4 research outputs found
μμν μλ£μμμ μ νλ μ νλ μ°λλΉλ₯Ό μ΄μ©ν κ³΅κ° κ²μ ν΅κ³λ μ°κ΅¬
Dept. of Biostatistics and Computing/μμ¬Spatial scan statistics proposed by Kulldorff are widely used as a technique to detect geographical disease clusters for different types of data such as Bernoulli, Poisson, ordinal, normal, and survival. The spatial scan statistic for ordinal data can be used to detect clusters indicating areas with high rates of more serious stages compared with the surrounding areas.
However, it has been pointed out that the Poisson-based spatial scan statistic tends to detect the most likely cluster much larger than the true cluster by absorbing insignificant neighbors with non-elevated risk. We suspect that the spatial scan statistic for ordinal data might also have the similar undesirable phenomena. Tango (2008) proposed to modify the spatial scan statistic using a restricted likelihood ratio for scanning only the regions with elevated risk. The method worked well for preventing over-detection but was evaluated only in the Poisson model.
In this paper, we propose to apply a restricted likelihood ratio into two spatial scan statistics to circumvent such a phenomenon in ordinal outcome data. Through a simulation study we compare the performance of the proposed methods with original spatial scan statistics. We calculate sensitivity, positive predicted value (PPV), usual power and bivariate power distribution as performance measures.
The simulation study results show that the proposed spatial scan statistics with a restricted likelihood ratio have a reasonable or better performance compared with original ones. The original methods for ordinal data tend to detect larger clusters than the true cluster, and our approach seems to reduce the undesirable property. We illustrate the proposed methods using a real data set of the 2014 Health Screening Program of Korea with the diagnosis results of normal, caution, suspected disease, and diagnosed with disease as an ordinal outcome.
곡κ°κ²μν΅κ³λ(spatial scan statistic)μ μ°λλΉ κ²μ μ κΈ°λ°μΌλ‘ νΉμ μ¬κ±΄μ λν λΆν¬κ° λ€λ₯Έ μ§μμ λΆν¬μ ν΅κ³μ μΌλ‘ μ μνκ² λ€λ₯Έ 곡κ°κ΅°μ§(spatial cluster)μ νμνλ λ°©λ²μΌλ‘ μ¬λ¬ λΆμΌμμ μ΄μ©λκ³ μλ€. μ΄ λ°©λ²μ μ°κ΅¬μκ° μ¬μ μ κ° μ§μμ μ€μ¬μ μ κΈ°μ€μΌλ‘ νμ±λλ ν보 κ΅°μ§(scanning window)μ λͺ¨μκ³Ό μ΅λ κ΅°μ§ ν¬κΈ°λ₯Ό μ€μ νλ€. ν보 κ΅°μ§μ λͺ¨μμ μν, νμν, λΉμ νμ΄ λ리 μ¬μ©λκ³ , μ΅λ κ΅°μ§ ν¬κΈ°λ λ³΄ν΅ μ 체 μΈκ΅¬μ 50%λ‘ μ€μ νλ€.
Kulldorff (1997)μ μν΄ μ μλ 곡κ°κ²μν΅κ³λμ΄ κ΅°μ§ νμμ μν λ°©λ²μΌλ‘ λ리 μ°μ΄λ, μ΄ λ°©λ²μ΄ μ€μ κ΅°μ§λ³΄λ€ λ λμ λ²μμ κ΅°μ§μ λμΆνλ€λ κ²μ΄ Tango (2007)μ μν΄ μλ €μ‘λ€. Tango (2008)λ λͺ¨μμ€νμ ν΅νμ¬ ν¬μμ‘ κΈ°λ°μ 곡κ°κ²μν΅κ³λμ΄ μ€μ κ΅°μ§ μ£Όλ³μ μ μνμ§ μλ μ§μλ€μ ν‘μν¨μΌλ‘μ¨ λ λμ μ§μμ κ΅°μ§μΌλ‘ λμΆνλ€λ μ¬μ€μ 보μκ³ , μ΄μ λν ν΄κ²°μ±
μΌλ‘ ν¬μμ‘ κΈ°λ°μ 곡κ°κ²μ ν΅κ³λμ μ νλ μ°λλΉλ₯Ό μ μ©ν¨μΌλ‘μ¨ μ μνμ§ μλ μ§μλ€μ μ¬μ μ μ κ±°νμ¬ κ΄μ¬ λμμ μ§μλ€ λ§μΌλ‘ κ΅°μ§μ λμΆνλ λ°©λ²μ μ μνμλ€. Tango (2008)κ° μ μν λ°©λ²μ΄ κΈ°μ‘΄μ λ°©λ²λ³΄λ€ μ€μ κ΅°μ§μ λΉκ΅μ λ μ νν μ°Ύμλμ λͺ¨μ μ€νμ ν΅ν΄ 보μλ€.
ννΈ μμν μλ£λ μ§λ³μ μ§νλ¨κ³μ κ°μ μμ λ²μ£Όλ₯Ό κ°μ§λ μλ£λ‘ μν λΆμΌμμ λΉλ²ν λνλλ€. μ΄λ¬ν μλ£λ₯Ό μν 곡κ°κ²μν΅κ³λμ λ립κ°μ€μ λ°λΌ λ κ°μ§ λ°©λ²μ΄ Jung et al. (2007)κ³Ό Jung and Lee (2011)μ μν΄ μ μλ λ°κ° μμΌλ©°, λ³Έ μ°κ΅¬μμλ μ΄ κ³΅κ°κ²μν΅κ³λλ€ λν μμ κ°μ νμμ λ³΄μΌ κ²μ΄λΌ μμνλ€. λ°λΌμ λ³Έ μ°κ΅¬μμλ μμν μλ£λ₯Ό μν 곡κ°κ²μν΅κ³λμ μ νλ μ°λλΉλ₯Ό μ μ©νλ λ°©μμ μ μνκ³ , λͺ¨μμ€νμ ν΅νμ¬ κΈ°μ‘΄μ λ°©λ²κ³Ό λΉκ΅ λ° νκ°ν΄ λ³΄κ³ μ νλ€.
κ·Έ κ²°κ³Ό, μμν μλ£λ₯Ό μν κΈ°μ‘΄μ 곡κ°κ²μν΅κ³λμ΄ μ°λ¦¬μ μμκ³Ό κ°μ΄ μ€μ κ΅°μ§λ³΄λ€ λ λμ μ§μμ κ΅°μ§μ λμΆνλ€λ κ²μ΄ λ°κ²¬λμκ³ , μ νλ μ°λλΉλ₯Ό μ μ©ν 곡κ°κ²μν΅κ³λ λ°©λ²μ΄ μ΄λ¬ν μ μ μ΄λ μ λ μ ν΄κ²°ν¨μ μ μ μμλ€. λν μ μλ λ°©λ²μ μ€μ λ°μ΄ν°μ μ μ©ν¨μΌλ‘μ¨ λ³Έ λ°©λ²μ νμμ±μ μ μνμλ€.ope
μ€νκ³νλ²μ μ΄μ©ν μλμ°¨μ© ν λμ€ν¬μ λ€λ¨νμ¬μ±ν κΈν μ€κ³
νμλ
Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :κΈ°κ³ν곡곡νλΆ,2003.Maste