| 1. |
- Bayes de Luna, Antonio, et al.
(författare)
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Interatrial blocks. A separate entity from left atrial enlargement: a consensus report
- 2012
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Ingår i: Journal of Electrocardiology. - : Elsevier BV. - 1532-8430 .- 0022-0736. ; 45:5, s. 445-451
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Tidskriftsartikel (refereegranskat)abstract
- Impaired interatrial conduction or interatrial block is well documented but is not described as an individual electrocardiographic (ECG) pattern in most of ECG books, although the term atrial abnormalities to encompass both concepts, left atrial enlargement (LAE) and interatrial block, has been coined. In fact, LAE and interatrial block are often associated, similarly to what happens with ventricular enlargement and ventricular block. The interatrial blocks, that is, the presence of delay of conduction between the right and left atria, are the most frequent atrial blocks. These may be of first degree (P-wave duration > 120 milliseconds), third degree (longer P wave with biphasic [+/-] morphology in inferior leads), and second degree when these patterns appear transiently in the same ECG recording (atrial aberrancy). There are evidences that these electrocardiographic P-wave patterns are due to a block because they may (a) appear transiently, (b) be without associated atrial enlargement, and (c) may be reproduced experimentally. The presence of interatrial blocks may be seen in the absence of atrial enlargement but often are present in case of LAE. The most important clinical implications of interatrial block are the following: (a) the first degree interatrial blocks are very common, and their relation with atrial fibrillation and an increased risk for global and cardiovascular mortality has been demonstrated; (b) the third degree interatrial blocks are less frequent but are strong markers of LAE and paroxysmal supraventricular tachyarrhythmias. Their presence has been considered a true arrhythmological syndrome. (C) 2012 Elsevier Inc. All rights reserved.
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| 2. |
- Bränberg, Kenny, 1956-
(författare)
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Observed score equating with covariates
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In test score equating the focus is on the problem of finding the relationship between the scales of different test forms. This can be done only if data are collected in such a way that the effect of differences in ability between groups taking different test forms can be separated from the effect of differences in test form difficulty. In standard equating procedures this problem has been solved by using common examinees or common items. With common examinees, as in the equivalent groups design, the single group design, and the counterbalanced design, the examinees taking the test forms are either exactly the same, i.e., each examinee takes both test forms, or random samples from the same population. Common items (anchor items) are usually used when the samples taking the different test forms are assumed to come from different populations. The thesis consists of four papers and the main theme in three of these papers is the use of covariates, i.e., background variables correlated with the test scores, in observed score equating. We show how covariates can be used to adjust for systematic differences between samples in a non-equivalent groups design when there are no anchor items. We also show how covariates can be used to decrease the equating error in an equivalent groups design or in a non-equivalent groups design. The first paper, Paper I, is the only paper where the focus is on something else than the incorporation of covariates in equating. The paper is an introduction to test score equating, and the author's thoughts on the foundation of test score equating. There are a number of different definitions of test score equating in the literature. Some of these definitions are presented and the similarities and differences between them are discussed. An attempt is also made to clarify the connection between the definitions and the most commonly used equating functions. In Paper II a model is proposed for observed score linear equating with background variables. The idea presented in the paper is to adjust for systematic differences in ability between groups in a non-equivalent groups design by using information from background variables correlated with the observed test scores. It is assumed that conditional on the background variables the two samples can be seen as random samples from the same population. The background variables are used to explain the systematic differences in ability between the populations. The proposed model consists of a linear regression model connecting the observed scores with the background variables and a linear equating function connecting observed scores on one test forms to observed scores on the other test form. Maximum likelihood estimators of the model parameters are derived, using an assumption of normally distributed test scores, and data from two administrations of the Swedish Scholastic Assessment Test are used to illustrate the use of the model. In Paper III we use the model presented in Paper II with two different data collection designs: the non-equivalent groups design (with and without anchor items) and the equivalent groups design. Simulated data are used to examine the effect - in terms of bias, variance and mean squared error - on the estimators, of including covariates. With the equivalent groups design the results show that using covariates can increase the accuracy of the equating. With the non-equivalent groups design the results show that using an anchor test together with covariates is the most efficient way of reducing the mean squared error of the estimators. Furthermore, with no anchor test, the background variables can be used to adjust for the systematic differences between the populations and produce unbiased estimators of the equating relationship, provided that the “right” variables are used, i.e., the variables explaining those differences. In Paper IV we explore the idea of using covariates as a substitute for an anchor test with a non-equivalent groups design in the framework of Kernel Equating. Kernel Equating can be seen as a method including five different steps: presmoothing, estimation of score probabilities, continuization, equating, and calculating the standard error of equating. For each of these steps we give the theoretical results when observations on covariates are used as a substitute for scores on an anchor test. It is shown that we can use the method developed for Post-Stratification Equating in the non-equivalent groups with anchor test design, but with observations on the covariates instead of scores on an anchor test. The method is illustrated using data from the Swedish Scholastic Assessment Test.
