Avoiding excessive enrollment of a single cohort in a clinical trial is prudent in order to avoid imbalances and to prevent one cohort from having a disproportionate influence on the results of a trial and perhaps eve...Avoiding excessive enrollment of a single cohort in a clinical trial is prudent in order to avoid imbalances and to prevent one cohort from having a disproportionate influence on the results of a trial and perhaps even negating positive findings of the clinical trial. Numerical criteria are provided here to evaluate the expected influence of a large cohort as a function of both its size and the relative effect of interventions, in comparison to those of other groups. Measures of expected influence are obtained as a function of the parameters of the distribution of statistics measuring influence. Calculated numerical criteria for the binomial, continuous and time-to-event contexts are presented. Details of the application of this method and sensitivity analyses conducted during the planning stages of a multiple myeloma clinical trial are provided. Numerical criteria are derived under asymptotic conditions and thus results hold for large cohorts. The numerical criteria are easy to compute and are useful tools to assess possible detrimental effects of large cohorts during the design of a study or during enrollment prior to any un-blinding. The numerical criteria allow for a-priori sensitivity analyses of the likely influence of large cohorts under varying conditions.展开更多
Objective:To compare the prognostic factors of mortality among melioidosis patients between lognormal accelerated failure time(AFT),Cox proportional hazards(PH),and Cox PH with time-varying coefficient(TVC)models.Meth...Objective:To compare the prognostic factors of mortality among melioidosis patients between lognormal accelerated failure time(AFT),Cox proportional hazards(PH),and Cox PH with time-varying coefficient(TVC)models.Methods:A retrospective study was conducted from 2014 to 2019 among 453 patients who were admitted to Hospital Sultanah Bahiyah,Kedah and Hospital Tuanku Fauziah,Perlis in Northern Malaysia due to confirmed-cultured melioidosis.The prognostic factors of mortality from melioidosis were obtained from AFT survival analysis,and Cox’s models and the findings were compared by using the goodness of fit methods.The analyses were done by using Stata SE version 14.0.Results:A total of 242 patients(53.4%)survived.In this study,the median survival time of melioidosis patients was 30.0 days(95%CI 0.0-60.9).Six significant prognostic factors were identified in the Cox PH model and Cox PH-TVC model.In AFT survival analysis,a total of seven significant prognostic factors were identified.The results were found to be only a slight difference between the identified prognostic factors among the models.AFT survival showed better results compared to Cox's models,with the lowest Akaike information criteria and best fitted Cox-snell residuals.Conclusions:AFT survival analysis provides more reliable results and can be used as an alternative statistical analysis for determining the prognostic factors of mortality in melioidosis patients in certain situations.展开更多
In clinical trials, the primary efficacy endpoint often corresponds to a so-called "composite endpoint". Composite endpoints combine several events of interest within a single outcome variable. Thereby it is...In clinical trials, the primary efficacy endpoint often corresponds to a so-called "composite endpoint". Composite endpoints combine several events of interest within a single outcome variable. Thereby it is intended to enlarge the expected effect size and thereby increase the power of the study. However, composite endpoints also come along with serious challenges and problems. On the one hand, composite endpoints may lead to difficulties during the planning phase of a trial with respect to the sample size calculation, asthe expected clinical effect of an intervention on the composite endpoint depends on the effects on its single components and their correlations. This may lead to wrong assumptions on the sample size needed. Too optimistic assumptions on the expected effect may lead to an underpowered of the trial, whereas a too conservatively estimated effect results in an unnecessarily high sample size. On the other hand, the interpretation of composite endpoints may be difficult, as the observed effect of the composite does not necessarily reflect the effects of the single components. Therefore the demonstration of the clinical efficacy of a new intervention by exclusively evaluating the composite endpoint may be misleading. The present paper summarizes results and recommendations of the latest research addressing the above mentioned problems in the planning, analysis and interpretation of clinical trials with composite endpoints, thereby providing a practical guidance for users.展开更多
The consideration of the time-varying covariate and time-varying coefficient effect in survival models are plausible and robust techniques. Such kind of analysis can be carried out with a general class of semiparametr...The consideration of the time-varying covariate and time-varying coefficient effect in survival models are plausible and robust techniques. Such kind of analysis can be carried out with a general class of semiparametric transformation models. The aim of this article is to develop modified estimating equations under semiparametric transformation models of survival time with time-varying coefficient effect and time-varying continuous covariates. For this, it is important to organize the data in a counting process style and transform the time with standard transformation classes which shall be applied in this article. In the situation when the effect of coefficient and covariates change over time, the widely used maximum likelihood estimation method becomes more complex and burdensome in estimating consistent estimates. To overcome this problem, alternatively, the modified estimating equations were applied to estimate the unknown parameters and unspecified monotone transformation functions. The estimating equations were modified to incorporate the time-varying effect in both coefficient and covariates. The performance of the proposed methods is tested through a simulation study. To sum up the study, the effect of possibly time-varying covariates and time-varying coefficients was evaluated in some special cases of semiparametric transformation models. Finally, the results have shown that the role of the time-varying covariate in the semiparametric transformation models was plausible and credible.展开更多
