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  • 1. Andersson, M
    et al.
    Ekdahl, K
    Mölstad, Sigvard
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Department of Health and Society, General Practice.
    Hansson, HB
    Persson, K
    Giesecke, J
    Modelling the spread of penicillin-resistant Streptococcus pneumoniae in day-care and evaluation of intervention2005In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 24, p. 3593-607Article in journal (Refereed)
  • 2.
    Austin, Peter C.
    et al.
    Inst Clin Evaluat Sci, G106,2075 Bayview Ave, Toronto, ON M4N 3M5, Canada.;Univ Toronto, Inst Hlth Management Policy & Evaluat, Toronto, ON, Canada.;Sunnybrook Res Inst, Schulich Heart Res Program, Toronto, ON, Canada..
    Wagner, Philippe
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centre for Clinical Research, County of Västmanland. Lund Univ, Unit Social Epidemiol, Fac Med, Malmo, Sweden..
    Merlo, Juan
    Lund Univ, Unit Social Epidemiol, Fac Med, Malmo, Sweden.;Region Skane, Ctr Primary Hlth Care Res, Malmo, Sweden..
    The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis2017In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 36, no 6, p. 928-938Article in journal (Refereed)
    Abstract [en]

    Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster-specific random effects which allow one to partition the total individual variance into between-cluster variation and between-individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time-to-event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., 'frailty') Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis.

  • 3.
    Berglund, Lars
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences.
    Garmo, Hans
    Lindbäck, Johan
    Svärdsudd, Kurt
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Family Medicine and Clinical Epidemiology.
    Zethelius, Björn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences.
    Maximum likelihood estimation of correction for dilution bias in simple linear regression using replicates from subjects with extreme first measurements2008In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 27, no 22, p. 4397-4407Article in journal (Refereed)
    Abstract [en]

    The least-squares estimator of the slope in a simple linear regression model is biased towards zero when the predictor is measured with random error. A corrected slope may be estimated by adding data from a reliability study, which comprises a subset of subjects from the main study. The precision of this corrected slope depends on the design of the reliability study and estimator choice.Previous work has assumed that the reliability study constitutes a random sample from the main study. A more efficient design is to use subjects with extreme values on their first measurement. Previously, we published a variance formula for the corrected slope, when the correction factor is the slope in the regression of the second measurement on the first. In this paper we show that both designs improve by maximum likelihood estimation (MLE). The precision gain is explained by the inclusion of data from all subjects for estimation of the predictor's variance and by the use of the second measurement for estimation of the covariance between response and predictor. The gain of MLE enhances with stronger true relationship between response and predictor and with lower precision in the predictor measurements. We present a real data example on the relationship between fasting insulin, a surrogate market, and true insulin sensitivity measured by a gold-standard euglycaemic insulin clamp, and simulations, where the behavior of profile-likelihood-based confidence intervals is examined. MLE was shown to be a robust estimator for non-normal distributions and efficient for small sample situations.

  • 4.
    Berglund, Lars
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm , UCR-Uppsala Clinical Research center.
    Garmo, Hans
    Regional Oncologic Center, University Hospital, Uppsala.
    Lindbäck, Johan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm , UCR-Uppsala Clinical Research center.
    Zethelius, Björn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences.
    Correction for regression dilution bias using replicates from subjects with extreme first measurements2007In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 26, no 10, p. 2246-2257Article in journal (Refereed)
    Abstract [en]

    The least squares estimator of the slope in a simple linear regression model will be biased towards zero when the predictor is measured with random error, i.e. intra-individual variation or technical measurement error. A correction factor can be estimated from a reliability study where one replicate is available on a subset of subjects from the main study. Previous work in this field has assumed that the reliability study constitutes a random subsample from the main study.We propose that a more efficient design is to collect replicates for subjects with extreme values on their first measurement. A variance formula for this estimator of the correction factor is presented. The variance for the corrected estimated regression coefficient for the extreme selection technique is also derived and compared with random subsampling. Results show that variances for corrected regression coefficients can be markedly reduced with extreme selection. The variance gain can be estimated from the main study data. The results are illustrated using Monte Carlo simulations and an application on the relation between insulin sensitivity and fasting insulin using data from the population-based ULSAM study.In conclusion, an investigator faced with the planning of a reliability study may wish to consider an extreme selection design in order to improve precision at a given number of subjects or alternatively decrease the number of subjects at a given precision.

  • 5.
    Bodnar, Olha
    et al.
    Physikalisch-Technische Bundesanstalt, Berlin, Germany.
    Link, Alfred
    Physikalisch-Technische Bundesanstalt, Berlin, Germany.
    Arendacká, Barbora
    Institut für Medizinische Statistik, Göttingen, Germany.
    Possolo, Antonio
    National Institute of Standards and Technology, Gaithersburg MD, USA.
    Elster, Clemens
    Physikalisch-Technische Bundesanstalt, Berlin, Germany.
    Bayesian estimation in random effects meta‐analysis using a non‐informative prior2017In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 36, no 2, p. 378-399Article in journal (Refereed)
    Abstract [en]

    Pooling information from multiple, independent studies (meta‐analysis) adds great value to medical research. Random effects models are widely used for this purpose. However, there are many different ways of estimating model parameters, and the choice of estimation procedure may be influential upon the conclusions of the meta‐analysis. In this paper, we describe a recently proposed Bayesian estimation procedure and compare it with a profile likelihood method and with the DerSimonian–Laird and Mandel–Paule estimators including the Knapp–Hartung correction. The Bayesian procedure uses a non‐informative prior for the overall mean and the between‐study standard deviation that is determined by the Berger and Bernardo reference prior principle. The comparison of these procedures focuses on the frequentist properties of interval estimates for the overall mean. The results of our simulation study reveal that the Bayesian approach is a promising alternative producing more accurate interval estimates than those three conventional procedures for meta‐analysis. The Bayesian procedure is also illustrated using three examples of meta‐analysis involving real data.

