This paper discusses various multiplicity issues arisen in clinical trials and possible statistical approaches to these issues. We especially stress the importance of the closed testing procedures (CTPs) in the setting of clinical trials: They include various modified Bonferroni procedures, e.g., step-down Dunnett procedure, hierarchical procedure, and Williams test. Moreover they can be even applied to adaptive designs in clinical trials. We illustrate the basic CTP procedures in detail.
{"title":"Statistical Approaches to Multiplicity Issues in Clinical Trials","authors":"T. Morikawa","doi":"10.5691/JJB.29.S15","DOIUrl":"https://doi.org/10.5691/JJB.29.S15","url":null,"abstract":"This paper discusses various multiplicity issues arisen in clinical trials and possible statistical approaches to these issues. We especially stress the importance of the closed testing procedures (CTPs) in the setting of clinical trials: They include various modified Bonferroni procedures, e.g., step-down Dunnett procedure, hierarchical procedure, and Williams test. Moreover they can be even applied to adaptive designs in clinical trials. We illustrate the basic CTP procedures in detail.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126379069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Assuming a random mating population of monoecious diploid, I derived an expression for the effective size of an age-structured population that varies the size over time in cycles of a given length. From the asymptotic contributions of age groups to the coancestry after many repetitions of cycles, an equation for the effective population size per cycle was derived, which showed a different expression from the previously published equation. Effect of the discrepancy was numerically evaluated with a hypothetical ladybird population with seasonal periodicity in size. The equation derived in this study gave a reasonably precise value, while the published one underestimated the effective size. The effect of estimation error of the census population size on the estimate of the effective size was also evaluated with the obtained formulae.
{"title":"Effective Size of Age-Structured Populations with Cyclic Change in Size","authors":"T. Nomura","doi":"10.5691/JJB.29.45","DOIUrl":"https://doi.org/10.5691/JJB.29.45","url":null,"abstract":"Assuming a random mating population of monoecious diploid, I derived an expression for the effective size of an age-structured population that varies the size over time in cycles of a given length. From the asymptotic contributions of age groups to the coancestry after many repetitions of cycles, an equation for the effective population size per cycle was derived, which showed a different expression from the previously published equation. Effect of the discrepancy was numerically evaluated with a hypothetical ladybird population with seasonal periodicity in size. The equation derived in this study gave a reasonably precise value, while the published one underestimated the effective size. The effect of estimation error of the census population size on the estimate of the effective size was also evaluated with the obtained formulae.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121919039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, flexible approaches with updating of sample size during the course of clinical trials have been proposed; the weighted Z-statistic approach and the 50 %-conditional power approach. In this paper, we propose a modification of the 50 %-conditional power approach, which increases the sample size only when the conditional power based on the unblinded interim results is greater than 50 %. Our method can control the type I error rate due to the restriction on the minimum required sample size ratio under the decision of increasing sample size. Simulation studies showed that the proposed method increased power about 10 % compared with the fixed sample size design and attained higher power than the original 50 %-conditional power approach. Compared with the weighted Z-statistic approach, the proposed method had several promising operating characteristics; a substantial gain in conditional power given the decision of sample size adjustment, a low probability of reaching the maximum sample size, a substantial decrease in the conditional type II error rate given the maximum sample size, and a conservative property of not increasing sample size erroneously under no treatment effect.
