B. Solomon, P. Boruta, Dagmar Horvath, John MacKella
{"title":"The Multiple Sclerosis Stress Equation","authors":"B. Solomon, P. Boruta, Dagmar Horvath, John MacKella","doi":"10.7243/2053-7662-11-1","DOIUrl":"https://doi.org/10.7243/2053-7662-11-1","url":null,"abstract":"","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71382507","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}
Testing effect size homogeneity is an essential part when conducting a meta-analysis. Comparative studies of effect size homogeneity tests in case of binary outcomes are found in the literature, but no test has come out as an absolute winner. A alternative approach would be to carry out multiple effect size homogeneity tests on the same meta-analysis and combine the resulting dependent p-values. In this article we applied the correlated Lancaster method for dependent statistical tests. To investigate the proposed approach’s performance, we applied eight different effect size homogeneity tests on a case study and on simulated datasets, and combined the resulting p-values. The proposed method has similar performance to that of tests based on the score function in the presence of a effect size when the number of studies is small, but outperforms these tests as the number of studies increases. However, the method’s performance is sensitive to the correlation coefficient value assumed between dependent tests, and only performs well when this value is high. More research is needed to investigate the method’s assumptions on correlation in case of effect size homogeneity tests, and to study the method’s performance in meta-analysis of continuous outcomes.
{"title":"Combining dependent p-values resulting from multiple effect\u0000size homogeneity tests in meta-analysis for binary outcomes","authors":"O. Almalik","doi":"10.7243/2053-7662-10-1","DOIUrl":"https://doi.org/10.7243/2053-7662-10-1","url":null,"abstract":"Testing effect size homogeneity is an essential part when conducting a meta-analysis. Comparative studies of effect size homogeneity tests in case of binary outcomes are found in the literature, but no test has come out as an absolute winner. A alternative approach would be to carry out multiple effect size homogeneity tests on the same meta-analysis and combine the resulting dependent p-values. In this article we applied the correlated Lancaster method for dependent statistical tests. To investigate the proposed approach’s performance, we applied eight different effect size homogeneity tests on a case study and on simulated datasets, and combined the resulting p-values. The proposed method has similar performance to that of tests based on the score function in the presence of a effect size when the number of studies is small, but outperforms these tests as the number of studies increases. However, the method’s performance is sensitive to the correlation coefficient value assumed between dependent tests, and only performs well when this value is high. More research is needed to investigate the method’s assumptions on correlation in case of effect size homogeneity tests, and to study the method’s performance in meta-analysis of continuous outcomes.","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45866587","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}
Lan Xu, Yangxin Huang, Henian Chen, A. Mbah, Feng Cheng
Background: Many clinical and public health researches collect data including multiple longitudinal measures and time-to-event outcomes, where characteristics of the pattern of exposure change and the association between features of longitudinal biomakers and the primary survival endpoint are of interest. Methods: Many existing statistical models for longitudinal-survival data might not provide robust inference when more than one longitudinal exposures which were significantly correlated and longitudinal measurements exhibit skewness and/or heavy tails; ignoring these data features may lead to biased estimation. In this article, we offered a multivariate joint model with the skew-normal (SN) distribution with application to the Mayo clinic primary biliary cirrhosis (PBC) study to assess simultaneous effects. Results: With the multivariate joint modeling associated with the skew-normal (SN) distribution, the subject-specific baseline (HR=2.390 with 95% CI: (1.429, 4.112)) and change rate (HR=2.588 with 95% CI: (1.845, 3.967)) of Bilirubin in natural log scale were positively associated with the risk of death; the higher the subject-specific change rate (HR=0.191 with 95% CI: (0.037, 0.915)) of Albumin in natural log scale was associated with a decrease in mortality rate; the subject-specific of SGOT levels in natural log scale did not affect the risk of death for PBC patients significantly. The results of the skewness parameters of natural log-transformed Bilirubin (δ1=0.42), Albumin (δ2=−0.03) and SGOT (δ3=0.095) were estimated to be significant, indicating the skewness of three biomarkers existed. Conclusions: Our results revealed the Bilirubin and Albumin levels may be involved in predicting risk of death for PBC patients, except for SGOT. The multivariate joint modeling associated with SN distribution provides better fit to the data, gives less biased parameter estimates for those longitudinal biomarkers in comparison with its counterpart where the normal distribution is assumed (data not shown here). The introduced modeling approach is generally applicable to other situations where longitudinal measurements and time-to-event outcomes are available.
