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PubMed2026

Diagnosing scientific replicability through probabilistic distinguishability.

MOTIVATION: Despite the widely recognized importance of replicability in biological research, computational methods to quantify irreplicability and identify irreplicable instances remain underdeveloped. This article presents an efficient and robust computational framework to address this gap. RESULTS: To tackle the challenge of defining an acceptable level of intrinsic heterogeneity among replicable studies, we introduce a distinguishability criterion, ensuring that replicable effects, while potentially heterogeneous, can be distinguished from zero effects and maintain consistent directions with high probability. We implement a Bayesian model criticism approach, reporting a Bayesian P-value to identify potential irreplicable instances. Through numerical experiments, we demonstrate the efficacy of the proposed methods in detecting batch effects in high-throughput experiments and identifying instances of the publication bias. Finally, we apply the framework to multi-tissue eQTL data from the GTEx consortium, uncovering tissue-specific eQTLs that represent biological heterogeneity across tissues. AVAILABILITY AND IMPLEMENTATION: An R package DiscRep implementing our method is available on GitHub (https://github.com/PengWang96/DiscRep).

Bayes Theorem↗
PubMed1993

Computer dosing program for the initiation of vancomycin therapy.

The predictive performance of a computer dosing program used for initiating vancomycin therapy was studied. Initial serum vancomycin concentrations in 31 adult patients receiving vancomycin were estimated by using a computer program (T.D.M.S.) incorporating a two-compartment open model. Sixty-two serum vancomycin concentrations at steady state (Css) were obtained before and after one-hour infusions and compared with estimated Css values. Bias and precision were evaluated by calculating median error (ME) and median absolute error (MAE), respectively. Population-based estimates of volume of distribution (V) and clearance (CL) were compared with those obtained by fitting each patient's data set by using Bayesian analysis (BA) and non-linear least-squares regression (NLLS). Median (mean +/- S.D.) bias and precision for peak Css were 7.7 (10.2 +/- 10.8) and 7.7 (10.6 +/- 10.5) mg/L, and for trough Css were 7.4 (7.7 +/- 7.6) and 7.4 (8.8 +/- 6.2) mg/L. The medians were significantly different from zero. Estimated median (mean +/- S.D.) V, CL, and half-life were 0.72 L/kg, 0.60 (0.67 +/- 0.21) mL/min/kg, and 11.59 (12.87 +/- 3.91) hours. Median (mean +/- S.D.) CL values determined by BA and NLLS were 0.86 (0.89 +/- 0.32) and 0.85 (0.92 +/- 0.34) mL/min/kg, respectively. Both CL values were significantly greater than the population-based estimate. However, median V values determined by BA and NLLS did not differ from the population-based estimate. A revised clearance model derived from Bayesian analysis of data for the first 21 patients was tested in the 10 other patients and appeared to improve the predictive performance of the a priori model.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMedDate not supplied

Solitary pulmonary nodules: Part II. Evaluation of the indeterminate nodule.

Various strategies may be used to evaluate indeterminate solitary pulmonary nodules. Growth rate assessment is an important and cost-effective step in the evaluation of these nodules. Clinical features (eg, patient age, history of prior malignancy, presenting symptoms, smoking history) can be useful in suggesting the diagnosis and aiding in management planning. Bayesian analysis allows more precise determination of the probability of malignancy (pCa). Decision analysis models suggest that the most cost-effective management strategy depends on the pCa for a given nodule. At contrast material-enhanced computed tomography, nodular enhancement of less than 15 HU is strongly predictive of a benign lesion, whereas enhancement of more than 20 HU typically indicates malignancy. At 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography, lesions with low FDG uptake are typically benign, whereas those with increased FDG uptake are typically malignant. Results of transthoracic needle aspiration biopsy influence management in approximately 50% of cases and, in indeterminate lesions with a pCa between 0.05 and 0.6, is the best initial diagnostic procedure. It is optimally used in peripheral nodules and has been reported to establish a benign diagnosis in up to 91% of cases. Although there is no one correct management approach, the ability to distinguish benign from malignant solitary pulmonary lesions has improved with the use of these strategies.

Bayes Theorem↗
PubMed1996

A knowledge-based model construction approach to medical decision making.