  • 6. Burgess, Stephen
    et al.
    Thompson, Simon G
    Andrews, G
    Samani, N J
    Hall, A
    Whincup, P
    Morris, R
    Lawlor, D A
    Davey Smith, G
    Timpson, N
    Ebrahim, S
    Ben-Shlomo, Y
    Davey Smith, G
    Timpson, N
    Brown, M
    Ricketts, S
    Sandhu, M
    Reiner, A
    Psaty, B
    Lange, L
    Cushman, M
    Hung, J
    Thompson, P
    Beilby, J
    Warrington, N
    Palmer, L J
    Nordestgaard, B G
    Tybjaerg-Hansen, A
    Zacho, J
    Wu, C
    Lowe, G
    Tzoulaki, I
    Kumari, M
    Sandhu, M
    Yamamoto, J F
    Chiodini, B
    Franzosi, M
    Hankey, G J
    Jamrozik, K
    Palmer, L
    Rimm, E
    Pai, J
    Psaty, B
    Heckbert, S
    Bis, J
    Anand, S
    Engert, J
    Collins, R
    Clarke, R
    Melander, O
    Berglund, G
    Ladenvall, P
    Johansson, L
    Jansson, Jan-Håkan
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Hallmans, Göran
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research.
    Hingorani, A
    Humphries, S
    Rimm, E
    Manson, J
    Pai, J
    Watkins, H
    Clarke, R
    Hopewell, J
    Saleheen, D
    Frossard, R
    Danesh, J
    Sattar, N
    Robertson, M
    Shepherd, J
    Schaefer, E
    Hofman, A
    Witteman, J C M
    Kardys, I
    Ben-Shlomo, Y
    Davey Smith, G
    Timpson, N
    de Faire, U
    Bennet, A
    Sattar, N
    Ford, I
    Packard, C
    Kumari, M
    Manson, J
    Lawlor, Debbie A
    Davey Smith, George
    Anand, S
    Collins, R
    Casas, J P
    Danesh, J
    Davey Smith, G
    Franzosi, M
    Hingorani, A
    Lawlor, D A
    Manson, J
    Nordestgaard, B G
    Samani, N J
    Sandhu, M
    Smeeth, L
    Wensley, F
    Anand, S
    Bowden, J
    Burgess, S
    Casas, J P
    Di Angelantonio, E
    Engert, J
    Gao, P
    Shah, T
    Smeeth, L
    Thompson, S G
    Verzilli, C
    Walker, M
    Whittaker, J
    Hingorani, A
    Danesh, J
    Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables2010In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 29, no 12, p. 1298-1311Article in journal (Refereed)
    Abstract [en]

    Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.

  • 7.
    Dosne, Anne-Gaëlle
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bergstrand, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Renard, Didier
    Novartis Pharma AG, Basel, Switzerland.
    Heimann, Guenter
    Novartis Pharma AG, Basel, Switzerland.
    Model averaging for robust assessment of QT prolongation by concentration-response analysis2017In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 36, no 24, p. 3844-3857Article in journal (Refereed)
    Abstract [en]

    Assessing the QT prolongation potential of a drug is typically done based on pivotal safety studies called thorough QT studies. Model-based estimation of the drug-induced QT prolongation at the estimated mean maximum drug concentration could increase efficiency over the currently used intersection-union test. However, robustness against model misspecification needs to be guaranteed in pivotal settings. The objective of this work was to develop an efficient, fully prespecified model-based inference method for thorough QT studies, which controls the type I error and provides satisfactory test power. This is achieved by model averaging: The proposed estimator of the concentration-response relationship is a weighted average of a parametric (linear) and a nonparametric (monotonic I-splines) estimator, with weights based on mean integrated square error. The desired properties of the method were confirmed in an extensive simulation study, which demonstrated that the proposed method controlled the type I error adequately, and that its power was higher than the power of the nonparametric method alone. The method can be extended from thorough QT studies to the analysis of QT data from pooled phase I studies.

  • 8.
    Hansson, Sven Ove
    KTH, Superseded Departments, History of Science and Technology.
    Replacing the no-effect level (NOEL) with bounded effect levels (OBEL and LEBEL)2002In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 21, no 20, p. 3071-3078Article in journal (Refereed)
    Abstract [en]

    From experiments or epidemiological studies designed to search for a particular toxic effect, it is in general possible to determine an upper bound for that effect. This observed bounded effect level (OBEL) is defined for both positive and negative experiments. It is non-zero even for negative experiments, and it is inversely related to the size of the exposed group. The OBEL can be used to determine the linearly extrapolated bounded effect level (LEBEL) for various effect doses. Contrary to no-observed-effect' levels (NOELs), LEBEL values are designed to protect against type 11 (false negative) errors. It is proposed that LEBEL values replace NOELs as a tool for decision-making.

