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PubMed2006

What does the population attributable fraction mean?

Recent controversy over the disagreement of population attributable fraction estimates for the obesity-total mortality relation has made the concept of attributable fraction visible in both scientific and popular news. Most of the attention in writings on the attributable fraction has focused on technical matters of estimation and on ensuring a causal relationship between exposure and outcome. Yet some of the most illuminating questions about the attributable fraction have to do with another causal question and how the measure is to be interpreted in light of the answer to this question: What interventions are available to cause the assumed reduction in risk among the exposed and the consequent estimated reduction in disease burden? In this paper, I discuss the limitations to the common interpretations of the attributable fraction and argue that these limitations cannot be overcome merely by better statistical modeling or by use of better data sets. They must be addressed through discussion of specific interventions and the hypothesized causal consequences of such specified interventions.

Bias↗

PubMed1990

Statistical analysis of pharmacokinetic data with special applications to bioequivalence studies.

The objectives of this investigation are: 1) to describe techniques for determining the validity of the assumptions; 2) to suggest data transformations which may validate the use of parametric procedures; and 3) to describe a non-parametric alternative to the analysis of variance for crossover designs. Two assumptions common to all parametric procedures include the underlying normal distribution of the observations and equality of variances across treatment groups. Normal probability plots and/or stem and leaf plots are good diagnostic techniques to address the assumption of normality, while Bartlett's test is the most common method of determining equality of variances. To evaluate bioequivalence data, the Food and Drug Administration suggests the use of analysis of variance for crossover designs. If the underlying assumptions are valid, the appropriate statistical models are well known. On the other hand, if the assumptions are not valid, the investigator has one of two choices: 1) transform the data in such a way as to satisfy the assumptions, or 2) use a non-parametric procedure. Square root or logarithmic transformations are commonly used in this situation. However, if a suitable transformation cannot be found, then a non-parametric procedure should be used. Koch (Biometrics (1972) 28, 577-584) developed a non-parametric crossover test, which is relatively easy to apply, but the corresponding power calculations required by the FDA are less obvious.

Animals↗

PubMed1990

Selecting experimental data for use in quantitative risk assessment: an expert-judgement approach.

In many cancer risk assessments the experimental data used in statistical modeling are selected by applying generic guidelines. The guidelines exclude use of some types of experimental data and often appear arbitrary since rules rather than scientific judgments guide selection of data. This paper implements an alternative approach in which data are selected based on the judgments of practicing scientists. Eight such scientists were identified through an explicit selection procedure to help select data for use in a dose-response assessment of formaldehyde. Judgements about appropriate data sets were then elicited in personal interviews using a formal interview protocol. Appropriate data sets were fit to the multistage model and used as the basis for low-dose extrapolation. Low-dose risk estimates are shown to be sensitive to the selection of data, especially the treatment of benign tumors. The recommendations of the experts also differ in some respects from the choices made in previously published risk assessments. This suggests that scientific judgement may be an appropriate method to augment guidelines when a broad range of data is available. The paper argues that the expert judgment approach has some advantages that are worth considering.

Air Pollutants↗

PubMed1990

Threshold altitude resulting in decompression sickness.

A review of case reports, hypobaric chamber training data, and experimental evidence indicated that the threshold for incidence of Altitude Decompression Sickness (DCS) was influenced by various factors such as prior denitrogenation, exercise or rest and period of exposure, in addition to individual susceptibility. Fitting these data with appropriate statistical models has the potential for estimating the frequency of occurrence of DCS at various altitudes under different experimental conditions and allows us to examine the influence of various factors on the threshold for DCS. This approach was illustrated by logistic regression analysis on the incidence of DCS below 9,144 m (30,000 ft). Estimations using these regressions showed that under a noprebreathe, 6-h exposure, simulated extravehicular activity profile, the threshold for symptoms occurred at approximately 3,353 m (11,000 ft); while under a no-prebreathe, 2-h exposure profile with knee-bends exercise, the threshold occurred at 7,925 m (26,000 ft). These examples showed that definition of threshold altitude should be qualified by the particular combination of experimental variables under which it was observed.

Aerospace Medicine↗

PubMed1988

Hyperparameter estimation using stochastic approximation with application to population pharmacokinetics.