{"title":"Joint Modeling Analysis of Multivariate Skewed-longitudinal and Time-to-event Data with Application to Primary Biliary Cirrhosis Study","authors":"Lan Xu, Yangxin Huang, Henian Chen, A. Mbah, Feng Cheng","doi":"10.7243/2053-7662-9-2","DOIUrl":"https://doi.org/10.7243/2053-7662-9-2","url":null,"abstract":"Background: Many clinical and public health researches collect data including multiple longitudinal measures and time-to-event outcomes, where characteristics of the pattern of exposure change and the association between features of longitudinal biomakers and the primary survival endpoint are of interest. Methods: Many existing statistical models for longitudinal-survival data might not provide robust inference when more than one longitudinal exposures which were significantly correlated and longitudinal measurements exhibit skewness and/or heavy tails; ignoring these data features may lead to biased estimation. In this article, we offered a multivariate joint model with the skew-normal (SN) distribution with application to the Mayo clinic primary biliary cirrhosis (PBC) study to assess simultaneous effects. Results: With the multivariate joint modeling associated with the skew-normal (SN) distribution, the subject-specific baseline (HR=2.390 with 95% CI: (1.429, 4.112)) and change rate (HR=2.588 with 95% CI: (1.845, 3.967)) of Bilirubin in natural log scale were positively associated with the risk of death; the higher the subject-specific change rate (HR=0.191 with 95% CI: (0.037, 0.915)) of Albumin in natural log scale was associated with a decrease in mortality rate; the subject-specific of SGOT levels in natural log scale did not affect the risk of death for PBC patients significantly. The results of the skewness parameters of natural log-transformed Bilirubin (δ1=0.42), Albumin (δ2=−0.03) and SGOT (δ3=0.095) were estimated to be significant, indicating the skewness of three biomarkers existed. Conclusions: Our results revealed the Bilirubin and Albumin levels may be involved in predicting risk of death for PBC patients, except for SGOT. The multivariate joint modeling associated with SN distribution provides better fit to the data, gives less biased parameter estimates for those longitudinal biomarkers in comparison with its counterpart where the normal distribution is assumed (data not shown here). The introduced modeling approach is generally applicable to other situations where longitudinal measurements and time-to-event outcomes are available.","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71382513","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":"Which is More Accurate in Measuring the Blood Pressure? Comparison of An manual Aneroid Sphygmomanometer, manual mercury or Digital Automated in Hypertension","authors":"A. Bener, Baris Sandal, C. Barışık, A. Toprak","doi":"10.7243/2053-7662-9-3","DOIUrl":"https://doi.org/10.7243/2053-7662-9-3","url":null,"abstract":"","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71382515","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}
Yangxin Huang, Jiaqing Chen, Lan Xu, Hanze Zhang, Yuanyuan Lu
{"title":"Bayesian MLIRT-based joint models for multivariate longitudinal and survival data with multiple features","authors":"Yangxin Huang, Jiaqing Chen, Lan Xu, Hanze Zhang, Yuanyuan Lu","doi":"10.7243/2053-7662-9-4","DOIUrl":"https://doi.org/10.7243/2053-7662-9-4","url":null,"abstract":"","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71382517","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 one-sample t test compares a sample to a known average. The standard deviation (SD) is known for the sample but not for the known average. This study compares p-values from one-sample versus two-sample t testing where SD is also known with the known average to see if the lack of information on SD makes a difference in p-values calculated with versus without the SD. Having confidence in a t test p-value is to have confidence in whether the difference in means happened by chance or not.
