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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Estimating Skin Cancer Risk: Evaluating Mobile Computer-Adaptive Testing.

Ngadiman Djaja¹, Monika Janda, Catherine M Olsen

¹School of Public Health and Social Work, Institute for Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.

Journal of Medical Internet Research

|January 24, 2016

Summary

Computer adaptive testing (CAT) significantly reduces participant burden for skin cancer risk assessment. This efficient method lowers response burden by up to 66% without sacrificing measurement precision.

Keywords:

Rasch analysis computer adaptive testing non adaptive test partial credit model skin cancer risk scale

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Area of Science:

Health Informatics
Psychometrics
Epidemiology

Background:

High response burden negatively impacts questionnaire completion rates.
Computer adaptive testing (CAT) offers potential for reduced item counts and precise measurement compared to non-adaptive testing.
Accurate skin cancer risk estimation is crucial for public health.

Purpose of the Study:

To compare the efficiency and precision of non-adaptive testing (NAT) versus CAT for skin cancer risk assessment.
To evaluate CAT's effectiveness when using Partial Credit Model (PCM)-derived calibration.
To quantify the reduction in response burden achieved by CAT.

Main Methods:

Utilized a large Australian population-based cohort study sample (N=43,794).
Calibrated a 30-item skin cancer risk scale using the Rasch Partial Credit Model (PCM).
Simulated 1000 cases using Rasch models under dichotomous, Rating Scale, and PCM scenarios to compare CAT and NAT efficiency and precision.

Main Results:

CAT demonstrated smaller person standard errors than NAT, indicating higher efficiency.
CAT achieved substantial reductions in response burden: 48% (dichotomous), 66% (Rating Scale Model), and 66% (PCM).
No loss of measurement precision was observed with CAT compared to NAT.

Conclusions:

CAT administration of the skin cancer risk scale can significantly decrease participant burden.
Measurement precision is maintained when utilizing CAT for skin cancer risk assessment.
A mobile CAT application was developed to facilitate efficient skin cancer risk assessment.