Thomas H. Scheike

Copenhagen University Hospital, København, Capital Region, Denmark

Are you Thomas H. Scheike?

Claim your profile

Publications (120)405.11 Total impact

  • Jianing Li, Thomas H Scheike, Mei-Jie Zhang
    [Show abstract] [Hide abstract]
    ABSTRACT: Recently, Fine and Gray (J Am Stat Assoc 94:496-509, 1999) proposed a semi-parametric proportional regression model for the subdistribution hazard function which has been used extensively for analyzing competing risks data. However, failure of model adequacy could lead to severe bias in parameter estimation, and only a limited contribution has been made to check the model assumptions. In this paper, we present a class of analytical methods and graphical approaches for checking the assumptions of Fine and Gray's model. The proposed goodness-of-fit test procedures are based on the cumulative sums of residuals, which validate the model in three aspects: (1) proportionality of hazard ratio, (2) the linear functional form and (3) the link function. For each assumption testing, we provide a [Formula: see text]-values and a visualized plot against the null hypothesis using a simulation-based approach. We also consider an omnibus test for overall evaluation against any model misspecification. The proposed tests perform well in simulation studies and are illustrated with two real data examples.
    Lifetime Data Analysis 11/2014; · 0.85 Impact Factor
  • Thomas H Scheike, Klaus K Holst, Jacob B Hjelmborg
    [Show abstract] [Hide abstract]
    ABSTRACT: We consider data from the Danish twin registry and aim to study in detail how lifetimes for twin-pairs are correlated. We consider models where we specify the marginals using a regression structure, here Cox's regression model or the additive hazards model. The best known such model is the Clayton-Oakes model. This model can be extended in several directions. One extension is to allow the dependence parameter to depend on covariates. Another extension is to model dependence via piecewise constant cross-hazard ratio models. We show how both these models can be implemented for large sample data, and suggest a computational solution for obtaining standard errors for such models for large registry data. In addition we consider alternative models that have some computational advantages and with different dependence parameters based on odds ratios of the survival function using the Plackett distribution. We also suggest a way of assessing how and if the dependence is changing over time, by considering either truncated or right-censored versions of the data to measure late or early dependence. This can be used for formally testing if the dependence is constant, or decreasing/increasing. The proposed procedures are applied to Danish twin data to describe dependence in the lifetimes of the twins. Here we show that the early deaths are more correlated than the later deaths, and by comparing MZ and DZ associations we suggest that early deaths might be more driven by genetic factors. This conclusion requires models that are able to look at more local dependence measures. We further show that the dependence differs for MZ and DZ twins and appears to be the same for males and females, and that there are indications that the dependence increases over calendar time.
    Lifetime Data Analysis 09/2014; · 0.85 Impact Factor
  • Klaus K. Holst, Thomas H. Scheike, Jacob B. Hjelmborg
    [Show abstract] [Hide abstract]
    ABSTRACT: Family studies provide an important tool for understanding etiology of diseases, with the key aim of discovering evidence of family aggregation and to determine if such aggregation can be attributed to genetic components. Heritability and concordance estimates are routinely calculated in twin studies of diseases, as a way of quantifying such genetic contribution. The endpoint in these studies are typically defined as occurrence of a disease versus death without the disease. However, a large fraction of the subjects may still be alive at the time of follow-up without having experienced the disease thus still being at risk. Ignoring this right-censoring can lead to severely biased estimates. We propose to extend the classical liability threshold model with inverse probability of censoring weighting of complete observations. This leads to a flexible way of modeling twin concordance and obtaining consistent estimates of heritability. We apply the method in simulations and to data from the population based Danish twin cohort where we describe the dependence in prostate cancer occurrence in twins.