  • 9.
    Lindhagen, Lars
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, UCR-Uppsala Clinical Research Center.
    Darkahi, Bahman
    Sandblom, Gabriel
    Berglund, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, UCR-Uppsala Clinical Research Center.
    Level-adjusted funnel plots based on predicted marginal expectations: an application to prophylactic antibiotics in gallstone surgery2014In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 33, no 21, p. 3655-3675Article in journal (Refereed)
    Abstract [en]

    Funnel plots are widely used to visualize grouped data, for example, in institutional comparison. This paper extends the concept to a multi-level setting, displaying one level at a time, adjusted for the other levels, as well as for covariates at all levels. These level-adjusted funnel plots are based on a Markov chain Monte Carlo fit of a random effects model, translating the estimated model parameters to predicted marginal expectations. Working within the estimation framework, we accommodate outlying institutions using heavy-tailed random effects distributions. We also develop computer-efficient methods to compute predicted probabilities in the case of dichotomous outcome data and various random effect distributions. We apply the method to a data set on prophylactic antibiotics in gallstone surgery.

  • 10.
    Lindmark, Anita
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Eriksson, Marie
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals2018In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 37, no 10, p. 1744-1762Article in journal (Refereed)
    Abstract [en]

    To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assumptions about unconfoundedness are required. Since these assumptions cannot be tested using the observed data, a mediation analysis should always be accompanied by a sensitivity analysis of the resulting estimates. In this article, we propose a sensitivity analysis method for parametric estimation of direct and indirect effects when the exposure, mediator, and outcome are all binary. The sensitivity parameters consist of the correlations between the error terms of the exposure, mediator, and outcome models. These correlations are incorporated into the estimation of the model parameters and identification sets are then obtained for the direct and indirect effects for a range of plausible correlation values. We take the sampling variability into account through the construction of uncertainty intervals. The proposed method is able to assess sensitivity to both mediator‐outcome confounding and confounding involving the exposure. To illustrate the method, we apply it to a mediation study based on the data from the Swedish Stroke Register (Riksstroke). An R package that implements the proposed method is available.

  • 11.
    Magnúsdóttir, Bergrún Tinna
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Nyquist, Hans
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Simultaneous estimation of parameters in the bivariate Emax model2015In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 34, no 28, p. 3714-3723Article in journal (Refereed)
    Abstract [en]

    In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation.

  • 12.
    Mandal, Saumen
    et al.
    Department of Statistics, University of Manitoba, Winnipeg, Canada.
    Arabi Belaghi, Reza
    Department of Statistics, University of Tabriz, Tabriz, Iran.
    Mahmoudi, Akram
    Department of Statistics, University of Tabriz, Tabriz, Iran.
    Aminnejad, Minoo
    Department of Statistics, Razi University, Kermanshah, Iran.
    Stein-type shrinkage estimators in gamma regression model with application to prostate cancer data2019In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 38, no 22, p. 4310-4322Article in journal (Refereed)
    Abstract [en]

    Gamma regression is applied in several areas such as life testing, forecasting cancer incidences, genomics, rainfall prediction, experimental designs, and quality control. Gamma regression models allow for a monotone and no constant hazard in survival models. Owing to the broad applicability of gamma regression, we propose some novel and improved methods to estimate the coefficients of gamma regression model. We combine the unrestricted maximum likelihood (ML) estimators and the estimators that are restricted by linear hypothesis, and we present Stein-type shrinkage estimators (SEs). We then develop an asymptotic theory for SEs and obtain their asymptotic quadratic risks. In addition, we conduct Monte Carlo simulations to study the performance of the estimators in terms of their simulated relative efficiencies. It is evident from our studies that the proposed SEs outperform the usual ML estimators. Furthermore, some tabular and graphical representations are given as proofs of our assertions. This study is finally ended by appraising the performance of our estimators for a real prostate cancer data. 

  • 13.
    Norén, G. Niklas
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Bate, Andrew
    Edwards, Ralph
    A statistical methodology for drug–drug interaction surveillance2008In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 27, no 16, p. 3057-3070Article in journal (Refereed)
    Abstract [en]

    Interaction between drug substances may yield excessive risk of adverse drug reactions (ADRs) when two drugs are taken in combination. Collections of individual case safety reports (ICSRs) related to suspected ADR incidents in clinical practice have proven to be very useful in post-marketing surveillance for pairwise drug–ADR associations, but have yet to reach their full potential for drug–drug interaction surveillance. In this paper, we implement and evaluate a shrinkage observed-to-expected ratio for exploratory analysis of suspected drug–drug interaction in ICSR data, based on comparison with an additive risk model. We argue that the limited success of previously proposed methods for drug–drug interaction detection based on ICSR data may be due to an underlying assumption that the absence of interaction is equivalent to having multiplicative risk factors. We provide empirical examples of established drug–drug interaction highlighted with our proposed approach that go undetected with logistic regression. A database wide screen for suspected drug–drug interaction in the entire WHO database is carried out to demonstrate the feasibility of the proposed approach. As always in the analysis of ICSRs, the clinical validity of hypotheses raised with the proposed method must be further reviewed and evaluated by subject matter experts.