A stochastic approximation algorithm is proposed for recursive estimation of the hyperparameters characterizing, in a population, the probability density function of the parameters of a statistical model. For a given population model defined by a parametric model of a biological process, an error model, and a class of densities on the set of the individual parameters, this algorithm provides a sequence of estimates from a sequence of individuals' observation vectors. Convergence conditions are verified for a class of population models including usual pharmacokinetic applications. This method is implemented for estimation of pharmacokinetic population parameters from drug multiple-dosing data. Its estimation capabilities are evaluated and compared to a classical method in population pharmacokinetics, the first-order method (NONMEM), on simulated data.

Algorithms↗

PubMed1988

Prognostic index for acute abdominal crisis (colic) in horses.

Selected physical and laboratory findings in 29 horses with acute abdominal crisis were evaluated retrospectively with a logistic regression equation to predict survival or death. Of 17 variables examined, 2 were found to have good predictive correlation. Serum lactate and packed cell volume had a combined predictive value of 94%. Packed cell volume mean values of 43 and 50%, respectively, differentiated survival from death. Serum lactate values of 28.18 mg/dl and 51.28 mg/dl differentiated survival and death. Analysis of data from a previous study of 36 horses with the logistic regression equation predicted survival rate of 96% accuracy, further substantiating the statistical model.

Age Factors↗

PubMed1995

[Statistical study of workload at a birthing unit].

The work consists of a statistical study of the workload on a labour ward. This included a study of the admission/birth frequency and an investigation of the factors that influenced the process of admissions and the time spent on the ward. Also the relation between the number of midwives and women in labour present on the ward was investigated. It was found that the variation in the number of spontaneous admissions/births could be described reasonably well by a Poisson distribution. A simple statistical model was proposed to calculate the number of midwives necessary at any given birth frequency. For a specific choice of parameters the model fitted the actual distribution very well. The relative frequency of complications was highest for women with no previous births and smallest for women with one previous birth. The time spent on the labour ward depended significantly on the number of times the woman had given birth before and on whether the present delivery was complicated.

Delivery Rooms↗

PubMedDate not supplied

Genetic susceptibility and occupational cancer.

This review describes statistical models for the biological interaction of susceptibility genes and environmental exposures, as observed in epidemiologic studies. The importance of metabolic transformation of industrial carcinogens and the potential role of genetic polymorphisms in metabolic enzyme activity are outlined. Several genetic polymorphisms have been associated with cancer risk, but the link with the relevant exposures has only infrequently been specified. For example, studies show that slow N-acetylation increased bladder cancer risk among workers exposed to some arylamines, as found among dye workers in England, but that this effect does not hold for benzidine exposure. This link of a genetic susceptibility factor with cancer risk due to some aromatic amines, but not to others, illustrated the specific nature of metabolic environment/gene interactions. Epidemiologic studies to investigate the role of genetic susceptibility in cancer development promise to further the identification of human carcinogens by focusing on susceptible individuals and, in turn, to enhance understanding of human cancer by relating cancer risk in populations to underlying biologic processes. Occupational studies are key to this effort.

Carcinogens↗

PubMed1995

Accurate prognostications of death. Opportunities and challenges for clinicians.

Linking survival time to an array of prognostic variables through a powerful statistical model can provide reliable, valid and potentially useful information for patient care. We present a summary of the recently developed SUPPORT model [Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment] for estimating survival time of seriously ill adult inpatients and illustrate the possible clinical use of such a model. This model is then translated into counseling. Clinicians are positioned to evaluate the relevance and validity of any model and to understand their persistent shortcomings.

Adult↗

PubMed1994

[Poisson regression in epidemiology].

The goals of Poisson regression are to estimate the effects of risk factors on incidence or mortality rates and to evaluate the dose-response relationship for variables representing quantitative levels of exposure. This statistical model presents, over standardization techniques, several advantages which are recalled here. The Poisson assumption, approximating the exact sampling distribution of rates is discussed. The model equation with internal and external standard rates is introduced, as well as summary measures of fit. Two examples are given: one from a cohort study, the other one from a geographical study, to help in the interpretation of Poisson regression results. Lastly, recent and on-going developments of these models are briefly considered.

