Raju Rimal

• Hestnes, Ingvild; Solberg, Lene Bergendal; Meyer, Haakon Eduard; Sundet, Mads; Rimal, Raju & Nordsletten, Lars [Show all 7 contributors for this article] (2024). The hip fracture incidence in the high-risk area Oslo continues to decline. Osteoporosis International. ISSN 0937-941X. 35, p. 1615–1623. doi: 10.1007/s00198-024-07156-8. Full text in Research Archive Show summary

  Summary Oslo in Norway has had the highest incidence of hip fractures in the world. The incidence in Oslo has been thoroughly described every decade since the late 1970s. The incidence in Oslo has previously been higher compared to the rest of Norway but has now decreased to a level below the country average. Purpose The purpose of this study was to report the incidence of hip fractures in Oslo in 2019 and compare it with the incidence rates from the previous four decades. Methods Patients residing in Oslo in 2019 with a new hip fracture identified by searching the Oslo hospital’s patient administrative systems and protocols from the operating theaters. The diagnosis was verified through medical records and/or radiographs. To compare with previous studies, the direct standardization method was used with the population of Oslo in 2019 as the standard. Results A total of 758 hip fractures, 70% women, were identified in 2019. The age-standardized incidence rates per 10,000 person-years in 2019 (95% CI) were 45 (41.1–48.8) for women and 30 (25.8–33.8) for men. In women, there has been a continuous decline in age-standardized rates the last three decades and in men the last two decades. The most pronounced decline was seen in the oldest age groups over 70 years. There has been a secular decline in both cervical and trochanteric fractures; however, the decrease in trochanteric fractures was most distinct for males, with more than two times higher risk in 1996/1997 compared to 2019. Conclusion Incidence rates for hip fractures in Oslo in 2019 were the lowest rate reported since 1978. The decrease was significant for both men and women. For the first time, the incidence rates are below the national rates of Norway. However, the rates are still among the highest worldwide.

• Rimal, Raju; Robsahm, Trude Eid; Green, Adele C.; Ghiasvand, Reza; Rueegg, Corina Silvia & Bassarova, Assia Vassileva [Show all 12 contributors for this article] (2024). Trends in Invasive Melanoma Thickness in Norway, 1983-2019. Acta Dermato-Venereologica. ISSN 0001-5555. 104. doi: 10.2340/actadv.v104.26110. Full text in Research Archive Show summary

  Monitoring melanoma incidence time trends by tumour thickness is essential to understanding the evolution of melanoma occurrence and guiding prevention strategies. To assess long-term incidence trends, tumour thickness was extracted from pathology reports in the Cancer Registry of Norway (1983–2007) and the Norwegian Melanoma Registry (2008–2019), n = 45,635 patients. Across all anatomic sites, T1 (≤ 1 mm) incidence increased most (men annual percentage change [AAPC] = 4.6, 95% confidence interval [95% CI] 4.2–5.0; women AAPC = 3.2, 95% CI 2.8–3.6); the increase was steep until 1989/90, followed by a plateau, and a further steep increase from 2004/05. Increased incidence was also observed for T2 (>1.0–2.0) melanoma (men AAPC = 2.8, 95% CI 2.4–3.2; women AAPC = 1.5, 95% CI 1.1–1.9), and T3 (>2.0–4.0) in men (AAPC = 1.4, 95% CI 0.9–1.9). T4 (>4.0) melanoma followed a similar overall pattern (men AAPC = 1.3, 95% CI 0.9–1.7, head/neck, upper limbs, and trunk; women AAPC = 0.9, 95% CI 0.4–1.4, upper limbs and trunk). Men had the highest T3 and T4 incidence and the sex difference increased with age. Regarding birth cohorts, age-specific incidence increased in all T categories in the oldest age groups, while stabilizing in younger patients born after 1950. Overall, the steep increase in T1 melanoma was not accompanied by a decrease in thick melanoma.

• 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 Show summary

  Little is known about if and how nevi and pigmentation are associated with melanoma-specific mortality. However, increased melanoma awareness in people with lighter pigmentation and many nevi may result in earlier diagnosis of thinner less-lethal tumors. The aim of this study was to investigate associations between nevus count (asymmetrical > 5 mm and small symmetrical), pigmentary characteristics (hair colour, eye colour, skin colour, freckling, pigmentary score), and melanoma-specific mortality in subjects with melanomas > 1 mm. Data from the Norwegian Women and Cancer cohort, established in 1991, with complete follow-up of melanoma patients until 2018 through the Cancer Registry of Norway, were used to estimate hazard ratios with 95% confidence intervals for the associations between nevus count, pigmentary characteristics, and melanoma-specific mortality, stratified by tumor thickness using Cox regression. Estimated hazard ratios consistently indicated a higher risk of melanoma death for those with darker vs lighter pigmentary characteristics in patients with tumors > 1.0–2.0 mm and > 2.0 mm thick (e.g. pigmentary score hazard ratio 1.25, 95% confidence interval (0.74–2.13)). Among women with melanomas >1.0 mm thick, lighter pigmentation and asymmetrical nevi may be associated with lower melanoma-specific mortality, suggesting that factors that increase the risk of melanoma may also be associated with decreased risk of death from melanoma.

