Comput Math Methods Med - Survival data analysis with time-dependent covariates using generalized additive models.

Tópicos

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{ chang(1828) time(1643) increas(1301) }
{ model(2341) predict(2261) use(1141) }
{ model(2220) cell(1177) simul(1124) }
{ data(3963) clinic(1234) research(1004) }
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{ age(1611) year(1155) adult(843) }
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{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ analysi(2126) use(1163) compon(1037) }
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Resumo

We discuss a flexible method for modeling survival data using penalized smoothing splines when the values of covariates change for the duration of the study. The Cox proportional hazards model has been widely used for the analysis of treatment and prognostic effects with censored survival data. However, a number of theoretical problems with respect to the baseline survival function remain unsolved. We use the generalized additive models (GAMs) with B splines to estimate the survival function and select the optimum smoothing parameters based on a variant multifold cross-validation (CV) method. The methods are compared with the generalized cross-validation (GCV) method using data from a long-term study of patients with primary biliary cirrhosis (PBC).

Resumo Limpo

discuss flexibl method model surviv data use penal smooth spline valu covari chang durat studi cox proport hazard model wide use analysi treatment prognost effect censor surviv data howev number theoret problem respect baselin surviv function remain unsolv use general addit model gam b spline estim surviv function select optimum smooth paramet base variant multifold crossvalid cv method method compar general crossvalid gcv method use data longterm studi patient primari biliari cirrhosi pbc

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