4 classes matched your search criteria.

Spring 2018  |  PUBH 8142 Section 001: Epidemiologic Uncertainty Analysis (60559)

Instructor(s)
Class Component:
Lecture
Credits:
2 Credits
Grading Basis:
S-N only
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
Regular Academic Session
 
01/16/2018 - 05/04/2018
Tue 12:00AM - 12:00AM
UMTC, East Bank
Also Offered:
Course Catalog Description:
Scientific interpretation of statistical analysis as dependent on both data and assumptions. Techniques that enable an investigator to incorporate uncertainty about assumptions into a quantitative analysis. prereq: 8140
Class Description:
An observed relative risk (RRobs) can be described mathematically as the product of the causal relative risk (RRcausal), a desired effect measure for etiologic epidemiologic studies, and error factors for the impact on study results of imperfections in the design, conduct and analysis of the study (uncontrolled confounding, losses-to-followup, nonrandom subject sampling, subject nonresponse, missing data, exposure and disease measurement error, unjustified statistical model assumptions, and random error). When viewed from this perspective, it becomes clear that RRcausal is not identifiable (i.e., cannot be validly estimated) without making assumptions about the values of the error-factor and random-error terms. A standard quantitative analysis does not account for most study imperfections. It therefore implicitly assumes that the product of the error factors equals 1.0. This standard-practice assumption, however, has neither theoretical nor empirical justification. We therefore advise epidemiologists to replace the standard assumption with more justifiable assumptions about the values of the error-factor terms. These more-justifiable assumptions can be incorporated into a quantitative analysis with uncertainty analysis (also known as bias modeling, probabilistic sensitivity analysis, Monte Carlo sensitivity analysis). We discuss this technique in this class.
Textbooks:
http://www.bookstores.umn.edu/buybooks.cgi?deptlookup=1&search=PUBH8142~001&term=1183
Instructor Supplied Information Last Updated:
10 January 2014

Spring 2016  |  PUBH 8142 Section 001: Epidemiologic Uncertainty Analysis (54433)

Instructor(s)
Class Component:
Lecture
Credits:
2 Credits
Grading Basis:
S-N only
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
Regular Academic Session
 
01/19/2016 - 05/06/2016
Tue 12:20PM - 02:15PM
UMTC, East Bank
Mayo Bldg/Additions 1155
Also Offered:
Course Catalog Description:
Scientific interpretation of statistical analysis as dependent on both data and assumptions. Techniques that enable an investigator to incorporate uncertainty about assumptions into a quantitative analysis. prereq: 8140
Class Description:
An observed relative risk (RRobs) can be described mathematically as the product of the causal relative risk (RRcausal), a desired effect measure for etiologic epidemiologic studies, and error factors for the impact on study results of imperfections in the design, conduct and analysis of the study (uncontrolled confounding, losses-to-followup, nonrandom subject sampling, subject nonresponse, missing data, exposure and disease measurement error, unjustified statistical model assumptions, and random error). When viewed from this perspective, it becomes clear that RRcausal is not identifiable (i.e., cannot be validly estimated) without making assumptions about the values of the error-factor and random-error terms. A standard quantitative analysis does not account for most study imperfections. It therefore implicitly assumes that the product of the error factors equals 1.0. This standard-practice assumption, however, has neither theoretical nor empirical justification. We therefore advise epidemiologists to replace the standard assumption with more justifiable assumptions about the values of the error-factor terms. These more-justifiable assumptions can be incorporated into a quantitative analysis with uncertainty analysis (also known as bias modeling, probabilistic sensitivity analysis, Monte Carlo sensitivity analysis). We discuss this technique in this class.
Textbooks:
http://www.bookstores.umn.edu/buybooks.cgi?deptlookup=1&search=PUBH8142~001&term=1163
Instructor Supplied Information Last Updated:
10 January 2014

Spring 2014  |  PUBH 8142 Section 001: Epidemiologic Uncertainty Analysis (60155)

Instructor(s)
Class Component:
Lecture
Credits:
2 Credits
Grading Basis:
S-N only
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
Delivery Medium
Times and Locations:
Regular Academic Session
 
01/21/2014 - 05/09/2014
Tue 12:20PM - 02:15PM
UMTC, East Bank
Mayo Bldg/Additions 1155
Also Offered:
Course Catalog Description:
Scientific interpretation of statistical analysis as dependent on both data and assumptions. Techniques that enable an investigator to incorporate uncertainty about assumptions into a quantitative analysis.
Class Description:
An observed relative risk (RRobs) can be described mathematically as the product of the causal relative risk (RRcausal), a desired effect measure for etiologic epidemiologic studies, and error factors for the impact on study results of imperfections in the design, conduct and analysis of the study (uncontrolled confounding, losses-to-followup, nonrandom subject sampling, subject nonresponse, missing data, exposure and disease measurement error, unjustified statistical model assumptions, and random error). When viewed from this perspective, it becomes clear that RRcausal is not identifiable (i.e., cannot be validly estimated) without making assumptions about the values of the error-factor and random-error terms. A standard quantitative analysis does not account for most study imperfections. It therefore implicitly assumes that the product of the error factors equals 1.0. This standard-practice assumption, however, has neither theoretical nor empirical justification. We therefore advise epidemiologists to replace the standard assumption with more justifiable assumptions about the values of the error-factor terms. These more-justifiable assumptions can be incorporated into a quantitative analysis with uncertainty analysis (also known as bias modeling, probabilistic sensitivity analysis, Monte Carlo sensitivity analysis). We discuss this technique in this class.
Textbooks:
http://www.bookstores.umn.edu/buybooks.cgi?deptlookup=1&search=PUBH8142~001&term=1143
Instructor Supplied Information Last Updated:
10 January 2014

Spring 2013  |  PUBH 8142 Section 001: Epidemiologic Uncertainty Analysis (55582)

Instructor(s)
Class Component:
Lecture
Credits:
2 Credits
Grading Basis:
S-N only
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
Delivery Medium
Times and Locations:
Regular Academic Session
 
01/22/2013 - 05/10/2013
Tue 12:20PM - 02:15PM
UMTC, East Bank
Mayo Bldg/Additions 1155
Also Offered:
Course Catalog Description:
Scientific interpretation of statistical analysis as dependent on both data and assumptions. Techniques that enable an investigator to incorporate uncertainty about assumptions into a quantitative analysis.
Class Description:
An observed relative risk (RRobs) can be described mathematically as the product of the causal relative risk (RRcausal)?a desired effect measure for etiologic epidemiologic studies?and error factors for the impact on study results of imperfections in the design, conduct and analysis of the study (uncontrolled confounding, losses-to-followup, nonrandom subject sampling, subject nonresponse, missing data, exposure and disease measurement error, unjustified statistical model assumptions, and random error). When viewed from this perspective, it becomes clear that RRcausal is not identifiable (i.e., cannot be validly estimated) without making assumptions about the values of the error-factor and random-error terms. A standard quantitative analysis does not account for most study imperfections. It therefore implicitly assumes that the product of the error factors equals 1.0. This standard-practice assumption, however, has neither theoretical nor empirical justification. We therefore advise epidemiologists to replace the standard assumption with more justifiable assumptions about the values of the error-factor terms. These more-justifiable assumptions can be incorporated into a quantitative analysis with uncertainty analysis (also known as bias modeling, probabilistic sensitivity analysis, Monte Carlo sensitivity analysis). We discuss this technique in this class.
Textbooks:
http://www.bookstores.umn.edu/buybooks.cgi?deptlookup=1&search=PUBH8142~001&term=1133
Instructor Supplied Information Last Updated:
6 November 2009

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