  • 14.
    Persson, Emma
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Waernbaum, Ingeborg
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Estimating a marginal causal odds ratio in a case-control design: analyzing the effect of low birth weight on the risk of type 1 diabetes mellitus2013In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 32, no 14, p. 2500-2512Article in journal (Refereed)
    Abstract [en]

    Estimation of marginal causal effects from case-control data has two complications: (i) confounding due to the fact that the exposure under study is not randomized, and (ii) bias from the case-control sampling scheme. In this paper, we study estimators of the marginal causal odds ratio, addressing these issues for matched and unmatched case-control designs when utilizing the knowledge of the known prevalence of being a case. The estimators are implemented in simulations where their finite sample properties are studied and approximations of their variances are derived with the delta method. Also, we illustrate the methods by analyzing the effect of low birth weight on the risk of type 1 diabetes mellitus using data from the Swedish Childhood Diabetes Register, a nationwide population-based incidence register.

  • 15.
    Persson, Emma
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Waernbaum, Ingeborg
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lind, Torbjörn
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Paediatrics.
    Estimating marginal causal effects in a secondary analysis of case-control data2017In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 36, no 15, p. 2404-2419Article in journal (Refereed)
    Abstract [en]

    When an initial case-control study is performed, data can be used in a secondary analysis to evaluate the effect of the case-defining event on later outcomes. In this paper, we study the example in which the role of the event is changed from a response variable to a treatment of interest. If the aim is to estimate marginal effects, such as average effects in the population, the sampling scheme needs to be adjusted for. We study estimators of the average effect of the treatment in a secondary analysis of matched and unmatched case-control data where the probability of being a case is known. For a general class of estimators, we show the components of the bias resulting from ignoring the sampling scheme and demonstrate a design-weighted matching estimator of the average causal effect. In simulations, the finite sample properties of the design-weighted matching estimator are studied. Using a Swedish diabetes incidence register with a matched case-control design, we study the effect of childhood onset diabetes on the use of antidepressant medication as an adult.

  • 16. Ravva, Patanjali
    et al.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    French, Jonathan L.
    A linearization approach for the model-based analysis of combined aggregate and individual patient data2014In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 33, no 9, p. 1460-1476Article in journal (Refereed)
    Abstract [en]

    The application of model-based meta-analysis in drug development has gained prominence recently, particularly for characterizing dose-response relationships and quantifying treatment effect sizes of competitor drugs. The models are typically nonlinear in nature and involve covariates to explain the heterogeneity in summary-level literature (or aggregate data (AD)). Inferring individual patient-level relationships from these nonlinear meta-analysis models leads to aggregation bias. Individual patient-level data (IPD) are indeed required to characterize patient-level relationships but too often this information is limited. Since combined analyses of AD and IPD allow advantage of the information they share to be taken, the models developed for AD must be derived from IPD models; in the case of linear models, the solution is a closed form, while for nonlinear models, closed form solutions do not exist. Here, we propose a linearization method based on a second order Taylor series approximation for fitting models to AD alone or combined AD and IPD. The application of this method is illustrated by an analysis of a continuous landmark endpoint, i.e., change from baseline in HbA1c at week 12, from 18 clinical trials evaluating the effects of DPP-4 inhibitors on hyperglycemia in diabetic patients. The performance of this method is demonstrated by a simulation study where the effects of varying the degree of nonlinearity and of heterogeneity in covariates (as assessed by the ratio of between-trial to within-trial variability) were studied. A dose-response relationship using an Emax model with linear and nonlinear effects of covariates on the emax parameter was used to simulate data. The simulation results showed that when an IPD model is simply used for modeling AD, the bias in the emax parameter estimate increased noticeably with an increasing degree of nonlinearity in the model, with respect to covariates. When using an appropriately derived AD model, the linearization method adequately corrected for bias. It was also noted that the bias in the model parameter estimates decreased as the ratio of between-trial to within-trial variability in covariate distribution increased. Taken together, the proposed linearization approach allows addressing the issue of aggregation bias in the particular case of nonlinear models of aggregate data.

  • 17.
    Rompaye, Bart Van
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
    Eriksson, Marie
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Goetghebeur, Els
    Evaluating hospital performance based on excess cause-specific incidence2015In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 34, no 8, p. 1334-1350Article in journal (Refereed)
    Abstract [en]

    Formal evaluation of hospital performance in specific types of care is becoming an indispensable tool for quality assurance in the health care system. When the prime concern lies in reducing the risk of a cause-specific event, we propose to evaluate performance in terms of an average excess cumulative incidence, referring to the center's observed patient mix. Its intuitive interpretation helps give meaning to the evaluation results and facilitates the determination of important benchmarks for hospital performance. We apply it to the evaluation of cerebrovascular deaths after stroke in Swedish stroke centers, using data from Riksstroke, the Swedish stroke registry. 

