the methods we conclude that the statistic for aggregation provides insight into clustering of life span and the statistic for linkage provides a new tool to include demographic information into the analysis.
Statistics in Medicine 10/2008; 28(1):140-51. · 1.88 Impact Factor
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ABSTRACT: Research suggests that chronically ill patients and their partners perceive illness differently, and that these differences have a negative impact on patients' quality of life (QoL). This study assessed whether illness perceptions of patients with Huntington's disease (HD) differ from those of their partners, and examined whether spousal illness perceptions are important for the QoL of the couples (n = 51 couples). Partners reported that their HD-patient spouses suffered more symptoms and experienced less control than the patients themselves reported. Illness perceptions of patients and partners correlated significantly with patient QoL. Partners' beliefs in a long duration of the patients' illness and less belief in cure, were associated with patient vitality scores. Suggestions for future research emphasize the importance of qualitative research approaches in combination with cognitive-behavioural approaches.
Quality of Life Research 07/2007; 16(5):793-801. · 2.30 Impact Factor
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ABSTRACT: The problem of estimating haplotype frequencies from unphased single nucleotide polymorphism (SNP) genotype data in sibships with and without parents is considered. We focus on the Fisher information of the haplotype frequencies of the parents in order to correctly deal with the dependence of haplotypes within sibships. We compare these Fisher information matrices with those obtained for unrelated individuals and study the relative efficiency of sibships with and without parents compared to unrelated individuals in estimating haplotype frequencies. Crudely summarizing, the second sib contributes half the information of the first, except for rare haplotypes, when the second sib counts almost as one. We argue that the relative efficiencies can also be used to correct for dependence in the calculation of standard errors after initially ignoring the dependence in the estimation phase.
Human Heredity 02/2007; 64(1):52-62. · 1.79 Impact Factor
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ABSTRACT: The mean identity-by-descent (IBD) specification used in the Generalized Estimating Equations (GEE) methodology for linkage is only valid, strictly speaking, under the assumption of fully polymorphic markers. In practice, markers often provide only partial IBD information, which can potentially result in inconsistency of the locus location and gene effect estimates obtained by the GEE method. Using both simulations and theory, we identify some realistic conditions about marker information under which the validity of the GEE linkage methods may be arguable. Namely, researchers should not trust the GEE parameters' estimates and their associated confidence intervals in areas of the genome where IBD information is sparse or when this information changes abruptly. We show that properly standardized statistics based on IBD sharing provide a valid alternative.
Genetic Epidemiology 02/2006; 30(1):94-100. · 3.44 Impact Factor
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ABSTRACT: The mean identity-by-descent (IBD) specification used in the Generalized Estimating Equations (GEE) methodology for linkage is only valid, strictly speaking, under the assumption of fully polymorphic markers. In practice, markers often provide only partial IBD information, which can potentially result in inconsistency of the locus location and gene effect estimates obtained by the GEE method. Using both simulations and theory, we identify some realistic conditions about marker information under which the validity of the GEE linkage methods may be arguable. Namely, researchers should not trust the GEE parameters' estimates and their associated confidence intervals in areas of the genome where IBD information is sparse or when this information changes abruptly. We show that properly standardized statistics based on IBD sharing provide a valid alternative. Genet. Epidemiol. 2006. © 2005 Wiley-Liss Inc.
Genetic Epidemiology 12/2005; 30(1):94 - 100. · 3.44 Impact Factor
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ABSTRACT: Randomized clinical trials with long-term survival data comparing two treatments often show Kaplan-Meier plots with crossing survival curves. Such behaviour implies a violation of the proportional hazards assumption for treatment. The Cox proportional hazards regression model with treatment as a fixed effect can therefore not be used to assess the influence of treatment of survival. In this paper we analyse long-term follow-up data from the Dutch Gastric Cancer Trial, a randomized study comparing limited (D1) lymph node dissection with extended (D2) lymph node dissection. We illustrate a number of ways of dealing with survival data that do not obey the proportional hazards assumption, each of which can be easily implemented in standard statistical packages.
