A cure rate model is a survival model incorporating the cure rate on the assumption that a population contains both uncured and cured individuals. It is a powerful statistical tool for cancer prognostic studies. In order to accurately predict longterm outcome the proportional hazards (PH) cure model requires variable selection methods. However, no specific variable selection method for the PH cure model has been established in practice. In this study, we present a stepwise variable selection method for the PH cure model with a logistic regression for the cure rate and a Cox regression for the hazard for uncured patients. We conducted simulation studies to evaluate the operating characteristics of the stepwise method in comparison to those of the best subset selection method based on Akaike information criterion and of the convenience variable selection method that puts all variables in the PH cure model and selects the significant ones. The results demonstrated that in many cases the stepwise method outperformed other methods with respect to false positive determinations and estimation bias for the survival curve. In addition, we demonstrated the usefulness of the stepwise method for the PH cure model by applying it to analyze clinical data on breast cancer patients.
{"title":"A Stepwise Variable Selection for a Cox Proportional Hazards Cure Model with Application to Breast Cancer Data","authors":"J. Asano, A. Hirakawa, C. Hamada","doi":"10.5691/JJB.34.21","DOIUrl":"https://doi.org/10.5691/JJB.34.21","url":null,"abstract":"A cure rate model is a survival model incorporating the cure rate on the assumption that a population contains both uncured and cured individuals. It is a powerful statistical tool for cancer prognostic studies. In order to accurately predict longterm outcome the proportional hazards (PH) cure model requires variable selection methods. However, no specific variable selection method for the PH cure model has been established in practice. In this study, we present a stepwise variable selection method for the PH cure model with a logistic regression for the cure rate and a Cox regression for the hazard for uncured patients. We conducted simulation studies to evaluate the operating characteristics of the stepwise method in comparison to those of the best subset selection method based on Akaike information criterion and of the convenience variable selection method that puts all variables in the PH cure model and selects the significant ones. The results demonstrated that in many cases the stepwise method outperformed other methods with respect to false positive determinations and estimation bias for the survival curve. In addition, we demonstrated the usefulness of the stepwise method for the PH cure model by applying it to analyze clinical data on breast cancer patients.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132501250","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}
要约放射线影响研究所追踪调查的原子弹爆炸受害者群体(寿命调查群体)因癌症以外的疾病死亡的剂量反应形状根据期间不同而不同。本研究将变化点模型和赤池信息量标准(AIC)应用于第13份公开的寿命调查报告(Preston et al. 2003)的数据。该数据包括86572名被调查者以及在1950—1997年跟踪调查期间因非癌症疾病死亡的31,881人。剂量反应使用变化点模型而背景不使用变化点模型的分析，显示了与寿命调查报告第13报相同的结果。也就是说，近距离原子弹爆炸受害者和远距离原子弹爆炸受害者因癌症以外的疾病导致的基准死亡率的差异随时间而变化，剂量反应的形状在1950—1967年呈线性二阶，在1968—1997年呈线性。但是，除了剂量反应之外，背景也使用了变化点模型的这次模型显示，近距离原子弹爆炸受害者和远距离原子弹爆炸受害者因癌症以外的疾病导致的基准死亡率的差异随着时间的变化而变化。几乎没有得到证据。剂量反应的形状在1950—1964年为纯二阶，在1965—1997年为线性。另外，将癌症以外的疾病死亡分为循环器官疾病和其他癌症以外的疾病时，剂量反应的形状不会因期间而改变。(循环器官疾病的剂量反应是线性的，其他非癌症疾病的剂量反应是纯次级的)。
{"title":"Application of a Change Point Model to Atomic-Bomb Survivor Data: Radiation Risk of Noncancer Disease Mortality","authors":"Yoshisuke Nonaka, Y. Shimizu, K. Ozasa, Munechika Misumi, H. Cullings, F. Kasagi","doi":"10.5691/JJB.32.75","DOIUrl":"https://doi.org/10.5691/JJB.32.75","url":null,"abstract":"要 約 放射線影響研究所が追跡調査している原爆被爆者集団(寿命調査集団)におけるがん以外の 疾患による死亡の線量反応の形状は期間により異なっている。本研究では、変化点モデルと赤池 情報量規準(AIC)を、公開されている寿命調査報告書第 13報(Preston et al.、2003年)のデータ に適用した。このデータには、対象者86,572人とその1950–1997年の追跡調査期間中のがん以外 の疾患死亡者 31,881 人が含まれている。線量反応には変化点モデルを用いたがバックグラウンド には変化点モデルを用いなかった解析では、寿命調査報告書第 13 報と同様の結果が示された。 つまり、近距離被爆者と遠距離被爆者のがん以外の疾患による基準死亡率の差が時間によって 変化し、線量反応の形状は 1950–1967 年では線形二次、1968–1997 年では線形であった。しかし、 線量反応だけでなくバックグラウンドにも変化点モデルを用いた今回のモデルでは、近距離被爆 者と遠距離被爆者のがん以外の疾患による基準死亡率の差が時間によって変化することを示す 証拠はほとんど得られなかった。線量反応の形状は、1950–1964 年では純粋な二次、1965–1997 年では線形であった。また、がん以外の疾患による死亡を循環器疾患とその他のがん以外の疾患 に分けた場合、線量反応の形状は期間によって変わらなかった。(循環器疾患の線量反応は線形、 その他のがん以外の疾患の線量反応は純粋な二次であった)。","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131879591","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}
{"title":"Model-Based Drug Developmentの事例","authors":"完爾 小松","doi":"10.5691/JJB.32.179","DOIUrl":"https://doi.org/10.5691/JJB.32.179","url":null,"abstract":"","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129231446","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 generalized estimating equation (GEE) method is a popular method for analyzing longitudinal data. An inappropriate specification of the working correlation structure reduces the effciency of the GEE estimation. Pan (2001a) and Hin and Wang (2009) proposed a quasi-likelihood under the independence model criterion (QIC) and a correlation information criterion (CIC) for selecting a proper working correlation structure, respectively. In this study, we proposed modifications to the QIC and CIC using the variance estimators of the GEE with improved small-sample properties. In a simulation study, the performance of the modified QIC and CIC was better than that of the original QIC and CIC. The modified methods were illustrated using the data for an air pollution study.