  • 18.
    Rowley, M.
    et al.
    Kings Coll London, Inst Math & Mol Biomed, Hodgkin Bldg, London SE1 1UL, England.;Saddle Point Sci, London, England..
    Garmo, H.
    Kings Coll London, Guys Hosp, Canc Epidemiol Grp, London, England..
    Van Hemelrijck, M.
    Kings Coll London, Guys Hosp, Canc Epidemiol Grp, London, England..
    Wulaningsih, W.
    Kings Coll London, Guys Hosp, Canc Epidemiol Grp, London, England..
    Grundmark, Birgitta
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Endocrine Surgery. Med Prod Agcy, Uppsala, Sweden.
    Zethelius, Björn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics. Med Prod Agcy, Uppsala, Sweden.
    Hammar, N.
    Karolinska Inst, Inst Environm Med, Dept Epidemiol, Stockholm, Sweden.;AstraZeneca Sverige, Sodertalje, Sweden..
    Walldius, G.
    Karolinska Inst, Inst Environm Med, Dept Cardiovasc Epidemiol, Stockholm, Sweden..
    Inoue, M.
    Waseda Univ, Dept Elect Engn & Biosci, Tokyo, Japan..
    Holmberg, L.
    Kings Coll London, Guys Hosp, Canc Epidemiol Grp, London, England..
    Coolen, A. C. C.
    Kings Coll London, Inst Math & Mol Biomed, Hodgkin Bldg, London SE1 1UL, England..
    A latent class model for competing risks2017In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 36, no 13, p. 2100-2119Article in journal (Refereed)
    Abstract [en]

    Survival data analysis becomes complex when the proportional hazards assumption is violated at population level or when crude hazard rates are no longer estimators of marginal ones. We develop a Bayesian survival analysis method to deal with these situations, on the basis of assuming that the complexities are induced by latent cohort or disease heterogeneity that is not captured by covariates and that proportional hazards hold at the level of individuals. This leads to a description from which risk-specific marginal hazard rates and survival functions are fully accessible, 'decontaminated' of the effects of informative censoring, and which includes Cox, random effects and latent classmodels as special cases. Simulated data confirm that our approach can map a cohort's substructure and remove heterogeneity-induced informative censoring effects. Application to data from the Uppsala Longitudinal Study of Adult Men cohort leads to plausible alternative explanations for previous counter-intuitive inferences on prostate cancer. The importance of managing cardiovascular disease as a comorbidity in women diagnosed with breast cancer is suggested on application to data from the Swedish Apolipoprotein Mortality Risk Study.

  • 19.
    Ryeznik, Yevgen
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sverdlov, Oleksandr
    Novartis Inst Biomed Res, Early Dev Biostat, E Hanover, NJ 07936 USA.
    A comparative study of restricted randomization procedures for multiarm trials with equal or unequal treatment allocation ratios2018In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 37, no 21, p. 3056-3077Article in journal (Refereed)
    Abstract [en]

    Randomization designs for multiarm clinical trials are increasingly used in practice, especially in phase II dose-ranging studies. Many new methods have been proposed in the literature; however, there is lack of systematic, head-to-head comparison of the competing designs. In this paper, we systematically investigate statistical properties of various restricted randomization procedures for multiarm trials with fixed and possibly unequal allocation ratios. The design operating characteristics include measures of allocation balance, randomness of treatment assignments, variations in the allocation ratio, and statistical characteristics such as type I error rate and power. The results from the current paper should help clinical investigators select an appropriate randomization procedure for their clinical trial. We also provide a web-based R shiny application that can be used to reproduce all results in this paper and run simulations under additional user-defined experimental scenarios.

  • 20.
    Salim, Agus
    et al.
    La Trobe University, Melbourne Vic, Australia.
    Ma, Xiangmei
    Saw Swee Hock School of Public Health, National University, Singapore, Singapore.
    Fall, Katja
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    Andrén, Ove
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Örebro University Hospital.
    Reilly, Marie
    Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Analysis of incidence and prognosis from 'extreme' case-control designs2014In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 33, no 30, p. 5388-5398Article in journal (Refereed)
    Abstract [en]

    The significant investment in measuring biomarkers has prompted investigators to improve cost-efficiency by sub-sampling in non-standard study designs. For example, investigators studying prognosis may assume that any differences in biomarkers are likely to be most apparent in an extreme sample of the earliest deaths and the longest-surviving controls. Simple logistic regression analysis of such data does not exploit the information available in the survival time, and statistical methods that model the sampling scheme may be more efficient. We derive likelihood equations that reflect the complex sampling scheme in unmatched and matched extreme' case-control designs. We investigated the performance and power of the method in simulation experiments, with a range of underlying hazard ratios and study sizes. Our proposed method resulted in hazard ratio estimates close to those obtained from the full cohort. The standard error estimates also performed well when compared with the empirical variance. In an application to a study investigating markers for lethal prostate cancer, an extreme case-control sample of lethal cases and the longest-surviving controls provided estimates of the effect of Gleason score in close agreement with analysis of all the data. By using the information in the sampling design, our method enables efficient and valid estimation of the underlying hazard ratio from a study design that is intuitive and easily implemented.