Statistics in Medicine 10/2005; 24(18):2807-21. · 1.88 Impact Factor
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ABSTRACT: In genetic epidemiological studies informative families are often oversampled to increase the power of a study. For a proband-family design, where relatives of probands are sampled, we derive the score statistic to test for clustering of binary and quantitative traits within families due to genetic factors. The derived score statistic is robust to ascertainment scheme. We considered correlation due to unspecified genetic effects and/or due to sharing alleles identical by descent (IBD) at observed marker locations in a candidate region. A simulation study was carried out to study the distribution of the statistic under the null hypothesis in small data-sets. To illustrate the score statistic, data from 33 families with type 2 diabetes mellitus (DM2) were analyzed. In addition to the binary outcome DM2 we also analyzed the quantitative outcome, body mass index (BMI). For both traits familial aggregation was highly significant. For DM2, also including IBD sharing at marker D3S3681 as a cause of correlation gave an even more significant result, which suggests the presence of a trait gene linked to this marker. We conclude that for the proband-family design the score statistic is a powerful and robust tool for detecting clustering of outcomes.
Annals of Human Genetics 08/2005; 69(Pt 4):373-81. · 2.57 Impact Factor
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ABSTRACT: Competing events concerning individual subjects are of interest in many medical studies. For example, leukemia-free patients surviving a bone marrow transplant are at risk of developing acute or chronic graft-versus-host disease, or they might develop infections. In this situation, competing risks models provide a natural framework to describe the disease. When incorporating covariates influencing the transition intensities, an obvious approach is to use Cox's proportional hazards model for each of the transitions separately. A practical problem then is how to deal with the abundance of regression parameters. Our objective is to describe the competing risks model in fewer parameters, both in order to avoid imprecise estimation in transitions with rare events and in order to facilitate interpretation of these estimates. Suppose that the regression parameters are gathered into a p x K matrix B, with p and K as the number of covariates and transitions, respectively. We propose the use of reduced rank models, where B is required to be of lower rank R, smaller than both p and K. One way to achieve this is to write B = AGamma(intercal) with A and Gamma matrices of dimensions p x R and K x R, respectively. We shall outline an algorithm to obtain estimates and their standard errors in a reduced rank proportional hazards model for competing risks and illustrate the approach on a competing risks model applied to 8966 leukemia patients from the European Group for Blood and Marrow Transplantation.
Biostatistics 08/2005; 6(3):465-78. · 2.14 Impact Factor
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ABSTRACT: Consistent average length differences between species and chromosome arm differences within species indicate that telomere length is genetically determined. This seems to contradict an observed large variation in lengths of the same human telomere between metaphases of the same individual. We examined the extent to which the variation in the telomeres of the human X and Y chromosomes is heritable, induced, or technical in origin.
Metaphase chromosomes were stained by fluorescence in situ hybridization with a telomere repeat-specific probe, and fluorescence intensities of the X and Y chromosomes were measured. If telomere length variation is predominantly genetically determined and a 50% probability of meiotic recombination between the pseudo-autosomal regions of Yp and Xp in the father is taken into account, one expects an equal chance that the Yp telomere of a son is derived from his father's Xp or Yp telomere. This implies that the Yp/Yq telomere ratios in fathers and sons will be identical in the absence of paternal meiotic recombination and different when recombination occurs.
Among five father-son pairs, four showed similar Yp/Yq ratios (P > 0.05), whereas one pair exhibited a large difference in the Yp/Yq ratio that was attributable to a significantly longer Xp than Yp telomere in the father and a presumptive meiotic exchange between X and Y during paternal meiosis. Further, the Xq telomere exhibited a generally shorter telomere length than the others.
The high variation in telomere length appeared to be intracellular (between sister chromatids) and, hence, technical in nature. We found no measurable induced variation in the cells studied, implying that, if induced variation exists, it is small compared with the technical variation.
