Iranian Journal of

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Background and objectives: There is growing interest in assessing gene-environment interaction in the course of case-control studies. Difficulties related to the sampling of controls have led to the development of a range of non-traditional methods that do not require controls for estimating gene-environment interaction. One of these new modalities is the case-only approach, in which the assessment of gene-environment interaction is based on information from the cases only. The present article describes the application of this approach to data from breast cancer patients and compares its efficacy with that of a traditional case-control analysis.
Methods: We used age at first pregnancy, number of live birth, menopause and the total number of post-menopausal years as the "environment" factors and family history of breast cancer as the "gene" factor. We computed standard errors, 95% confidence intervals and (-2 log likelihood) to compare efficiency between case-control and case-only analyses.
Results: We observed significant interaction between menopause and family history of breast cancer by both methods (OR=4.32 CI: 1.10-16.90 for case-control analysis & OR=3.40 CI: 1.17-9.87 for case-only analysis). There was also a significant interaction effect between total years after menopause and family history of breast cancer (OR=1.07 CI: 0.98-1.16 in case-control analysis & OR=1.07 CI: 1.01-1.12 in case-only analysis). The case-only approach yielded narrower confidence intervals for the odds ratio, and the (-2 log likelihood) values computed by this method were correspondingly smaller.
Conclusions: Comparison of confidence intervals and (-2 log likelihood) values shows that the estimation of gene-environment interaction in breast cancer would be more efficient with the case-only approach than with the traditional case-control analysis.

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Background & Objectives: We studied the risk of Chronic Kidney Disease (CKD), Metabolic Syndrome (MetS), and their interaction on the incidence of Coronary Heart Disease (CHD).
Methods: A population of 6568 participants (43.4% male) with a mean age of 48.4 years for males and 46.7 years for females and a median follow-up of 10.1 years was investigated. They were divided into 4 groups at baseline: CKD-/MetS-, CKD+/MetS-, CKD-/MetS+, CKD+/MetS+. Hazard Ratios (HRs) were calculated for each group and were compared to the first group using multivariate Cox regression analysis adjusted for age, education, smoking, total cholesterol, and the family history of cardiovascular diseases.
 Results: Men with CKD (without MetS) showed an HR of 1.74 (CI 95%: 1.16-2.60) for CHD events. The measured value was 2.34 (1.77-3.08) for men with MetS (without CKD). The respective results were in women 1.18 (0.64-2.19) and 2.59 (1.73-3.88). CKD and MetS had a significant negative interaction with CHD events (HR=0.40, 0.24-0.66). The interaction was not significant in women (P value=0.48).
Conclusion: The results of this study indicated that CKD without MetS was a risk factor for coronary heart disease in men but not in women.

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Background & Objectives: The case-cohort study is one of the youngest designs in epidemiology and some methodological aspects of it are still in debate. This study aimed at comparing the estimated hazard ratio, standard error, and interaction hazard ratio between the case-cohort and cohort studies for assessing the relationship between diabetes and cardiovascular diseases.

Methods: A total of 1701 men and 2253 women aged between 40 and 75 years were considered as the main cohort. Subcohort sampling was performed using simple random sampling with a sampling fraction of 0.3%. The hazard ratio of the cohort study was calculated using Cox regression model and the 3 methods of Prentice, Self-Prentice, and Barlow were used for calculating the hazard ratio of the case-cohort study. The mentioned regression models were used to assess the interactions.

Results: The results of the two studies were similar in populations with higher incidence (cohort of men) and lower incidence (the cohort of women) when frequency percent of exposure variable was greater than 10%. When the sample size of the initial cohort was less than 1250 subjects, discrepancies were observed between the results of the two studies. In addition, the standard error of the case-cohort study was higher than the cohort study. The results of both studies were similar in assessing the considered interactions.

Conclusion: The results are similar when the initial cohort sample sizes are sufficient. Meanwhile, unlike the percentage of exposure frequency, the outcome incidence has a negligible impact on the discrepancy between the results while the effect of the relative frequency of the exposure levels on the results discrepancy is noticeable.

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  Background & Objectives : The identification of risk factors and their interactions is important in medical studies. The aim of this study was to identify the interaction of risk factors of cerebral palsy in 1-6 years-old children with classification regression methods.

  Methods : The data of this cross-sectional study which was conducted on 225 children aged 1-6 years was collected during 2008- 2009. Classification regression methods (classification and regression tree (CART), adapting boosting (AdaBoost), bagging, and C4.5 algorithm) were used to identify interactions between risk factors. Data analysis was carried out with R3.0.1 software.

  Results : The identified interactions of the factors by a) the AdaBoost method were (consanguinity: sex, previous pregnancies: vaginal delivery, consanguinity: sex: preterm, history of the disease: preterm: asphyxia, consanguinity: sex: asphyxia, history of the disease: sex: small size relative to gestational age, neonatal infection: asphyxia: small size relative to gestational age, history of the disease: sex: asphyxia, preterm: asphyxia: vaginal delivery) by b) the bagging method were (consanguinity: asphyxia, consanguinity: preterm: asphyxia), by c) the C4.5 algorithm were (asphyxia: preterm, asphyxia: consanguinity: history of the disease: preterm), and by d) the CART method were (asphyxia: consanguinity). The sensitivity and specificity of the AdaBoost method was better than other methods (0.941±0.029 and 0.951±0.030, respectively).

  Conclusion : The AdaBoost method could better recognize and model potential interactions between risk factors of cerebral palsy.