    07/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Background: Prostate cancer is thought to be the most heritable cancer, although little is known about how this genetic contribution varies across age. Methods: To address this question, we undertook the world's largest prospective study in the Nordic Twin Study of Cancer cohort, including 18,680 monozygotic and 30,054 dizygotic same sex male twin pairs. We incorporated time-to-event analyses to estimate the risk concordance and heritability while accounting for censoring and competing risks of death, essential sources of biases that have not been accounted for in previous twin studies modeling cancer risk and liability. Results: The cumulative risk of prostate cancer was similar to that of the background population. The cumulative risk for twins whose co-twin was diagnosed with prostate cancer was greater for MZ than for DZ twins across all ages. Among concordantly affected pairs, the time between diagnoses was significantly shorter for MZ than DZ pairs (median 3.8 versus 6.5 years, respectively). Genetic differences contributed substantially to variation in both the risk and the liability (heritability=58% (95% CI 52%-63%) of developing prostate cancer. The relative contribution of genetic factors was constant across age through late life with substantial genetic heterogeneity even when diagnosis and screening procedures vary. Conclusions: Results from the population based twin cohort, indicate a greater genetic contribution to the risk of developing prostate cancer when addressing sources of bias. The role of genetic factors is consistently high across age Impact: Findings impact the search for genetic and epigenetic markers and frame prevention efforts.
    Cancer Epidemiology Biomarkers &amp Prevention 05/2014; · 4.56 Impact Factor
  • Thomas H Scheike, Klaus K Holst, Jacob B Hjelmborg
    [Show abstract] [Hide abstract]
    ABSTRACT: For twin time-to-event data, we consider different concordance probabilities, such as the casewise concordance that are routinely computed as a measure of the lifetime dependence/correlation for specific diseases. The concordance probability here is the probability that both twins have experienced the event of interest. Under the assumption that both twins are censored at the same time, we show how to estimate this probability in the presence of right censoring, and as a consequence, we can then estimate the casewise twin concordance. In addition, we can model the magnitude of within pair dependence over time, and covariates may be further influential on the marginal risk and dependence structure. We establish the estimators large sample properties and suggest various tests, for example, for inferring familial influence. The method is demonstrated and motivated by specific twin data on cancer events with the competing risk death. We thus aim to quantify the degree of dependence through the casewise concordance function and show a significant genetic component. Copyright © 2013 John Wiley & Sons, Ltd.
    Statistics in Medicine 10/2013; · 2.04 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Do birthweight (BW) and co-twin sex influence the age at menarche in twins? BW, but not co-twin sex, was associated with age at menarche in twins. However, BW was not associated with age at menarche after controlling for genetics and shared rearing environment. Nutritional deprivation during critical developmental periods can trigger long-term effects on health. A small size at birth has been associated with early age at menarche in singletons. However, the relative influence of genetics and environmental factors on this association remains unresolved. In total, 2505 twin pairs were included in this cohort study. All participants were recruited from the Danish Twin Register. Data on the age at menarche were collected by questionnaire and combined with data on BW, birth length (BL) and gestational age (GA) from the Danish Medical Birth register. The BW for GA standard deviation score (BW-SDS) was calculated. BW-SDS [hazard ratio (HR) 0.96; 95% confidence interval (CI): 0.93-0.00], P = 0.04], but not BW, BL or GA (P ≥ 0.15), was positively associated with age at menarche in all twins after adjustment for zygosity and year of birth. However, BW-SDS was not associated with menarcheal age within twin pairs (HR 1.01; 95% CI: 0.91-1.12, P = 0.88). No differences were found in the age at menarche or birth size between twin girls from same sex and twin girls from opposite-sex pregnancies. Heritability of menarcheal age and BW were estimated to be 0.61 (95% CI: 0.38-0.84) and 0.27 (95% CI: 0.18-0.38), respectively. Both BW and menarcheal age were influenced by genetic and environmental factors. A limitation of this study is recall bias on the age at menarche. It is also not clear how these results should be extrapolated to the non-twin population. lower BW for GA is associated with earlier age at menarche in twin girls. However, the lack of within-pair differences in menarcheal age between even markedly BW-discordant twins indicates that this association is governed by environmental or genetic factors shared by both twins. Thus, within-pair differences in intrauterine nutritional factors leading to discordant growth do not seem to influence timing of menarche. The authors have nothing to declare. Departmental funds were used to support all authors during the study period and preparation.