  • 21.
    Sjölander, Arvid
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden .
    Lichtenstein, Paul
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden .
    Larsson, Henrik
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Pawitan, Yudi
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden .
    Between-within models for survival analysis.2013In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 32, no 18, p. 3067-3076Article in journal (Refereed)
    Abstract [en]

    A popular way to control for confounding in observational studies is to identify clusters of individuals (e.g., twin pairs), such that a large set of potential confounders are constant (shared) within each cluster. By studying the exposure-outcome association within clusters, we are in effect controlling for the whole set of shared confounders. An increasingly popular analysis tool is the between-within (BW) model, which decomposes the exposure-outcome association into a 'within-cluster effect' and a 'between-cluster effect'. BW models are relatively common for nonsurvival outcomes and have been studied in the theoretical literature. Although it is straightforward to use BW models for survival outcomes, this has rarely been carried out in practice, and such models have not been studied in the theoretical literature. In this paper, we propose a gamma BW model for survival outcomes. We compare the properties of this model with the more standard stratified Cox regression model and use the proposed model to analyze data from a twin study of obesity and mortality. We find the following: (i) the gamma BW model often produces a more powerful test of the 'within-cluster effect' than stratified Cox regression; and (ii) the gamma BW model is robust against model misspecification, although there are situations where it could give biased estimates.

  • 22.
    Svensson, Elisabeth
    Örebro University, Örebro University School of Business.
    Different ranking approaches defining association and agreement measures of paired ordinal data2012In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 31, no 26, p. 3104-3117Article in journal (Refereed)
    Abstract [en]

    Rating scales are common for self-assessments of qualitative variables and also for expert-rating of the severity of disability, outcomes, etc. Scale assessments and other ordered classifications generate ordinal data having rank-invariant properties only. Hence, statistical methods are often based on ranks. The aim is to focus at the differences in ranking approaches between measures of association and of disagreement in paired ordinal data. The Spearman correlation coefficient is a measure of association between two variables, when each data set is transformed to ranks. The augmented ranking approach to evaluate disagreement takes account of the information given by the pairs of data, and provides identification and measures of systematic disagreement, when present, separately from measures of additional individual variability in assessments. The two approaches were applied to empirical data regarding relationship between perceived pain and physical health and reliability in pain assessments made by patients. The art of disagreement between the patients' perceived levels of outcome after treatment and the doctor's criterion-based scoring was also evaluated. The comprehensive evaluation of observed disagreement in terms of systematic and individual disagreement provides valuable interpretable information of their sources. The presence of systematic disagreement can be adjusted for and/or understood. Large individual variability could be a sign of poor quality of a scale or heterogeneity among raters. It was also demonstrated that a measure of association must not be used as a measure of agreement, even though such misuse of correlation coefficients is common.

  • 23.
    Sverdlov, Oleksandr
    et al.
    Early Development Biostatistics, Novartis Institutes for Biomedical Research .
    Ryeznik, Yevgen
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Implementing Unequal Randomization in Clinical Trials with Heterogeneous Treatment Costs2019In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 38, no 16, p. 2905-2927Article in journal (Refereed)
    Abstract [en]

    Equal randomization has been a popular choice in clinical trial practice. However, in trials with heterogeneous variances and/or variable treatment costs, as well as in the settings where maximization of every trial participant’s benefit is an important design consideration, optimal allocation proportions may be unequal across study treatment arms. In this paper, we investigate optimal allocation designs minimizing study cost under statistical efficiency constraints for parallel group clinical trials comparing several investigational treatments against the control. We show theoretically that equal allocation designs may be suboptimal, and unequal allocation designs can provide higher statistical power for the same budget, or result in a smaller cost for the same level of power. We also show how the optimal allocation can be implemented in practice by means of restricted randomization procedures, and how to perform statistical inference following these procedures, using invoked population-based or randomization-based approaches. Our results provide further support to some previous findings in the literature that unequal randomization designs can be cost-efficient and can be successfully implemented in practice. We conclude that the choice of the target allocation, the randomization procedure and the statistical methodology for data analysis are essential components to ensure valid, powerful, and robust clinical trial results.

  • 24.
    Sysoev, Oleg
    et al.
    Linköping Univ, Dept Comp & Informat Sci, Linköping, Sweden.
    Bartoszek, Krzysztof
    Linköping Univ, Dept Comp & Informat Sci, Linköping, Sweden.
    Ekström, Eva-Charlotte
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, International Maternal and Child Health (IMCH).
    Ekholm Selling, Katarina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, International Maternal and Child Health (IMCH).
    PSICA: Decision trees for probabilistic subgroup identification with categorical treatments2019In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 38, no 22, p. 4436-4452Article in journal (Refereed)
    Abstract [en]

    Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine, which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments. A number of approaches based on decision trees have been proposed to identify such subgroups, but most of them focus on two-arm trials (control/treatment) while a few methods consider quantitative treatments (defined by the dose). However, no subgroup identification method exists that can predict the best treatments in a scenario with a categorical set of treatments. We propose a novel method for subgroup identification in categorical treatment scenarios. This method outputs a decision tree showing the probabilities of a given treatment being the best for a given group of patients as well as labels showing the possible best treatments. The method is implemented in an R package psica available on CRAN. In addition to a simulation study, we present an analysis of a community-based nutrition intervention trial that justifies the validity of our method.

