This paper describes a method proposed for a censored linear regression model that can be used in the context of survival analysis. The method has the important characteristic of allowing estimation and inference without knowing the distribution of the duration variable. Moreover, it does not need the assumption of proportional hazards. Therefore, it can be an interesting alternative to the Cox proportional hazards models when this assumption does not hold. In addition, implementation and interpretation of the results is simple. In order to analyse the performance of this methodology, we apply it to two real examples and we carry out a simulation study. We present its results together with those obtained with the traditional Cox model and AFT parametric models. The new proposal seems to lead to more precise results.

SummaryIn this paper we propose a new method to estimate nonparametrically a time varying parameter model when some qualitative information from outside data (e.g. seasonality) is available. In this framework we make two main contributions. First, the resulting estimator is shown to belong to the class of generalized ridge estimators and under some conditions its rate of convergence is optimal within its smoothness class. Furthermore, if the outside data information is full lled by the underlying model, the estimator shows e ciency gains in small sample sizes. Second, for the implementation process, since the estimation procedure envolves the computation of the inverse of a high order matrix we provide an algorithm that avoids this computation and, also, a data-driven method is derived to select the control parameters. The practical performance of the method is demonstrated in a simulation study and in an application to the demand of soft drinks in Canada.

In this work we study the effect of several covariates on a censored response variable with unknown probability distribution. A semiparametric model is proposed to consider situations where the functional form of the effect of one or more covariates is unknown, as is the case in the application presented in this work. We provide its estimation procedure and, in addition, a bootstrap technique to make inference on the parameters. A simulation study has been carried out to show the good performance of the proposed estimation process and to analyse the effect of the censorship. Finally, we present the results when the methodology is applied to AIDS diagnosed patients.

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We analyzed whether male Spanish elite soccer players live longer than the general population. Secondly, we compared their mortality with a cohort of soccer players who continued working as soccer elite coaches after retirement. Using age and calendar-date adjusted life tables, we analyzed the mortality hazard ratio of 1333 Spanish male players born before 1950, and who played in elite leagues from 1939, compared with the Spanish population. Using Cox proportional hazards model we compared their mortality with a cohort of 413 players who continued as coaches. Players showed significantly lower mortality than the general population, but this advantage decreased with advanced age, disappearing after 80 years. Coaches showed a similar pattern. Comparing players versus coaches, date of birth and years as professional were associated with survival, but debut age and player position were not. Unadjusted median survival time was 79.81 years (IQR 72.37–85.19) for players and 81.8 years (IQR 74.55–86.73) for coaches. Kaplan-Meier estimator adjusted for covariables showed no difference between cohorts (p=0.254). In conclusion, former Spanish male players showed lower mortality than the general population, but this effect disappeared after 80 years of age. Continuing their career as coaches after retirement from playing did not confer major benefits.