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| 3. |
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| 4. |
- de Luna, Xavier, et al.
(författare)
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Covariate selection for the non-parametric estimation of an average treatment effect
- 2011
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Ingår i: Biometrika. - : Oxford University Press. - 0006-3444 .- 1464-3510. ; 98:4, s. 861-875
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Tidskriftsartikel (refereegranskat)abstract
- Observational studies in which the effect of a nonrandomized treatment on an outcome of interest is estimated are common in domains such as labour economics and epidemiology. Such studies often rely on an assumption of unconfounded treatment when controlling for a given set of observed pre-treatment covariates. The choice of covariates to control in order to guarantee unconfoundedness should primarily be based on subject matter theories, although the latter typically give only partial guidance. It is tempting to include many covariates in the controlling set to try to make the assumption of an unconfounded treatment realistic. Including unnecessary covariates is suboptimal when the effect of a binary treatment is estimated nonparametrically. For instance, when using a n1/2-consistent estimator, a loss of efficiency may result from using covariates that are irrelevant for the unconfoundedness assumption. Moreover, bias may dominate the variance when many covariates are used. Embracing the Neyman–Rubin model typically used in conjunction with nonparametric estimators of treatment effects, we characterize subsets from the original reservoir of covariates that are minimal in the sense that the treatment ceases to be unconfounded given any proper subset of these minimal sets. These subsets of covariates are shown to be identified under mild assumptions. These results lead us to propose data-driven algorithms for the selection of minimal sets of covariates.
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| 5. |
- de Luna, Xavier, 1968-, et al.
(författare)
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Non-parametric inference for the effect of a treatment on survival times with application in the health and social sciences
- 2010
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Ingår i: Journal of Statistical Planning and Inference. - : Elsevier. - 0378-3758 .- 1873-1171. ; 140:7, s. 2122-2137
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we perform inference on the effect of a treatment on survival times in studies where the treatment assignment is not randomized and the assignment time is not known in advance. Two such studies are discussed: a heart transplant program and a study of Swedish unemployed eligible for employment subsidy. We estimate survival functions on a treated and a control group which are made comparable through matching on observed covariates. The inference is performed by conditioning on waiting time to treatment, that is, time between the entrance in the study and treatment. This can be done only when sufficient data are available. In other cases, averaging over waiting times is a possibility, although the classical interpretation of the estimated survival functions is lost unless hazards are not functions of waiting time. To show unbiasedness and to obtain an estimator of the variance, we build on the potential outcome framework, which was introduced by J. Neyman in the context of randomized experiments, and adapted to observational studies by D.B. Rubin. Our approach does not make parametric or distributional assumptions. In particular, we do not assume proportionality of the hazards compared. Small sample performance of the estimator and a derived test of no treatment effect are studied in a Monte Carlo study.
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| 6. |
- de Luna, Xavier, 1968-, et al.
(författare)
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Testing for the Unconfoundedness Assumption Using an Instrumental Assumption
- 2014
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Ingår i: Journal of Causal Inference. - : Walter de Gruyter GmbH. - 2193-3677 .- 2193-3685. ; 2:2, s. 187-199
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Tidskriftsartikel (refereegranskat)abstract
- The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption. In this paper, we present a set of assumptions on an instrumental variable which allows us to test for the unconfoundedness assumption, although they do not necessarily yield nonparametric identification of an average causal effect. We propose a test for the unconfoundedness assumption based on the instrumental assumptions introduced and give conditions under which the test has power. We perform a simulation study and apply the results to a case study where the interest lies in evaluating the effect of job practice on employment.