  • 25.
    Sysoev, Oleg
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Bartoszek, Krzysztof
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Ekström, Eva-Charlotte
    Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden.
    Ekström Selling, Katarina
    Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden.
    PSICA: Decision trees for probabilistic subgroup identification with categorical treatments2019In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 38, no 22, p. 4436-4452Article in journal (Refereed)
    Abstract [en]

    Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine, which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments. A number of approaches based on decision trees have been proposed to identify such subgroups, but most of them focus on two‐arm trials (control/treatment) while a few methods consider quantitative treatments (defined by the dose). However, no subgroup identification method exists that can predict the best treatments in a scenario with a categorical set of treatments. We propose a novel method for subgroup identification in categorical treatment scenarios. This method outputs a decision tree showing the probabilities of a given treatment being the best for a given group of patients as well as labels showing the possible best treatments. The method is implemented in an R package psica available on CRAN. In addition to a simulation study, we present an analysis of a community‐based nutrition intervention trial that justifies the validity of our method.

  • 26.
    Thulin, Måns
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
    Two-sample tests and one-way MANOVA for multivariate biomarker data with nondetects2016In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 35, no 20, p. 3623-3644Article in journal (Refereed)
    Abstract [en]

    Testing whether the mean vector of a multivariate set of biomarkers differs between several populations is an increasingly common problem in medical research. Biomarker data is often left censored because some measurements fall below the laboratory's detection limit. We investigate how such censoring affects multivariate two-sample and one-way multivariate analysis of variance tests. Type I error rates, power and robustness to increasing censoring are studied, under both normality and non-normality. Parametric tests are found to perform better than non-parametric alternatives, indicating that the current recommendations for analysis of censored multivariate data may have to be revised.

  • 27. Van Belle, Vanya
    et al.
    Pelckmans, Kristiaan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Suykens, Johan A. K.
    Van Huffel, Sabine
    Additive survival least-squares support vector machines2010In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 29, no 2, p. 296-308Article in journal (Refereed)
  • 28. Varewyck, M.
    et al.
    Vansteelandt, S.
    Eriksson, Marie
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Goetghebeur, E.
    On the practice of ignoring center-patient interactions in evaluating hospital performance2016In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 35, no 2, p. 227-238Article in journal (Refereed)
    Abstract [en]

    We evaluate the performance of medical centers based on a continuous or binary patient outcome (e.g., 30-day mortality). Common practice adjusts for differences in patient mix through outcome regression models, which include patient-specific baseline covariates (e.g., age and disease stage) besides center effects. Because a large number of centers may need to be evaluated, the typical model postulates that the effect of a center on outcome is constant over patient characteristics. This may be violated, for example, when some centers are specialized in children or geriatric patients. Including interactions between certain patient characteristics and the many fixed center effects in the model increases the risk for overfitting, however, and could imply a loss of power for detecting centers with deviating mortality. Therefore, we assess how the common practice of ignoring such interactions impacts the bias and precision of directly and indirectly standardized risks. The reassuring conclusion is that the common practice of working with the main effects of a center has minor impact on hospital evaluation, unless some centers actually perform substantially better on a specific group of patients and there is strong confounding through the corresponding patient characteristic. The bias is then driven by an interplay of the relative center size, the overlap between covariate distributions, and the magnitude of the interaction effect. Interestingly, the bias on indirectly standardized risks is smaller than on directly standardized risks. We illustrate our findings by simulation and in an analysis of 30-day mortality on Riksstroke.

  • 29.
    Waernbaum, Ingeborg
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Model misspecification and robustness in causal inference: comparing matching with doubly robust estimation2012In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 31, no 15, p. 1572-1581Article in journal (Refereed)
    Abstract [en]

    In this paper we compare the robustness properties of a matching estimator with a doubly robust estimator. We describe the robustness properties of matching and subclassification estimators by showing how misspecification of the propensity score model cam result in consistent estimation of the average causal effect. The propensity scores are covariate scores, which are a class of functions that removes bias due to all observed covariates. When matching on a parametric model (e.g. a propensity or prognostic score), the matching estimator is robust to model misspecifications if the misspecified model belongs to the class of covariate scores. The implication is that there are multiple possibilities for the matching estimator in contrast to the doubly robust estimator in which the researcher has two chances to make reliable inference. In simulations, we compare the finite sample properties of the matching estimator with a simple inverse probability weighting estimator and a doubly robust estimator. For the misspecifications in our study the mean square error of the matching estimator is smaller than the mean square error of both the simple inverse probability weighting estimator and the doubly robust estimator-

  • 30. Weidemann, Felix
    et al.
    Dehnert, Manuel
    Koch, Judith
    Wichmann, Ole
    Höhle, Michael
    Stockholm University, Faculty of Science, Department of Mathematics. Robert Koch Institute, Germany.
    Bayesian parameter inference for dynamic infectious disease modelling: Rotavirus in Germany2014In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 33, no 9, p. 1580-1599Article in journal (Refereed)
    Abstract [en]

    Understanding infectious disease dynamics using epidemic models based on ordinary differential equations requires the calibration of model parameters from data. A commonly used approach in practice to simplify this task is to fix many parameters on the basis of expert or literature information. However, this not only leaves the corresponding uncertainty unexamined but often also leads to biased inference for the remaining parameters because of dependence structures inherent in any given model. In the present work, we develop a Bayesian inference framework that lessens the reliance on such external parameter quantifications by pursuing a more data-driven calibration approach. This includes a novel focus on residual autocorrelation combined with model averaging techniques in order to reduce these estimates’ dependence on the underlying model structure. We applied our methods to the modelling of age-stratified weekly rotavirus incidence data in Germany from 2001 to 2008 using a complex susceptible–infectious–susceptible-type model complemented by the stochastic reporting of new cases. As a result, we found the detection rate in the eastern federal states to be more than four times higher compared with that of the western federal states (19.0% vs 4.3%), and also the infectiousness of symptomatically infected individuals was estimated to be more than 10 times higher than that of asymptomatically infected individuals (95% credibility interval: 8.1–19.6). Not only do these findings give valuable epidemiological insight into the transmission processes, we were also able to  examine the considerable impact on the model-predicted transmission dynamics when fixing parameters beforehand.

