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International audienceThe simultaneous modeling of ageing and maintenance efficiency of repairable systems is a major issue in reliability. Many imperfect maintenance models have been proposed. To analyze a dataset, it is necessary to check whether these models are adapted or not. In this paper, we propose a general methodology for testing the goodness of fit of any kind of imperfect maintenance model. Two families of tests are presented, based respectively on martingale residuals and probability integral transforms. The quantiles of the test statistics distributions under the null hypothesis are computed with parametric bootstrap methods. An extensive simulation study is provided, from which we recommend the use of two tests in practice, one from each family. Finally, the tests are applied to several real datasets

We describe a unified framework within which we can build survival models. The motivation for this work comes from a study on the prediction of relapse among breast cancer patients treated at the Curie Institute in Paris, France. Our focus is on how to best code, or characterize, the effects of the variables, either alone or in combination with others. We consider simple graphical techniques that not only provide an immediate indication as to the goodness of fit but, in cases of departure from model assumptions, point in the direction of a more involved alternative model. These techniques help support our intuition. This intuition is backed up by formal theorems that underlie the process of building richer models from simpler ones. Goodness-of-fit techniques are used alongside measures of predictive strength and, again, formal theorems show that these measures can be used to help identify models closest to the unknown non-proportional hazards mechanism that we can suppose generates the observations. We consider many examples and show how these tools can be of help in guiding the practical problem of efficient model construction for survival data.

Assuming some regression model, it is common to study the conditional distribution of survival given covariates. Here, we consider the impact of further conditioning, specifically conditioning on a marginal survival function, known or estimated. We investigate to what purposes any such information can be used in a proportional or non-proportional hazards regression analysis of time on the covariates. It does not lead to any improvement in efficiency when the form of the assumed proportional hazards model is valid. However, when the proportional hazards model is not valid, the usual partial likelihood estimator is not consistent and depends heavily on the unknown censoring mechanism. In this case we show that the conditional estimate that we propose is consistent for a parameter that has a strong interpretation independent of censoring. Simulations and examples are provided.