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A latent class analysis of Parkinson's disease symptoms

Cummin, Graham (2020) A latent class analysis of Parkinson's disease symptoms. Master's Thesis, University of Pittsburgh. (Unpublished)

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Parkinson’s disease is a neurological disease in which the dopamine releasing brain cells degenerate die and are not replaced. Affecting mostly persons older than age 60, in the US population the fraction above 60 is nearly 40 percent. This population is also growing more elderly. The public health importance of correctly assessing Parkinson’s disease and the accompanying symptom burden in order to effectively and efficiently treat the growing elderly population in the US and in order to keep costs and expectation managed is high. The ability to identify clusters of symptoms could improve awareness of how to treat and counsel patients. Latent Class Analysis is a method which can be used to predict classes, or clusters, and which can be used with categorical outcomes. In this thesis, the Parkinson’s symptoms were clustered into four classes characterized in part by sex and age of the patient. Unique symptoms predicted at greater than 50% were identified for three of these classes, the first and reference class reported very few symptoms. Relative to the first class, the second class was more likely to have a younger age at onset, but was not more likely to be male or female, and uniquely reported Mood swings and depression (76%). The third class was more likely to be male, but was not more likely to be older or younger at age of onset, and uniquely reported difficulty standing from a chair (73%). The fourth class was more likely to be female and to be younger at age of onset relative to class 1, and uniquely reported 4 unique symptoms, sweating (62%), muscle spasm (70%), hot flashes or chills (57%) and persistent dull pain (59%).This LCA model predicts divisions across gender and sex in the specific symptoms and their associations in keeping with clinical expectations.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Cummin, Grahamglc33@pitt.eduglc33
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairBuchanich,
Committee MemberYouk,
Committee MemberChahine,
Date: 29 January 2020
Date Type: Publication
Defense Date: 2 December 2019
Approval Date: 29 January 2020
Submission Date: 20 November 2019
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 28
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: cluster, sex differences in parkinson's disease,
Date Deposited: 29 Jan 2020 19:21
Last Modified: 01 Jan 2021 06:15


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