• Journal of Preventive Medicine and Public Health
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• v.23 no.3 s.31
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• pp.247-254
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• 1990

짝짓기 방법은 교란변수를 통제하기 가장 좋은 방법으로 알려져 있으나, 모수추정시 그 계산방법이 복잡하고, 포함된 모든 정보를 이용할 수 없다는 단점을 갖고 있다. 그럼에도 불구하고, conditional 모델을 이용한 matched 분석법은 짝지은 자료 분석시 가장 좋은 방법으로 인정되고 있다. 그러나 명확한 confounding 현상을 통제할 목적이 아닌 상태에서 짝지워진 자료를 matched 분석법으로 모수추정하는 경우나, 올바로 짝지워진 자료를 분석법의 편이성 때문에 unmatched 분석을 시도하는 경우, 오히려 estimation bias가 야기될 수 있다. 이러한 estimation bias의 통제능력을 몇 가지 분석방법을 이용하여 비교하고자, 1:2로 대응된 한 환자-대조군 자료를 이용하여 Mantel-Haenszel 분석법, 두가지의 unconditional model을 이용한 다변량분석법의 결과를 conditional model을 이용한 matched 분석법의 결과와 비교하였다. 1. Matched 분석법의 대용방법으로 사용된 세 가지 방법들은 모수추정면에서나 가설검정능력면에서 차이를 서로 보이지 않았다. 2. 짝짓기에 사용된 변수가 분석자료내에서 confounder나 effect modifier로 작용되지 않았음이 명백한 경우에는 이들 세 가지 통제 방법과 matched 분석법간에 차이가 없었다. 3. 짝짓기에 사용된 변수가 분석자료내에서 effect modifier로 작용하지는 않았으나, Confounder로 작용한 것으로 추정되는 경우, unmatched 분석법으로 인해 야기된 estimation bias의 통제능력이 이들 세 가지 대용방안 모두에서 인정되었다. 4. 짝짓기에 사용된 변수가 분석자료내에서 effect modifier로 작용하고 있음을 직접 확인할 수 있는 경우에는, overmatching에 의한 estimation bias를 의심할 수 있었으며, 이들 세 가지 통제방법은 오히려 unmatched 분석 방법에 가까운 모수를 추정하였다.

• Journal of Preventive Medicine and Public Health
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• v.15 no.1
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• pp.179-186
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• 1982

The methods controlling the confounding factors were discussed using the data of secondary infertility with induced abortion. Mantel-Haenszel method and logistic model were applied in the analysis to find out which factors were confounding and/or effect modification variables. In the logistic analysis, the main effect of induced abortion, spontaneous abortion, age and interaction effect between induced abortion and spontaneous abortion were chosen as independent variables being regressed into logistic functions. Spontaneons abortion was interpreted as a potential confounder and at the same time potential effect modifier and age was interpreted as potential confounder. Spontaneous abortion was shown to be more important influencing factor than age to the secondary infertility. In the course of logistic analysis, the problem of parameter estimation and hypothesis testing, assessing the fitness of a model, and selection of the best model were briefly explained. For the program of logistic model, FUNCAT Procedure of SAS package was chosen.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.197-201
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• 2002

The healthy worker effect is an important issue in occupational epidemiology. We proposed a new statistical method to test the relationship between exposure and time to death in the presence of the healthy worker effect. In this study, we considered the healthy worker hire effect to operate as a confounder and the healthy worker survival effect to operate as a confounder and an intermediate variable. The basic idea of the proposed method reflects the length bias-sampling caused by changing one's employment status. Simulation studies were also carried out to compare the proposed method with the Cox proportional hazards models. According to our simulation studies, both the proposed test and the test based on the Cox model having the change of the employment status as a time-dependent covariate seem to be satisfactory at an upper 5% significance level. The Cox models, however, are inadequate with the change, if any, of the employment status as time-independent covariate. The proposed test is superior in power to the test based on the Cox model including the time-dependent employment status.