广义估计方程(GEE)法是一种常用的纵向数据分析方法。工作相关结构的不适当规范降低了GEE估计的效率。Pan (2001a)和Hin and Wang(2009)分别提出了独立模型准则下的准似然准则(QIC)和相关信息准则(CIC)来选择合适的工作关联结构。在本研究中，我们使用改进小样本性质的GEE方差估计器对QIC和CIC进行了修改。在仿真研究中，改进后的QIC和CIC的性能都优于原QIC和CIC。用一项空气污染研究的数据说明了改进的方法。
{"title":"Modifications of QIC and CIC for Selecting a Working Correlation Structure in the Generalized Estimating Equation Method","authors":"M. Gosho, C. Hamada, I. Yoshimura","doi":"10.5691/JJB.32.1","DOIUrl":"https://doi.org/10.5691/JJB.32.1","url":null,"abstract":"The generalized estimating equation (GEE) method is a popular method for analyzing longitudinal data. An inappropriate specification of the working correlation structure reduces the effciency of the GEE estimation. Pan (2001a) and Hin and Wang (2009) proposed a quasi-likelihood under the independence model criterion (QIC) and a correlation information criterion (CIC) for selecting a proper working correlation structure, respectively. In this study, we proposed modifications to the QIC and CIC using the variance estimators of the GEE with improved small-sample properties. In a simulation study, the performance of the modified QIC and CIC was better than that of the original QIC and CIC. The modified methods were illustrated using the data for an air pollution study.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132982702","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}
As clinical trials with “positive” results are more likely to be published, a meta-analysis of only published trials may be biased toward positive results (referred to as “publication bias”). A number of statistical tests have been proposed to detect publication bias. However, they have undesirable properties, particularly, the inflation of type I error and low power. A primordial countermeasure has been launched. In September 2004, the International Committee of Medical Journal Editors announced that they would no longer publish trials that were not registered in a public registry in advance. They embraced the WHO trial registration set consisting of 20 items including target sample size, which is related to the publication of results. The aim of this paper is to propose a new approach with a higher statistical power for detecting publication bias by using information on the sample sizes of all trials, including unpublished trials from the registry. We compared the proposed method to commonly used methods via simulations. The proposed method was found to have a higher power than the other methods in many situations. It will be useful for detecting publication bias because clinical trial registration will be more widespread in the near future.
{"title":"Improvement of Statistical Power to Detect Publication Bias in Meta-analysis Using the Clinical Trial Registration System","authors":"N. Matsuoka, H. Horio, C. Hamada","doi":"10.5691/JJB.32.13","DOIUrl":"https://doi.org/10.5691/JJB.32.13","url":null,"abstract":"As clinical trials with “positive” results are more likely to be published, a meta-analysis of only published trials may be biased toward positive results (referred to as “publication bias”). A number of statistical tests have been proposed to detect publication bias. However, they have undesirable properties, particularly, the inflation of type I error and low power. A primordial countermeasure has been launched. In September 2004, the International Committee of Medical Journal Editors announced that they would no longer publish trials that were not registered in a public registry in advance. They embraced the WHO trial registration set consisting of 20 items including target sample size, which is related to the publication of results. The aim of this paper is to propose a new approach with a higher statistical power for detecting publication bias by using information on the sample sizes of all trials, including unpublished trials from the registry. We compared the proposed method to commonly used methods via simulations. The proposed method was found to have a higher power than the other methods in many situations. It will be useful for detecting publication bias because clinical trial registration will be more widespread in the near future.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134172036","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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