{"title":"A Modification of the 50 %-Conditional Power Approach for Increasing the Sample Size Based on an Interim Estimate of Treatment Difference","authors":"K. Uemura, Y. Matsuyama, Y. Ohashi","doi":"10.5691/JJB.29.19","DOIUrl":"https://doi.org/10.5691/JJB.29.19","url":null,"abstract":"Recently, flexible approaches with updating of sample size during the course of clinical trials have been proposed; the weighted Z-statistic approach and the 50 %-conditional power approach. In this paper, we propose a modification of the 50 %-conditional power approach, which increases the sample size only when the conditional power based on the unblinded interim results is greater than 50 %. Our method can control the type I error rate due to the restriction on the minimum required sample size ratio under the decision of increasing sample size. Simulation studies showed that the proposed method increased power about 10 % compared with the fixed sample size design and attained higher power than the original 50 %-conditional power approach. Compared with the weighted Z-statistic approach, the proposed method had several promising operating characteristics; a substantial gain in conditional power given the decision of sample size adjustment, a low probability of reaching the maximum sample size, a substantial decrease in the conditional type II error rate given the maximum sample size, and a conservative property of not increasing sample size erroneously under no treatment effect.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127508368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For the analysis of count data from comparative clinical study with patient screening which refers to the baseline observation, Cook and Wei (2003) proposed a conditional Gamma-Poisson model as a natural extension of ANCOVA. However, in some cases this model suffers from its insufficient capacity in expressing the inter-patient heterogeneity. As alternative we propose extended models that include an additional random effect into the conventional Poisson mixture, which can be estimated through conditioning by total sum of count or baseline. The resulting models can offer improved summary of patient heterogeneity, as well as other population parameters. The proposed models are illustrated with seizure count data from a clinical experiment for an anti-epileptic drug.
{"title":"Mixture Models for Mixed Poisson Processes with Baseline Counts in Randomized Controlled Trials","authors":"H. Uehara, T. Tango","doi":"10.5691/JJB.29.1","DOIUrl":"https://doi.org/10.5691/JJB.29.1","url":null,"abstract":"For the analysis of count data from comparative clinical study with patient screening which refers to the baseline observation, Cook and Wei (2003) proposed a conditional Gamma-Poisson model as a natural extension of ANCOVA. However, in some cases this model suffers from its insufficient capacity in expressing the inter-patient heterogeneity. As alternative we propose extended models that include an additional random effect into the conventional Poisson mixture, which can be estimated through conditioning by total sum of count or baseline. The resulting models can offer improved summary of patient heterogeneity, as well as other population parameters. The proposed models are illustrated with seizure count data from a clinical experiment for an anti-epileptic drug.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130834571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fixation or extinction of neutral genes by genetic drift is especially important for understanding the evolution of small populations. Assuming a monoecious diploid species, I derived expressions for the fixation probability of a neutral gene in age-structured populations with cyclic change in size. Stochastic simulation with a small population showed that the obtained formulae give a good prediction of the fixation probability. Extension to a dioecious diploid species was also presented.
{"title":"Fixation Probability of a Neutral Gene in an Age-Structured Population with Cyclic Change in Size","authors":"T. Nomura","doi":"10.5691/JJB.29.35","DOIUrl":"https://doi.org/10.5691/JJB.29.35","url":null,"abstract":"Fixation or extinction of neutral genes by genetic drift is especially important for understanding the evolution of small populations. Assuming a monoecious diploid species, I derived expressions for the fixation probability of a neutral gene in age-structured populations with cyclic change in size. Stochastic simulation with a small population showed that the obtained formulae give a good prediction of the fixation probability. Extension to a dioecious diploid species was also presented.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131146306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Japanese Foundation for Cancer Research (JFCR) has provided a large database on gastric cancer for open use comprised of data on survival times after surgery and related covariates including prognostic factors. Data analysis based on the generalized hazards model incorporating the B-spline function (GHMBS) was performed using a dataset (JFCR dataset) composed of 9,631 cases within this database. A model selection method was adopted for clarifying the meaning of estimated parameters, because the GHMBS was comprised of the proportional hazards model (PHM) and accelerated failure time model (AFTM) as submodels. A preliminary simulation experiment to examine the performance of the model selection method based on the GHMBS was conducted under the condition that multiple covariates were considered, where one was the target covariate and the other was covariate for adjustment. After validation of the method by this simulation experiment, the method was applied to the JFCR dataset to estimate the period effect for prolonging survival time with an adjustment for the stage effect. The analysis revealed that the PHM was suitable for the period effect, while a mixture of the PHM and AFTM was for the stage effect and treatment for gastric cancer made steady progress from the 1950s through the 1990s.
