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Use of clinical trial data to calculate attributable risks
Abstracts
245 management system that allows the clinical center to perform a variety of functions locally, including data entry, data editing and report generation (both preprogrammed and ad hoc clinicprogrammed reports). The system was implemented using a full-featured relational database management system on microcomputers. This presentation is a progress report of that experience. The initial philosophy and final reality of the implementation are contrasted and major problems are highlighted. A number of suggestions, including hardware, software, and operating system selection, are given for other groups contemplating this approach. Guidelines on averting potential problems stemming from study planning decisions are also presented. Many problems encountered during the implementation of the system can be directly traced to decisions made during the planning phase of the study. Other topics addressed include data transfer, remote installation of software, system security, and clinic personnel. Finally, alternative approaches are presented and evaluated, including a "what-we-would-do-now" scenario that will take into account the recent advances in hardware and software.
Use of Clinical Trial Data to Calculate Attributable Risks Jacques B e n i c h o u a n d Mitchell Gaff N a t i o n a l C a n c e r Institute, Bethesda, M a r y l a n d (10) A large clinical trial for cardiovascular disease may afford an opportunity to study risk factors for cancer, such as initial serum concentrations of micronutrients, because the cohort is followed closely for a variety of disease outcomes, and initial sera are often frozen for future studies. We obtain point estimates and confidence intervals for the cancer risk attributable to such exposure under a piecewise exponential survival model with proportional hazards. This model uses age, rather than time on study, as the time scale, and therefore risk sets are not nested. Variance calculations are complicated because the age-specific proportion exposed is a function of agespecific baseline hazard rates and the relative risk. Comparisons are drawn with epidemiologic methods to calculate attributable risks in stratified case-control studies.
Problems in Applying the Bootstrap to Censored Data with Covariates T h e o d o r e Karrison
University of Chicago, Chicago, Illinois (11) Efron described the bootstrap as a "simple and straightforward method for calculating approximated biases, standard deviations, confidence intervals, and so forth in almost any nonparametric estimation problem." However, in the case of censored survival data with covariates, application of the bootstrap is not so straightforward---careful attention must be given to the problem of conditioning on the observed covariate information. Other problems arise when parameter estimates fail to converse for a particular bootstrap sample. We present two bootstrap methods for censored data with covariates--a simple method that does not condition on the observed covariate information and a second, more complicated, algorithm that does condition on the covariates, but that requires knowledge of, or approximation to, the censoring distribution. The methods are used to examine the properties of estimators of the survival curve and regression coefficients based upon a piecewise exponential model. Specifically, the estimates of standard error provided by the large sample theory are compared with the bootstrap estimates of error (under the two methods) and the distributions of related pivotal quantities are investigated. A F a m i l y of Residuals for the Cox Regression Model William E. Barlow
University of Southern California, Los Angeles, California (12) The proportional hazards model of Cox is widely used for the analysis of survival data. Adequate methods for evaluating the goodness-of-fit remain to be developed, however. Barlow and Prentice (Biometrika, 1988) have proposed residuals for the Cox model that include "generalized residuals" and the score residuals of Schoenfeld (Biometrika, 1982) as special cases. One member of this family is shown to be an unstandardized estimate of the influence function, the impact of any individual observation on the parameter estimates. The usefulness of this residual is investigated by simulation. The influence function estimate is compared to the actual change in the parameter estimates when each observation is deleted. Further, the ability of the residuals to support the correct model and refute an incorrect model is also considered.
245 management system that allows the clinical center to perform a variety of functions locally, including data entry, data editing and report generation (both preprogrammed and ad hoc clinicprogrammed reports). The system was implemented using a full-featured relational database management system on microcomputers. This presentation is a progress report of that experience. The initial philosophy and final reality of the implementation are contrasted and major problems are highlighted. A number of suggestions, including hardware, software, and operating system selection, are given for other groups contemplating this approach. Guidelines on averting potential problems stemming from study planning decisions are also presented. Many problems encountered during the implementation of the system can be directly traced to decisions made during the planning phase of the study. Other topics addressed include data transfer, remote installation of software, system security, and clinic personnel. Finally, alternative approaches are presented and evaluated, including a "what-we-would-do-now" scenario that will take into account the recent advances in hardware and software.
Use of Clinical Trial Data to Calculate Attributable Risks Jacques B e n i c h o u a n d Mitchell Gaff N a t i o n a l C a n c e r Institute, Bethesda, M a r y l a n d (10) A large clinical trial for cardiovascular disease may afford an opportunity to study risk factors for cancer, such as initial serum concentrations of micronutrients, because the cohort is followed closely for a variety of disease outcomes, and initial sera are often frozen for future studies. We obtain point estimates and confidence intervals for the cancer risk attributable to such exposure under a piecewise exponential survival model with proportional hazards. This model uses age, rather than time on study, as the time scale, and therefore risk sets are not nested. Variance calculations are complicated because the age-specific proportion exposed is a function of agespecific baseline hazard rates and the relative risk. Comparisons are drawn with epidemiologic methods to calculate attributable risks in stratified case-control studies.
Problems in Applying the Bootstrap to Censored Data with Covariates T h e o d o r e Karrison
University of Chicago, Chicago, Illinois (11) Efron described the bootstrap as a "simple and straightforward method for calculating approximated biases, standard deviations, confidence intervals, and so forth in almost any nonparametric estimation problem." However, in the case of censored survival data with covariates, application of the bootstrap is not so straightforward---careful attention must be given to the problem of conditioning on the observed covariate information. Other problems arise when parameter estimates fail to converse for a particular bootstrap sample. We present two bootstrap methods for censored data with covariates--a simple method that does not condition on the observed covariate information and a second, more complicated, algorithm that does condition on the covariates, but that requires knowledge of, or approximation to, the censoring distribution. The methods are used to examine the properties of estimators of the survival curve and regression coefficients based upon a piecewise exponential model. Specifically, the estimates of standard error provided by the large sample theory are compared with the bootstrap estimates of error (under the two methods) and the distributions of related pivotal quantities are investigated. A F a m i l y of Residuals for the Cox Regression Model William E. Barlow
University of Southern California, Los Angeles, California (12) The proportional hazards model of Cox is widely used for the analysis of survival data. Adequate methods for evaluating the goodness-of-fit remain to be developed, however. Barlow and Prentice (Biometrika, 1988) have proposed residuals for the Cox model that include "generalized residuals" and the score residuals of Schoenfeld (Biometrika, 1982) as special cases. One member of this family is shown to be an unstandardized estimate of the influence function, the impact of any individual observation on the parameter estimates. The usefulness of this residual is investigated by simulation. The influence function estimate is compared to the actual change in the parameter estimates when each observation is deleted. Further, the ability of the residuals to support the correct model and refute an incorrect model is also considered.