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Literature DB >> 2068426

A note on the calculation of expected survival, illustrated by the survival of liver transplant patients.

Abstract

Based on historical regression analyses of survival one may calculate expected survival curves for patients in a study group, based on their observed covariates. This note discusses this calculation and illustrates it on a comparison of the survival of a certain group of liver transplant patients to their prognosis under conservative treatment.

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Year: 1991 PMID： 2068426 DOI： 10.1002/sim.4780100508

Source DB: PubMed Journal: Stat Med ISSN： 0277-6715 Impact factor: 2.373

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Cited

11 in total

1. Assessing effects on long-term survival after early termination of randomized trials.

Authors: Y Shen; T R Fleming
Journal: Lifetime Data Anal Date: 1999 Impact factor: 1.588

2. Adjusting and comparing survival curves by means of an additive risk model.

Authors: P H Zahl; O O Aalen
Journal: Lifetime Data Anal Date: 1998 Impact factor: 1.588

Review 3. Historical controls and modern survival analysis.

Authors: N Keiding
Journal: Lifetime Data Anal Date: 1995 Impact factor: 1.588

4. Scoring system for nosocomial pneumonia in ICUs.

Authors: A Kropec; G Schulgen; H Just; K Geiger; M Schumacher; F Daschner
Journal: Intensive Care Med Date: 1996-11 Impact factor: 17.440

5. Survival of anti-mitochondrial antibody-positive and -negative primary biliary cirrhosis patients on ursodeoxycholic acid treatment.

Authors: Meri Koulentaki; Joanna Moscandrea; Philipos Dimoulios; Costas Chatzicostas; Elias A Kouroumalis
Journal: Dig Dis Sci Date: 2004-08 Impact factor: 3.199

A note on the calculation of expected survival, illustrated by the survival of liver transplant patients.

1. Assessing effects on long-term survival after early termination of randomized trials.

2. Adjusting and comparing survival curves by means of an additive risk model.

Review 3. Historical controls and modern survival analysis.

4. Scoring system for nosocomial pneumonia in ICUs.

5. Survival of anti-mitochondrial antibody-positive and -negative primary biliary cirrhosis patients on ursodeoxycholic acid treatment.

6. Extending the Peters-Belson approach for assessing disparities to right censored time-to-event outcomes.

7. Landmark estimation of survival and treatment effects in observational studies.

8. Survival data analysis with time-dependent covariates using generalized additive models.

Review 9. Estimating relative survival among people registered with cancer in England and Wales.

10. Analysis of heart transplant survival data using generalized additive models.