| 1. |
- Cipriano, Mariateresa, et al.
(author)
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Association between cannabinoid CB1 receptor expression and Akt signalling in prostate cancer
- 2013
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:6, s. e65798-
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Journal article (peer-reviewed)abstract
- Background: In prostate cancer, tumour expression of cannabinoid CB1 receptors is associated with a poor prognosis. One explanation for this association comes from experiments with transfected astrocytoma cells, where a high CB receptor expression recruits the Akt signalling survival pathway. In the present study, we have investigated the association between CB1 receptor expression and the Akt pathway in a well-characterised prostate cancer tissue microarray.Methodology/Principal Findings: Phosphorylated Akt immunoreactivity (pAkt-IR) scores were available in the database. CB1 receptor immunoreactivity (CB1IR) was rescored from previously published data using the same scale as pAkt-IR. There was a highly significant correlation between CB1IR and pAkt-IR. Further, cases with high expression levels of both biomarkers were much more likely to have a more severe form of the disease at diagnosis than those with low expression levels. The two biomarkers had additive effects, rather than an interaction, upon disease-specific survival.Conclusions/Significance: The present study provides data that is consistent with the hypothesis that at a high CB1 receptor expression, the Akt signalling pathway becomes operative.
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| 2. |
- Hammarsten, Peter, 1977-, et al.
(author)
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ErbB2 Receptor Immunoreactivity in Prostate Cancer : Relationship to the Androgen Receptor, Disease Severity at Diagnosis and Disease Outcome
- 2014
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In: PLOS ONE. - : Public Library Science. - 1932-6203. ; 9:9
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Journal article (peer-reviewed)abstract
- BACKGROUND: ErbB2 is a member of the epidermal growth factor family of tyrosine kinases that is centrally involved in the pathogenesis of prostate cancer and several studies have reported that a high expression of this protein has prognostic value. In the present study, we have investigated whether tumour ErbB2 immunoreactivity (ErbB2-IR) has clinically useful prognostic value, i.e. that it provides additional prognostic information to that provided by routine clinical tests (Gleason score, tumour stage).METHODOLOGY/PRINCIPAL FINDINGS: ErbB2-IR was measured in a well-characterised tissue microarray of tumour and non-malignant samples obtained at diagnosis. Additionally, mRNA levels of ErbB2-IR in the prostate were determined in the rat following manipulation of circulating androgen levels. Tumour ErbB2-IR was significantly associated with the downstream signalling molecule phosphorylated-Akt and with the cell proliferation marker Ki-67. The significant association of tumour ErbB2-IR with the Gleason score at diagnosis was lost when controlled for the association of both parameters with Ki-67. In the rat prostate, mRNA for ErbB2 was inversely associated with circulating androgen levels. There was no association between ErbB2-IR and the androgen receptor (AR)-IR in the tumours, but an interaction between the two parameters was seen with respect to their association with the tumour stage. Tumour ErbB2-IR was confirmed to be a prognostic marker for disease-specific survival, but it did not provide significant additive information to the Gleason score or to Ki-67.CONCLUSIONS/SIGNIFICANCE: It is concluded that tumour ErbB2-IR is of limited clinical value as a prognostic marker to aid treatment decisions, but could be of pathophysiological importance in prostate cancer.
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| 3. |
- Häggström, Jenny
(author)
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Bandwidth selection for backfitting estimation of semiparametric additive models : a simulation study
- 2013
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In: Computational Statistics & Data Analysis. - Amsterdam : Elsevier. - 0167-9473 .- 1872-7352. ; 62, s. 136-148
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Journal article (peer-reviewed)abstract
- A data-driven bandwidth selection method for backfitting estimation of semiparametric additive models, when the the parametric part is of main interest, is proposed. The proposed method is a double smoothing estimator of the mean-squared error of the backfitting estimator of the parametric terms. The performance of the proposed method is evaluated and compared with existing bandwidth selectors by means of a simulation study.
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| 4. |
- Häggström, Jenny, 1980-, et al.
