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The performance of some two-sample survival time tests in small samples with censoring

Stephan Ogenstad

The performance of some two-sample survival time tests in small samples with censoring

by Stephan Ogenstad

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Published by Dept. of Statistics, Univ. of Stockholm in Stockholm .
Written in English


The Physical Object
Pagination30 p.
Number of Pages30
ID Numbers
Open LibraryOL24715259M

The example you love to hate I Let f(T i; i)gn i=1 denote observation times subject to non-informative right-censoring I Assume the true survival distribution is exponential with density f(t;) = 1 exp t= construct a 95% CI for I Answer: on board I Problem! The log-LH is not approximated well by a quadratic in small-samples when the amount of censoring is largeFile Size: KB. type of censoring is right censoring when the observed time is shorter than the actual failure time. There are other types of censoring, such as left censoring and interval censoring. Our focus in this thesis is on the case of right censoring. Before introducing models to analyze survival data, we first give the notation of the survival data.

  Length of testFor type ll testing, length of the test depends on no. of units being tested no. of failures to be observed time to failure ed test time to generate r failures is r x MTTFUnder CFR model if n units are placed on test until r failures are observed the expected test time.   Machine Learning for Survival Analysis The observed event time of the uncensored instance is smaller than the censoring time of the censored instance. A node of a survival tree is considered “pure” if all the patients in the node survive for an identical span of time. The logrank test is most commonly used dissimilarity measure that.

SURVIVAL/FAILURE ANALYSIS Rafael Hidalgo Gonzalez HISTORY Peter L. Berstein in his book ‘Against the Gods the remarkable story of risk’ narrates how the small book published in London and titled Natural and Political Obsrvations made upon the Bills of Mortality made history. The book contained a compilation of birth and deaths in London from to Mann -Whitney test The means of 2 paired (matched) samples e.g. weight before and after a diet for one group of subjects Continuous/ scale Time variable (time 1 = before, time 2 = after) Paired t-test Wilcoxon signed rank test The means of 3+ independent groups Continuous/ scale Categorical/ nominal One-way ANOVA Kruskal-Wallis testFile Size: KB.


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The performance of some two-sample survival time tests in small samples with censoring by Stephan Ogenstad Download PDF EPUB FB2

Motivated by the poor performance of the log-rank test in settings where the sample sizes in one or both groups is small and where the underlying censoring distributions of the groups may differ, and by the lack of interval estimation methods for such settings, we develop 2 methods by adapting hypothetical permutation methods that could be used when the censoring distributions in 2 groups were equal or when the underlying survival and censoring times Cited by: Besides, the standard assumption that survival time and censoring time are conditionally independent given the treatment, required for the regular two-sample tests, may not be realistic in observational studies.

Moreover, treatment-specific hazards are often non-proportional, resulting in small power for the log-rank by: 3. The powers of several two-sample tests are compared by simulation for small samples from exponential and Weibull distributions with and without censoring.

The tests con-sidered include the F test, a modification for samples that are from Weibull distributions, Cox's test, Peto & Peto's log rank test, their generalized Wilcoxon test, a modified log.

1. I NTRODUCTION. The log-rank test and virtually equivalent score, likelihood ratio, or Wald tests arising from fitting Cox's proportional hazards model (Cox, ; Peto and Peto, ; Klein and Moeschberger, ) are the most commonly used statistical methods for comparing 2 groups with respect to a time-to-event end tests are computationally simple to evaluate, Cited by: More powerful logrank permutation tests for two-sample survival data and robustness under unequal censoring in both samples.

At the same time the power of the conditional and unconditional. the two-sample cases studied the Mantel-Haenszel statistic and other nonparametric methods provide counterparts to the tests based on the partial likelihood.

Small-sample properties of some of these test statistics have been examined (see Lee, Desu and Gehan, ). Some key results from our Monte Carlo study are now summarized briefly.

Table 1 The empirical type I error rates of the tests at the significance level of when the survival times have the same uniform, exponential, or log-normal distributions with sample sizes n 1 =n 2 =n 3 =n 4 = (simulation study w replicates for.

