Raju Rimal

• Ahimbisibwe, Ashley; Valberg, Morten; Green, Adele C.; Ghiasvand, Reza; Rüegg, Corina Silvia & Rimal, Raju [Show all 10 contributors for this article] (2023). Nevus Count, Pigmentary Characteristics, and Melanoma-specific Mortality among Norwegian Women with Melanoma >1.0 mm Thick. Acta Dermato-Venereologica. ISSN 0001-5555. 103. doi: 10.2340/actadv.v103.4403. Full text in Research Archive
• Rimal, Raju; Almøy, Trygve & Sæbø, Solve (2020). Comparison of multi-response estimation methods. Chemometrics and Intelligent Laboratory Systems. ISSN 0169-7439. 205. doi: 10.1016/j.chemolab.2020.104093. Full text in Research Archive
• Rimal, Raju; Almøy, Trygve & Sæbø, Solve (2019). Comparison of multi-response prediction methods. Chemometrics and Intelligent Laboratory Systems. ISSN 0169-7439. 190, p. 10–21. doi: 10.1016/j.chemolab.2019.05.004. Full text in Research Archive Show summary

  While data science is battling to extract information from the enormous explosion of data, many estimators and algorithms are being developed for better prediction. Researchers and data scientists often introduce new methods and evaluate them based on various aspects of data. However, studies on the impact of/on a model with multiple response variables are limited. This study compares some newly-developed (envelope) and well-established (PLS, PCR) prediction methods based on real data and simulated data specifically designed by varying properties such as multicollinearity, the correlation between multiple responses and position of relevant principal components of predictors. This study aims to give some insight into these methods and help the researcher to understand and use them in further studies.

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• Rimal, Raju; Robsahm, Trude Eid; Green, Adele C.; Ghiasvand, Reza; Rueegg, Corina Silvia & Bassarova, Assia Vassileva [Show all 12 contributors for this article] (2023). Melanoma survival by tumour thickness in Norway, 1983–2019.
• Rimal, Raju; Robsahm, Trude Eid; Green, Adèle C.; Ghiasvand, Reza; Rueegg, Corina Silvia & Bassarova, Assia Vassileva [Show all 11 contributors for this article] (2022). Trends in melanoma tumour thickness in Norway, 1983–2019. Show summary

  Aim: Describe the trends in melanoma tumour thickness in Norway by T category, overall and in important subgroups such as sex, age, anatomic site, and histopathological subtype, over 35-year.

• Rimal, Raju (2021). Long-term Trend on Tumour Thickness of Melanoma in Norway. Show summary

  Introduction: Norway ranks fifth in incidence and second in mortality of cutaneous melanoma worldwide. Tumour thickness at diagnosis is the cornerstone of melanoma classification and the most important prognostic factor for clinically localized primary melanoma. Increased incidence of thin tumours may be a result of increased awareness and changes in pathological practice. Recently digitized tumour thickness data (1980-2007) and data from the Melanoma Registry (2008-), enable investigating long-term trends in melanoma thickness. Aim: To investigate trends in tumour thickness, overall and in important subgroups in a nationwide case series from 1980 to 2019. Methods: Tumour thickness was categorized in T-categories: T1 (≤1.0 mm), T2 (1.0-2.0 mm), T3 (>2.0-4.0 mm), and T4 (>4.0 mm). Incidence rates were age-standardized using the European standard population. Trends and changes in incidence rate over time were analysed with annual percentage changes (APC) and average APC. Conclusions: T1 melanomas had the largest increase in incidence over time. An increasing trend was also observed in thicker tumours, suggesting that the rise in melanoma incidence is due to not only overdiagnosis/pathological practice.

• Rimal, Raju; Robsahm, Trude Eid; Green, Adèle C.; Ghiasvand, Reza; Rueegg, Corina Silvia & Bassarova, Assia Vassileva [Show all 11 contributors for this article] (2021). Long-term trends in melanoma tumour thickness in Norway. Show summary

