Model-based estimation of measures of association for time-to-event outcomes

BMC Medical Research Methodology

Patrizia Boracchi²  Elia Biganzoli¹  Federico Ambrogi² 

[1] Fondazione IRCCS Istituto Nazionale Tumori, Via Venezian 1, 20133 Milan, Italy;Department of Clinical Sciences and Community Health, University of Milan, Via Venezian 1, 20133 Milan, Italy

关键词: Numbers needed to treat;    Link functions;    Pseudo-values;    Transformation models;    Survival analysis;   

Others  :  1091255
DOI  :  10.1186/1471-2288-14-97

 received in 2013-12-23, accepted in 2014-07-25,  发布年份 2014

【 摘 要 】

Background

Hazard ratios are ubiquitously used in time to event applications to quantify adjusted covariate effects. Although hazard ratios are invaluable for hypothesis testing, other adjusted measures of association, both relative and absolute, should be provided to fully appreciate studies results. The corrected group prognosis method is generally used to estimate the absolute risk reduction and the number needed to be treated for categorical covariates.

Methods

The goal of this paper is to present transformation models for time-to-event outcomes to obtain, directly from estimated coefficients, the measures of association widely used in biostatistics together with their confidence interval. Pseudo-values are used for a practical estimation of transformation models.

Results

Using the regression model estimated through pseudo-values with suitable link functions, relative risks, risk differences and the number needed to treat, are obtained together with their confidence intervals. One example based on literature data and one original application to the study of prognostic factors in primary retroperitoneal soft tissue sarcomas are presented. A simulation study is used to show some properties of the different estimation methods.

Conclusions

Clinically useful measures of treatment or exposure effect are widely available in epidemiology. When time to event outcomes are present, the analysis is performed generally resorting to predicted values from Cox regression model. It is now possible to resort to more general regression models, adopting suitable link functions and pseudo values for estimation, to obtain alternative measures of effect directly from regression coefficients together with their confidence interval. This may be especially useful when, in presence of time dependent covariate effects, it is not straightforward to specify the correct, if any, time dependent functional form. The method can easily be implemented with standard software.

【 授权许可】

   
2014 Ambrogi et al.; licensee BioMed Central Ltd.

【 图 表 】

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