We present a framework for representing the probabilistic effects of actions and contingent treatment plans. Our language has a well-defined declarative semantics and we have developed an implemented algorithm (named BNG) that generates Bayesian networks (BN) to compute the posterior probabilities of queries. In this paper we address the problem of projecting a contingent treatment plan by automatically constructing a structure of interrelated BNs, which we call a BN-graph, and applying the available propagation procedures on it. To address the optimal plan generation, we base our approach on the observation that normally the target plan space has a well-defined structure. We provide a language to describe plan spaces which resembles a programming language with loops and conditionals. We briefly present the procedures for finding the optimal plan(s) from such specified plan spaces.

Acute Disease↗
PubMed1993

Population pharmacokinetic analysis of new aminoglycosides, astromicin and isepamicin, and evaluation of Bayesian prediction method for approximation of individual clearance of drug.

Pharmacokinetic data obtained from healthy subjects after a single i.v. infusion over 30 min of either astromicin (AST) or isepamicin (ISP), the newly developed aminoglycoside antibiotics, were analyzed by a computer program, NONMEM, together with those in patients having impaired renal functions of various degrees, which were cited from the literature. A two-compartment open model was utilized for the analysis, assuming that the total body clearance of drug (ClB) is linearly correlated with endogeneous creatinine clearance (Clcr). By the analysis, it was found that the body weight explains some part of interindividual variability in ClB of ISP, although it did not hold true in the case of AST. For each drug, the means and variances of ClB and the distribution volume of central compartment, and only the means of two intercompartmental constants (K12 and K21), all of which were obtained by the NONMEM analysis, were implemented in a Bayesian prediction program for a microcomputer. This, thus, clarified a point as to when blood sample should be collected in order to get the best prediction of individual ClB with the use of the above Bayesian program and the measurement of drug concentration in the sampled blood as a feedback information. For this purpose, the drug concentrations in plasma obtained in the multiple-dose study of each drug, in which the drug was administered in healthy subjects as i.v. infusion over 1 h every 12 h for 4.5 days, were used.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed1994

Afterimages: integration of diagnostic information through Bayesian enhancement of scintigraphic images.

Although the diagnostic accuracy of myocardial perfusion scintigraphy can be improved by additional consideration of clinical and exercise information, multivariate prediction models are infrequently used for this purpose in the clinical setting. We therefore developed a Bayesian algorithm that instead transforms the scintigraphic image itself, modifying defect contrast as a function of the pretest likelihood of coronary artery disease. The algorithm was tested in computer simulations of myocardial perfusion scintigraphy with data from 378 patients (166 from California and 212 from West Virginia) who underwent planar exercise thallium-201 scintigraphy and coronary angiography. Images were interpreted before and after enhancement by eight readers (four at each medical center) with different training orientations (internist, radiologist, cardiologist, nuclear cardiologist, and nuclear medicine technologist) who used a four-point score (from 0, normal to 3, severe defect). Accuracy was quantified as area under a receiver-operating characteristic (ROC) curve. Improvements in accuracy obtained by the algorithm were compared to those provided by multiple logistic regression. Overall, Bayesian enhancement increased ROC area from 0.63 +/- 0.04 to 0.71 +/- 0.04 (p < 0.01). The improvement was consistent for all 16 reading sets (eight readers multiplied by two patient populations; p < 0.05). In comparison, multiple logistic regression increased ROC area from 0.63 +/- 0.04 to 0.79 +/- 0.03 (p < 0.01), outperforming interpretation of the enhanced images in 13 of the 16 reading sets. Bayesian enhancement improves diagnostic accuracy of conventional scintigraphic image interpretation. The improvement is stable across individuals, training orientations, and patient populations. Although this approach is not as accurate as multiple logistic regression, it may be more practical for widespread clinical application.

Algorithms↗
PubMed1991

Nomogram for dosing warfarin at steady state.

The predictive performance of a nomogram for dosing warfarin was compared with that of a computer program. The nomogram and the computer program were developed from the log-linear model describing warfarin pharmacodynamics at steady state. The nomogram's dose-response curves were generated by using previously reported pharmacodynamic and pharmacokinetic values for an outpatient population receiving warfarin. The series of dose-response curves were plotted by altering the pharmacodynamic values over a range of 3 standard deviations. The ability of the nomogram to predict the steady-state prothrombin time ratio (PTR) after an adjustment in the dosage of warfarin was evaluated, and the results were compared with those of a commercially available program involving Bayesian regression. Data for 65 outpatients were evaluated. The mean +/- S.D. nomogram-predicted, computer-predicted, and measured PTRs were 1.63 +/- 0.27, 1.64 +/- 0.24, and 1.66 +/- 0.23, respectively. The mean prediction errors for the nomogram and the computer program were -0.037 and -0.026, respectively, and the mean percent absolute prediction errors were 11.6% and 11.0%, respectively. Neither method was biased, and differences between the results for the two methods were not significant. The predictive performance of the warfarin dosing nomogram was comparable to that of the computer program.