    Human Reproduction 08/2013; · 4.67 Impact Factor
  • Thomas H Scheike, Klaus K Holst, Jacob B Hjelmborg
    [Show abstract] [Hide abstract]
    ABSTRACT: There has been considerable interest in studying the magnitude and type of inheritance of specific diseases. This is typically derived from family or twin studies, where the basic idea is to compare the correlation for different pairs that share different amount of genes. We here consider data from the Danish twin registry and discuss how to define heritability for cancer occurrence. The key point is that this should be done taking censoring as well as competing risks due to e.g. death into account. We describe the dependence between twins on the probability scale and show that various models can be used to achieve sensible estimates of the dependence within monozygotic and dizygotic twin pairs that may vary over time. These dependence measures can subsequently be decomposed into a genetic and environmental component using random effects models. We here present several novel models that in essence describe the association in terms of the concordance probability, i.e., the probability that both twins experience the event, in the competing risks setting. We also discuss how to deal with the left truncation present in the Nordic twin registries, due to sampling only of twin pairs where both twins are alive at the initiation of the registries.
    Lifetime Data Analysis 02/2013; · 0.85 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we consider a problem from hematopoietic cell transplant (HCT) studies where there is interest on assessing the effect of haplotype match for donor and patient on the cumulative incidence function for a right censored competing risks data. For the HCT study, donor's and patient's genotype are fully observed and matched but their haplotypes are missing. In this paper we describe how to deal with missing covariates of each individual for competing risks data. We suggest a procedure for estimating the cumulative incidence functions for a flexible class of regression models when there are missing data, and establish the large sample properties. Small sample properties are investigated using simulations in a setting that mimics the motivating haplotype matching problem. The proposed approach is then applied to the HCT study.
    Lifetime Data Analysis 09/2012; · 0.85 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: BACKGROUND & AIMS: Hyperaminoacidemia stimulates myofibrillar fractional synthesis rate (myoFSR) transiently in resting skeletal muscle. We investigated whether light-load resistance exercise can extent this responsiveness. METHODS: Ten healthy males exercised one leg with a light-load resistance-like exercise at 16% of 1 repetition maximum and received oral protein boluses every hour for a 10-h period. Their myoFSR was determined by [1-(13)C]-leucine incorporation. Muscle biopsies were obtained from the resting (REST) and exercised (EXC) muscles every 2.5-h in the protein-fed period. RESULTS: Protein feeding significantly elevated plasma leucine and essential amino acids by an average of 39 ± 9% (mean ± SEM) and 20 ± 4%, respectively, compared to the basal concentrations: 197 ± 12 μmol L(-1) and 854 ± 35 μmol L(-1), respectively. The myoFSR was similar in EXC and REST muscles in the first 8 h (all time intervals p > 0.05). After 8 h the myoFSR dropped in the REST muscle to 0.041 ± 0.005%·h(-1), which was 65 ± 5% of the rate in EXC leg at the same time point (0.062 ± 0.004%·h(-1)) and 80 ± 14% of the level in REST leg from 0.5 to 8 h (0.056 ± 0.005%·h(-1)) (interaction p < 0.05). CONCLUSIONS: Compared to rest, light-load exercise prolonged the stimulatory effect of dietary protein on muscle biosynthesis providing perspectives for a muscle restorative effect in clinical settings where strenuous activity is intolerable.
