Evaluating the association between latent classes and competing risks outcomes with multi‐phenotype data

Biometrics ◽  
2021 ◽  
Author(s):  
Teng Fei ◽  
John Hanfelt ◽  
Limin Peng
Keyword(s):  
Competing Risks ◽  
Latent Classes ◽  
Phenotype Data

1249: Internal Validation of a Competing-Risks Survival Nomogram for Patients Treated with Cystectomy for Bladder Cancer

2006 ◽  
Vol 175 (4S) ◽  
pp. 402-402
Author(s):  
Alberto Briganti ◽  
K.-H. Felix Chun ◽  
Shahrokh F. Shariat ◽  
Yair Lotan ◽  
Ganesh S. Palapattu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Competing Risks ◽  
Internal Validation

On Permissible Attribute Classes in Noncompensatory Cognitive Diagnosis Models

Methodology ◽  
2014 ◽  
Vol 10 (3) ◽  
pp. 100-107 ◽  
Author(s):  
Jürgen Groß ◽  
Ann Cathrice George
Keyword(s):  
Diagnostic Information ◽  
Cognitive Diagnosis ◽  
Latent Classes ◽  
Psychometric Test ◽  
Cognitive Diagnosis Models ◽  
Dina Model

When a psychometric test has been completed by a number of examinees, an afterward analysis of required skills or attributes may improve the extraction of diagnostic information. Relying upon the retrospectively specified item-by-attribute matrix, such an investigation may be carried out by classifying examinees into latent classes, consisting of subsets of required attributes. Specifically, various cognitive diagnosis models may be applied to serve this purpose. In this article it is shown that the permission of all possible attribute combinations as latent classes can have an undesired effect in the classification process, and it is demonstrated how an appropriate elimination of specific classes may improve the classification results. As an easy example, the popular deterministic input, noisy “and” gate (DINA) model is applied to Tatsuoka’s famous fraction subtraction data, and results are compared to current discussions in the literature.

Obtaining measurement-invariant latent classes across hierarchical units

2010 ◽  
Author(s):  
Louis Tay ◽  
Ed Diener ◽  
Fritz Drasgow
Keyword(s):  
Latent Classes
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