%0 Journal Article %1 Bull1997 %A Bull, K %A Spiegelhalter, D J %D 1997 %J Statistics in medicine %K Algorithms Humans LogisticModels MathematicalComputing Models OutcomeandProcessAssessment(HealthCare) OutcomeandProcessAssessment(HealthCare):stat ProportionalHazardsModels PulmonaryAtresia RandomAllocation SelectionBias Statistical StatisticsasTopic StatisticsasTopic:methods SurvivalAnalysis TimeFactors %N 9 %P 1041-74 %T Survival analysis in observational studies. %U http://www.ncbi.nlm.nih.gov/pubmed/9160498 %V 16 %X Multi-centre databases are making an increasing contribution to medical understanding. While the statistical handling of randomized experimental studies is well documented in the medical literature, the analysis of observational studies requires the addressing of additional important issues relating to the timing of entry to the study and the effect of potential explanatory variables not introduced until after that time. A series of analyses is illustrated on a small data set. The influence of single and multiple explanatory variables on the outcome after a fixed time interval and on survival time until a specific event are examined. The analysis of the effect on survival of factors that only come into play during follow-up is then considered. The aim of each analysis, the choice of data used, the essentials of the methodology, the interpretation of the results and the limitations and underlying assumptions are discussed. It is emphasized that, in contrast to randomized studies, the basis for selection and timing of interventions in observational studies is not precisely specified so that attribution of a survival effect to an intervention must be tentative. A glossary of terms is provided.

@article{Bull1997, abstract = {Multi-centre databases are making an increasing contribution to medical understanding. While the statistical handling of randomized experimental studies is well documented in the medical literature, the analysis of observational studies requires the addressing of additional important issues relating to the timing of entry to the study and the effect of potential explanatory variables not introduced until after that time. A series of analyses is illustrated on a small data set. The influence of single and multiple explanatory variables on the outcome after a fixed time interval and on survival time until a specific event are examined. The analysis of the effect on survival of factors that only come into play during follow-up is then considered. The aim of each analysis, the choice of data used, the essentials of the methodology, the interpretation of the results and the limitations and underlying assumptions are discussed. It is emphasized that, in contrast to randomized studies, the basis for selection and timing of interventions in observational studies is not precisely specified so that attribution of a survival effect to an intervention must be tentative. A glossary of terms is provided.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Bull, K and Spiegelhalter, D J}, biburl = {https://www.bibsonomy.org/bibtex/24dc5dcc1ebf46c6a9becb1a982735867/jepcastel}, interhash = {b6b68516edf0e879365b0918529e5874}, intrahash = {4dc5dcc1ebf46c6a9becb1a982735867}, issn = {0277-6715}, journal = {Statistics in medicine}, keywords = {Algorithms Humans LogisticModels MathematicalComputing Models OutcomeandProcessAssessment(HealthCare) OutcomeandProcessAssessment(HealthCare):stat ProportionalHazardsModels PulmonaryAtresia RandomAllocation SelectionBias Statistical StatisticsasTopic StatisticsasTopic:methods SurvivalAnalysis TimeFactors}, month = {5}, note = {2873<m:linebreak></m:linebreak>Anàlisi de supervivència}, number = 9, pages = {1041-74}, pmid = {9160498}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Survival analysis in observational studies.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9160498}, volume = 16, year = 1997 }