    Clinical nutrition (Edinburgh, Scotland) 08/2012; · 3.27 Impact Factor
  • Source
    Thomas A Gerds, Thomas H Scheike, Per K Andersen
    [Show abstract] [Hide abstract]
    ABSTRACT: In survival analysis with competing risks, the transformation model allows different functions between the outcome and explanatory variables. However, the model's prediction accuracy and the interpretation of parameters may be sensitive to the choice of link function. We review the practical implications of different link functions for regression of the absolute risk (or cumulative incidence) of an event. Specifically, we consider models in which the regression coefficients β have the following interpretation: The probability of dying from cause D during the next t years changes with a factor exp(β) for a one unit change of the corresponding predictor variable, given fixed values for the other predictor variables. The models have a direct interpretation for the predictive ability of the risk factors. We propose some tools to justify the models in comparison with traditional approaches that combine a series of cause-specific Cox regression models or use the Fine-Gray model. We illustrate the methods with the use of bone marrow transplant data. Copyright © 2012 John Wiley & Sons, Ltd.
    Statistics in Medicine 08/2012; · 2.04 Impact Factor
  • Thomas H Scheike, Yanqing Sun
    [Show abstract] [Hide abstract]
    ABSTRACT: The cross-odds ratio is defined as the ratio of the conditional odds of the occurrence of one cause-specific event for one subject given the occurrence of the same or a different cause-specific event for another subject in the same cluster over the unconditional odds of occurrence of the cause-specific event. It is a measure of the association between the correlated cause-specific failure times within a cluster. The joint cumulative incidence function can be expressed as a function of the marginal cumulative incidence functions and the cross-odds ratio. Assuming that the marginal cumulative incidence functions follow a generalized semiparametric model, this paper studies the parametric regression modeling of the cross-odds ratio. A set of estimating equations are proposed for the unknown parameters and the asymptotic properties of the estimators are explored. Non-parametric estimation of the cross-odds ratio is also discussed. The proposed procedures are applied to the Danish twin data to model the associations between twins in their times to natural menopause and to investigate whether the association differs among monozygotic and dizygotic twins and how these associations have changed over time.
    Biostatistics 06/2012; 13(4):680-94. · 2.43 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Insurance customers usually hold more than one contract with the same insurer. A generalization of classical survival analysis methods is used to examine the risk of losing a customer once an initial insurance policy cancellation has occurred. This method does not assume that the model parameters are fixed over time, but rather that the parameters may fluctuate. Our results suggest that the kind of contracts held by customers and the concurrence of an external competitor strongly influence customer loyalty right after that cancellation, but those factors become much less significant some months later. Our study shows how predictions of the probability of losing a customer can be readjusted and improves the way companies manage business risk.
    Expert Systems with Applications 02/2012; 39:3551-3558. · 1.85 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To produce a fetal weight chart representative of a Tanzanian population, and compare it to weight charts from Sub-Saharan Africa and the developed world. A longitudinal observational study in Northeastern Tanzania. Pregnant women were followed throughout pregnancy with serial trans-abdominal ultrasound. All pregnancies with pathology were excluded and a chart representing the optimal growth potential was developed using fetal weights and birth weights. The weight chart was compared to a chart from Congo, a chart representing a white population, and a chart representing a white population but adapted to the study population. The prevalence of SGA was assessed using all four charts. A total of 2193 weight measurements from 583 fetuses/newborns were included in the fetal weight chart. Our chart had lower percentiles than all the other charts. Most importantly, in the end of pregnancy, the 10(th) percentiles deviated substantially causing an overestimation of the true prevalence of SGA newborns if our chart had not been used. We developed a weight chart representative for a Tanzanian population and provide evidence for the necessity of developing regional specific weight charts for correct identification of SGA. Our weight chart is an important tool that can be used for clinical risk assessments of newborns and for evaluating the effect of intrauterine exposures on fetal and newborn weight.
