SMR analysis of historical follow-up studies with missing death certificates

The evaluation of epidemiological follow-up studies is frequently based on a comparison of the number O of deaths observed in the cohort from a specified cause with the expected number E calculated from person years in the cohort and mortality rates from a reference population. The ratio SMR = 100 x O/E is called the standardized mortality ratio (SMR). While person years can easily be calculated from the cohort and reference rates are generally available from the national statistical offices or the World Health Organization (WHO), problems can arise with the accessibility of the causes of death of the deceased study participants. However, the information that a person has died may be available, e.g., from population registers. In this paper, a statistical model for this situation is developed to derive a maximum likelihood (ML) estimator for the true (but unknown) number O* of deaths from a specified cause, which uses the known number O of deaths from this cause and the proportion p of all known causes of death among all decreased participants. It is shown that the standardized mortality ratio SMR* based on this estimated number is just SMR* = SMR/p. Easily computable confidence limits can be obtained by dividing the usual confidence limits of the SMR by the opposite limit of the proportion p. However, the confidence level alpha has to be adjusted appropriately.     

Appropriate characters for racial classification in maize

To determine the relative importance of the genotype, the environment, and their interaction on the expression of morphological characters in maize races, 50 Mexican races and 24 races from Central America, South America, and the U.S. were grown in several locations and seasons in México and 47 characters were measured directly. Estimates of the ratio of variance components, rc = [Vc/(Ve+VCs/], were used as criteria to determine the appropriate characters for racial classification. Twenty-four useful variables were identified. Analysis of the structure of the data matrix facilitated visual examination of correlations among the variables and of the variability represented by each variable. Based on these analyses, a minimum list of 9 characters was suggested to be appropriate variables for racial classification: number of leaves per plant, branched part length/tassel length, central spike internode length, male glume length, kernel width, rachis segment length, pith diameter, ear diameter/length, and kernel width/length.     

The validity of cancer information on death certificates in Norway and the impact of death certificate initiated cases on cancer incidence and survival

BackgroundDeath certificates are an important source of information for cancer registries. The aim of this study was to validate the cancer information on death certificates, and to investigate the effect of including death certificate initiated (DCI) cases in the Cancer Registry of Norway when estimating cancer incidence and survival.MethodsAll deaths in Norway in the period 2011–2015 with cancer mentioned on the death certificates were linked to the cancer registry. Notifications not registered from other sources were labelled death certificate notifications (DCNs), and considered as either cancer or not, based on available information in the registry or from trace-back to another source.ResultsFrom the total of 65 091 cancers mentioned on death certificates in the period 2011–2015, 58,425 (89.8%) were already in the registry. Of the remaining 6 666 notifications, 2 636 (2 129 with cancer as underlying cause) were not regarded to be new cancers, which constitutes 4.0% of all cancers mentioned on death certificates and 39.5% of the DCNs. Inclusion of the DCI cases increased the incidence of all cancers combined by 2.6%, with largest differences for cancers with poorer prognosis and for older age groups. Without validation, including the 2 129 disregarded death certificates would over-estimate the incidence by 1.3%. Including DCI cases decreased the five-year relative survival estimate for all cancer sites combined with 0.5% points.ConclusionIn this study, almost 40% of the DCNs were regarded not to be a new cancer case, indicating unreliability of death certificate information for cancers that are not already registered from other sources. The majority of the DCNs where, however, registered as new cases that would have been missed without death certificates. Both including and excluding the DCI cases will potentially bias the survival estimates, but in different directions. This biases were shown to be small in the Cancer Registry of Norway.     

