• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.543-553
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• 2016

The validity of statistical inference depends on proper randomization methods. However, even with proper randomization, we can have imbalanced with respect to important characteristics. In this paper, we introduce a method based on ranked auxiliary variables for treatment allocation in crossover designs using Latin squares models. We evaluate the improvement of the efficiency in treatment comparisons using the proposed method. Our simulation study reveals that our proposed method provides a more powerful test compared to simple randomization with the same sample size. The proposed method is illustrated by conducting an experiment to compare two different concentrations of titanium dioxide nanofiber (TDNF) on rats for the purpose of comparing weight gain.

• 대한예방의학회:학술대회논문집
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• 2001.10a
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• pp.432-432
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• 2001

• Korean Journal of Radiology
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• v.25 no.2
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• pp.123-125
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• 2024

• International Journal of Reliability and Applications
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• v.4 no.3
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• pp.97-111
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• 2003

By applying Theorem 2.6.4 (Fang and Zhang, 1990, p.66) the dispersion matrix of a multivariate power exponential (MPE) distribution is derived. It is shown that the MPE and the gamma distributions are related and thus the MPE and chi-square distributions are related. By extending Fang and Xu's Theorem (1987) from the normal distribution to the Univariate Power Exponential (UPE) distribution an explicit expression is derived for calculating the probability of an UPE random variable over an interval. A representation of the characteristic function (c.f.) for an UPE distribution is given. Based on the MPE distribution the probability density functions of the generalized non-central chi-square, the generalized non-central t, and the generalized non-central F distributions are derived.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.5657-5661
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• 2013

In India, among males, leukemia rates vary across the country. The present unmatched hospital-based case-control study conducted at Tata Memorial Hospital included subjects registered between the years 1997-99. There were 246 leukemia cases and 1,383 normal controls. Data on demographics, lifestyle, diet and occupation history were recorded. Cigarette (OR=2.1) and bidi smoking (OR=3.4) showed excess risk for leukemia. Odds ratios were 3.9 for fish-eaters, 0.40 for chilli eaters, 1.5 for milk drinkers and 0.60 for coffee drinkers, compared to non-drinkers/eaters. However, neither exposure to use of pesticides nor cotton dust showed any excess risk for leukemia.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.1-13
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• 2018

In many studies, a researcher attempts to describe a population where units are measured for multiple outcomes, or responses. In this paper, we present an efficient procedure based on ranked set sampling to estimate and perform hypothesis testing on a multivariate mean. The method is based on ranking on an auxiliary covariate, which is assumed to be correlated with the multivariate response, in order to improve the efficiency of the estimation. We showed that the proposed estimators developed under this sampling scheme are unbiased, have smaller variance in the multivariate sense, and are asymptotically Gaussian. We also demonstrated that the efficiency of multivariate regression estimator can be improved by using Ranked set sampling. A bootstrap routine is developed in the statistical software R to perform inference when the sample size is small. We use a simulation study to investigate the performance of the method under known conditions and apply the method to the biomarker data collected in China Health and Nutrition Survey (CHNS 2009) data.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4109-4115
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• 2014

Background: Accurate assessment of disease progression requires proper understanding of natural disease process which is often hidden and unobservable. For this purpose, disease status should be clearly detected. But in most diseases it is not possible to detect such status. This study, therefore, aims to present a model which both investigates the unobservable disease process and considers the error probability in diagnosis of disease states. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at the Iran Cancer Institute from 1995 to 1999 were analyzed. Moreover, to estimate and assess the effect of demographic, diagnostic and clinical factors as well as medical and post-surgical variables on transition rates and the probability of misdiagnosis of relapse, a hidden Markov multi-state model was employed. Results: Classification errors of patients in alive state without a relapse ($e_{21}$) and with a relapse ($e_{12}$) were 0.22 (95% CI: 0.04-0.63) and 0.02 (95% CI: 0.00-0.09), respectively. Only variables of age and number of renewed treatments affected misdiagnosis of relapse. In addition, patient age and distant metastasis were among factors affecting the occurrence of relapse (state1${\rightarrow}$state2) while the number of renewed treatments and the type and extent of surgery had a significant effect on death hazard without relapse (state2${\rightarrow}$state3)and death hazard with relapse (state2${\rightarrow}$state3). Conclusions: A hidden Markov multi-state model provides the possibility of estimating classification error between different states of disease. Moreover, based on this model, factors affecting the probability of this error can be identified and researchers can be helped with understanding the mechanisms of classification error.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.441-447
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• 2014