{"title":"Survival Analysis for Gastric Cancer Using the Japanese Foundation Database Based on a Generalized Hazards Model Incorporating B-spline Functions","authors":"Hisao Takeuchi, I. Yoshimura, C. Hamada","doi":"10.5691/JJB.28.59","DOIUrl":"https://doi.org/10.5691/JJB.28.59","url":null,"abstract":"The Japanese Foundation for Cancer Research (JFCR) has provided a large database on gastric cancer for open use comprised of data on survival times after surgery and related covariates including prognostic factors. Data analysis based on the generalized hazards model incorporating the B-spline function (GHMBS) was performed using a dataset (JFCR dataset) composed of 9,631 cases within this database. A model selection method was adopted for clarifying the meaning of estimated parameters, because the GHMBS was comprised of the proportional hazards model (PHM) and accelerated failure time model (AFTM) as submodels. A preliminary simulation experiment to examine the performance of the model selection method based on the GHMBS was conducted under the condition that multiple covariates were considered, where one was the target covariate and the other was covariate for adjustment. After validation of the method by this simulation experiment, the method was applied to the JFCR dataset to estimate the period effect for prolonging survival time with an adjustment for the stage effect. The analysis revealed that the PHM was suitable for the period effect, while a mixture of the PHM and AFTM was for the stage effect and treatment for gastric cancer made steady progress from the 1950s through the 1990s.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125480955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Positivity of anti-thyroglobulin antibody (TgAb) is one of the markers of chronic thyroiditis (Hashimoto disease). From 2000 to 2003, a thyroid disease prevalence study was conducted at the Radiation Effects Research Foundation, in Hiroshima and Nagasaki. Utilizing the study's results, we show that via EM algorithm log-transformed TgAb level is compatible with a two-component mixture normal distribution, with the smaller normal distribution corresponding to the TgAb negative group but the larger distribution not necessarily corresponding to the TgAb positive group. A subject is determined to be TgAb positive if TgAb level is greater than a given cutoff. We compared the cutoff values from population-based methods and the laboratory method. The population-based methods consist of a simple method, a receiver operating characteristic (ROC) curve method, and a minimum misclassification rate (MMR) method. The simple method is used to determine positivity from only TgAb negative populations. Since the ROC curve and MMR methods are valid only when TgAb positivity and negativity are known but the simple method is valid only when TgAb negativity is known, the simple method was deemed useful for determining the cutoff in our data. In comparison with the simple, population-based method, we show that the cutoff from the laboratory method is appropriate and that the TgAb positive rates from various methods are approximately equal. With the two-component mixture normal distribution in TgAb level, our simple population-based method for determination of cutoff is another more practical example of handling the clinical measurement than the method given in Thompson et al. (Applied Statistics 1998).
{"title":"Finite Mixture Models in Assessing Anti-thyroglobulin Antibody Positivity as a Marker of Chronic Thyroiditis","authors":"E. Nakashima, Y. Fujii, M. Imaizumi, K. Ashizawa","doi":"10.5691/JJB.28.79","DOIUrl":"https://doi.org/10.5691/JJB.28.79","url":null,"abstract":"Positivity of anti-thyroglobulin antibody (TgAb) is one of the markers of chronic thyroiditis (Hashimoto disease). From 2000 to 2003, a thyroid disease prevalence study was conducted at the Radiation Effects Research Foundation, in Hiroshima and Nagasaki. Utilizing the study's results, we show that via EM algorithm log-transformed TgAb level is compatible with a two-component mixture normal distribution, with the smaller normal distribution corresponding to the TgAb negative group but the larger distribution not necessarily corresponding to the TgAb positive group. A subject is determined to be TgAb positive if TgAb level is greater than a given cutoff. We compared the cutoff values from population-based methods and the laboratory method. The population-based methods consist of a simple method, a receiver operating characteristic (ROC) curve method, and a minimum misclassification rate (MMR) method. The simple method is used to determine positivity from only TgAb negative populations. Since the ROC curve and MMR methods are valid only when TgAb positivity and negativity are known but the simple method is valid only when TgAb negativity is known, the simple method was deemed useful for determining the cutoff in our data. In comparison with the simple, population-based method, we show that the cutoff from the laboratory method is appropriate and that the TgAb positive rates from various methods are approximately equal. With the two-component mixture normal distribution in TgAb level, our simple population-based method for determination of cutoff is another more practical example of handling the clinical measurement than the method given in Thompson et al. (Applied Statistics 1998).","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125425539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meta-analysis of randomized controlled trials is a widely used study methodology and it is considered to provide the highest level of evidence. The results of such analysis, however, by the nature of this methodology, may be affected by a very serious bias referred to as publication bias. Although the trim-and-fill method has been proposed as a means of adjusting for publication bias, it does not necessarily provide a suitable correction under realistic circumstances. This article proposes a new method to correct for publication bias based on p-value and evaluates the performance of this method by means of simulations. It is shown that the performance of the proposed method is superior to that of the trim-and-fill method under realistic situations.
