| 1. |
- Bossoli, D, et al.
(författare)
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Marginal quantile regression for dependent data with a working odds-ratio matrix
- 2018
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Ingår i: Biostatistics (Oxford, England). - : Oxford University Press (OUP). - 1468-4357 .- 1465-4644. ; 19:4, s. 529-545
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Tidskriftsartikel (refereegranskat)abstract
- Dependent data arise frequently in applied research and several approaches to adjusting for the dependence among observations have been proposed in quantile regression. Cluster bootstrap is generally inefficient and computationally demanding, especially when the number of clusters is large. When the primary interest is on marginal quantiles, estimating equations have been proposed that estimate a working correlation matrix from the regression residuals’ sign. However, the Pearson’s correlation coefficient is an inadequate measure of dependence between binary variables because its range depends on their marginal probabilities. Instead, we propose to model the working correlation matrix through odds ratios. Different working structures can be easily estimated by suitable logistic regression models. These structures can be parametrized to depend on covariates and clusters. Simulations show that the proposed estimator has similar behavior to that of generalized estimating equations applied to regression for the mean. We study marginal quantiles of cognitive behavior with data from a randomized trial for treatment of obsessive compulsive disorder.
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| 2. |
- Gabriel, EE, et al.
(författare)
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Cross-direct effects in settings with two mediators
- 2023
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Ingår i: Biostatistics (Oxford, England). - : Oxford University Press (OUP). - 1468-4357 .- 1465-4644. ; 24:4, s. 1017-1030
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Tidskriftsartikel (refereegranskat)abstract
- When multiple mediators are present, there are additional effects that may be of interest beyond the well-known natural (NDE) and controlled direct effects (CDE). These effects cross the type of control on the mediators, setting one to a constant level and one to its natural level, which differs across subjects. We introduce five such estimands for the cross-CDE and -NDE when two mediators are measured. We consider both the scenario where one mediator is influenced by the other, referred to as sequential mediators, and the scenario where the mediators do not influence each other. Such estimands may be of interest in immunology, as we discuss in relation to measured immunological responses to SARS-CoV-2 vaccination. We provide identifying expressions for the estimands in observational settings where there is no residual confounding, and where intervention, outcome, and mediators are of arbitrary type. We further provide tight symbolic bounds for the estimands in randomized settings where there may be residual confounding of the outcome and mediator relationship and all measured variables are binary.
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| 3. |
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| 4. |
- Gabriel, EE, et al.
(författare)
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Predictive cluster level surrogacy in the presence of interference
- 2020
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Ingår i: Biostatistics (Oxford, England). - : Oxford University Press (OUP). - 1468-4357 .- 1465-4644. ; 21:2, s. E33-E46
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Tidskriftsartikel (refereegranskat)abstract
- Surrogate evaluation is a difficult problem that is made more so by the presence of interference. Our proposed procedure can allow for relatively easy evaluation of surrogates for indirect or spill-over clinical effects at the cluster level. Our definition of surrogacy is based on the causal-association paradigm (Joffe and Greene, 2009. Related causal frameworks for surrogate outcomes. Biometrics65, 530–538), under which surrogates are evaluated by the strength of the association between a causal treatment effect on the clinical outcome and a causal treatment effect on the candidate surrogate. Hudgens and Halloran (2008, Toward causal inference with interference. Journal of the American Statistical Association103, 832–842) introduced estimators that can be used for many of the marginal causal estimands of interest in the presence of interference. We extend these to consider surrogates for not just direct effects, but indirect and total effects at the cluster level. We suggest existing estimators that can be used to evaluate biomarkers under our proposed definition of surrogacy. In our motivating setting of a transmission blocking malaria vaccine, there is expected to be no direct protection to those vaccinated and predictive surrogates are urgently needed. We use a set of simulated data examples based on the proposed Phase IIb/III trial design of transmission blocking malaria vaccine to demonstrate how our definition, proposed criteria and procedure can be used to identify biomarkers as predictive cluster level surrogates in the presence of interference on the clinical outcome.
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| 5. |
- Jauhiainen, Alexandra, 1981, et al.