Bayes Theorem↗
PubMed1987

Expert systems in psychiatry. A review.

Existing computer-based decision aids in the areas of psychiatric diagnosis and consultation are reviewed, and the prospects for expert system development within the mental health field are discussed. Emphasis is placed upon the decision-making models used in these systems rather than on their particular application area. The decision-making paradigms discussed are (1) data bank analysis, (2) statistical pattern recognition, (3) Bayesian analysis, (4) logical flow chart method, and (5) knowledge-based (expert system) approaches. For each paradigm, its essential features, its strengths and weaknesses, and some example applications are presented.

Diagnosis, Computer-Assisted↗
PubMed1998

Acute middle ear infection in small children: a Bayesian analysis using multiple time scales.

The study is based on a sample of 965 children living in Oulu region (Finland), who were monitored for acute middle ear infections from birth to the age of two years. We introduce a nonparametrically defined intensity model for ear infections, which involves both fixed and time dependent covariates, such as calendar time, current age, length of breast-feeding time until present, or current type of day care. Unmeasured heterogeneity, which manifests itself in frequent infections in some children and rare in others and which cannot be explained in terms of the known covariates, is modelled by using individual frailty parameters. A Bayesian approach is proposed to solve the inferential problem. The numerical work is carried out by Monte Carlo integration (Metropolis-Hastings algorithm).

Acute Disease↗
PubMed1999

Predicting the course of meningococcal disease outbreaks in closed subpopulations.

A stochastic epidemic model was applied to meningococcal disease outbreaks in defined small populations such as military garrisons and schools. Meningococci are spread primarily by asymptomatic carriers and only a small proportion of those infected develop invasive disease. Bayesian predictions of numbers of invasive cases were developed, based on observed data using a stochastic epidemic model. We used additional data sets to model both disease probability and duration of carriage. Markov chain Monte Carlo sampling techniques were used to compute the full posterior distribution which summarized all information drawn together from multiple sources.

Adult↗
PubMedDate not supplied

Continuous trees and NEVADA simulation: a quadrature approach to modeling continuous random variables in decision analysis.

This paper introduces an improved technique for modeling risk and decision problems that have continuous random variables and probabilistic dependence. Variables are modeled with mixtures of four-parameter random variables, called "continuous trees." Functions of random variables are calculated using gaussian quadrature in a manner called "Nevada simulation" (NumErical Integration of Variance And probabilistic Dependence Analyzer). This technique is compared with traditional decision-tree modeling in terms of analytic technique, solution-time complexity, and accuracy. Nevada simulation takes advantage of the probabilistic independence in a decision problem while allowing for probabilistic dependence to achieve polynomial computational-time complexity for many decision problems. It improves on the accuracy of traditional decision trees by employing larger approximations than traditional decision analysis. It improves on traditional decision analysis by modeling continuous variables with continuous, rather than discrete, distributions. A Bayesian analysis using a mixed discrete-continuous probability distribution for cigarette smoking rate is presented.

Algorithms↗
PubMed1994

A physician-based architecture for the construction and use of statistical models.

Physicians need specially tailored computer tools to take advantage of published research results. We present a knowledge-based computer framework--the physician-based (PB) architecture--for constructing such tools, and we use the problem of physicians' interpretation of two-arm parallel randomized clinical trials (TAPRCT) as a working example. Statistical models are represented by influence diagrams. The interpretation of influence-diagram elements are mapped into users' language in a domain-specific, physician-based user interface, called a patient-flow diagram. Statistical-model transformations that maintain the semantic relationships of the model and that embody clinical-epidemiological knowledge are encoded in a mediating structure called the cohort-state diagram. The algorithm that coordinates the interactions among the knowledge representations uses modular actions called construction steps. This architecture has been implemented in a Bayesian system, called THOMAS, that supports physician decision making in light of TAPRCT data. This support entails assessing clinical significance, prior beliefs, and methodological concerns. We suggest that the PB architecture applies to a wide range of statistical tools and users.