{"title":"Comparison of p-value results between one versus two sample t testing: A case study","authors":"J. Hart","doi":"10.7243/2053-7662-9-1","DOIUrl":"https://doi.org/10.7243/2053-7662-9-1","url":null,"abstract":"The one-sample t test compares a sample to a known average. The standard deviation (SD) is known for the sample but not for the known average. This study compares p-values from one-sample versus two-sample t testing where SD is also known with the known average to see if the lack of information on SD makes a difference in p-values calculated with versus without the SD. Having confidence in a t test p-value is to have confidence in whether the difference in means happened by chance or not.","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71382511","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 objective of this work is to predict the spread of COVID-19 starting from observed data, using a forecast method inspired by probabilistic weather prediction systems operational today. Results show that this method works well for China: on day 25 we could have predicted well the outcome for the next 35 days. The same method has been applied to Italy and South Korea, and forecasts for the forthcoming weeks are included in this work. For Italy, forecasts based on data collected up to today (24 March) indicate that number of observed cases could grow from the current value of 69,176, to between 101k-180k, with a 50% probability of being between 110k-135k. For South Korea, it suggests that the number of observed cases could grow from the current value of 9,018 (as of the 23rd of March), to values between 8,500 and 9,300, with a 50% probability of being between 8,700 and 8,900. We conclude by suggesting that probabilistic disease prediction systems are possible and could be developed following key ideas and methods from weather forecasting. Having access to skilful daily updated forecasts could help taking better informed decisions on how to manage the spread of diseases such as COVID-19.
{"title":"Weather-inspired ensemble-based probabilistic prediction of COVID-19","authors":"R. Buizza","doi":"10.7243/2053-7662-8-4","DOIUrl":"https://doi.org/10.7243/2053-7662-8-4","url":null,"abstract":"The objective of this work is to predict the spread of COVID-19 starting from observed data, using a forecast method inspired by probabilistic weather prediction systems operational today. \u0000Results show that this method works well for China: on day 25 we could have predicted well the outcome for the next 35 days. The same method has been applied to Italy and South Korea, and forecasts for the forthcoming weeks are included in this work. For Italy, forecasts based on data collected up to today (24 March) indicate that number of observed cases could grow from the current value of 69,176, to between 101k-180k, with a 50% probability of being between 110k-135k. For South Korea, it suggests that the number of observed cases could grow from the current value of 9,018 (as of the 23rd of March), to values between 8,500 and 9,300, with a 50% probability of being between 8,700 and 8,900. \u0000We conclude by suggesting that probabilistic disease prediction systems are possible and could be developed following key ideas and methods from weather forecasting. Having access to skilful daily updated forecasts could help taking better informed decisions on how to manage the spread of diseases such as COVID-19.","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42926149","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}
Abstract Background: Clostridioides difficile infection causes chronic and sometimes life-threatening diarrhea in patients as a consequence of antibiotics overuse. A promising experimental procedure for
{"title":"Survival analysis to assess of the effectiveness of fecal microbiota transplantation for recurrent and/or refractory Clostridioides difficile infection","authors":"T. Petukhova, P. Kim, A. Desmond","doi":"10.7243/2053-7662-8-1","DOIUrl":"https://doi.org/10.7243/2053-7662-8-1","url":null,"abstract":"Abstract \u0000Background: Clostridioides difficile infection causes chronic and sometimes life-threatening diarrhea in patients as a consequence of antibiotics overuse. A promising experimental procedure for","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41632043","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}
Abstract Likelihood ratio test is widely used for detecting adverse reactions (ARs) of single drug in biomedical studies. However, it is difficult to detect adverse reactions of multiple drugs simultaneously. Corresponding to this, we
{"title":"A Generalized Likelihood Ratio Test for Estimating Drug Adverse Reactions","authors":"Yeqian Liu","doi":"10.7243/2053-7662-7-4","DOIUrl":"https://doi.org/10.7243/2053-7662-7-4","url":null,"abstract":"Abstract \u0000Likelihood ratio test is widely used for detecting adverse reactions (ARs) of single drug in biomedical studies. However, it is difficult to detect adverse reactions of multiple drugs simultaneously. Corresponding to this, we","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43409404","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}
Abstract Background: The purpose of this research was to calculate the minimum sample size needed to obtain reliable results from crowdsourced retrospective online surveys of IVF clinics, where the sample was IVF cycles
{"title":"Can We Learn from the“Wisdom of the Crowd”? Finding the Sample-Size Sweet Spot – an Analysis of Internet-Based Crowdsourced Surveys of Fertility Professionals","authors":"Gon Shoham, M. Leong, A. Weissman, Y. Yaron","doi":"10.7243/2053-7662-7-3","DOIUrl":"https://doi.org/10.7243/2053-7662-7-3","url":null,"abstract":"Abstract \u0000Background: The purpose of this research was to calculate the minimum sample size needed to obtain reliable results from crowdsourced retrospective online surveys of IVF clinics, where the sample was IVF cycles","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46589244","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：