(författare)
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Transcriptional and metabolic data integration and modeling for identification of active pathways
- 2012
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Ingår i: Biostatistics. - : Oxford University Press (OUP). - 1465-4644 .- 1468-4357. ; 13:4, s. 748-761
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Tidskriftsartikel (refereegranskat)abstract
- With the growing availability of omics data generated to describe different cells and tissues, the modeling and interpretation of such data has become increasingly important. Pathways are sets of reactions involving genes, metabolites, and proteins highlighting functional modules in the cell. Therefore, to discover activated or perturbed pathways when comparing two conditions, for example two different tissues, it is beneficial to use several types of omics data. We present a model that integrates transcriptomic and metabolomic data in order to make an informed pathway-level decision. Since metabolites can be seen as end-points of perturbations happening at the gene level, the gene expression data constitute the explanatory variables in a sparse regression model for the metabolite data. Sophisticated model selection procedures are developed to determine an appropriate model. We demonstrate that the transcript profiles can be used to informatively explain the metabolite data from cancer cell lines. Simulation studies further show that the proposed model offers a better performance in identifying active pathways than, for example, enrichment methods performed separately on the transcript and metabolite data.
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| 6. |
- Josefsson, Maria, 1979-, et al.
(författare)
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A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout
- 2023
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Ingår i: Biostatistics. - : Oxford University Press. - 1465-4644 .- 1468-4357. ; 24:2, s. 372-387
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Tidskriftsartikel (refereegranskat)abstract
- Studies of memory trajectories using longitudinal data often result in highly non-representative samples due to selective study enrollment and attrition. An additional bias comes from practice effects that result in improved or maintained performance due to familiarity with test content or context. These challenges may bias study findings and severely distort the ability to generalize to the target population. In this study we propose an approach for estimating the finite population mean of a longitudinal outcome conditioning on being alive at a specific time point. We develop a flexible Bayesian semi-parametric predictive estimator for population inference when longitudinal auxiliary information is known for the target population. We evaluate sensitivity of the results to untestable assumptions and further compare our approach to other methods used for population inference in a simulation study. The proposed approach is motivated by 15-year longitudinal data from the Betula longitudinal cohort study. We apply our approach to estimate lifespan trajectories in episodic memory, with the aim to generalize findings to a target population.
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| 7. |
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| 8. |
- Liu, Mengling, et al.
(författare)
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Estimation and selection of complex covariate effects in pooled nested case-control studies with heterogeneity
- 2013
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Ingår i: Biostatistics. - : Oxford University Press. - 1465-4644 .- 1468-4357. ; 14:4, s. 682-694
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Tidskriftsartikel (refereegranskat)abstract
- A major challenge in cancer epidemiologic studies, especially those of rare cancers, is observing enough cases. To address this, researchers often join forces by bringing multiple studies together to achieve large sample sizes, allowing for increased power in hypothesis testing, and improved efficiency in effect estimation. Combining studies, however, renders the analysis difficult owing to the presence of heterogeneity in the pooled data. In this article, motivated by a collaborative nested case-control (NCC) study of ovarian cancer in three cohorts from United States, Sweden, and Italy, we investigate the use of penalty regularized partial likelihood estimation in the context of pooled NCC studies to achieve two goals. First, we propose an adaptive group lasso (gLASSO) penalized approach to simultaneously identify important variables and estimate their effects. Second, we propose a composite agLASSO penalized approach to identify variables with heterogeneous effects. Both methods are readily implemented with the group coordinate gradient decent algorithm and shown to enjoy the oracle property. We conduct simulation studies to evaluate the performance of our proposed approaches in finite samples under various heterogeneity settings, and apply them to the pooled ovarian cancer study.
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| 9. |
- Lönnstedt, Ingrid, et al.
(författare)
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Hierarchical Bayes models for cDNA microarray gene expression
- 2005
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Ingår i: Biostatistics. - : Oxford University Press (OUP). - 1465-4644 .- 1468-4357. ; 6:2, s. 279-291
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Tidskriftsartikel (refereegranskat)abstract
- cDNA microarrays are used in many contexts to compare mRNA levels between samples of cells. Microarray experiments typically give us expression measurements on 1000-20 000 genes, but with few replicates for each gene. Traditional methods using means and standard deviations to detect differential expression are not satisfactory in this context. A handful of alternative statistics have been developed, including several empirical Bayes methods. In the present paper we present two full hierarchical Bayes models for detecting gene expression, of which one (D) describes our microarray data very well. We also compare the full Bayes and empirical Bayes approaches with respect to model assumptions, false discovery rates and computer running time. The proposed models are compared to existing empirical Bayes models in a simulation study and for a set of data (Yuen et al., 2002), where 27 genes have been categorized by quantitative real-time PCR. It turns out that the existing empirical Bayes methods have at least as good performance as the full Bayes ones.