Interaction profile-based protein classification of death domain

Background

The increasing number of protein sequences and 3D structure obtained from genomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimental and computational efforts on the creation and analysis of information derived from 3D structure. In particular, the high-throughput generation of protein-protein interaction data from a few organisms makes such an approach very important towards understanding the molecular recognition that make-up the entire protein-protein interaction network. Since the generation of sequences, and experimental protein-protein interactions increases faster than the 3D structure determination of protein complexes, there is tremendous interest in developing in silico methods that generate such structure for prediction and classification purposes. In this study we focused on classifying protein family members based on their protein-protein interaction distinctiveness. Structure-based classification of protein-protein interfaces has been described initially by Ponstingl et al. [1] and more recently by Valdar et al. [2] and Mintseris et al. [3], from complex structures that have been solved experimentally. However, little has been done on protein classification based on the prediction of protein-protein complexes obtained from homology modeling and docking simulation.

Results

We have developed an in silico classification system entitled HODOCO (Homology modeling, Docking and Classification Oracle), in which protein Residue Potential Interaction Profiles (RPIPS) are used to summarize protein-protein interaction characteristics. This system applied to a dataset of 64 proteins of the death domain superfamily was used to classify each member into its proper subfamily. Two classification methods were attempted, heuristic and support vector machine learning. Both methods were tested with a 5-fold cross-validation. The heuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an 89% average accuracy.

Conclusion

We have confirmed the reliability and potential value of classifying proteins via their predicted interactions. Our results are in the same range of accuracy as other studies that classify protein-protein interactions from 3D complex structure obtained experimentally. While our classification scheme does not take directly into account sequence information our results are in agreement with functional and sequence based classification of death domain family members.

     

Mortality in neurofibromatosis 1: an analysis using U.S. death certificates

Although neurofibromatosis 1 (NF1) is a relatively common autosomal dominant condition, information about its effect on mortality is limited. We used Multiple-Cause Mortality Files, compiled from U.S. death certificates by the National Center for Health Statistics, for 1983 through 1997. We identified 3,770 cases of presumed NF1 among 32,722,122 deaths in the United States, a frequency of 1/8,700, which is one-third to one-half the estimated prevalence. Mean and median ages at death for persons with NF1 were 54.4 and 59 years, respectively, compared with 70.1 and 74 years in the general population. Results of proportionate mortality ratio (PMR) analyses showed that persons with NF1 were 34 times more likely (PMR=34.3, 95% confidence interval [CI] 30.8-38.0) to have a malignant connective or other soft-tissue neoplasm listed on their death certificates than were persons without NF1. Overall, persons with NF1 were 1.2 times more likely than expected (PMR=1.21, 95% CI 1.14-1.28) to have a malignant neoplasm listed on their death certificates, but the PMR was 6.07 (95% CI 4.88-7.45) for persons who died at 10-19 years of age and was 4.93 (95% CI 4.14-5.82) for those who died at 20-29 years of age. Similarly, vascular disease was recorded more often than expected on death certificates of persons with NF1 who died at <30 years of age (PMR=3.26, 95% CI 1.31-6.71 at age <10 years; PMR=2.68, 95% CI 1.38-4.68 at age 10-19 years; and PMR=2.25, 95% CI 1.46-3.32 at 20-29 years) but not in older persons. This study supports previous findings of decreased life expectancy for persons with NF1 and, within the limitations of death certificates, provides population-based data about NF1 morbidity and mortality that are useful to clinicians caring for patients with NF1.     

The races of maize: III. choices of appropriate characters for racial classification

Analyses of variance for 111 characters from 55 races and subraces of maize from eastern South America grown at Piracicaba, S. P., Brazil, between 1960 and 1965, indicated that those characters which were least affected by environmental factors and interactions were reproductive characters. In particular, the component of variance due to differences among races for certain ear and kernel characters was greater than the sum of the corresponding components due to differences among years and race by year interactions. The converse was true for all vegetative characters. Tassel characters tended to be intermediate between ear and plant characters. While some indices had larger components of variance attributable to racial differences than to the effects of environment and/or environmental interaction, some commonly used ones, such as cob/rachis and rachilla/kernel indices, proved to be quite susceptible to environmental influences. Again, indices based upon solely vegetative characters were consistently influenced more strongly by environmental factors and interaction than were those based on reproductive characters.