    PLoS ONE 01/2012; 7(9):e44773. · 3.53 Impact Factor
  • Torben Martinussen, Thomas H. Scheike, David M. Zucker
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we consider clustered right-censored time-to-event data. Such data can be analysed either using a marginal model if one is interested in population effects or using so-called frailty models if one is interested in covariate effects on the individual level and in estimation of correlation. The Cox frailty model has been studied extensively in the last decade or so and estimation techniques and large sample results are now available. It is, however, difficult to deal with time-changing covariate effects when using the Cox model. An appealing alternative model is the Aalen additive hazards model, in which it is easy to work with time dynamics. In this paper, we describe an innovative approach to estimation in the Aalen additive gamma frailty hazards model. We give the large sample properties of the estimators and investigate their small sample properties by Monte Carlo simulation. A real example is provided for illustration. Copyright 2011, Oxford University Press.
    Biometrika 11/2011; 98(4). · 1.65 Impact Factor
  • Source
    Thomas H Scheike, Torben Martinussen, Mei-Jie Zhang
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we consider a problem from bone marrow transplant (BMT) studies where there is interest on assessing the effect of haplotype match for donor and patient on the overall survival. The BMT study we consider is based on donors and patients that are genotype matched, and this therefore leads to a missing data problem. We show how Aalen's additive risk model can be applied in this setting with the benefit that the time-varying haplo-match effect can be easily studied. This problem has not been considered before, and the standard approach where one would use the EM-algorithm cannot be applied for this model because the likelihood is hard to evaluate without additional assumptions. We suggest an approach based on multivariate estimating equations that are solved using a recursive structure. This approach leads to an estimator where the large sample properties can be developed using product-integration theory. Small sample properties are investigated using simulations in a setting that mimics the motivating haplo-match problem.
    Scandinavian Journal of Statistics 09/2011; 38(3):409-423. · 1.17 Impact Factor
  • Source
    Anders Gorst-Rasmussen, Thomas H. Scheike
    [Show abstract] [Hide abstract]
    ABSTRACT: In data sets with many more features than observations, independent screening based on all univariate regression models leads to a computationally convenient variable selection method. Recent efforts have shown that in the case of generalized linear models, independent screening may suffice to capture all relevant features with high probability, even in ultra-high dimension. It is unclear whether this formal sure screening property is attainable when the response is a right-censored survival time. We propose a computationally very efficient independent screening method for survival data which can be viewed as the natural survival equivalent of correlation screening. We state conditions under which the method admits the sure screening property within a general class of single-index hazard rate models with ultra-high dimensional features. An iterative variant is also described which combines screening with penalized regression in order to handle more complex feature covariance structures. The methods are evaluated through simulation studies and through application to a real gene expression dataset.
    Journal of the Royal Statistical Society Series B (Statistical Methodology) 05/2011; · 4.81 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Principal component analysis (PCA) has been used extensively in the field of nutritional epidemiology to derive patterns that summarize food and nutrient intake, but interpreting it can be difficult. The authors propose the use of a new statistical technique, the treelet transform (TT), as an alternative to PCA. TT combines the quantitative pattern extraction capabilities of PCA with the interpretational advantages of cluster analysis and produces patterns involving only naturally grouped subsets of the original variables. The authors compared patterns derived using TT with those derived using PCA in a study of dietary patterns and risk of myocardial infarction among 26,155 male participants in a prospective Danish cohort. Over a median of 11.9 years of follow-up, 1,523 incident cases of myocardial infarction were ascertained. The 7 patterns derived with TT described almost as much variation as the first 7 patterns derived with PCA, for which interpretation was less clear. When the authors used multivariate Cox regression models to estimate relative risk of myocardial infarction, the significant risk factors were comparable whether the model was based on PCA or TT factors. The present study shows that TT may be a useful alternative to PCA in epidemiologic studies, leading to patterns that possess comparable explanatory power and are simple to interpret.