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| 10. |
- Sachs, MC, et al.
(författare)
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Flexible evaluation of surrogacy in platform studies
- 2023
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Ingår i: Biostatistics (Oxford, England). - : Oxford University Press (OUP). - 1468-4357 .- 1465-4644. ; 25:1, s. 220-236
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Tidskriftsartikel (refereegranskat)abstract
- Trial-level surrogates are useful tools for improving the speed and cost effectiveness of trials but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial-level surrogate evaluation, but none, to our knowledge, for the specific setting of platform studies. As platform studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate evaluation that would not normally be possible. However, they also offer a set of statistical issues including heterogeneity of the study population, treatments, implementation, and even potentially the quality of the surrogate. We propose the use of a hierarchical Bayesian semiparametric model for the evaluation of potential surrogates using nonparametric priors for the distribution of true effects based on Dirichlet process mixtures. The motivation for this approach is to flexibly model relationships between the treatment effect on the surrogate and the treatment effect on the outcome and also to identify potential clusters with differential surrogate value in a data-driven manner so that treatment effects on the surrogate can be used to reliably predict treatment effects on the clinical outcome. In simulations, we find that our proposed method is superior to a simple, but fairly standard, hierarchical Bayesian method. We demonstrate how our method can be used in a simulated illustrative example (based on the ProBio trial), in which we are able to identify clusters where the surrogate is, and is not useful. We plan to apply our method to the ProBio trial, once it is completed.
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| 11. |
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| 12. |
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| 13. |
- Sjolander, A
(författare)
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Estimation of marginal causal effects in the presence of confounding by cluster
- 2021
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Ingår i: Biostatistics (Oxford, England). - : Oxford University Press (OUP). - 1468-4357 .- 1465-4644. ; 22:3, s. 598-612
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Tidskriftsartikel (refereegranskat)abstract
- A popular way to control for unmeasured confounders is to utilize clusters (e.g. sets of siblings), in which a potentially large set of confounders are constant. By estimating the exposure–outcome association within clusters, rather than between unrelated subjects, all cluster-constant confounders are implicitly controlled for. To analyze such clustered data, it is common to use fixed effects models, which absorb all cluster-constant confounders into a cluster-specific intercept. In this article, we show how linear and log-linear fixed effects models can be used to estimate marginal counterfactual means. These counterfactual means can be estimated and presented for each exposure level separately, or contrasted to form a wide range of marginal causal effects. For binary outcomes, we propose to estimate marginal causal effects with marginal logistic between-within models. These models include a constant intercept common for all clusters, and control for unmeasured cluster-constant confounders by adding the mean exposure level in each cluster to the model. We illustrate the proposed methods by re-analyzing data from a co-twin control study on birth weight and Attention-Deficit/Hyperactivity Disorder.
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| 14. |
- Soneson, Charlotte, et al.
(författare)
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A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities.
- 2012
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Ingår i: Biostatistics. - : Oxford University Press (OUP). - 1468-4357 .- 1465-4644. ; 13, s. 129-141
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Tidskriftsartikel (refereegranskat)abstract
- Analysis of multivariate data sets from, for example, microarray studies frequently results in lists of genes which are associated with some response of interest. The biological interpretation is often complicated by the statistical instability of the obtained gene lists, which may partly be due to the functional redundancy among genes, implying that multiple genes can play exchangeable roles in the cell. In this paper, we use the concept of exchangeability of random variables to model this functional redundancy and thereby account for the instability. We present a flexible framework to incorporate the exchangeability into the representation of lists. The proposed framework supports straightforward comparison between any 2 lists. It can also be used to generate new more stable gene rankings incorporating more information from the experimental data. Using 2 microarray data sets, we show that the proposed method provides more robust gene rankings than existing methods with respect to sampling variations, without compromising the biological significance of the rankings.
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| 15. |
- Stjernqvist, Susann, et al.
