This paper concerns the Log-rank test for comparing survival curves of neonatal mortality characteristic groups in River Nile State, Sudan. In this paper, log-rank test is used to compare two or more survival curves f...This paper concerns the Log-rank test for comparing survival curves of neonatal mortality characteristic groups in River Nile State, Sudan. In this paper, log-rank test is used to compare two or more survival curves for the characteristics of newborn associated with newborn death after using Kaplan-Meier methods to estimate and graph survival curves for the variable of interest as (sex of newborn, weight of newborn, gestational age, mode of delivery and resident type), at the hospital of River Nile state—Sudan, with a sample size 700 of newborn in which the admission to the Neonatal Intensive Care Unit (NICU) of those hospitals during the period 2018-2020. In term of risk of death for newborn we found that 25% of sample study for newborns who were born in River Nile State-Sudan died. In addition, we conclude that after the log-rank statistics and Kaplan-Meier methods were applied, gender does not affect the newborn’s risk of survival, while the risk of survival increases when the birth weight is greater than 4.35 kg and the gestational age is greater than 42 weeks. There is no difference in the probability of survival for newborns whether the delivery is normal or cesarean. However, newborns are significantly more likely to survive in urban areas than in rural areas.展开更多
目的:L og rank检验是生存资料比较的标准方法,但无与之匹配的样本量测定方法。论述了这种检验的功效,为样本量研究提供依据。方法:由L ach in-Fou lkes法计算期望功效作为参照,回顾L og rank检验的3种形式,按M on te C arlo方法分别计...目的:L og rank检验是生存资料比较的标准方法,但无与之匹配的样本量测定方法。论述了这种检验的功效,为样本量研究提供依据。方法:由L ach in-Fou lkes法计算期望功效作为参照,回顾L og rank检验的3种形式,按M on te C arlo方法分别计算其观测功效,然后作对比分析。结果:所得观测功效在多数试验集均低于期望功效。与上半部相比,寿命表下半部期望和观测功效均较低。所得观测功效在不同终检水平或不同生存分布各不相同。结论:L ach in-Fou lkes法产生的样本量偏小,不能满足L og rank检验的预定功效。L og rank检验所需样本量因终检水平、生存时间或生存分布而异,L ach in-Fou lkes法无视这些事实,无法作出切合实际的测定,因此必须寻求与这种检验匹配的样本量测定方法。展开更多
Objective This paper propses a family of summary chi square tests for comparing survival rates at all points of time between two groups. Methods They are respectively derived from the Peto et al. expression for the lo...Objective This paper propses a family of summary chi square tests for comparing survival rates at all points of time between two groups. Methods They are respectively derived from the Peto et al. expression for the log rank test, the Mantel Haenszel expression for the log rank test, and the generalized Wilcoxon test by means of using the homogenetic effective sample size in place of the number at risk and using the corresponding numerator of the conditional probability surviving in place of the death number. Results After such derivations they become clearer in clinical significance, more powerful, and free from the assumption of proportional hazard. Conclusion These tests can be employed in analyzing the clinical data of cancer. A worked example illustrates the methodology.展开更多
Recurrent event time data are common in biomedical follow-up studies, in which a study subject may experience repeated occurrences of an event of interest. In this paper, we evaluate two popular nonparametric tests fo...Recurrent event time data are common in biomedical follow-up studies, in which a study subject may experience repeated occurrences of an event of interest. In this paper, we evaluate two popular nonparametric tests for recurrent event time data in terms of their relative effciency. One is the log-rank test for classical survival data and the other a more recently developed nonparametric test based on comparing mean recurrent rates. We show analytically that, somewhat surprisingly, the log-rank test that only makes use of time to the first occurrence could be more effcient than the test for mean occurrence rates that makes use of all available recurrence times, provided that subject-to-subject variation of recurrence times is large. Explicit formula are derived for asymptotic relative effciencies under the frailty model. The findings are demonstrated via extensive simulations.展开更多
Breast cancer is one of the leading diseases that affect women’s lives. It affects their lives in so many ways by denying them the required standard of health needed to carry out all of their daily activities for som...Breast cancer is one of the leading diseases that affect women’s lives. It affects their lives in so many ways by denying them the required standard of health needed to carry out all of their daily activities for some days, weeks, months or years before eventually causing death. This research estimates the survival rate of breast cancer patients and investigates the effects of stage of tumor, gender, age, ethnic group, occupation, marital status and type of cancer upon the survival of patients. Data used for the study were extracted from the case file of patients in the Radiation Oncology Department, University College Hospital, Ibadan using a well-structured pro forma in which 74 observations were censored and 30 events occurred. The Kaplan-Meier estimator was used to estimate the overall survival probability of breast cancer patients following their recruitment into the study and determine the mean and median survival times of breast cancer patients following their time of recruitment into the study. Since there are different groups with respect to the stages of tumor at the time of diagnosis, the log-rank test was used to compare the survival curve of the stages of tumor with considering p-values below 0.05 as statistically significant. Multivariate Cox regression was used to investigate the effects of some variables on the survival of patients. The overall cumulative survival probability obtained is 0.175 (17.5%). The overall estimated mean time until death is 28.751 weeks while the median time between admission and death is 23 weeks. As the p-value (0.000032) of the log-rank test for comparing stages of tumor is less than 0.05, it is concluded that there is significant evidence of a difference in survival times for the stages of tumor. The survival function plot for the stages of tumor shows that patients with stage III tumor are less likely to survive. From the estimated mean time until death for the stages of tumor, it was deduced that stage I tumor patients have an increased chance of survival. Types of cancer, gender, marital status, ethnic group, occupation and patient’s age at entry into the study are not important predictors of chances of survival.展开更多
文摘This paper concerns the Log-rank test for comparing survival curves of neonatal mortality characteristic groups in River Nile State, Sudan. In this paper, log-rank test is used to compare two or more survival curves for the characteristics of newborn associated with newborn death after using Kaplan-Meier methods to estimate and graph survival curves for the variable of interest as (sex of newborn, weight of newborn, gestational age, mode of delivery and resident type), at the hospital of River Nile state—Sudan, with a sample size 700 of newborn in which the admission to the Neonatal Intensive Care Unit (NICU) of those hospitals during the period 2018-2020. In term of risk of death for newborn we found that 25% of sample study for newborns who were born in River Nile State-Sudan died. In addition, we conclude that after the log-rank statistics and Kaplan-Meier methods were applied, gender does not affect the newborn’s risk of survival, while the risk of survival increases when the birth weight is greater than 4.35 kg and the gestational age is greater than 42 weeks. There is no difference in the probability of survival for newborns whether the delivery is normal or cesarean. However, newborns are significantly more likely to survive in urban areas than in rural areas.
