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Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance
 In Bayesian Statistics 5
, 1995
"... Survival analysis is concerned with finding models to predict the survival of patients or to assess the efficacy of a clinical treatment. A key part of the modelbuilding process is the selection of the predictor variables. It is standard to use a stepwise procedure guided by a series of significanc ..."
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Cited by 39 (12 self)
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Survival analysis is concerned with finding models to predict the survival of patients or to assess the efficacy of a clinical treatment. A key part of the modelbuilding process is the selection of the predictor variables. It is standard to use a stepwise procedure guided by a series of significance tests to select a single model, and then to make inference conditionally on the selected model. However, this ignores model uncertainty, which can be substantial. We review the standard Bayesian model averaging solution to this problem and extend it to survival analysis, introducing partial Bayes factors to do so for the Cox proportional hazards model. In two examples, taking account of model uncertainty enhances predictive performance, to an extent that could be clinically useful. 1 Introduction From 1974 to 1984 the Mayo Clinic conducted a doubleblinded randomized clinical trial involving 312 patients to compare the drug DPCA with a placebo in the treatment of primary biliary cirrhosis...
Bayesian Model Averaging in proportional hazard models: Assessing the risk of a stroke
 Applied Statistics
, 1997
"... Evaluating the risk of stroke is important in reducing the incidence of this devastating disease. Here, we apply Bayesian model averaging to variable selection in Cox proportional hazard models in the context of the Cardiovascular Health Study, a comprehensive investigation into the risk factors for ..."
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Cited by 29 (5 self)
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Evaluating the risk of stroke is important in reducing the incidence of this devastating disease. Here, we apply Bayesian model averaging to variable selection in Cox proportional hazard models in the context of the Cardiovascular Health Study, a comprehensive investigation into the risk factors for stroke. We introduce a technique based on the leaps and bounds algorithm which e ciently locates and ts the best models in the very large model space and thereby extends all subsets regression to Cox models. For each independent variable considered, the method provides the posterior probability that it belongs in the model. This is more directly interpretable than the corresponding Pvalues, and also more valid in that it takes account of model uncertainty. Pvalues from models preferred by stepwise methods tend to overstate the evidence for the predictive value of a variable. In our data Bayesian model averaging predictively outperforms standard model selection methods for assessing
Health effects of gasoline exposure. II. Mortality patterns of distribution workers
 in the United States. Environ. Health Perspect. 101 (Suppl
, 1993
"... In this study, the cohort consisted of 18,135 distribution employees with potential exposure to gasoline for at least one year at landbased terminals (n = 9,026) or on marine vessels (n = 9,109) between 1946 and 1985. The primary objective of the study was to determine the relationshipA if any, bet ..."
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Cited by 10 (1 self)
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In this study, the cohort consisted of 18,135 distribution employees with potential exposure to gasoline for at least one year at landbased terminals (n = 9,026) or on marine vessels (n = 9,109) between 1946 and 1985. The primary objective of the study was to determine the relationshipA if any, between exposure to gasoline and mortality from kidney cancer or leukemia. In addition, other causes of death of secondary interest included multiple myeloma and heart diseases. The mortality of the cohort was observed through June 30,1989. The results of this study indicated that there was no increased mortality from either kidney cancer or leukemia among marketing and marine distribution employees who were exposed to gasoline in the petroleum industry when compared to the general population. Among the landbased terminal employees, the kidney cancer standardized mortality ratio (SMR) was 65.4 (12 deaths) and leukemia SMR was 89.1 (27 deaths). For the marine cohort, the SMRs were 83.7 for kidney cancer (12 deaths) and 70.0 for leukemia (16 deaths), respectively. More importantly, based on internal comparisons, there was no association between mortality from kidney cancer or leukemia and various indices of gasoline exposure. In particular, neither duration of employment,
A SAS Macro For Estimation Of Direct Adjusted Survival Curves Based On A Stratified Cox Regression Model
"... Abstract. Often in biomedical research the aim of a study is to compare the outcomes of several treatment arms while adjusting for multiple clinical prognostic factors. In this paper we focus on computation of the direct adjusted survival curves for different treatment groups based on an unstratifie ..."