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| 7. |
- Fahlén, Jessica, 1973-
(författare)
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Essays on spatial point processes and bioinformatics
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis consists of two separate parts. The first part consists of one paper and considers problems concerning spatial point processes and the second part includes three papers in the field of bioinformatics. The first part of the thesis is based on a forestry problem of estimating the number of trees in a region by using the information in an aerial photo, showing the area covered by the trees. The positions of the trees are assumed to follow either a binomial point process or a hard-core Strauss process. Furthermore, discs of equal size are used to represent the tree-crowns. We provide formulas for the expectation and the variance of the relative vacancy for both processes. The formulas are approximate for the hard-core Strauss process. Simulations indicate that the approximations are accurate. The second part of this thesis focuses on pre-processing of microarray data. The microarray technology can be used to measure the expression of thousands of genes simultaneously in a single experiment. The technique is used to identify genes that are differentially expressed between two populations, e.g. diseased versus healthy individuals. This information can be used in several different ways, for example as diagnostic tools and in drug discovery. The microarray technique involves a number of complex experimental steps, where each step introduces variability in the data. Pre-processing aims to reduce this variation and is a crucial part of the data analysis. Paper II gives a review over several pre-processing methods. Spike-in data are used to describe how the different methods affect the sensitivity and bias of the experiment. An important step in pre-processing is dye-normalization. This normalization aims to remove the systematic differences due to the use of different dyes for coloring the samples. In Paper III a novel dye-normalization, the MC-normalization, is proposed. The idea behind this normalization is to let the channels’ individual intensities determine the correction, rather than the average intensity which is the case for the commonly used MA-normalization. Spike-in data showed that the MC-normalization reduced the bias for the differentially expressed genes compared to the MA-normalization. The standard method for preserving patient samples for diagnostic purposes is fixation in formalin followed by embedding in paraffin (FFPE). In Paper IV we used tongue-cancer microRNA-microarray data to study the effect of FFPE-storage. We suggest that the microRNAs are not equally affected by the storage time and propose a novel procedure to remove this bias. The procedure improves the ability of the analysis to detect differentially expressed microRNAs.
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| 8. |
- Holmström, Anna, et al.
(författare)
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SIMSAM-nätverket i Umeå strävar mot att bli ett framstående center för registerforskning som knyter samman barndomen med livslång hälsa och välfärd
- 2011
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Ingår i: SVEPET - Medlemstidning för Svensk Epidemiologisk Förening (SVEP). - Göteborg : Svensk epidemiologisk förening. - 1101-4385. ; 29:3, s. 8-9
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Vetenskapsrådets SIMSAM initiativ syftar till att stärka multidisciplinär registerforskning i Sverige. Inom SIMSAM-nätverket i Umeå arbetar vi tvärvetenskapligt med sikte på att utvecklas till ett center med excellens kring mikrodataforskning som knyter samman barndomen med livslång hälsa och välfärd. Just nu fokuserar vi på att få tillgång till sammanlänkade data från ett flertal nationella och regionala register för att komma vidare med vår planerade forskning. Dessutom har Umeå-nätverket nyligen fått i uppdrag att leda den nationella samordningen av SIMSAM initiativet.
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| 9. |
- Häggström, Jenny, 1980-, et al.
(författare)
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Estimating prediction error : cross-validation vs. accumulated prediction error
- 2010
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Ingår i: Communications in statistics. Simulation and computation. - : Informa plc.. - 0361-0918 .- 1532-4141. ; 39:5, s. 880-898
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Tidskriftsartikel (refereegranskat)abstract
- We study the validation of prediction rules such as regression models and classification algorithms through two out-of-sample strategies, cross-validation and accumulated prediction error. We use the framework of Efron (1983) where measures of prediction errors are defined as sample averages of expected errors and show through exact finite sample calculations that cross-validation and accumulated prediction error yield different smoothing parameter choices in nonparametric regression. The difference in choice does not vanish as sample size increases.
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| 10. |
- Häggström, Jenny, 1980-
(författare)
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Selection of smoothing parameters with application in causal inference
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is a contribution to the research area concerned with selection of smoothing parameters in the framework of nonparametric and semiparametric regression. Selection of smoothing parameters is one of the most important issues in this framework and the choice can heavily influence subsequent results. A nonparametric or semiparametric approach is often desirable when large datasets are available since this allow us to make fewer and weaker assumptions as opposed to what is needed in a parametric approach. In the first paper we consider smoothing parameter selection in nonparametric regression when the purpose is to accurately predict future or unobserved data. We study the use of accumulated prediction errors and make comparisons to leave-one-out cross-validation which is widely used by practitioners. In the second paper a general semiparametric additive model is considered and the focus is on selection of smoothing parameters when optimal estimation of some specific parameter is of interest. We introduce a double smoothing estimator of a mean squared error and propose to select smoothing parameters by minimizing this estimator. Our approach is compared with existing methods.The third paper is concerned with the selection of smoothing parameters optimal for estimating average treatment effects defined within the potential outcome framework. For this estimation problem we propose double smoothing methods similar to the method proposed in the second paper. Theoretical properties of the proposed methods are derived and comparisons with existing methods are made by simulations.In the last paper we apply our results from the third paper by using a double smoothing method for selecting smoothing parameters when estimating average treatment effects on the treated. We estimate the effect on BMI of divorcing in middle age. Rich data on socioeconomic conditions, health and lifestyle from Swedish longitudinal registers is used.
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