文摘目的:L og rank检验是生存资料比较的标准方法,但无与之匹配的样本量测定方法。论述了这种检验的功效,为样本量研究提供依据。方法:由L ach in-Fou lkes法计算期望功效作为参照,回顾L og rank检验的3种形式,按M on te C arlo方法分别计算其观测功效,然后作对比分析。结果:所得观测功效在多数试验集均低于期望功效。与上半部相比,寿命表下半部期望和观测功效均较低。所得观测功效在不同终检水平或不同生存分布各不相同。结论:L ach in-Fou lkes法产生的样本量偏小,不能满足L og rank检验的预定功效。L og rank检验所需样本量因终检水平、生存时间或生存分布而异,L ach in-Fou lkes法无视这些事实,无法作出切合实际的测定,因此必须寻求与这种检验匹配的样本量测定方法。
文摘Objective This paper propses a family of summary chi square tests for comparing survival rates at all points of time between two groups. Methods They are respectively derived from the Peto et al. expression for the log rank test, the Mantel Haenszel expression for the log rank test, and the generalized Wilcoxon test by means of using the homogenetic effective sample size in place of the number at risk and using the corresponding numerator of the conditional probability surviving in place of the death number. Results After such derivations they become clearer in clinical significance, more powerful, and free from the assumption of proportional hazard. Conclusion These tests can be employed in analyzing the clinical data of cancer. A worked example illustrates the methodology.
基金supported by US National Science Foundation (Grant No. DMS-0504269)
文摘Recurrent event time data are common in biomedical follow-up studies, in which a study subject may experience repeated occurrences of an event of interest. In this paper, we evaluate two popular nonparametric tests for recurrent event time data in terms of their relative effciency. One is the log-rank test for classical survival data and the other a more recently developed nonparametric test based on comparing mean recurrent rates. We show analytically that, somewhat surprisingly, the log-rank test that only makes use of time to the first occurrence could be more effcient than the test for mean occurrence rates that makes use of all available recurrence times, provided that subject-to-subject variation of recurrence times is large. Explicit formula are derived for asymptotic relative effciencies under the frailty model. The findings are demonstrated via extensive simulations.
文摘Breast cancer is one of the leading diseases that affect women’s lives. It affects their lives in so many ways by denying them the required standard of health needed to carry out all of their daily activities for some days, weeks, months or years before eventually causing death. This research estimates the survival rate of breast cancer patients and investigates the effects of stage of tumor, gender, age, ethnic group, occupation, marital status and type of cancer upon the survival of patients. Data used for the study were extracted from the case file of patients in the Radiation Oncology Department, University College Hospital, Ibadan using a well-structured pro forma in which 74 observations were censored and 30 events occurred. The Kaplan-Meier estimator was used to estimate the overall survival probability of breast cancer patients following their recruitment into the study and determine the mean and median survival times of breast cancer patients following their time of recruitment into the study. Since there are different groups with respect to the stages of tumor at the time of diagnosis, the log-rank test was used to compare the survival curve of the stages of tumor with considering p-values below 0.05 as statistically significant. Multivariate Cox regression was used to investigate the effects of some variables on the survival of patients. The overall cumulative survival probability obtained is 0.175 (17.5%). The overall estimated mean time until death is 28.751 weeks while the median time between admission and death is 23 weeks. As the p-value (0.000032) of the log-rank test for comparing stages of tumor is less than 0.05, it is concluded that there is significant evidence of a difference in survival times for the stages of tumor. The survival function plot for the stages of tumor shows that patients with stage III tumor are less likely to survive. From the estimated mean time until death for the stages of tumor, it was deduced that stage I tumor patients have an increased chance of survival. Types of cancer, gender, marital status, ethnic group, occupation and patient’s age at entry into the study are not important predictors of chances of survival.
