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| 2. |
- Columbu, S, et al.
(författare)
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Modeling sign concordance of quantile regression residuals with multiple outcomes
- 2023
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Ingår i: The international journal of biostatistics. - : Walter de Gruyter GmbH. - 1557-4679 .- 2194-573X. ; 19:1, s. 97-110
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Tidskriftsartikel (refereegranskat)abstract
- Quantile regression permits describing how quantiles of a scalar response variable depend on a set of predictors. Because a unique definition of multivariate quantiles is lacking, extending quantile regression to multivariate responses is somewhat complicated. In this paper, we describe a simple approach based on a two-step procedure: in the first step, quantile regression is applied to each response separately; in the second step, the joint distribution of the signs of the residuals is modeled through multinomial regression. The described approach does not require a multidimensional definition of quantiles, and can be used to capture important features of a multivariate response and assess the effects of covariates on the correlation structure. We apply the proposed method to analyze two different datasets.
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| 3. |
- Fenske, Nora, et al.
(författare)
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Boosting Structured Additive Quantile Regression for Longitudinal Childhood Obesity Data
- 2013
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Ingår i: The International Journal of Biostatistics. - : Walter de Gruyter GmbH. - 1557-4679 .- 2194-573X. ; 9:1, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.
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| 4. |
- Forkman, Johannes
(författare)
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Generalized Confidence Intervals for Intra- and Inter-subject Coefficients of Variation in Linear Mixed-effects Models
- 2017
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Ingår i: International Journal of Biostatistics. - : Walter de Gruyter GmbH. - 2194-573X .- 1557-4679. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Linear mixed-effects models are linear models with several variance components. Models with a single random-effects factor have two variance components: the random-effects variance, i.e., the inter-subject variance, and the residual error variance, i.e., the intra-subject variance. In many applications, it is practice to report variance components as coefficients of variation. The intra- and inter-subject coefficients of variation are the square roots of the corresponding variances divided by the mean. This article proposes methods for computing confidence intervals for intra- and inter-subject coefficients of variation using generalized pivotal quantities. The methods are illustrated through two examples. In the first example, precision is assessed within and between runs in a bioanalytical method validation. In the second example, variation is estimated within and between main plots in an agricultural split-plot experiment. Coverage of generalized confidence intervals is investigated through simulation and shown to be close to the nominal value.
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| 5. |
- Kum, Cletus Kwa, et al.
(författare)
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On the Effects of Malaria Treatment on Parasite Drug Resistance : Probability Modelling of Genotyped Malaria Infections
- 2013
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Ingår i: The International Journal of Biostatistics. - : Walter de Gruyter GmbH. - 1557-4679 .- 2194-573X. ; 9:1, s. 135-148
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Tidskriftsartikel (refereegranskat)abstract
- We compare the frequency of resistant genes of malaria parasites before treatment and at first malaria incidence after treatment. The data come from a clinical trial at two health facilities in Tanzania and concerns single nucleotide polymorphisms (SNPs) at three positions believed to be related to resistance to malaria treatment. A problem is that mixed infections are common, which both obscures the underlying frequency of alleles at each locus as well as the associations between loci in samples where alleles are mixed. We use combinatorics and quite involved probability methods to handle multiple infections and multiple haplotypes. The infection with the different haplotypes seemed to be independent of each other. We showed that at two of the three studied SNPs, the proportion of resistant genes had increased after treatment with sulfadoxine-pyrimethamine alone but when treated in combination with artesunate, no effect was noticed. First recurrences of malaria associated more with sulfadoxine-pyrimethamine alone as treatment than when in combination with artesunate. We also found that the recruited children had two different ongoing malaria infections where the parasites had different gene types.
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| 6. |
- Le Goff, Line Chloe, et al.
(författare)
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Parameter Estimation of a Two-Colored Urn Model Class
- 2017
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Ingår i: INTERNATIONAL JOURNAL OF BIOSTATISTICS. - : WALTER DE GRUYTER GMBH. - 2194-573X .- 1557-4679. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Though widely used in applications, reinforced randomwalk on graphs have never been the subject of a valid statistical inference. We develop in this paper a statistical framework for a general two-colored urn model. The probability to draw a ball at each step depends on the number of balls of each color and on a multidimensional parameter through a function, called choice function. We introduce two estimators of the parameter: the maximum likelihood estimator and a weighted least squares estimator which is less efficient, but is closer to the calibration techniques used in the applied literature. In general, the model is an inhomogeneous Markov chain and this property makes the estimation of the parameter impossible on a single path, even if it were infinite. Therefore we assume that we observe i.i.d. experiments, each of a predetermined finite length. This is coherent with the usual experimental set-ups. We apply the statistical framework to a real life experiment: the selection of a path among pre-existing channels by an ant colony. We performed experiments, which consisted of letting ants pass through the branches of a fork. We consider the particular urn model proposed by J.-L. Deneubourg et al. in 1990 to describe this phenomenon. We simulate this model for several parameter values in order to assess the accuracy of the MLE and the WLSE. Then we estimate the parameter from the experimental data and evaluate confident regions with Bootstrap algorithms. The findings of this paper do not contradict the biological literature, but give statistical significance to the values of the parameter found therein.
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| 7. |
- Grünewald, Maria, 1976-, et al.
(författare)
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A Stochastic EM Type Algorithm for Parameter Estimation in Models with Continuous Outcomes, under Complex Ascertainment
- 2010
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Ingår i: The International Journal of Biostatistics. - : Walter de Gruyter GmbH. - 1557-4679. ; 6:1, s. Article 23-
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Tidskriftsartikel (refereegranskat)abstract
- Outcome-dependent sampling probabilities can be used to increase efficiency in observational studies. For continuous outcomes, appropriate consideration of sampling design in estimating parameters of interest is often computationally cumbersome. In this article, we suggest a Stochastic EM type algorithm for estimation when ascertainment probabilities are known or estimable. The computational complexity of the likelihood is avoided by filling in missing data so that an approximation of the full data likelihood can be used. The method is not restricted to any specific distribution of the data and can be used for a broad range of statistical models.
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