The Effect of 6-Mercaptopurine on the Duration of Steroid-induced Remissions in Acute Leukemia: A Model for Evaluation of Other Potentially Useful Therapy

Freireich, Emil J.; Gehan, Edmund A.; Frei, Emil; Schroeder, Leslie R.; Wolman, Irving J.; Anbari, Rachad; Burgert, E. Omar; Mills, Stephen D.; Pinkel, Donald; Selawry, Oleg S.; Moon, John H.; Gendel, Benjamin R.; Spurr, Charles L.; Storrs, Robert; Haurani, Farid I.; Hoogstraten, Barth; Lee, Stanley Chun-Wei

doi:10.1182/blood.v21.6.699.699

Cited by 264 publications

(19 citation statements)

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“…A presentation of the different methods available for estimating RMST, applied to the classical Freireich data [ 28 ], can be found in the supplementary material [see Additional file 1 ].…”

Section: Methodsmentioning

confidence: 99%

Analyzing differences between restricted mean survival time curves using pseudo-values

Ambrogi

Iacobelli

Andersen

2022

BMC Med Res Methodol

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Hazard ratios are ubiquitously used in time to event analysis to quantify treatment effects. Although hazard ratios are invaluable for hypothesis testing, other measures of association, both relative and absolute, may be used to fully elucidate study results. Restricted mean survival time (RMST) differences between groups have been advocated as useful measures of association. Recent work focused on model-free estimates of the difference in restricted mean survival through follow-up times, instead of focusing on a single time horizon. The resulting curve can be used to quantify the association in time units with a simultaneous confidence band. In this work a model-based estimate of the curve is proposed using pseudo-values allowing for possible covariate adjustment. The method is easily implementable with available software and makes possible to compute a simultaneous confidence region for the curve. The pseudo-values regression using multiple restriction times is in good agreement with the estimates obtained by standard direct regression models fixing a single restriction time. Moreover, the proposed method is flexible enough to reproduce the results of the non-parametric approach when no covariates are considered. Examples where it is important to adjust for baseline covariates will be used to illustrate the different methods together with some simulations.

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“…A presentation of the different methods available for estimating RMST, applied to the classical Freireich data [ 28 ], can be found in the supplementary material [see Additional file 1 ].…”

Section: Methodsmentioning

confidence: 99%

Analyzing differences between restricted mean survival time curves using pseudo-values

Ambrogi

Iacobelli

Andersen

2022

BMC Med Res Methodol

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“…To further elaborate more these methods, the second group of the same leukemia data is considered, which consists of events only. The leukemia data are taken from Freireich et al ( 8 ). The dataset is given below:…”

Section: Analysis Of Datasetsmentioning

confidence: 99%

Methods for Improving the Variance Estimator of the Kaplan–Meier Survival Function, When There Is No, Moderate and Heavy Censoring-Applied in Oncological Datasets

Khan

Zaman

Azmi

et al. 2022

Front. Public Health

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In case of heavy and even moderate censoring, a common problem with the Greenwood and Peto variance estimators of the Kaplan–Meier survival function is that they can underestimate the true variance in the left and right tails of the survival distribution. Here, we introduce a variance estimator for the Kaplan–Meier survival function by assigning weight greater than zero to the censored observation. On the basis of this weight, a modification of the Kaplan–Meier survival function and its variance is proposed. An advantage of this approach is that it gives non-parametric estimates at each point whether the event occurred or not. The performance of the variance of this new method is compared with the Greenwood, Peto, regular, and adjusted hybrid variance estimators. Several combinations of these methods with the new method are examined and compared on three datasets, such as leukemia clinical trial data, thalassaemia data as well as cancer data. Thalassaemia is an inherited blood disease, very common in Pakistan, where our data are derived from.

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“…The MatSurv output is comparable to that from proc lifetest in SAS and ggsurvplot in R. Code for reproducing similar output in R and SAS are shown below as well as the output from all 3 statistical programs (R, SAS and MatSurv). The data used is from a classic and frequently used example by Freireich (Freireich et al, 1963). In addition, we have also used the Acute Myeloid Leukemia (LAML) dataset (Ley et al, 2013) from The Cancer Genome Atlas (TCGA).…”

Section: Comparisonmentioning

confidence: 99%

MatSurv: Survival analysis and visualization in MATLAB

Creed¹,

Gerke²,

Berglund³

2020

JOSS

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Survival analysis is a set of methods for evaluating time-to-event data that is widely applied across research disciplines. For example, it is commonly used in clinical trials to compare the effect of treatments. In cancer biology, it can be used to understand how low-or highexpression of genes affect the aggressiveness of the tumor. Time-to-event data frequently include censored data points, samples where no event was observed. An event is, for example, death, relapse of disease, or a new metastatic tumor. If none of these events occur during the study period, the time to-to-event is unknown, we only know that no events were observed during the study time. The methods described below were developed for this kind of data. For an in-depth introduction to survival analysis, we can recommend the book by Kleinbaum and David (Kleinbaum, 1998). In fact, much of the code used in MatSurv is based on the equations given in the book. Commonly reported elements of survival analysis include log-rank tests, hazard ratios (HR) and Kaplan-Meier (KM) curves. KM-curves are used to compare survival durations between two or more groups and give users a particular estimate of survival probability at a given time; log-rank tests are used to conduct statistical inference on survival durations between groups; and HRs provide a ratio of the hazard rates between groups. To further improve the KM-plot, it has been suggested that the KM-plot should always be accomplished by a table that describes the number of patients that are still "at-risk" at a specific timepoint (Morris et al., 2019). MATLAB (MATLAB, 2019) currently lacks functions to easily create KM-plots with accompanying risk tables. Furthermore, MATLAB does not have a built-in log-rank test, nor is one available in any of the existing toolboxes, including the Statistics and Machine Learning Toolbox. The Statistics and Machine Learning Toolbox support Cox proportional hazards regression using the coxphfit function and KM-plots can be created using the plot or stairs functions. Our goal for MatSurv is to provide an easy-to-use tool that creates publication quality KM-plots with corresponding risk tables. The statistical procedures built into MatSurv can be used to compare two or multiple groups. In addition, MatSurv allows the user to easily modify the appearance of the created figure. The graphics were inspired by the survminer R-package (Kassambara, 2018).

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The Effect of 6-Mercaptopurine on the Duration of Steroid-induced Remissions in Acute Leukemia: A Model for Evaluation of Other Potentially Useful Therapy

Cited by 264 publications

References 0 publications

Analyzing differences between restricted mean survival time curves using pseudo-values

Analyzing differences between restricted mean survival time curves using pseudo-values

Methods for Improving the Variance Estimator of the Kaplan–Meier Survival Function, When There Is No, Moderate and Heavy Censoring-Applied in Oncological Datasets

MatSurv: Survival analysis and visualization in MATLAB

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