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DTSTART;VALUE=DATE:20251003
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SUMMARY:Thematic Workshop: Inequality in Myeloproliferative Diseases and Acute Leukaemia Mortality and Surveillance. Historical and Contemporary Perspectives
DESCRIPTION:The workshop will address key issues\, including disparities in mortality and treatment access for myeloproliferative diseases (MPDs) and acute leukemias. It will also include discussions on methodologies for managing mortality data\, focusing on statistics\, the International Classification of Diseases (ICD)\, and database management. \nDay 1: October 3\, 2025 (Friday)\n• Start after 15:00 to accommodate participants’ schedules.\n• Registration\, welcome coffee\, and opening remarks.\n• Keynote lectures on historical trends and modern perspectives of MPDs and acute leukemias.\n• Cultural evening with traditional Armenian music and dinner.\nDay 2: October 4\, 2025 (Saturday)\n• Dedicated sessions focusing on specific MPDs (Polycythemia Vera\, Essential Thrombocythemia\, and Myelofibrosis).\n• Discussions on mortality trends\, surveillance\, and data management for MPDs.\nDay 3: October 5\, 2025 (Sunday)\n• Morning sessions on acute leukemias\, covering mortality data and surveillance methodologies.\n• Conclude by 15:00 to accommodate departures. \nYou can register here. The deadline for registration is June 1\, 2025.
URL:https://greatleap.eu/event/thematic-workshop-inequality-in-myeloproliferative-diseases-and-acute-leukaemia-mortality-and-surveillance-historical-and-contemporary-perspectives/
CATEGORIES:GREATLEAP
ORGANIZER;CN="Dr. Lusine Sahakyan":MAILTO:lusisahakyan@gmail.com
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DTSTART;TZID=Europe/Helsinki:20251003T140000
DTEND;TZID=Europe/Helsinki:20251003T150000
DTSTAMP:20260427T150504
CREATED:20250912T091404Z
LAST-MODIFIED:20251105T114933Z
UID:6439-1759500000-1759503600@greatleap.eu
SUMMARY:Online lecture 8 - Session 1: OLS regression models and causal inference
DESCRIPTION:Lecturers: Dr. Auke Rijpma\, Research Institute for History and Art History\, Utrecht University \nContent:Historical demographers often rely on survival models to analyze events like mortality and health outcomes. While these are powerful and appropriate tools\, the more basic OLS (Ordinary Least Squares) model is a very interesting and often robust alternative\, especially when data doesn’t perfectly adhere to the requirements of more complex models. What’s more\, there is a well-developed literature on how to perform causal inference with OLS models\, allowing researchers to go beyond simple correlation to identify meaningful causal relationships. This lecture will introduce the basic OLS model and then cover a number of key causal inference strategies. We will also demonstrate practical estimation procedures using the powerful fixest package in R\, highlighting how these methods can be applied to historical demographic data. \nObjectives: \n\nDiscuss the basics of the OLS model\nIntroduce important causal inference strategies that can be performed with extensions of the OLS model\nDemonstrate OLS estimation in R’s fixest library\n\nRequirements: Active participation. \nRecommended reading: \n\nAngrist\, Joshua D.\, and Jörn-Steffen Pischke. 2015. Mastering ’metrics: The Path from Cause to Effect. Princeton University Press.\nZwart\, Pim de\, Daniel Gallardo-Albarrán\, and Auke Rijpma. 2022. ‘The Demographic Effects of Colonialism: Forced Labor and Mortality in Java\, 1834–1879’. The Journal of Economic History 82 (1): 211–49. https://doi.org/10.1017/S0022050721000577.\n\nSlides for Online lecture 8 – Session 1- OLS regression models and causal inference \n 
URL:https://greatleap.eu/event/online-lecture-8-session-1-ols-regression-models-and-causal-inference/
LOCATION:MS Teams
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DTSTART;TZID=Europe/Helsinki:20251003T150000
DTEND;TZID=Europe/Helsinki:20251003T160000
DTSTAMP:20260427T150504
CREATED:20250912T092252Z
LAST-MODIFIED:20251105T115838Z
UID:6445-1759503600-1759507200@greatleap.eu
SUMMARY:Online lecture 8 - Session 2: Multinomial regression models by and for historians/historical demographers
DESCRIPTION:Lecturers: Dr. Mayra Murkens\, Faculty of Arts\, University of Groningen \nContent: One of the main issues historical demographers face\, is the lack of a proper estimation of the population at risk. Multinomial logistic regression models can approximate differences between groups in past societies without the information on the population at risk. In this lecture\, the idea behind multinomial logistic regressions and how to structure your data to perform these models will be discussed.  \nObjectives: \n\nShow how multinomial logistic regression analyses can offer a solution when a population at risk is missing. \nShow what is needed to perform a multinomial logistic regression.\n\nRequirements: Active participation. \nRecommended reading: \n\nRenzo Derosas\, Cristina Munno\, The Place to Heal and the Place to Die. Patients and Causes of Death in Nineteenth-Century Venice\, Social History of Medicine\, Volume 35\, Issue 4\, November 2022\, Pages 1140–1161\, https://doi.org/10.1093/shm/hkaa050\nMurkens\, M.\, Pelzer\, B.\, & Janssens\, A. (2022). Transitory inequalities: how individual-level cause-specific death data can unravel socioeconomic inequalities in infant mortality in Maastricht\, the Netherlands\, 1864–1955. The History of the Family\, 28(1)\, 95–131. https://doi.org/10.1080/1081602X.2022.2084442\n\n 
URL:https://greatleap.eu/event/online-lecture-8-session-2-multinomial-regression-models-by-and-for-historians-historical-demographers/
LOCATION:MS Teams
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