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Cited by 2 (0 self)
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Abstract. Often in biomedical research the aim of a study is to compare the outcomes of several treatment arms while adjusting for multiple clinical prognostic factors. In this paper we focus on computation of the direct adjusted survival curves for different treatment groups based on an unstratified or a stratified Cox model. The estimators are constructed by taking the average of the individual predicted survival curves. The method of direct adjustment controls for possible confounders due to an imbalance of patient characteristics between treatment groups. This adjustment is especially useful for nonrandomized studies. We have written a SAS Macro to estimate and compare the direct adjusted survival curves. We illustrate the SAS Macro through the examples analyzing stem cell transplant data and Ewingâ€™s sarcoma data.
Confidence bands for the difference of two survival curves under proportional hazards model
, 1998
"... A common approach to testing for differences between the survival rates of two therapies is to use a proportional hazards regression model which allows for an adjustment of the two survival functions for any imbalance in prognostic factors in the comparison. An alternative approach to this problem i ..."
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Cited by 1 (1 self)
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A common approach to testing for differences between the survival rates of two therapies is to use a proportional hazards regression model which allows for an adjustment of the two survival functions for any imbalance in prognostic factors in the comparison. An alternative approach to this problem is to plot the difference between the two predicted survival functions with a confidence band that provides information about when these two treatments differ. Such a band will depend on the covariate values of a given patient. In this paper we show how to construct a confidence band for the difference of two survival functions based on the proportional hazards model. A simulation approach is used to generate the bands. This approach is used to compare the survival probabilities of chemotherapy and allogeneic bone marrow transplants for chronic leukemia.
Regression Diagnostics: Mechanical and Structural Aspects . . .
, 1986
"... One of the problems that can undermine the estimation of parameters for a statistical model is when two or more of the explanatory variables are correlated. The presence of this collinearity causes an instability in the estimation of the parameters in the model and may even cause a change in the sig ..."
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One of the problems that can undermine the estimation of parameters for a statistical model is when two or more of the explanatory variables are correlated. The presence of this collinearity causes an instability in the estimation of the parameters in the model and may even cause a change in the sign of the estimate. Another important consideration wnen building a statistical model, is the assessment of the individual effect of each observation on the final parameter estimate. In order to assess the potential effect of collinearity on the parameter estimates, the data matrix (X) is evaluated. When the Xmatrix is illconditioned, it is computationally mo;e difficult to estimate the model parameters. Assessing the severity of the computational problems gives a measure of the collinearity in the data. In this
Statistical Methods for Censored Survival Data
"... Methods of statistical analysis of censored survival times are brieflyreviewed and illustrated by application to clinical trials data. These include estimation of the survival curve, nonparametric tests to compare several survival curves, tests for trend, and regression analysis. Extensions of the ..."
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Methods of statistical analysis of censored survival times are brieflyreviewed and illustrated by application to clinical trials data. These include estimation of the survival curve, nonparametric tests to compare several survival curves, tests for trend, and regression analysis. Extensions of the methodology are made for application to epidemiologic casecontrol studies. These are used to estimate relative risks for leukemia asociated with radiation exposures. A final section provides some annotated references to the recent literature.
Determining When One Treatment is Different From Another Based On A Censored Data Regression Model
"... Often when comparing the survival rates of individuals given either of two treatments the analysis stops with a test of the hypothesis of no treatment difference and perhaps a plot of the two survival functions. The hypothesis test is usually a comparison of the two survival curves over the entire o ..."
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Often when comparing the survival rates of individuals given either of two treatments the analysis stops with a test of the hypothesis of no treatment difference and perhaps a plot of the two survival functions. The hypothesis test is usually a comparison of the two survival curves over the entire observational period. An alternative approach to this problem is to provide an investigator with a confidence set for the set of times at which the survival rates of the two treatments are the same. We discuss how such confidence sets can be constructed when the proportional hazards or additive regression model is used to adjust the comparison of interest for other factors which may influence survival. These approaches are illustrated on retrospective data gathered to compare the survival rates of allogeneic and autologous bone marrow transplants for acute leukemia.
RESEARCH ARTICLE Open Access
"... to handle mortality when investigating length of hospital stay and time to clinical stability ..."
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to handle mortality when investigating length of hospital stay and time to clinical stability