  Norway has the second-highest mortality rate of cutaneous melanoma worldwide and ranks fifth in incidence. Tumour (Breslow) thickness at diagnosis is the primary determinant of the T category in the tumour, nodes, metastasis staging system, and the most important prognostic factor for survival after localized melanoma. This ongoing study investigates long-term trends in tumour thickness, and the corresponding T categories, overall and in important subgroups, in a nationwide case series over a 40-year time period. The population-based Cancer Registry of Norway (CRN) provided all first primary invasive melanoma cases for 1980-2019. Tumour thickness was available from the Norwegian Melanoma Registry (within the CRN) for all cases diagnosed in 2008-2019 and was manually extracted from the paper notifications archived in the CRN for the cases diagnosed in 1980-2007. Covariates include sex, age, residential geographical region, anatomic site, histopathological subtype, clinical stage, and ulceration. Descriptive summaries are presented as frequencies (numbers, %) and medians with interquartile ranges (IQR). In total, 47,439 morphologically verified first primary invasive melanoma cases were diagnosed in Norway in 1980-2019. In both men and women, median age at diagnosis increased from 1980-2000 to 2008-2019 (Table 1). Trunk was the most common anatomic site in men (59.5%), and trunk (32.3%) and lower extremities (35.9%) the most common sites in women. The dominant subtype was superficial spreading melanoma (54.8%), followed by nodular melanoma (18.4%). In total, 90.0% of the cases were diagnosed in a local stage, whereas 5.6% had regional metastases and 4.4% had distant metastases. Women were diagnosed at a thinner stage than men. In men, median (IQR) tumour thickness decreased from 1.4 mm (0.75–3) in 1980-1999 to 1 mm (0.6–2.3) in 2008-2019, and in women from 1 mm (0.6–2) to 0.9 mm (0.5–1.8) (Table 1). Tumour thickness was missing in the pathology reports for more than 25% of the cases until 1990. Reporting of ulceration started in 2000, but with a large proportion of missing values. After the Norwegian Melanoma Registry was established in 2008, the proportions of missing ulceration decreased dramatically. This unique time series of national melanoma tumour thickness data will identify trends in tumour thickness, overall and in subgroups of the population, as well as identify potential effects of changing exposure patterns and earlier detection.

• Rimal, Raju; Almøy, Trygve & Sæbø, Solve (2019). A prediction comparison of some multivariate methods. Show summary

  A linear model is a widely used relationship structure which we encounter. A versatile tool to simulate linear model data controlling various aspects of it can be useful not only for comp aring methods, algorithms and models but also accessing and understanding their properties. Here we will present an R-package `simrel` that can control properties such as collinearity bet ween the variables, information content in predictors that is relevant for responses and position of the predictor components that contain this information. With few parameters, the packa ge can simulate data with a wide range of properties. Various multivariate prediction methods are developed over time to address the problem related to such properties. In the second part of the talk, we will compare relatively established methods such as Principal Component Regression, Partial Least Squares Regression together with newly developed envelope methods using the multi-response data simulated with varying properties. These methods deal with the relevant and irrelevant regression structure in a different way. This part will pr esent these differences and make some comparisons on these methods.

• Rimal, Raju; Almøy, Trygve & Sæbø, Solve (2018). A versatile tool for simulating linear model data. Show summary

  Data science is generating enormous amounts of data, and new and advanced analytical methods are constantly being developed to cope with the challenge of extracting information from such “big-data”. Researchers often use simulated data to assess and document the properties of these new methods, and in this paper, we present an extension to the R-package simrel, which is a versatile and transparent tool for simulating linear model data with an extensive range of adjustable properties. The method is based on the concept of relevant components and is equivalent to the newly developed envelope model. It is a multi-response extension of R-package simrel which is available in R-package repository CRAN, and as simrel the new approach is essentially based on random rotations of latent relevant components to obtain a predictor matrix X, but in addition we introduce random rotations of latent components spanning a response space in order to obtain a multivariate response matrix Y. The properties of the linear relation between X and Y are defined by a small set of input parameters which allow versatile and adjustable simulations. The method is implemented as an update to the R-package simrel.

• Rimal, Raju (2019). Exploration of Multi-response Multivariate Methods. Norwegian University of Life Sciences. ISSN 978-82-575-1636-9. Show summary

  A linear regression model defines a linear relationship between two or more random variables. The random variables that depend on other random variables are often called response variables and the independent random variables are called predictor variables. In most cases not all variation is relevant for regression, i.e. only a certain amount of the variation in the predictors is relevant and only so for a part of the variation in the response. This leads to a reduction of the linear regression model where one can imagine a subspace of the space spanned by the predictor variables that contains all the relevant information for a subspace of the space spanned by the response variables. In this thesis we attempt to compare some new methods which are based on the envelope model and some established methods such as principal components regression (PCR) and partial least squares regression (PLS). The comparison tests these methods on their performance of producing minimum prediction and estimation error while modelling data simulated with specifically designed properties. For the simulation we have also created an R-package called simrel with a web interface. A simulation model for a multi-response multivariate linear model, on which the simulation tool is based, is discussed in the first paper. This paper prepares a basic foundation for the simulations with the concept of reduction of regression models. The second paper discusses the similarities of the envelope, PCR and PLS population models. This paper compares the prediction performance of several multivariate methods using a model with a single response. The final two papers make an extensive investigation evaluating the pre- diction and estimation performance of established (PCR, PLS1 and PLS2) and newly developed envelope based (Xenv and Senv) methods. Unsurpris- ingly the study found that not one method dominates in all situations, but their performance depend on the properties of the data they model. How- ever, the envelope based methods have shown remarkable performance in many cases, both in prediction and estimation. The study also recommend researchers to use and evaluate the envelope methods.

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