{"title":"A Practical Method Adjusting for Publication Bias in Meta-analysis Based on p-value","authors":"N. Matsuoka, Chihiro Hasegawa, C. Hamada","doi":"10.5691/JJB.28.19","DOIUrl":"https://doi.org/10.5691/JJB.28.19","url":null,"abstract":"Meta-analysis of randomized controlled trials is a widely used study methodology and it is considered to provide the highest level of evidence. The results of such analysis, however, by the nature of this methodology, may be affected by a very serious bias referred to as publication bias. Although the trim-and-fill method has been proposed as a means of adjusting for publication bias, it does not necessarily provide a suitable correction under realistic circumstances. This article proposes a new method to correct for publication bias based on p-value and evaluates the performance of this method by means of simulations. It is shown that the performance of the proposed method is superior to that of the trim-and-fill method under realistic situations.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125497086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michiels et al. (2005) showed that a list of genes identified as predictors of prognosis via a non-repeated training — validation approach is unstable and advocate the validation by repeated random sampling. They considered that the genes which were selected as top 50 genes in more than half of their jackknife samples were stable for prediction. However, there is no rationale of the determination of the length of the gene list and the threshold of stability. Since evaluating an accumulation of low p-values in the repeated random sampling is essentially required for a stability assessment, it is better to compare the distribution of p-values of a gene observed with the distribution of p-values under the null hypothesis directly. In this study, the Quantile-Quantile plot (Q-Q plot) of p-values with null reference was proposed for this purpose. We applied the proposed method to a clinical data for primary breast cancer. The Q-Q plot approach can reveal that the genes with a similar p-value in the ordinary analysis have different p-value distributions in the repeated random sampling, and the gene with low p-values accumulated in the repeated random sampling could be evaluated according to the reference lines in the Q-Q plot.
Michiels et al.(2005)表明，通过非重复训练验证方法确定的预后预测因子基因列表是不稳定的，并主张通过重复随机抽样进行验证。他们认为，在一半以上的折刀样本中，被选为前50个基因的基因是稳定的，可以预测。然而，目前还没有确定基因列表长度和稳定性阈值的基本原理。由于评估重复随机抽样中低p值的积累对于稳定性评估是必不可少的，因此最好直接将观察到的基因p值分布与零假设下的p值分布进行比较。为此，本研究提出了无参考p值的分位数-分位数图(Q-Q图)。我们将提出的方法应用于原发性乳腺癌的临床资料。Q-Q图方法可以揭示在常规分析中p值相近的基因在重复随机抽样中p值分布不同，在重复随机抽样中积累的p值较低的基因可以根据Q-Q图中的参考线进行评价。
{"title":"The Q-Q Plot of p-values for Predicting Outcomes with the Gene Expression Data","authors":"Y. Ito, Y. Fujiwara, Y. Ohashi","doi":"10.5691/JJB.28.37","DOIUrl":"https://doi.org/10.5691/JJB.28.37","url":null,"abstract":"Michiels et al. (2005) showed that a list of genes identified as predictors of prognosis via a non-repeated training — validation approach is unstable and advocate the validation by repeated random sampling. They considered that the genes which were selected as top 50 genes in more than half of their jackknife samples were stable for prediction. However, there is no rationale of the determination of the length of the gene list and the threshold of stability. Since evaluating an accumulation of low p-values in the repeated random sampling is essentially required for a stability assessment, it is better to compare the distribution of p-values of a gene observed with the distribution of p-values under the null hypothesis directly. In this study, the Quantile-Quantile plot (Q-Q plot) of p-values with null reference was proposed for this purpose. We applied the proposed method to a clinical data for primary breast cancer. The Q-Q plot approach can reveal that the genes with a similar p-value in the ordinary analysis have different p-value distributions in the repeated random sampling, and the gene with low p-values accumulated in the repeated random sampling could be evaluated according to the reference lines in the Q-Q plot.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130532444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Suganami, K. Kano, Y. Kuwayama, C. Hamada, I. Yoshimura