(författare)
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A continuous-index hidden Markov jump process for modeling DNA copy number data
- 2009
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Ingår i: Biostatistics. - : Oxford University Press (OUP). - 1465-4644 .- 1468-4357. ; 10:4, s. 773-778
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Tidskriftsartikel (refereegranskat)abstract
- The number of copies of DNA in human cells can be measured using array comparative genomic hybridization (aCGH), which provides intensity ratios of sample to reference DNA at genomic locations corresponding to probes on a microarray. In the present paper, we devise a statistical model, based on a latent continuous-index Markov jump process, that is aimed to capture certain features of aCGH data, including probes that are unevenly long, unevenly spaced, and overlapping. The model has a continuous state space, with 1 state representing a normal copy number of 2, and the rest of the states being either amplifications or deletions. We adopt a Bayesian approach and apply Markov chain Monte Carlo (MCMC) methods for estimating the parameters and the Markov process. The model can be applied to data from both tiling bacterial artificial chromosome arrays and oligonucleotide arrays. We also compare a model with normal distributed noise to a model with t-distributed noise, showing that the latter is more robust to outliers.
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| 16. |
- Stocks, Theresa, et al.
(författare)
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Model selection and parameter estimation for dynamic epidemic models via iterated filtering : application to rotavirus in Germany
- 2020
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Ingår i: Biostatistics. - : Oxford University Press (OUP). - 1465-4644 .- 1468-4357. ; 21:3, s. 400-416
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Tidskriftsartikel (refereegranskat)abstract
- Despite the wide application of dynamic models in infectious disease epidemiology, the particular modeling of variability in the different model components is often subjective rather than the result of a thorough model selection process. This is in part because inference for a stochastic transmission model can be difficult since the likelihood is often intractable due to partial observability. In this work, we address the question of adequate inclusion of variability by demonstrating a systematic approach for model selection and parameter inference for dynamic epidemic models. For this, we perform inference for six partially observed Markov process models, which assume the same underlying transmission dynamics, but differ with respect to the amount of variability they allow for. The inference framework for the stochastic transmission models is provided by iterated filtering methods, which are readily implemented in the R package pomp by King and others (2016, Statistical inference for partially observed Markov processes via the R package pomp. Journal of Statistical Software 69, 1–43). We illustrate our approach on German rotavirus surveillance data from 2001 to 2008, discuss practical difficulties of the methods used and calculate a model based estimate for the basic reproduction number R0 using these data.
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| 17. |
- Sundberg, Rolf, 1942-, et al.
(författare)
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Statistical modeling in case-control real-time RT-PCR assays, for identification of differentially expressed genes in schizophrenia
- 2006
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Ingår i: Biostatistics. - : Oxford University Press (OUP). - 1465-4644 .- 1468-4357. ; 7:1, s. 130-144
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Tidskriftsartikel (refereegranskat)abstract
- Aspects of experimental design, statistical modeling, and statistical inference in case-control real-time reverse transcription-polymerase chain reaction (RT-PCR) assays are discussed. The background is mRNA expression data from an investigation of genes previously suggested to be schizophrenia related. Real-time RT-PCR allows large samples of individuals. However, with more individuals than positions per plate, incomplete designs are required. A basic multivariate (for several genes jointly) random-effects analysis of covariance model, incorporating heterogeneity both between and within individuals, is formulated. The use of reference genes to form additional regressors is found to be highly efficient. Because regressions between and within individuals are usually different, it is important first to average over the intraindividual replicates. This has consequences for the influence of plate effects. Topics also discussed are testing for a significant mean disease effect, differential coregulation, and the difficulty of identifying genes affected in only a subgroup of cases.
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| 18. |
- Varewyck, Machteld, et al.
(författare)
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On shrinkage and model extrapolation in the evaluation of clinical center performance
- 2014
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Ingår i: Biostatistics. - : Oxford University Press. - 1465-4644 .- 1468-4357. ; 15:4, s. 651-664
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Tidskriftsartikel (refereegranskat)abstract
- We consider statistical methods for benchmarking clinical centers based on a dichotomous outcome indicator. Borrowing ideas from the causal inference literature, we aim to reveal how the entire study population would have fared under the current care level of each center. To this end, we evaluate direct standardization based on fixed versus random center effects outcome models that incorporate patient-specific baseline covariates to adjust for differential case-mix. We explore fixed effects (FE) regression with Firth correction and normal mixed effects (ME) regression to maintain convergence in the presence of very small centers. Moreover, we study doubly robust FE regression to avoid outcome model extrapolation. Simulation studies show that shrinkage following standard ME modeling can result in substantial power loss relative to the considered alternatives, especially for small centers. Results are consistent with findings in the analysis of 30-day mortality risk following acute stroke across 90 centers in the Swedish Stroke Register.