  • 31.
    Wienke, Andreas
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Ripatti, Samuli
    Palmgren, Juni
    Stockholm University, Faculty of Science, Department of Mathematics.
    Yashin, Anatoli
    A bivariate survival model with compound Poisson frailty2010In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 29, no 2, p. 275-283Article in journal (Refereed)
    Abstract [en]

    A correlated frailty model is suggested for analysis of bivariate time-to-event data. The model is an extension of the correlated power variance function (PVF) frailty model (correlated three-parameter frailty model) (J. Epidemiol. Biostat. 1999; 4:53-60). It is based on a bivariate extension of the compound Poisson frailty model in univariate survival analysis (Ann. Appl. Probab. 1992; 4:951-972). It allows for a non-susceptible fraction (of zero frailty) in the population, overcoming the common assumption in survival analysis that all individuals are susceptible to the event under study. The model contains the correlated gamma frailty model and the correlated inverse Gaussian frailty model as special cases. A maximum likelihood estimation procedure for the parameters is presented and its properties are studied in a small simulation study. This model is applied to breast cancer incidence data of Swedish twins. The proportion of women susceptible to breast cancer is estimated to be 15 per cent.

  • 32.
    Winell, Henric
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics. Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden.
    Lindbäck, Johan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, UCR-Uppsala Clinical Research Center.
    A general score-independent test for order-restricted inference2018In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 37, no 21, p. 3078-3090Article in journal (Refereed)
    Abstract [en]

    In the analysis of ordered categorical data, the categories are often assigned a set of subjectively chosen order-restricted scores. To overcome the arbitrariness involved in the assignment of the scores, several score-independent tests have been proposed. However, these methods are limited to 2 x K contingency tables, where K is the number of ordered categories. We present an efficiency robust score-independent test that is applicable to more general situations. The test is embedded into a flexible framework for conditional inference and provides a natural generalization of many familiar tests involving ordered categorical data, such as the generalized Cochran-Mantel-Haenszel test for singly or doubly ordered contingency tables, the Page test for randomized block designs and the Tarone-Ware trend test for survival data. The proposed method is illustrated by several numerical examples.

  • 33. Yin, Li
    et al.
    Sundberg, Rolf
    Wang, Xiaoqin
    University of Gävle, Department of Mathematics, Natural and Computer Sciences, Ämnesavdelningen för matematik och statistik.
    Rubin, Donald
    Control of confounding through secondary samples2006In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 25, no 22, p. 3814-3825Article in journal (Refereed)
    Abstract [en]

    The control of confounding is essential in many statistical problems, especially in those that attempt to estimate exposure effects. In some cases, in addition to the 'primary' sample, there is another 'secondary' sample which, though having no direct information about the exposure effect, contains information about the confounding factors. The purpose of this article is to study the influence of the secondary sample on likelihood inference for the exposure effect. In particular, we investigate the interplay between the efficiency improvement and the possible bias introduced by the secondary sample as a function of the degree of confounding in the primary sample and the sizes of the primary and secondary samples. In the case of weak confounding, the secondary sample can only little improve estimation of the exposure effect, whereas with strong confounding the secondary sample can be much more useful. On the other hand, it will be more important to consider possible biasing effects in the latter case. For illustration, we use a formal example of a generalized linear model and a real example with sparse data from a case-control study of the association between gastric cancer and HM-CAP/Band 120. Copyright (c) 2006 John Wiley & Sons, Ltd.

  • 34. Yip, Benjamin H
    et al.
    Björk, Camilla
    Lichtenstein, Paul
    Hultman, Christina M
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Psychiatry, Ulleråker, University Hospital.
    Pawitan, Yudi
    Covariance component models for multivariate binary traits in family data analysis2008In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 27, no 7, p. 1086-1105Article in journal (Refereed)
    Abstract [en]

    For family studies, there is now an established analytical framework for binary-trait outcomes within the generalized linear mixed models (GLMMs). However, the corresponding analysis of multivariate binary-trait (MBT) outcomes is still limited. Certain diseases, such as schizophrenia and bipolar disorder, have similarities in epidemiological features, risk factor patterns and intermediate phenotypes. To have a better etiological understanding, it is important to investigate the common genetic and environmental factors driving the comorbidity of the diseases. In this paper, we develop a suitable GLMM for MBT outcomes from extended families, such as nuclear, paternal- and maternal-halfsib families. We motivate our problem with real questions from psychiatric epidemiology and demonstrate how different substantive issues of comorbidity between two diseases can be put into the analytical framework.

1 - 34 of 34
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