Background: Multi-state models are appropriate for cancer studies such as gastrectomy which have high mortality statistics. These models can be used to better describe the natural disease process. But reaching that goal requires making assumptions like Markov and homogeneity with time. The present study aims to investigate these hypotheses. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. To assess Markov assumption and time homogeneity in modeling transition rates among states of multi-state model, Cox-Snell residuals, Akaikie information criteria and Schoenfeld residuals were used, respectively. Results: The assessment of Markov assumption based on Cox-Snell residuals and Akaikie information criterion showed that Markov assumption was not held just for transition rate of relapse (state 1 ${\rightarrow}$ state 2) and for other transition rates - death hazard without relapse (state 1 ${\rightarrow}$ state 3) and death hazard with relapse (state 2 ${\rightarrow}$ state 3) - this assumption could also be made. Moreover, the assessment of time homogeneity assumption based on Schoenfeld residuals revealed that this assumption - regarding the general test and each of the variables in the model- was held just for relapse (state 1 ${\rightarrow}$ state 2) and death hazard with a relapse (state 2 ${\rightarrow}$ state 3). Conclusions: Most researchers take account of assumptions such as Markov and time homogeneity in modeling transition rates. These assumptions can make the multi-state model simpler but if these assumptions are not made, they will lead to incorrect inferences and improper fitting.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.523-528
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• 2013

Background: The possibility that electromagnetic fields (EMF) exposure may increase male breast cancer risk has been discussed for a long time. However, arguments have been presented that studies limited by poor quality could have led to statistically significant results by chance or bias. Moreover, data fo the last 10 years have not been systematically summarized. Methods and Results: To confirm any possible association, a meta-analysis was performed by a systematic search strategy. Totals of 7 case-control and 11 cohort studies was identified and pooled ORs with 95% CIs were used as the principal outcome measures. Data from these studies were extracted with a standard meta-analysis procedure and grouped in relation to study design, cut-off point, exposure assessment method, adjustment and exposure model. A statistical significant increased risk of male breast cancer with EMF exposure was defined (pooled ORs = 1.32, 95% CI = 1.14-1.52, P < 0.001), and subgroup analyses also showed similar results. Conclusions: This meta-analysis suggests that EMF exposure may be associated with the increase risk of male breast cancer despite the arguments raised.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6369-6373
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• 2013

Background: Gastric cancer is one of the most common causes of cancer deaths all over the world and the most important reason for its high rate of death is its belated diagnosis at advanced stages of the disease. Events occur in patients which are regarded not only as themselves factors affecting patients' survival but also which can be affected by other factors. This study was designed and implemented aiming to identify these events and to investigate factors affecting their occurrence. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at the Iran Cancer Institute from 1995-1999 were analyzed. The survival time of these patients was determined after surgery and the effects of various factors including demographic, diagnostic and clinical as well as medical, and post-surgical varuiables on the occurrence of death hazard without relapse, hazard of relapse, and death hazard with a relapse were assessed. Results: The median survival time for these patients was 16.3 months and the 5-year survival rate was 21.6%. Based on the results of multi-state model, age and distant metastases affected relapse whereas disease stage, type and extent of surgery, lymph nodes metastases, and number of renewed treatments affected death hazard without relapse. Moreover, age, type and extent of surgery, number of renewed treatments, and liver metastases were identified as factors affecting death hazard in patients with relapse. Conclusions: Most cancer studies pay heed to factors which have effect on death occurrence, but some events occur which should be taken into consideration to better describe the natural process of the disease and provide researchers with more accurate data.