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| 19. |
- Zetterqvist, J, et al.
(författare)
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Doubly robust methods for handling confounding by cluster
- 2016
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Ingår i: Biostatistics (Oxford, England). - : Oxford University Press (OUP). - 1468-4357 .- 1465-4644. ; 17:2, s. 264-276
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Tidskriftsartikel (refereegranskat)abstract
- In clustered designs such as family studies, the exposure-outcome association is usually confounded by both cluster-constant and cluster-varying confounders. The influence of cluster-constant confounders can be eliminated by studying the exposure-outcome association within (conditional on) clusters, but additional regression modeling is usually required to control for observed cluster-varying confounders. A problem is that the working regression model may be misspecified, in which case the estimated within-cluster association may be biased. To reduce sensitivity to model misspecification we propose to augment the standard working model for the outcome with an auxiliary working model for the exposure. We derive a doubly robust conditional generalized estimating equation (DRCGEE) estimator for the within-cluster association. This estimator combines the two models in such a way that it is consistent if either model is correct, not necessarily both. Thus, the DRCGEE estimator gives the researcher two chances instead of only one to make valid inference on the within-cluster association. We have implemented the estimator in an R package and we use it to examine the association between smoking during pregnancy and cognitive abilities in offspring, in a sample of siblings.
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| 20. |
- Ängquist, Lars, et al.
(författare)
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Improving the calculation of statistical significance in genome-wide scans
- 2005
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Ingår i: Biostatistics. - : Oxford University Press (OUP). - 1468-4357 .- 1465-4644. ; 6:4, s. 520-538
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Tidskriftsartikel (refereegranskat)abstract
- Calculations of the significance of results from linkage analysis can be performed by simulation or by theoretical approximation, with or without the assumption of perfect marker information. Here we concentrate on theoretical approximation. Our starting point is the asymptotic approximation formula presented by Lander and Kruglyak (1995, Nature Genetics, 11, 241-247), incorporating the effect of finite marker spacing as suggested by Feingold et al. (1993, American Journal of Human Genetics, 53, 234-251). We consider two distinct ways in which this formula can be improved. Firstly, we present a formula for calculating the crossover rate rho for a pedigree of general structure. For a pedigree set, these values may then be weighted into an overall crossover rate which can be used as input to the original approximation formula. Secondly, the unadjusted p-value formula is based on the assumption of a Normally distributed nonparametric linkage (NPL) score. This leads to conservative or anticonservative p-values of varying magnitude depending on the pedigree set structure. We adjust for non-Normality by calculating the marginal distribution of the NPL score under the null hypothesis of no linkage with an arbitrarily small error. The NPL score is then transformed to have a marginal standard Normal distribution and the transformed maximal NPL score, together with a slightly corrected value of the overall crossover rate, is inserted into the original formula in order to calculate the p-value. We use pedigrees of seven different structures to compare the performance of our suggested approximation formula to the original approximation formula, with and without skewness correction, and to results found by simulation. We also apply the suggested formula to two real pedigree set structure examples. Our method generally seems to provide improved behavior, especially for pedigree sets which show clear departure from Normality, in relation to the competing approximations.
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| 21. |
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| 22. |
- Crowther, MJ, et al.
(författare)
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A flexible parametric accelerated failure time model and the extension to time-dependent acceleration factors
- 2023
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Ingår i: Biostatistics (Oxford, England). - : Oxford University Press (OUP). - 1468-4357. ; 24:3, s. 811-831
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Tidskriftsartikel (refereegranskat)abstract
- Accelerated failure time (AFT) models are used widely in medical research, though to a much lesser extent than proportional hazards models. In an AFT model, the effect of covariates act to accelerate or decelerate the time to event of interest, that is, shorten or extend the time to event. Commonly used parametric AFT models are limited in the underlying shapes that they can capture. In this article, we propose a general parametric AFT model, and in particular concentrate on using restricted cubic splines to model the baseline to provide substantial flexibility. We then extend the model to accommodate time-dependent acceleration factors. Delayed entry is also allowed, and hence, time-dependent covariates. We evaluate the proposed model through simulation, showing substantial improvements compared to standard parametric AFT models. We also show analytically and through simulations that the AFT models are collapsible, suggesting that this model class will be well suited to causal inference. We illustrate the methods with a data set of patients with breast cancer. Finally, we provide highly efficient, user-friendly Stata, and R software packages.
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| 23. |
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