Observational Studies

  • A cartoon to illustrate the value of statistics in epidemiology, especially in developing causal evidence for the harmful effects of smoking based on observational data.  The cartoon was drawn in 2013 by British cartoonist John Landers based on an idea by Dennis Pearl from Ohio State University.  This item is part of the cartoons and readings from the “World Without Statistics” series that provided cartoons and readings on important applications of statistics created for celebration of 2013 International Year of Statistics.  The series may be found at https://online.stat.psu.edu/stat100/lesson/1/1.4

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  • A joke to use in discussing Meta Analyses.  The joke was written in 2019 by Larry Lesser from The University of Texas at El Paso.

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  • A cartoon suitable for use in teaching about cohort effects versus age effects in epidemiological studies. The cartoon is number 2080 (December, 2018) from the webcomic series at xkcd.com created by Randall Munroe. Free to use in the classroom and on course web sites under a creative commons attribution-non-commercial 2.5 license.

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  • A joke to aid in discussing Confirmation Bias (bias introduced in surveys because respondents tend to interpret things in a way that confirms their preexisting beliefs).  The joke was written by Larry Lesser from The Universisty of Texas at El Paso and Dennis Pearl from The Pennsylvania State University in October, 2018.

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  • Explore the Hubble Deep Fields from a statistical point of view.  Watch out for the booby traps of bias, the vagueness of variability, and the shiftiness of sample size as we travel on a photo safari through the Hubble Deep Fields (HDFs).

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  • This article gives a brief overview of the role of a biostatistician at NASA.  It also provides names of those one can contact in this area.  

     

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Pearson's chi-square; the empirical logit; and prospective, case-control, and cross-sectional studies.

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Pearson's chi-square; the empirical logit; and prospective, case-control, and cross-sectional studies.

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  • This presentation discusses modeling cluster correlation explicitly through random effects, yielding a generalized linear mixed effects models (GLMM). Part II contains many examples of application to different studies.

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  • This is a graduate level survey course that stresses the concepts of statistical design and analysis in biomedical research, with special emphasis on clinical trials. Perfect for students and teachers wanting to learn/acquire materials for this topic.

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