Biomedical subjects

N Schenker

Publications and source records attributed to N Schenker.

7 recordsLinked to original sources

PubMed2002

Humans and great apes share a large frontal cortex.

Some of the outstanding cognitive capabilities of humans are commonly attributed to a disproportionate enlargement of the human frontal lobe during evolution. This claim is based primarily on comparisons between the brains of humans and of other primates, to the exclusion of most great apes. We compared the relative size of the frontal cortices in living specimens of several primate species, including all extant hominoids, using magnetic resonance imaging. Human frontal cortices were not disproportionately large in comparison to those of the great apes. We suggest that the special cognitive abilities attributed to a frontal advantage may be due to differences in individual cortical areas and to a richer interconnectivity, none of which required an increase in the overall relative size of the frontal lobe during hominid evolution.

Animals↗

PubMed2000

Analysing the effects of anaemia on local recurrence of head and neck cancer when covariate values are missing.

We present a case study in the analysis of the prognostic effects of anaemia and other covariates on the local recurrence of head and neck cancer in patients who have been treated with radiation therapy. Because it is believed that a large fraction of the patients are cured by the therapy, we use a failure time mixture model for the outcomes, which simultaneously models both the relationship of the covariates to cure and the relationship of the covariates to local recurrence times for subjects who are not cured. A problematic feature of the data is that two covariates of interest having missing values, so that only 75 per cent of the subjects have complete data. We handle the missing-data problem by jointly modelling the covariates and the outcomes, and then fitting the model to all of the data, including the incomplete cases. We compare our approach to two traditional methods for handling missingness, that is, complete-case analysis and the use of an indicator variable for missingness. The comparison with complete-case analysis demonstrates gains in efficiency for joint modelling as well as sensitivity of some results to the method used to handle missing data. The use of an indicator variable yields results that are very similar to those from joint modelling for our data. We also compare the results obtained for the mixture model with results obtained for a standard (non-mixture) survival model. It is seen that the mixture model separates out effects in a way that is not possible with a standard survival model. In particular, conditional on other covariates, we find strong evidence of an association between anaemia and cure, whereas the evidence of an association between anaemia and time to local recurrence for patients who are not cured is weaker.

Anemia↗

PubMed1999

Fitting the log-F accelerated failure time model with incomplete covariate data.

Data obtained from studies in the health sciences often have incompletely observed covariates as well as censored outcomes. In this paper, we present methods for fitting the log-F accelerated failure time model with incomplete continuous and/or categorical time-independent covariates using the Gibbs sampler. A general location model that allows different covariance structures across cells is specified for the covariates, and ignorable missingness of the covariates is assumed. Techniques that accommodate standard assumptions of ignorable censoring as well as certain types of nonignorable censoring are developed. We compare our approach to traditional complete-case analysis in an application to data obtained from a study of melanoma. The comparison indicates that substantial gains in efficiency are possible with our approach.

Biometry↗

PubMed1993

Multiple imputation for threshold-crossing data with interval censoring.

Medical statistics often involve measurements of the time when a variable crosses a threshold value. The time to threshold crossing may be the outcome variable in a survival analysis, or a time-dependent covariate in the analysis of a subsequent event. This paper presents new methods for analysing threshold-crossing data that are interval censored in that the time of threshold crossing is known only within a specified interval. Such data typically arise in event-history studies when the threshold is crossed at some time between data-collection points, such as visits to a clinic. We propose methods based on multiple imputation of the threshold-crossing time with use of models that take into account values recorded at the times of visits. We apply the methods to two real data sets, one involving hip replacements and the other on the prostate specific antigen (PSA) assay for prostate cancer. In addition, we compare our methods with the common practice of imputing the threshold-crossing time as the right endpoint of the interval. The two examples require different imputation models, but both lead to simple analyses of the multiply imputed data that automatically take into account variability due to imputation.

Data Interpretation, Statistical↗

PubMed1993

Analyses of public use decennial census data with multiply imputed industry and occupation codes.

"This paper gives a brief introduction to multiple imputation for handling non-response in surveys. We then describe a recently completed project in which multiple imputation was used to recalibrate industry and occupation codes in 1970 U.S. census public use samples to the 1980 standard. Using analyses of data from the project, we examine the utility of analysing a large data set having imputed values compared with analysing a small data set having true values, and we provide examples of the amount by which variability is underestimated by using just one imputation rather than multiple imputations."

Americas↗

PubMed1991

Multiple imputation in health-care databases: an overview and some applications.

Multiple imputation for non-response replaces each missing value by two or more plausible values. The values can be chosen to represent both uncertainty about the reasons for non-response and uncertainty about which values to impute assuming the reasons for non-response are known. This paper provides an overview of methods for creating and analysing multiply-imputed data sets, and illustrates the dramatic improvements possible when using multiple rather than single imputation. A major application of multiple imputation to public-use files from the 1970 census is discussed, and several exploratory studies related to health care that have used multiple imputation are described.

Databases, Factual↗

PubMed1988

[Handling missing data in coverage estimation, with application to the 1986 Test of Adjustment Related Operations].

"This paper discusses methods used to handle missing data in post-enumeration surveys for estimating census coverage error, as illustrated for the 1986 Test of Adjustment Related Operations (Diffendal 1988). The methods include imputation schemes based on hot-deck and logistic regression models as well as weighting adjustments. The sensivity of undercount estimates from the 1986 test to variations in the imputation models is also explored." The test was carried out in Central Los Angeles County, California.

Americas↗