Median sample size in the surveyed articles was (range ), and the median proportion of censoring was 60% (range %). This note shows that values of median follow-up may differ substantially depending on the method used.

Results of survival analysis apply to the time frame in which most of the individuals were by: order kis a general way of constructing statistical tests. Here I develop such smooth tests for some two-sample problems and some tests of t in regression models. Neyman’s embedding idea is one of two main ingredients applied in this thesis.

The second one is the idea of data-driven tests which is due to Ledwina (). Data-driven tests were 1. We then discuss the two-sample problem and the usage of the log-rank test for comparing survival distributions between groups.

Lastly, we discuss in some detail the proportional hazards model, which is a semiparametric regression model specifically developed for censored data. All methods are illustrated with artificial or real data sets. Abstract. A theoretical analysis is made of the properties of various methods for comparing two distributions of survival time.

The results are intended primarily to guide the choice of method of analysis for such simple comparisons as of a treatment versus a control, but the main implications are fairly general, illustrating the performance of different models in a range of : Christiana Kartsonaki, D.

Cox. Survival analysis is a branch of statistics, which is focussed on the analysis of time- to-event data. In multivariate survival analysis, the proportional hazards (PH) is the most popular model in.

Inference on Weibull Parameters Under a Balanced Two Sample Type-II Progressive Censoring Scheme Shuvashree Mondal 1, Debasis Kundu;2 Abstract The progressive censoring scheme has received considerable amount of attention in the last fteen years.

During the last few years joint progressive censoring scheme has gained some by: 1. The sampling distribution of V in (3) under the null hypothesis is hard to derive explicitly since are correlated. However, its p‐value can be estimated using bootstrap (Qiu and Sheng, ).

Next, we want to generalize the statistics U and V to cases with multiple hazard rate functions (i.e.,).To this end, we will obtain pairs of U and V through comparing two hazard rate functions by: 7.

The Use of Survival Analysis Techniques Among Highly Censored Data Sets. Shelby Marie Cummings. Ap Abstract. The purpose of this research project was to look at the various survival analysis techniques and determine if there was either a way of fixing these methods or a better method to use in the case of data sets with a large percentage of censored data points.

Two commonly used tests for comparison of survival curves are the generalized Wilcoxon procedure of Gehan () and Breslow () and the logrank test proposed by Mantel () and Cox ().

In applications, the logrank test is often used after checking for validity of the proportional hazards (PH) assumption, with Wilcoxon being the fallback method when the PH Cited by: Furthermore, hazard functions of commonly used Survival distributions are described.

Some of the under rated areas due to lack of software’s are also discussed. Key words: Censoring, Cox regression model, Failure time, Kaplan-Meier survival function, Logrank test, Proportional hazards assumption Corresponding author:File Size: KB.

Nonparametric generalized ducial inference for survival functions under censoring and in some cases superior performance to the methods in the literature. In particular, (Fay et al.,;Fay and Brittain,) in various settings with small samples and/or heavy censoring. Additionally we also consider the setting ofBarber and Jennison Author: Yifan Cui, Jan Hannig.

Prognostic studies of time-to-event data, where researchers aim to develop or validate multivariable prognostic models in order to predict survival, are commonly seen in the medical literature; however, most are performed retrospectively and few consider sample size prior to analysis.

Events per variable rules are sometimes cited, but these are based on bias and coverage of confidence. where is the largest survival time less than or equal to t and is the number of subjects alive just before time (the ith ordered survival time), denotes the number who died at time where i can be any value between 1 and p.

For censored observations = 0. Method. Order the survival time by increasing duration starting with the shortest one. The book demonstrates the advantages of the copula-based methods in the context of medical research, especially with regard to cancer patients’ survival data.

Needless to say, the statistical methods presented here can also be applied to many other branches of science, especially in reliability, where survival analysis plays an important role.Increasing hazard ratio; Two-sample problem. 1 Introduction The proportional hazards (PH) assumption has been used widely for modeling and analysis of survival data.

A test of this assumption is not only an important two-sample problem, it is also relevant as a diagnostic .The log-rank test is a hypothesis test to compare the survival distributions of two samples. Log-rank test 2.

They used the log rank test for the equality of survivor functions to determine whether there was a significant difference (p.