Lifetime Data Anal - The k-sample problem in a multi-state model and testing transition probability matrices.

Tópicos

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Resumo

The choice of multi-state models is natural in analysis of survival data, e.g., when the subjects in a study pass through different states like 'healthy', 'in a state of remission', 'relapse' or 'dead' in a health related quality of life study. Competing risks is another common instance of the use of multi-state models. Statistical inference for such event history data can be carried out by assuming a stochastic process model. Under such a setting, comparison of the event history data generated by two different treatments calls for testing equality of the corresponding transition probability matrices. The present paper proposes solution to this class of problems by assuming a non-homogeneous Markov process to describe the transitions among the health states. A class of test statistics are derived for comparison of [Formula: see text] treatments by using a 'weight process'. This class, in particular, yields generalisations of the log-rank, Gehan, Peto-Peto and Harrington-Fleming tests. For an intrinsic comparison of the treatments, the 'leave-one-out' jackknife method is employed for identifying influential observations. The proposed methods are then used to develop the Kolmogorov-Smirnov type supremum tests corresponding to the various extended tests. To demonstrate the usefulness of the test procedures developed, a simulation study was carried out and an application to the Trial V data provided by International Breast Cancer Study Group is discussed.

Resumo Limpo

choic multist model natur analysi surviv data eg subject studi pass differ state like healthi state remiss relaps dead health relat qualiti life studi compet risk anoth common instanc use multist model statist infer event histori data can carri assum stochast process model set comparison event histori data generat two differ treatment call test equal correspond transit probabl matric present paper propos solut class problem assum nonhomogen markov process describ transit among health state class test statist deriv comparison formula see text treatment use weight process class particular yield generalis logrank gehan petopeto harringtonflem test intrins comparison treatment leaveoneout jackknif method employ identifi influenti observ propos method use develop kolmogorovsmirnov type supremum test correspond various extend test demonstr use test procedur develop simul studi carri applic trial v data provid intern breast cancer studi group discuss

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