    American journal of epidemiology 05/2011; 173(10):1097-104. · 5.59 Impact Factor
  • Source
    C B Pipper, C Ritz, T H Scheike
    [Show abstract] [Hide abstract]
    ABSTRACT: An additive hazards model may be used to quantify the effect of genetic and environmental predictors on flowering of sugar beet plants recorded as data-grouped time-to-event data. Estimated predictor effects have an intuitive interpretation rooted in the underlying time dynamics of the flowering process. However, agricultural experiments are often designed using several plots containing a large number of plants that are subsequently being monitored. In this article, we consider an additive hazards model with an additional plot structure induced by latent shared frailty variables. This approach enables us to derive a method to assess the quality of predictors in terms of how much plot variation they explain. We apply the method to a large data set exploring flowering of sugar beet and conclude that the genetic predictor biotype, which has a strong effect, also explains a substantial amount of the plot variation. The method is also applied to a data set from medical research concerning days to virus positivity of serum samples in AIDS patients.
    Biometrics 04/2011; 67(4):1361-8. · 1.41 Impact Factor
  • Source
    American journal of epidemiology 04/2011; · 5.59 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In sub-Saharan Africa (SSA), malaria caused by Plasmodium falciparum has historically been a major contributor to morbidity and mortality. Recent reports indicate a pronounced decline in infection and disease rates which are commonly ascribed to large-scale bed net programmes and improved case management. However, the decline has also occurred in areas with limited or no intervention. The present study assessed temporal changes in Anopheline populations in two highly malaria-endemic communities of NE Tanzania during the period 1998-2009. Between 1998 and 2001 (1st period) and between 2003 and 2009 (2nd period), mosquitoes were collected weekly in 50 households using CDC light traps. Data on rainfall were obtained from the nearby climate station and were used to analyze the association between monthly rainfall and malaria mosquito populations. The average number of Anopheles gambiae and Anopheles funestus per trap decreased by 76.8% and 55.3%, respectively over the 1st period, and by 99.7% and 99.8% over the 2nd period. During the last year of sampling (2009), the use of 2368 traps produced a total of only 14 Anopheline mosquitoes. With the exception of the decline in An. gambiae during the 1st period, the results did not reveal any statistical association between mean trend in monthly rainfall and declining malaria vector populations. A longitudinal decline in the density of malaria mosquito vectors was seen during both study periods despite the absence of organized vector control. Part of the decline could be associated with changes in the pattern of monthly rainfall, but other factors may also contribute to the dramatic downward trend. A similar decline in malaria vector densities could contribute to the decrease in levels of malaria infection reported from many parts of SSA.
    Malaria Journal 01/2011; 10:188. · 3.49 Impact Factor

Publication Stats

4k Citations
405.11 Total Impact Points

Institutions

  • 2013
    • Copenhagen University Hospital
      København, Capital Region, Denmark
  • 1996–2013
    • University of Copenhagen
      • • Section of Biostatistics
      • • Department of Basic Sciences and Environment
      København, Capital Region, Denmark
  • 2009–2011
    • National Institute for Medical Research (NIMR)
      Dār es Salām, Dar es Salaam, Tanzania
    • University of Southern Denmark
      • Institute of Public Health
      Odense, South Denmark, Denmark
  • 2000–2011
    • IT University of Copenhagen
      København, Capital Region, Denmark
    • Odense University Hospital
      Odense, South Denmark, Denmark
  • 2008–2009
    • University of Padova
      • Department of Statistical Sciences
      Padova, Veneto, Italy
    • Medical College of Wisconsin
      • Division of Biostatistics
      Milwaukee, Wisconsin, United States
  • 1997–2009
    • Rigshospitalet
      • • Department of Cardiology
      • • Department of Growth and Reproduction
      Copenhagen, Capital Region, Denmark
  • 2005
    • Imperial College London
      • Department of Medicine
      London, ENG, United Kingdom
  • 2004
    • Institut national d'études démographiques
      Lutetia Parisorum, Île-de-France, France
  • 2002
    • Aalborg University
      • Department of Mathematical Sciences
      Aalborg, Region North Jutland, Denmark
  • 1993
    • University of California, Berkeley
      Berkeley, California, United States