• The Journal of the Korean dental association
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• v.44 no.10 s.449
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• pp.658-670
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• 2006

1980년대 후반기부터 치주질환과 뇌경색(ischemic stroke)자료의 연관성을 모색하는 시도가 있어왔다. 이번 연구의 목적은 치주질환과 뇌경색 유무와의 어떤 관계가 있는지를 60세 이상의 노인을 대상으로 조사, 통계 분석하였다. 자료는 미국의 총 국민조사 격인 The Third Nation Health and Nutrition Examination Survey (NHANES III)를 이용하였다. 이번 연구에서 unadjusted logistic model 통계법을 이용하여 치아 상실수와 뇌경색 경험이 통계학적으로 유의한 수치의 상관성이 있음을 알게 되었다. 또한 나이와 흡연유무를 고려, 조정한 후 multiple logistic model 통계법으로 잔존치아가 적을수록 더욱 뇌경색에 걸릴 확률이 높음을 보였다. 그러나 두 질병에 동시에 선택된 중요한 위험인자 (risk factor)를 모두 고려, 조정 한 후에는 통계학적인 유의성을 찾지 못했다. 치은퇴축, 치주낭 깊이, 치석, 탐침시 출혈과 뇌경색 경험은 각각의 비교법에서 약간의 상관성을 보이나, 모든 통계법을 통해 일괄된 결과를 얻을 수는 없었다.

• Journal of the Korean Society of Food Culture
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• v.7 no.2
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• pp.149-155
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• 1992

This study was carried out to find out the effect of dietary intake on cognitive function retardation in old age using dietary survey and cognitive function test. The subjects were 332 men of 50-94 years old and their activities of daily living were very similar. The cognitive function was tested by Mini Mental State Examination (MMSE)-K which was translated from MMSE, and the 24-hour recall method was used for dietary survey. Scoring of MMSE-K was a little different from MMSE, that is, in case of no education, one to four points were added to exclude the effect of education which has been considered as a confounder by many researchers. The number of subjects belonging to below 23 of MMSE-K score was increased by increasing age. Even though points were added in case of no education, the ratio of below 23 MMSE-K score group was diminished by increasing education. Therefore, education seems not to be a confounder but a independent variable on cognitive function. Income, past occupation, family type, self-evaluated health status did not play any effect on cognitive function significantly. On the other hand, the correlation between each nutrients and the score of cognitive function test showed that the more consumption of vitamin A and protein, the higher cognitive function score was obtained. In case of iron and Ca, even though it was not statistically significant, there was a tendency of increasing cognitive function score by increasing the intake of those nutrients. This study suggests that micronutrient intakes might be more related to cognitive function than macronutrients.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.415-445
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• 2023

Oftentimes, the time dependent treatment covariate and the time dependent confounders exist in observation studies. It is an important problem to correctly adjust for the time dependent confounders in the propensity score analysis. Recently, In the survival data, Hade et al. (2020) used a propensity score matching method to correctly estimate the treatment delay effect when the time dependent confounder affects time to the treatment time, where the treatment delay effects is defined to the delay in treatment reception. In this paper, we proposed the Cox model based marginal structural model (Cox-MSM) framework to estimate the treatment delay effect and conducted extensive simulation studies to compare our proposed Cox-MSM with the propensity score matching method proposed by Hade et al. (2020). Our simulation results showed that the Cox-MSM leads to more exact estimate for the treatment delay effect compared with two sequential matching schemes based on propensity scores. Example from study in treatment discontinuation in conjunction with simulated data illustrates the practical advantages of the proposed Cox-MSM.

