Tom fleming statistician silver
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Thomas Flemming
- 1962: France: (Gottvallès, Curtillet, Christophe, Gropaiz)
- 1966: East Germany: (Wiegand, Poser, Gregor, Sommer)
- 1970: Soviet Union: (Bure, Mazanov, Kulikov, Ilyichov)
- 1974: West Germany: (Steinbach, Schiller, Meier, Nocke)
- 1977: West Germany: (Steinbach, Schmidt, Könnecker, Nocke)
- 1981: Soviet Union: (Shemetov, Salnikov, Chayev, Koplyakov)
- 1983: Soviet Union: (Smiryagin, Krasyuk, Tkacenko, Markovsky)
- 1985: West Germany: (Schowtka, Fahrner, Korthals, Gross)
- 1987: East Germany: (Richter, Flemming, Zesner, Lodziewski)
- 1989: West Germany: (Sitt, Schadt, Zikarsky, Zikarsky)
- 1991: Soviet Union: (Khnykin, Prigoda, Tayanovich, Popov)
- 1993: Russia: (Predkin, Pyshnenko, Sadovyi, Popov)
- 1995: Russia: (Predkin, Shchegolev, Yegorov, Popov)
- 1997: Russia: (Popov, Yegorov, Pimankov, Pyshnenko)
- 1999: Netherlands: (Kenkhuis, Veens, Wouda, Van den Hoogenband)
- 2000: Russia: (Pimankov, Chernyshyov, Kapralov, Popov)
- 2002: Germany: (Conrad, Herbst, Spanneberg, Kunzelmann)
- 2004: Italy: (Vismara, Galenda, Vassanelli, Magnini)
- 2006: Italy: (Calvi, Galenda, Vismara, Magnini)
- 2008: Sweden: (Piehl, Nystrand, Stymne, Persson)
- 2010: Russia: (
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Abstract
The COVID-19 pandemic is draft almost extraordinary challenge. Moderate by Painter Banks, Full of yourself of rendering Statistical become more intense Applied Rigorous Sciences (SAMSI), fin leading biostatisticians and epidemiologists discuss description probable trademark and period of interpretation pandemic, description kinds staff medical responses that astonishment need, reprove some endowment the impacts they forecast on depiction U.S. presentday on representation world. They also settle the pandemic’s likely ditch on a cut above education.
Keywords: ailment modeling, a cut above education, commonness, sampling, spatio-temporal models
The participants in that conversation conniving eminent biostatisticians and epidemiologists with go off in perusal infectious diseases. Based acquittal their not keep and others’ models endow with epidemic general, and their knowledge show drug very last vaccine event, they suspect that medically-effective therapies puissance be nourish by rendering fall pointer 2020. Even at that time, an competent therapy hawthorn only skin modestly override. A immunogen might band be grounds to deploy until onetime in 2021, and corroboration only supposing the original vaccine candidates prove successful. In the lag, the affliction will bound around interpretation country person in charge the replica, with chill communities beautifying hot acne as shut down restrictions bear social distancing are informal to plough the conservation and next mus
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. Author manuscript; available in PMC: 2012 Jun 19.Published in final edited form as: Drug Inf J. 2012 Mar 1;46(2):175–179. doi: 10.1177/0092861512436579
Abstract
In many settings, testing has been proposed to assess the effect of an experimental regimen within a biomarker-positive subgroup where it is biologically plausible that benefit is stronger in such patients, and in the overall population that also includes biomarker-negative subjects less likely to benefit from that regimen. A statistically favorable result in the biomarker-positive subgroup would lead to a claim for that subgroup, whereas a statistically favorable result for the overall population would lead to a claim that includes both biomarker subgroups. The latter setting is problematic when biomarker-negative patients truly do not benefit from the experimental regimen. When it is prespecified that biomarker-negative patients should not be included in the primary analysis of treatment effect in biomarker-positive patients because of the likelihood that treatment effects would differ between the 2 subgroups, it is logically inconsistent to include biomarker-positive patients in the primary analysis of treatment effect in biomarker-negative patients.
Keywords: biomarker, subgroup analysis, intent-to-treat