Cytometry Part A 05/2005; 65(1):35-9. · 3.73 Impact Factor
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ABSTRACT: We present a unified approach to selection and linkage analysis of selected samples, for both quantitative and dichotomous complex traits. It is based on the score test for the variance attributable to the trait locus and applies to general pedigrees. The method is equivalent to regressing excess IBD sharing on a function of the traits. It is shown that when population parameters for the trait are known, such inversion does not entail any loss of information. For dichotomous traits, pairs of pedigree members of different phenotypic nature (e.g., affected sib pairs and discordant sib pairs) can easily be combined as well as populations with different trait prevalences.
Genetic Epidemiology 10/2004; 27(2):97-108. · 3.44 Impact Factor
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ABSTRACT: We present a unified approach to selection and linkage analysis of selected samples, for both quantitative and dichotomous complex traits. It is based on the score test for the variance attributable to the trait locus and applies to general pedigrees. The method is equivalent to regressing excess IBD sharing on a function of the traits. It is shown that when population parameters for the trait are known, such inversion does not entail any loss of information. For dichotomous traits, pairs of pedigree members of different phenotypic nature (e.g., affected sib pairs and discordant sib pairs) can easily be combined as well as populations with different trait prevalences. © 2004 Wiley-Liss, Inc.
Genetic Epidemiology 08/2004; 27(2):97 - 108. · 3.44 Impact Factor
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ABSTRACT: Risk estimation in breast cancer families is often estimated by use of the Claus tables. We analyzed the family histories of 196 counselees; compared the Claus tables with the Claus, the BRCA1/2, the BRCA1/2/ models; and performed linear regression analysis to extend the Claus tables with characteristics of hereditary breast cancer. Finally, we compared the Claus extended method with the Claus, the BRCA1/2, and the BRCA1/2/u models. We found 47% agreement for Claus table versus Claus model; 39% agreement for Claus table versus BRCA1/2 model; 48% agreement for Claus table versus BRCA1/2/u model; 37% agreement for Claus extended method versus Claus model; 44% agreement for Claus extended model versus BRCA1/2 model; and 66% agreement for Claus extended method versus BRCA1/2/u model. The regression formula (Claus extended method) for the lifetime risk for breast cancer was 0.08 + 0.40 (*) Claus Table + 0.07 (*) ovarian cancer + 0.08 (*) bilateral breast cancer + 0.07 (*) multiple cases. This new method for risk estimation, which is an extension of the Claus tables, incorporates information on the presence of ovarian cancer, bilateral breast cancer, and whether there are more than two affected relatives with breast cancer. This extension might offer a good alternative for breast cancer risk estimation in clinical practice.
Cancer Epidemiology Biomarkers & Prevention 02/2004; 13(1):87-93. · 4.12 Impact Factor
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Journal of Medical Genetics 08/2003; 40(7):e83. · 6.36 Impact Factor
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Journal of Medical Genetics 04/2003; 40(3):e25. · 6.36 Impact Factor
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ABSTRACT: The two most popular methods to detect linkage of a quantitative trait to a marker are the Haseman-Elston regression method and the variance components likelihood-ratio test. In the literature, these methods are frequently compared and the relative advantages and disadvantages of each method are well known. In this article, we derive a score test for the variance component attributable to a specific quantitative trait locus and show that for sib-pairs it is mathematically equivalent to a recently proposed version of the Haseman-Elston method that optimally combines the sum squared and the difference squared of the centered phenotype values of the sibs. Because score tests and likelihood-ratio tetsts are equivalent for large sample sizes, the variance components likelihood-ratio test is also asymptotically equivalent to this optimal Haseman-Elston test. This fact gives a theoretical explanation of the empirical observation from simulation studies reporting similar power of the variance components likelihood-ratio test and the optimal Haseman-Elston method. Perhaps more importantly for practical purposes, the score test can also be extended in a natural way to support the simultaneous analysis of more than two subjects and multivariate phenotypes.
Genetic Epidemiology 05/2002; 22(4):345-55. · 3.44 Impact Factor