• 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 Show summary

  Prediction performance does not always reflect the estimation behaviour of a method. High error in estimation may necessarily not result in high prediction error, but can lead to an unreliable prediction if test data lie in a slightly different subspace than the training data. In addition, high estimation error often leads to unstable estimates, and consequently, the estimated effect of predictors on the response can not have a valid interpretation. Many research fields show more interest in the effect of predictor variables than actual prediction performance. This study compares some newly-developed (envelope) and well-established (PCR, PLS) estimation methods using simulated data with specifically designed properties such as Multicollinearity in the predictor variables, the correlation between multiple responses and the position of principal components corresponding to predictors that are relevant for the response. This study aims to give some insights into these methods and help the researchers to understand and use them for further study. Here we have, not surprisingly, found that no single method is superior to others, but each has its strength for some specific nature of data. In addition, the newly developed envelope method has shown impressive results in finding relevant information from data using significantly fewer components than the other methods.

• 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.

• Rimal, Raju; Almøy, Trygve & Sæbø, Solve (2018). A tool for simulating multi-response linear model data. Chemometrics and Intelligent Laboratory Systems. ISSN 0169-7439. 176, p. 1–10. doi: 10.1016/j.chemolab.2018.02.009. Full text in Research Archive 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. Sub-space rotations also allow for generating data suitable for testing variable selection methods in multi-response settings. The method is implemented as an update to the R-package simrel.

• Helland, Inge Svein; Sæbø, Solve; Almøy, Trygve & Rimal, Raju (2018). Model and estimators for partial least squares regression. Journal of Chemometrics. ISSN 0886-9383. 32(9). doi: 10.1002/cem.3044. Full text in Research Archive Show summary

  Partial least squares (PLS) regression has been a very popular method for prediction. The method can in a natural way be connected to a statistical model, which now has been extended and further developed in terms of an envelope model. Concentrating on the univariate case, several estimators of the regression vector in this model are defined, including the ordinary PLS estimator, the maximum likelihood envelope estimator, and a recently proposed Bayes PLS estimator. These are compared with respect to prediction error by systematic simulations. The simulations indicate that Bayes PLS performs well compared with the other methods.

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• Perrier, Flavie; Rimal, Raju; Green, Adele C; Rueegg, Corina Silvia; Ghiasvand, Reza & Gjersvik, Petter [Show all 13 contributors for this article] (2026). Breslow thickness and melanoma-specific death in patients diagnosed with thin cutaneous melanoma in 1983-2019. Full text in Research Archive
• Veierød, Marit Bragelien & Rimal, Raju (2024). The incidence of thin melanoma is increasing the most. [Internet]. uio.no. Full text in Research Archive
• Veierød, Marit Bragelien & Rimal, Raju (2024). Forekomsten av tynne melanomer øker mest. [Internet]. uio.no. Full text in Research Archive
• Veierød, Marit Bragelien & Rimal, Raju (2024). Disse kreftflekkene øker mest. [Journal]. forskning.no. Full text in Research Archive
• 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. Full text in Research Archive
• 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. Full text in Research Archive 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; 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. Full text in Research Archive 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 (2021). Long-term Trend on Tumour Thickness of Melanoma in Norway. Full text in Research Archive 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; Almøy, Trygve & Sæbø, Solve (2019). A prediction comparison of some multivariate methods. Full text in Research Archive 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. Full text in Research Archive 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 (2016). A comparetive study on PCR, PLS, Envelope and BayesPLS models. Full text in Research Archive
• Rimal, Raju (2015). Multimatrix Extension of Partial Least Square. Full text in Research Archive
• Rimal, Raju; Sæbø, Solve & Almøy, Trygve (2019). Exploration of multi-response multivariate methods. Norges miljø- og biovitenskapelige universitet (NMBU). ISSN 9788257516369. Full text in Research Archive Show summary

  En lineær regresjonsmodell definerer et lineært forhold mellom to eller flere tilfeldige variabler. De tilfeldige variablene som er avhengige av andre tilfeldige variabler, kalles ofte responsvariabler, og de uavhengige tilfeldige variablene kalles prediktorvariabler. I de fleste tilfeller er ikke all variasjon relevant for regresjon, dvs. bare en viss mengde variasjonen i prediktorene er relevante, og bare for en del av variasjonen i responsen. Dette fører til en reduksjon av den lineære regresjonsmodellen der man kan forestille seg et underrom av rommet som spennesut av prediktorvariablene som inneholder all relevant informasjon for et underrom av rommet spent ut av responsvariablene. I denne avhandlingen prøver vi å sammenligne noen nye metoder som er basert på Envelopemodellen og noen etablerte metoder som principal komponent regresjon (PCR) og partiell minste kvadraters regresjon (PLS). Sammenligningen tester disse metodene på deres ytelse til å produsere minimum prediksjon- og estimeringsfeil, mens modelleringsdata simuleres med spesielt designede egenskaper. For simuleringen har vi også laget en R-pakke kalt simrel med et webgrensesnitt. En simuleringsmodell for multirespons, multivariat lineær modell, som simuleringsverktøyet bygger på, diskuteres i den første artikkelen. Denne artikkelen utarbeider et grunnleggende fundament for simuleringene basert på konseptet om reduksjon av regresjonsmodeller. Den andre artikkelen diskuterer likhetene i Envelope-, PCR- og PLS-populasjonsmodellene. Denne artikkelen sammenligner prediksjonsytelsen til flere multivariate metoder ved bruk av en modell med en enkelt respons. De to siste artiklene gir en grundig evaluering av prediksjons- og estimeringsegenskapene til etablerte metoder (PCR, PLS1 og PLS2) og nyutviklede envelope-baserte metoder (Xenv og Senv). Ikke uventet fant studien at det ikke finnes en enkelt metode som dominerer i alle situasjoner, men resultatene deres avhenger av egenskapene til dataene de modellerer. Imidlertid har envelope-baserte metoder vist bemerkelsesverdig resultater i mange tilfeller, både når det gjelder prediksjon og estimering. Studien anbefaler også forskere å bruke og evaluere envelope-metodene.

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