(author)
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Estimating prediction error : cross-validation vs. accumulated prediction error
- 2010
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In: Communications in statistics. Simulation and computation. - : Informa plc.. - 0361-0918 .- 1532-4141. ; 39:5, s. 880-898
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Journal article (peer-reviewed)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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| 5. |
- Häggström, Jenny, et al.
(author)
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Optimal Bandwidth Selection in Observed-Score Kernel Equating
- 2014
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In: Journal of educational measurement. - : Wiley. - 0022-0655 .- 1745-3984. ; 51:2, s. 201-211
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Journal article (peer-reviewed)abstract
- The selection of bandwidth in kernel equating is important because it has a direct impact on the equated test scores. The aim of this article is to examine the use of double smoothing when selecting bandwidths in kernel equating and to compare double smoothing with the commonly used penalty method. This comparison was made using both an equivalent groups design and a nonequivalent group with anchor test design. The performance of the methods was evaluated through simulation studies using both symmetric and skewed score distributions. In addition, the bandwidth selection methods were applied to real data from a college admissions test. The results show that the traditional penalty method works well although double smoothing is a viable alternative because it performs reasonably well compared to the traditional method.
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| 6. |
- Häggström, Jenny, et al.
(author)
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Potential upstream regulators of cannabinoid receptor 1 signaling in prostate cancer : A Bayesian network analysis of data from a tissue microarray
- 2014
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In: The Prostate. - : Wiley. - 0270-4137 .- 1097-0045. ; 74:11, s. 1107-1117
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Journal article (peer-reviewed)abstract
- BACKGROUND The endocannabinoid system regulates cancer cell proliferation, and in prostate cancer a high cannabinoid CB1 receptor expression is associated with a poor prognosis. Down-stream mediators of CB1 receptor signaling in prostate cancer are known, but information on potential upstream regulators is lacking. RESULTS Data from a well-characterized tumor tissue microarray were used for a Bayesian network analysis using the max-min hill-climbing method. In non-malignant tissue samples, a directionality of pEGFR (the phosphorylated form of the epidermal growth factor receptor) CB1 receptors were found regardless as to whether the endocannabinoid metabolizing enzyme fatty acid amide hydrolase (FAAH) was included as a parameter. A similar result was found in the tumor tissue, but only when FAAH was included in the analysis. A second regulatory pathway, from the growth factor receptor ErbB2 FAAH was also identified in the tumor samples. Transfection of AT1 prostate cancer cells with CB1 receptors induced a sensitivity to the growth-inhibiting effects of the CB receptor agonist CP55,940. The sensitivity was not dependent upon the level of receptor expression. Thus a high CB1 receptor expression alone does not drive the cells towards a survival phenotype in the presence of a CB receptor agonist. CONCLUSIONS The data identify two potential regulators of the endocannabinoid system in prostate cancer and allow the construction of a model of a dysregulated endocannabinoid signaling network in this tumor. Further studies should be designed to test the veracity of the predictions of the network analysis in prostate cancer and other solid tumors.
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| 7. |
- Häggström, Jenny, 1980-
(author)
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Selection of smoothing parameters with application in causal inference
- 2011
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Doctoral thesis (other academic/artistic)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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| 8. |
- Häggström, Jenny, et al.
(author)
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Targeted smoothing parameter selection for estimating average causal effects
- 2014
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In: Computational statistics (Zeitschrift). - : Springer. - 0943-4062 .- 1613-9658. ; 29:6, s. 1727-1748
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Journal article (peer-reviewed)abstract
- The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as kernel, splines or local polynomial regression. Such regression methods are tuned via smoothing parameters which regulates the amount of degrees of freedom used in the fit. In this paper we propose data-driven methods for selecting smoothing parameters when the targeted parameter is an average causal effect. For this purpose, we propose to estimate the exact expression of the mean squared error of the estimators. Asymptotic approximations indicate that the smoothing parameters minimizing this mean squared error converges to zero faster than the optimal smoothing parameter for the estimation of the regression functions. In a simulation study we show that the proposed data-driven methods for selecting the smoothing parameters yield lower empirical mean squared error than other methods available such as, e.g., cross-validation.
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