In randomized clinical trials with right-censored time-to-event outcomes,the popular log-rank test without adjusting for baseline covariates is asymptotically valid for treatment effect under simple randomization of t...In randomized clinical trials with right-censored time-to-event outcomes,the popular log-rank test without adjusting for baseline covariates is asymptotically valid for treatment effect under simple randomization of treatments but is too conservative under covariate-adaptive random-ization.The stratified log-rank test,which adjusts baseline covariates in the test procedure by stratification,is asymptotically valid regardless of what treatment randomization is applied.In the literature,however,under simple randomization there is no affirmative conclusion about whether the stratified log-rank test is asymptotically more powerful than the unstratified log-rank test.In this article we show when the stratified and unstratified log-rank tests aim for the same null hypothesis and that,under simple randomization,the stratified log-rank test is asymp-totically more powerful than the unstratified log-rank test in the region of alternative hypothesis that is specified by a Cox proportional hazards model.We also provide some discussion about why we do not have an affirmative conclusion in general.展开更多
In this paper, for time-to-event data, we propose a new statistical framework for casual inference in evaluating clinical utility of predictive biomarkers and in selecting an optimal treatment for a particular patient...In this paper, for time-to-event data, we propose a new statistical framework for casual inference in evaluating clinical utility of predictive biomarkers and in selecting an optimal treatment for a particular patient. This new casual framework is based on a new concept, called Biomarker Adjusted Treatment Effect （BATE） curve. The BATE curve can be used for assessing clinical utility of a predictive biomarker, for designing a subsequent confirmation trial, and for guiding clinical practice. We then propose semi-p^rametric methods for estimating the BATE curves of biomarkers and establish asymptotic results of the proposed estimators for the BATE curves. We also conduct extensive simulation studies to evaluate finite-sample properties of the proposed estimation methods. Finally, we illustrate the application of the proposed method in a real-world data set.展开更多
文摘Avoiding excessive enrollment of a single cohort in a clinical trial is prudent in order to avoid imbalances and to prevent one cohort from having a disproportionate influence on the results of a trial and perhaps even negating positive findings of the clinical trial. Numerical criteria are provided here to evaluate the expected influence of a large cohort as a function of both its size and the relative effect of interventions, in comparison to those of other groups. Measures of expected influence are obtained as a function of the parameters of the distribution of statistics measuring influence. Calculated numerical criteria for the binomial, continuous and time-to-event contexts are presented. Details of the application of this method and sensitivity analyses conducted during the planning stages of a multiple myeloma clinical trial are provided. Numerical criteria are derived under asymptotic conditions and thus results hold for large cohorts. The numerical criteria are easy to compute and are useful tools to assess possible detrimental effects of large cohorts during the design of a study or during enrollment prior to any un-blinding. The numerical criteria allow for a-priori sensitivity analyses of the likely influence of large cohorts under varying conditions.
文摘Objective:To compare the prognostic factors of mortality among melioidosis patients between lognormal accelerated failure time(AFT),Cox proportional hazards(PH),and Cox PH with time-varying coefficient(TVC)models.Methods:A retrospective study was conducted from 2014 to 2019 among 453 patients who were admitted to Hospital Sultanah Bahiyah,Kedah and Hospital Tuanku Fauziah,Perlis in Northern Malaysia due to confirmed-cultured melioidosis.The prognostic factors of mortality from melioidosis were obtained from AFT survival analysis,and Cox’s models and the findings were compared by using the goodness of fit methods.The analyses were done by using Stata SE version 14.0.Results:A total of 242 patients(53.4%)survived.In this study,the median survival time of melioidosis patients was 30.0 days(95%CI 0.0-60.9).Six significant prognostic factors were identified in the Cox PH model and Cox PH-TVC model.In AFT survival analysis,a total of seven significant prognostic factors were identified.The results were found to be only a slight difference between the identified prognostic factors among the models.AFT survival showed better results compared to Cox's models,with the lowest Akaike information criteria and best fitted Cox-snell residuals.Conclusions:AFT survival analysis provides more reliable results and can be used as an alternative statistical analysis for determining the prognostic factors of mortality in melioidosis patients in certain situations.