Adult↗

PubMed1993

Statistical analysis of in vivo tumor growth experiments.

We review and compare statistical methods for the analysis of in vivo tumor growth experiments. The methods most commonly used are deficient in that they have either low power or misleading type I error rates. We propose a set of multivariate statistical modeling methods that correct these problems, illustrating their application with data from a study of the effect of alpha-difluoromethylornithine on growth of the BT-20 human breast tumor in nude mice. All the methods find significant differences between the alpha-difluoromethylornithine dose groups, but recommended sample sizes for a subsequent study are much smaller with the multivariate methods. We conclude that the multivariate methods are preferable and present guidelines for their use.

Animals↗

PubMed1995

Structural and conjunctural compensation method for hospital budgetary allocation on the basis of DRGs.

We propose here a structural and conjunctural compensation method to improve budgetary allocation which could be based on Diagnosis Related Groups. This method consists in the determination of sub-group costs within DRGs. The specification of these sub-groups is possible by introducing clinical and social parameters in the statistical model. Hospitals could then compare their sub-group proportions and analyze their differences in relation to conjunctural factors (recruitment, medical practices) and structural factors (technical team, local medical structure). This method also allows an identification of specialty hospitals (outliers) and a compensation allocation for budgeting for these hospitals.

Budgets↗

PubMed1996

[Age of onset of rhegmatogenous retinal detachment analyzed by Weibull distribution function].

We reviewed a series of 426 case or rhegmatogenous retinal detachment due to idiopathic retinal break and characterized the age at onset using a vital statistics model, Weibull distribution. When cases were divided into groups based on the type of retinal break, the cumulative frequency of each group could be described by the model. The equatorial atrophic hole group showed a frequency distribution which starts at about 7.5 years, culminates at about 20 years and terminates at about 40 years. The tractional tear group revealed two distinct distributions depending upon refractive errors; the non-high myopic subgroup showed a distribution which starts at 30 years and peaks at 60 years, and the high myopic subgroup had a distribution which starts at about 15 years and peaks at about 40 years. The macular hole group showed a distribution which starts in the second half of the third decade and peaks at about 60 years. The findings are consistent with the view that pathomechanisms having differing time courses are responsible for the age distribution at onset of rhegmatogenous retinal detachment caused by various types of retinal break.

Adolescent↗

PubMed1997

A likelihood-based approach to the estimation of relative DNA copy number by comparative genomic hybridization.

A general approach is described for efficiently and objectively analyzing comparative genomic hybridization (CGH) profile data based on the use of realistic statistical models and the application of standard likelihood-based inference. In contrast to other methods in current use, the approach provides a most parsimonious explanation by identifying the smallest number of relative DNA copy number changes consistent with the data, together with estimates of their levels and positions and of their standard errors. By making efficient use of available data, it has the potential to enhance the resolution of CGH technology. The computational feasibility of the method is illustrated by application to real CGH profile data from human chromosome 4.

Chromosomes, Human, Pair 4↗

PubMedDate not supplied

[The efficiency of the calcitonin-transporting activity of the hemato-C-cellular system in the thyroid gland].

The efficiency of hemato-C-cellular transport of calcitonin into the systemic blood flow was evaluated by the methods of statistical modelling. The linear regression model obtained on 36 rats suggests essential predetermination of the serum concentrations of calcitonin (CT) by the intensity of incretory activity of the thyroid parafollicular epithelium: CT = 13.733+ (0.318 Nvsg; rxy = 0.830; p < 0.001 (Nvsg is numerical density of the sublemmal granules on the C-cells vascular pole). At the same time, the resulting index (CT) depends not only on Nvsg. This index is also influenced by the factors not controlled by the model, such as Hemato-C-cellular delay of calcitonin. These factors determine each certain proportion of the error of prognostication of the calcitonin serum content according to Nvsg. A covariation model CT = bNvsg was plotted in order to determine the total influence of uncontrolled factors. Its error of prognostication was subject to analysis of variance and a confidence interval of biological accessibility of endogenous calcitonin was calculated (73% < FCT < or = 100%; p > or = 0.95). The results obtained suggest the absence of essential intrathyroid loss of calcitonin at the transinterstitial and transmural stages of hemato-C-cellular hormone transfer.

