Biomedical subjects

C Kooperberg

Publications and source records attributed to C Kooperberg.

5 recordsLinked to original sources

PubMed1997

Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions.

We explore the ability of a simple simulated annealing procedure to assemble native-like structures from fragments of unrelated protein structures with similar local sequences using Bayesian scoring functions. Environment and residue pair specific contributions to the scoring functions appear as the first two terms in a series expansion for the residue probability distributions in the protein database; the decoupling of the distance and environment dependencies of the distributions resolves the major problems with current database-derived scoring functions noted by Thomas and Dill. The simulated annealing procedure rapidly and frequently generates native-like structures for small helical proteins and better than random structures for small beta sheet containing proteins. Most of the simulated structures have native-like solvent accessibility and secondary structure patterns, and thus ensembles of these structures provide a particularly challenging set of decoys for evaluating scoring functions. We investigate the effects of multiple sequence information and different types of conformational constraints on the overall performance of the method, and the ability of a variety of recently developed scoring functions to recognize the native-like conformations in the ensembles of simulated structures.

Bayes Theorem↗

PubMed1997

Hazard regression with interval-censored data.

In a recent paper, Kooperberg, Stone, and Truong (1995a) introduced hazard regression (HARE), in which linear splines and their tensor products are used to estimate the conditional log-hazard function based on possibly censored, positive response data and one or more covariates. Model selection is carried out in an adaptive fashion using maximum likelihood estimation of the unknown coefficients, Rao and Wald statistics to carry out stepwise addition and deletion of basis functions, and the Bayesian Information Criterion (BIC) to select the final model. In the present paper, the HARE methodology is extended to accommodate interval-censored data, time-dependent covariates, and cubic splines. The presence of interval-censored data means that the log-likelihood function may no longer be concave, presenting additional numerical challenges. The extended methodology is applied to a data set containing both interval-censoring and time-dependent covariates. The new software will be available in a future release of S-Plus.

Acquired Immunodeficiency Syndrome↗

PubMed1996

Statistical modeling to predict elective surgery time. Comparison with a computer scheduling system and surgeon-provided estimates.

BACKGROUND: Accurate estimation of operating times is a prerequisite for the efficient scheduling of the operating suite. The authors, in this study, sought to compare surgeons' time estimates for elective cases with those of commercial scheduling software, and to ascertain whether improvements could be made by regression modeling. METHODS: The study was conducted at the University of Washington Medical Center in three phases. Phase 1 retrospectively reviewed surgeons' time estimates and the scheduling system's estimates throughout 1 yr. In phase 2, data were collected prospectively from participating surgeons by means of a data entry form completed at the time of scheduling elective cases. Data included the procedure code, estimated operating time, estimated case difficulty, and potential factors that might affect the duration. In phase 3, identical data were collected from five selected surgeons by personal interview. RESULTS: In phase 1, 26 of 43 surgeons provided significantly better estimates than did the scheduling system (P < 0.01), and no surgeon was significantly worse, although the absolute errors were large (34% of 157 min average case length). In phase 2, modeling improved the accuracy of the surgeons' estimates by 11.5%, compared with the scheduling system. In phase 3, applying the model from phase 2 improved the accuracy of the surgeons' estimates by 18.2%. CONCLUSIONS: Surgeons provide more accurate time estimates than does the scheduling software as it is used in our institution. Regression modeling effects modest improvements in accuracy. Further improvements would be likely if the hospital information system could provide timely historical data and feedback to the surgeons.

Appointments and Schedules↗

PubMed1995

Trees and splines in survival analysis.

During the past few years several nonparametric alternatives to the Cox proportional hazards model have appeared in the literature. These methods extend techniques that are well known from regression analysis to the analysis of censored survival data. In this paper we discuss methods based on (partition) trees and (polynomial) splines, analyse two datasets using both Survival Trees and HARE, and compare the strengths and weaknesses of the two methods. One of the strengths of HARE is that its model fitting procedure has an implicit check for proportionality of the underlying hazards model. It also provides an explicit model for the conditional hazards function, which makes it very convenient to obtain graphical summaries. On the other hand, the tree-based methods automatically partition a dataset into groups of cases that are similar in survival history. Results obtained by survival trees and HARE are often complementary. Trees and splines in survival analysis should provide the data analyst with two useful tools when analysing survival data.

Algorithms↗

PubMed1991

Using logistic regression to estimate the adjusted attributable risk of low birthweight in an unmatched case-control study.

Other authors have shown how to estimate attributable risk based on stratification. In this paper, we show how to estimate adjusted attributable risks, standard errors, and confidence intervals from an unmatched case-control study that has population-based controls and uses the logistic regression model to estimate relative risk. We apply the method to data from a case-control study of low birthweight. The method is conceptually simple, has no assumptions beyond those of the logistic model, makes use of computer-intensive statistical techniques (the bootstrap), and extends to interactions. A Fortran computer program to carry out the computations is available from the authors upon request.

Case-Control Studies↗