文摘In clinical trials, the primary efficacy endpoint often corresponds to a so-called "composite endpoint". Composite endpoints combine several events of interest within a single outcome variable. Thereby it is intended to enlarge the expected effect size and thereby increase the power of the study. However, composite endpoints also come along with serious challenges and problems. On the one hand, composite endpoints may lead to difficulties during the planning phase of a trial with respect to the sample size calculation, asthe expected clinical effect of an intervention on the composite endpoint depends on the effects on its single components and their correlations. This may lead to wrong assumptions on the sample size needed. Too optimistic assumptions on the expected effect may lead to an underpowered of the trial, whereas a too conservatively estimated effect results in an unnecessarily high sample size. On the other hand, the interpretation of composite endpoints may be difficult, as the observed effect of the composite does not necessarily reflect the effects of the single components. Therefore the demonstration of the clinical efficacy of a new intervention by exclusively evaluating the composite endpoint may be misleading. The present paper summarizes results and recommendations of the latest research addressing the above mentioned problems in the planning, analysis and interpretation of clinical trials with composite endpoints, thereby providing a practical guidance for users.
文摘The consideration of the time-varying covariate and time-varying coefficient effect in survival models are plausible and robust techniques. Such kind of analysis can be carried out with a general class of semiparametric transformation models. The aim of this article is to develop modified estimating equations under semiparametric transformation models of survival time with time-varying coefficient effect and time-varying continuous covariates. For this, it is important to organize the data in a counting process style and transform the time with standard transformation classes which shall be applied in this article. In the situation when the effect of coefficient and covariates change over time, the widely used maximum likelihood estimation method becomes more complex and burdensome in estimating consistent estimates. To overcome this problem, alternatively, the modified estimating equations were applied to estimate the unknown parameters and unspecified monotone transformation functions. The estimating equations were modified to incorporate the time-varying effect in both coefficient and covariates. The performance of the proposed methods is tested through a simulation study. To sum up the study, the effect of possibly time-varying covariates and time-varying coefficients was evaluated in some special cases of semiparametric transformation models. Finally, the results have shown that the role of the time-varying covariate in the semiparametric transformation models was plausible and credible.
文摘In randomized clinical trials with right-censored time-to-event outcomes,the popular log-rank test without adjusting for baseline covariates is asymptotically valid for treatment effect under simple randomization of treatments but is too conservative under covariate-adaptive random-ization.The stratified log-rank test,which adjusts baseline covariates in the test procedure by stratification,is asymptotically valid regardless of what treatment randomization is applied.In the literature,however,under simple randomization there is no affirmative conclusion about whether the stratified log-rank test is asymptotically more powerful than the unstratified log-rank test.In this article we show when the stratified and unstratified log-rank tests aim for the same null hypothesis and that,under simple randomization,the stratified log-rank test is asymp-totically more powerful than the unstratified log-rank test in the region of alternative hypothesis that is specified by a Cox proportional hazards model.We also provide some discussion about why we do not have an affirmative conclusion in general.
基金supported by a Core Investigator,Research Career Scientist(Grant No.RCS OS-196)Biostatistics Unit Director at the Northwest HSR&D Center of Excellence,Department of Veterans Affairs Medical Center,Seattle,WA and Department of Veterans Affairs,Veterans Health Administration,Health Services Research and Development Service,project(Grant No.XVA61-036)
文摘In this paper, for time-to-event data, we propose a new statistical framework for casual inference in evaluating clinical utility of predictive biomarkers and in selecting an optimal treatment for a particular patient. This new casual framework is based on a new concept, called Biomarker Adjusted Treatment Effect （BATE） curve. The BATE curve can be used for assessing clinical utility of a predictive biomarker, for designing a subsequent confirmation trial, and for guiding clinical practice. We then propose semi-p^rametric methods for estimating the BATE curves of biomarkers and establish asymptotic results of the proposed estimators for the BATE curves. We also conduct extensive simulation studies to evaluate finite-sample properties of the proposed estimation methods. Finally, we illustrate the application of the proposed method in a real-world data set.