Glaucoma is the primary cause of vision loss in Japan. The most important glaucoma therapy is to decrease intraocular pressure (IOP) for preventing visual field defects in the pre-stage of vision loss. Considering a systematic diurnal variation of IOP, Kuwayama et al. (2006) proposed to use a circular linear mixed effect (CLME) model for evaluating the efficacy of therapy on IOP decrease for patients with normal tension glaucoma (NTG) and applied it to the data analysis in a clinical trial (Nipradilol trial) with 28 NTG patients. In this application, there occurred an issue that the parameter estimates were different depending on the method of estimation and the best method was not identified. We, therefore, compared six methods for parameter estimation (standard two-stage (STS) method, global two-stage (GTS) method, first order approximation (FOA) method, Laplacian approximation (LAP) method, Monte Carlo integration (MCI) method and Gaussian quadrature (GAUS) method) through a simulation experiment with the bias and square root of mean squared error as the criteria for evaluation. The GAUS method proved to be superior to others in realizing least bias and mean squared error under various simulation conditions, although it was most time consuming.
在日本，青光眼是导致视力丧失的主要原因。在视力丧失前期，降低眼压是预防视野缺损最重要的治疗方法。Kuwayama et al.(2006)考虑到IOP的系统性日变化，提出使用循环线性混合效应(circular linear mixed effect, CLME)模型评价治疗对正常紧张性青光眼(NTG)患者IOP降低的疗效，并将其应用于28例NTG患者的临床试验(Nipradilol试验)数据分析。在这个应用程序中，出现了一个问题，即参数估计根据估计方法的不同而不同，并且没有确定最佳方法。因此，我们通过以偏置和均方误差的平方根为评价标准，比较了六种参数估计方法(标准两阶段法(STS)、全局两阶段法(GTS)、一阶近似法(FOA)、拉普拉斯近似法(LAP)、蒙特卡罗积分法(MCI)和高斯正交法(GAUS))。GAUS方法虽然耗时较长，但在各种仿真条件下均方误差最小，优于其他方法。
{"title":"Comparison of Methods for Parameter Estimation in a Circular Linear Mixed Effect Model Incorporating the Diurnal Variation for Evaluating the Treatment Effects of Glaucoma Therapy","authors":"H. Suganami, K. Kano, Y. Kuwayama, C. Hamada, I. Yoshimura","doi":"10.5691/JJB.28.1","DOIUrl":"https://doi.org/10.5691/JJB.28.1","url":null,"abstract":"Glaucoma is the primary cause of vision loss in Japan. The most important glaucoma therapy is to decrease intraocular pressure (IOP) for preventing visual field defects in the pre-stage of vision loss. Considering a systematic diurnal variation of IOP, Kuwayama et al. (2006) proposed to use a circular linear mixed effect (CLME) model for evaluating the efficacy of therapy on IOP decrease for patients with normal tension glaucoma (NTG) and applied it to the data analysis in a clinical trial (Nipradilol trial) with 28 NTG patients. In this application, there occurred an issue that the parameter estimates were different depending on the method of estimation and the best method was not identified. We, therefore, compared six methods for parameter estimation (standard two-stage (STS) method, global two-stage (GTS) method, first order approximation (FOA) method, Laplacian approximation (LAP) method, Monte Carlo integration (MCI) method and Gaussian quadrature (GAUS) method) through a simulation experiment with the bias and square root of mean squared error as the criteria for evaluation. The GAUS method proved to be superior to others in realizing least bias and mean squared error under various simulation conditions, although it was most time consuming.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126114161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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