• Communications for Statistical Applications and Methods
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• v.23 no.1
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• pp.1-20
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• 2016

Causal inference methodologies have been developed for the past decade to estimate the unconfounded effect of an exposure under several key assumptions. These assumptions include, but are not limited to, the stable unit treatment value assumption, the strong ignorability of treatment assignment assumption, and the assumption that propensity scores be bounded away from zero and one (the positivity assumption). Of these assumptions, the first two have received much attention in the literature. Yet the positivity assumption has been recently discussed in only a few papers. Propensity scores of zero or one are indicative of deterministic exposure so that causal effects cannot be defined for these subjects. Therefore, these subjects need to be removed because no comparable comparison groups can be found for such subjects. In this paper, using currently available causal inference methods, we evaluate the effect of arbitrary cutoffs in the distribution of propensity scores and the impact of those decisions on bias and efficiency. We propose a tree-based method that performs well in terms of bias reduction when the definition of positivity is based on a single confounder. This tree-based method can be easily implemented using the statistical software program, R. R code for the studies is available online.

• Genomics & Informatics
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• v.5 no.2
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• pp.61-67
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• 2007

The allele frequencies of markers as well as linkage disequilibrium (LD) can be changed in cases due to the LD between markers and the disease allele, exhibiting spurious associations of markers. To identify the true association, classical statistical tests for dealing with confounders have been applied to draw a conclusion as to whether the association of variants comes from LD with the known disease allele. However, a more direct test considering LD using estimated haplotype frequencies may be more efficient. The null hypothesis is that the different allele frequencies of a variant between cases and controls come solely from the increased disease allele frequency and the LD relationship with the disease allele. The haplotype frequencies of controls are estimated using the expectation maximization (EM) algorithm from the genotype data. The estimated frequencies are applied to calculate the expected haplotype frequencies in cases corresponding to the increase or decrease of the causative or protective alleles. The suggested method was applied to previously published data, and several APOE variants showed association with Alzheimer's disease independent from the APOE ${\varepsilon}4$ variant, rs429358, regardless of LD showing significant simulated p-values. The test results support the possibility that there may be more than one common disease variant in a locus.

• Sleep Medicine and Psychophysiology
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• v.16 no.2
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• pp.61-64
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• 2009

Obstructive sleep apnea (OSA) has been definitively shown to be a risk factor for the development of cardiovascular disease and mortality. Recent reports have indicated that obstructive sleep apnea is associated with insulin resistance and impaired glucose metabolism, also have type 2 diabetes. The potential mechanisms leading to the development of type 2 diabetes in OSA patients are likely to be various. Reduced physical activity resulting from daytime somnolence, sympathetic nervous system activation, intermittent hypoxia, sleep fragmentation and sleep loss, dysregulation of the hypothalamic-pituitary axis, alteration in adipokine profiles, and activation of inflammatory pathways have been proposed. Based on the current evidence, clinicians should assess the risk of OSA in patients with type 2 diabetes and, conversely, consider that possibility of glucose intolerance in patients with OSA. Further large-scale and long-term follow-up studies in patient populations with selected by reliable but inexpensive diagnostic measures, controlled for potential confounder factor, are needed.

• Health Policy and Management
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• v.26 no.1
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• pp.71-78
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• 2016

The value of using health insurance claim database is continuously rising in healthcare research. In studies where comorbidities act as a confounder, comorbidity adjustment holds importance. Yet researchers are faced with a myriad of options without sufficient information on how to appropriately adjust comorbidity. The purpose of this study is to assist in selecting an appropriate index, look back period, and data range for comorbidity adjustment. No consensus has been formed regarding the appropriate index, look back period and data range in comorbidity adjustment. This study recommends the Charlson comorbidity index be selected when predicting the outcome such as mortality, and the Elixhauser's comorbidity measures be selected when analyzing the relations between various comorbidities and outcomes. A longer look back period and inclusion of all diagnoses of both inpatient and outpatient data led to increased prevalence of comorbidities, but contributed little to model performance. Limited data range, such as the inclusion of primary diagnoses only, may complement limitations of the health insurance claim database, but could miss important comorbidities. This study suggests that all diagnoses of both inpatients and outpatients data, excluding rule-out diagnosis, be observed for at least 1 year look back period prior to the index date. The comorbidity index, look back period, and data range must be considered for comorbidity adjustment. To provide better guidance to researchers, follow-up studies should be conducted using the three factors based on specific diseases and surgeries.