Animals↗

PubMed2006

Informed prognosis [corrected] after abdominal aortic aneurysm repair using predictive modeling techniques [corrected].

OBJECTIVE: To identify the best method for the prediction of postoperative mortality in individual abdominal aortic aneurysm surgery (AAA) patients by comparing statistical modelling with artificial neural networks' (ANN) and clinicians' estimates. METHODS: An observational multicenter study was conducted of prospectively collected postoperative Acute Physiology and Chronic Health Evaluation II data for a 9-year period from 24 intensive care units (ICU) in the Thames region of the United Kingdom. The study cohort consisted of 1205 elective and 546 emergency AAA patients. Four independent physiologic variables-age, acute physiology score, emergency operation, and chronic health evaluation-were used to develop multiple regression and ANN models to predict in-hospital mortality. The models were developed on 75% of the patient population and their validity tested on the remaining 25%. The results from these two models were compared with the observed outcome and clinicians' estimates by using measures of calibration, discrimination, and subgroup analysis. RESULTS: Observed in-hospital mortality for elective surgery was 9.3% (95% confidence interval [CI], 7.7% to 11.1%) and for emergency surgery, 46.7% (95% CI, 42.5 to 51.0%). The ANN and the statistical models were both more accurate than the clinicians' predictions. Only the statistical model was internally valid, however, when applied to the validation set of observations, as evidenced by calibration (Hosmer-Lemeshow C statistic, 14.97; P = .060), discrimination properties (area under receiver operating characteristic curve, 0.869; 95% CI, 0.824 to 0.913), and subgroup analysis. CONCLUSIONS: The prediction of in-hospital mortality in AAA patients by multiple regression is more accurate than clinicians' estimates or ANN modelling. Clinicians can use this statistical model as an objective adjunct to generate informed prognosis.

Aortic Aneurysm, Abdominal↗

PubMed1991

Thomas: building Bayesian statistical expert systems to aid in clinical decision making.

Knowledge-based system for classical statistical analysis must separate the task of analyzing data from that of using the results of the analysis. In contrast, a Bayesian framework for building biostatistical expert system allows for the integration of the data-analytic and decision-making tasks. The architecture of such a framework entails enabling the system (1) to make its recommendations on decision-analytic grounds; (2) to construct statistical models dynamically; (3) to update a statistical model based on the user's prior beliefs and on data from, the methodological concerns evinced by, the study. This architecture permits the knowledge engineer to represent a variety of types of statistical and domain knowledge. Construction of such systems requires that the knowledge engineer reinterpret traditional statistical concerns, such as by replacing the notion of statistical significance with that of a pragmatic clinical threshold. The clinical user of such a system can interact with the system at a semantic level appropriate to her fund of methodological knowledge, rather than at the level of statistical details. We demonstrate these issues with a prototype system called THOMAS which helps a physician decision maker interpret the results of a published randomized clinical trial.

Artificial Intelligence↗

PubMed2003

Statistical inversion for medical x-ray tomography with few radiographs: II. Application to dental radiology.

Diagnostic and operational tasks in dental radiology often require three-dimensional information that is difficult or impossible to see in a projection image. A CT-scan provides the dentist with comprehensive three-dimensional data. However, often CT-scan is impractical and, instead, only a few projection radiographs with sparsely distributed projection directions are available. Statistical (Bayesian) inversion is well-suited approach for reconstruction from such incomplete data. In statistical inversion, a priori information is used to compensate for the incomplete information of the data. The inverse problem is recast in the form of statistical inference from the posterior probability distribution that is based on statistical models of the projection data and the a priori information of the tissue. In this paper, a statistical model for three-dimensional imaging of dentomaxillofacial structures is proposed. Optimization and MCMC algorithms are implemented for the computation of posterior statistics. Results are given with in vitro projection data that were taken with a commercial intraoral x-ray sensor. Examples include limited-angle tomography and full-angle tomography with sparse projection data. Reconstructions with traditional tomographic reconstruction methods are given as reference for the assessment of the estimates that are based on the statistical model.

Algorithms↗