Algorithms↗
PubMed1987

A decision-driven system to collect the patient history.

We have developed a computer-administered history designed to directly interview hospitalized patients with pulmonary disease. A frame-based decision system is used to direct the history and to generate a one- to five-member differential diagnostic list based on this history. This system incorporates a cognitive model of question selection and a Bayesian scoring algorithm. Structures to control the choice of questions are embedded in the diagnostic frames and in a QUERY program that makes the final choice of questions. We have compared the behavior of this decision-driven approach with a history taken using a paper questionnaire. The paper-based history presents 182 questions to every patient and captured 75% of 85 pulmonary diseases in its differential lists. The decision-driven system asks 50.7 +/- 31.0 (mean +/- standard deviation) and captured 74% of 61 pulmonary diseases. Our experience suggests that the use of a computerized diagnostic knowledge base to direct the selection of pertinent questions can substantially reduce the number of questions necessary to collect a diagnostically useful patient history.

Artificial Intelligence↗
PubMed1994

The design and construction of a medical simulation model.

This paper describes the design, construction and validation of a probabilistic simulation model of patients who present with abdominal pain. The model incorporates text-book medical knowledge, clinical judgment, and statistics collected from real cases. The knowledge representation combines techniques of Bayesian network modelling with ideas of logistic discrimination. The model is shown to generate convincing, realistic cases; large numbers of artificial cases with no missing observations can be generated quickly. This should make the model a useful tool for investigating factors which limit achievable computer accuracy in the diagnosis of abdominal pain.

Abdominal Pain↗
PubMedDate not supplied

Designing for nonparametric Bayesian survival analysis using historical controls.

This paper gives a method for choosing the number of patients N0 out of N available patients to be randomized to current controls om a two-arm study when comparison of nonparametric survival curves is the anticipated method of data analysis. The criterion imposed is that of choosing N0 to minimize the posterior variance of the difference between the current control and experimental survival curves. A nonparametric Bayesian argument incorporating the survival curve of available historical controls establishes the criterion. Formulas and tables which facilitate this computation are presented.

Bayes Theorem↗
PubMed1998

Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms.

We present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with magnetic resonance imaging (MRI) or computed tomography (CT). Because we allow for mixtures of materials and treat voxels as regions, our technique reduces errors that other classification techniques can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume data. It also has the potential to make volume measurements more accurately and classifies noisy, low-resolution data well. There are two unusual aspects to our approach. First, we assume that, due to partial-volume effects, or blurring, voxels can contain more than one material, e.g., both muscle and fat; we compute the relative proportion of each material in the voxels. Second, we incorporate information from neighboring voxels into the classification process by reconstructing a continuous function, rho(x), from the samples and then looking at the distribution of values that rho(x) takes on within the region of a voxel. This distribution of values is represented by a histogram taken over the region of the voxel; the mixture of materials that those values measure is identified within the voxel using a probabilistic Bayesian approach that matches the histogram by finding the mixture of materials within each voxel most likely to have created the histogram. The size of regions that we classify is chosen to match the spacing of the samples because the spacing is intrinsically related to the minimum feature size that the reconstructed continuous function can represent.

Algorithms↗
PubMed2000

Strategies for estimating the parameters needed for different test-day models.

Currently, most analyses of parameters in test-day models involve two types of models: random regression, where various functions describe variability of (co)variances with regard to days in milk, and multiple traits, where observations in adjacent days in milk are treated as one trait. The methodologies used for estimation of parameters included Bayesian via Gibbs sampling, and REML in the form of derivative-free, expectation-maximization, or average-information algorithms. The first method is simpler and uses less memory but may need many rounds to produce posterior samples. In REML, however, the stopping point is well established. Because of computing limitations, the largest estimations of parameters were on fewer than 20,000 animals. The magnitude and pattern of heritabilities varied widely, which could be caused by simplifications in the model, overparameterization, small sample size, and unrepresentative samples. Patterns of heritability differ among random regression and multiple-trait models. Accurate parameters for large multi-trait random regression models may be difficult to obtain at the present time. Parameters that are sufficiently accurate in practice may be obtained outside the complete prediction model by a constructive approach, where parameters averaged over the lactation would be combined with several typical curves for (co)variances for days in milk. Obtained parameters could be used for any model, and could also aid in comparison of models.

Analysis of Variance↗