PubMed · 10382180
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M Fishman, M Brush, P Frey, C Welles. 1999. Testing forms with transplant protocols.. https://pubmed.ncbi.nlm.nih.gov/10382180/
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Although intensive chemotherapy has improved event-free survival for most children with lymphoblastic leukaemia there remain up to 10% who have not benefited from this approach. These include infants, children with Ph1 positive leukaemia, with near-haploidy, and slow remitters in most of whom event free survival remains below 40%. Evaluation of the benefits of Bone Marrow Transplantation in high risk ALL is fraught with difficulties and to date has not produced clear evidence of benefit. The way forward lies in prospective evaluation of BMT in tightly defined subsets of highest risk children, a task which will require international collaboration.
An important but difficult problem in clinical trials is to determine the presence of cured patients when long-term survivors are observed. The likelihood ratio test has been studied for this purpose in the gamma mixture model. However, its asymptotic null distribution is not readily available due to a violation of boundary conditions in the standard asymptotic theory. In this paper, a simulation study is employed to examine a proposed asymptotic null distribution of the likelihood ratio test. We find that the distribution can also be used to approximate the asymptotic null distribution of the likelihood ratio test in the Weibull and log-normal mixture models when the censoring rate is not too light. However, the simulation study also shows that null distribution of the likelihood ratio test deviates significantly from the suggested distribution under moderate sample sizes when the censoring rate is small or the hazard rate is large. Consequently caution is needed in this case to determine the presence of cured patients. Finally, the results are used to confirm the presence of cured patients in a leukaemia study.
Multi-state models have proved versatile and useful in the statistical analysis of the complicated course of events after bone marrow transplantation. Working from data from the International Bone Marrow Transplant Registry, we show that summary probability calculations may be useful to explore hypothetical scenarios where some transition intensities are set by the researcher. A multi-state Markov process model is specified with six states: the initial state 0; acute; chronic and both acute and chronic graft-versus-host disease A, C and AC; relapse R and death in remission D. Transition rates between the states are estimated using Nelson-Aalen estimators and Cox regression models and combined to transition probability estimators using Aalen-Johansen product integration. Besides the estimated transition probabilities to D and R we explore hypothetical probabilities obtained by artificially changing certain transition intensities, with the general purposes of getting summary views of the development for actual patients 'in this world' and of exploring the intrinsic information from real patients about consequences of various changed conditions.