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PubMed2001

Lies, damned lies, and DNA statistics: DNA match testing Bayes' Theorem, and the criminal courts.

This study explains the correct method for the interpretation of DNA matches by using Bayesian Probability, and its various traps: the DNA fallacies. The current approach of the Court of Appeal to the use and presentation of DNA evidence is outlined. A new Bayesian Fallacy is explored. Specific focus is on Norman Fenton and Martin Neil's recent research (Fenton and Neil, 2000) which suggests a new dimension in the presentation of DNA evidence in court.

Bayes Theorem↗

PubMedDate not supplied

The D2 dopamine receptor gene as a predictor of compulsive disease: Bayes' theorem.

The dopaminergic system, and in particular the dopamine D2 receptor, has been profoundly implicated in reward mechanisms in the meso-limbic circuitry of the brain. Dysfunction of the D2 dopamine receptors leads to aberrant substance (alcohol, drug, tobacco and food) seeking behavior. Decades of research indicate that genetics play an important role in vulnerability to severe substance seeking behavior. We propose that variants of the D2 dopamine receptor gene are important common genetic determinants in predicting compulsive disease.

Alleles↗

PubMed1991

Three-locus linkage analysis using recombinant inbred strains and Bayes' theorem.

Recombinant inbred (RI) strains are useful in linkage analysis and gene mapping. However, the generally small number of strains in an RI strain set limits the power of RI strains in linkage detection. Several methods for increasing the power of RI strains have been used, including summing data across RI strain sets and excluding linkage to genomic regions. In this paper, Bayesian analysis is applied to three-locus linkage data. This method further increases the power of RI strains to detect linkage and gives estimations of the probability of each of the three possible gene orders if the test locus is linked to the pair of marker loci. Several examples are presented, including reconsideration of the position of the proto-oncogene L-myc on the mouse linkage map.

Animals↗

PubMed1997

Vector diagnostics in dementia derived from Bayes' theorem.

This paper introduces the concept of vector diagnostics. In contrast to the conventional approach where one diagnosis takes precedence, the authors propose an alternative strategy that addresses the clinical reality of comorbidity and multiple diagnoses for an individual. Based on a Bayesian approach, the probability distribution for the etiologically heterogeneous dementia diagnoses is estimated from the Canadian Study of Health and Aging database. These data were collected between February 1991 and May 1992. This method facilitates the establishment of a probability for more than one diagnosis within a given individual. By analyzing the correspondence between diagnostic groups, it is demonstrated that some clinical diagnoses are not reliably distinguished on the basis of the considered subset of symptoms and signs. As a consequence, the conventional diagnostic categories might require revision. The resulting probabilistic algorithm allows for the mining of existing epidemiologic databases for patterns of signs and symptoms that characterize emerging diagnostic categories which might better account for the heterogeneity of the dementia subtypes and individual variability.

Aged↗

PubMed1987

[Rational choice of a diagnostic test for the detection of bone metastases in breast cancer as a function of prevalence (based on the Bayes theorem)].

Since 1976 to 1978 inclusive, 465 patients with primary breast cancers underwent whole body analogical scintiscans, 456 numerical scans, and 453 were X-rayed. Ninety per cent received axillary node ablation, 62% presented adenopathies of which a third were capsular ruptures and 54% were T3 and T4. In 53 patients, the presence of metastasis was estimated by clinical evaluation. In 38 patients, metastasis were confirmed. Probability for the patient to present metastasis when the test is positive, is: P (M/T +) = Se.p/(Se.p) + (1-p) + (1-p) (1-Spe) Probability when the test is negative is: P (M/T -) = p(1-Spe)/p(1-Se) + (1-p)Spe p = prevalence Se = sensibility Spe = specificity Performance of the test is the probability of obtaining a correct result is: P = (f1) - (f2) = (f3) The (f3) curves were plotted for each of the three tests. For a prevalence of 11% (clinical estimate) the X-ray or analogical scintiscans tests achieve the same performance, but that for a prevalence of 8% (exact number) the radiological examination yields the best. In two cases, numerical scan is better.

Analysis of Variance↗

PubMed2006

Variable performance of weaning-predictor tests: role of Bayes' theorem and spectrum and test-referral bias.

OBJECTIVE: We examined whether variation in reported reliability of the frequency-to-tidal volume ratio (f/V(T)) in predicting weaning success is explained by spectrum and test-referral bias, as reflected by variation in pretest probability of success. DESIGN: Two authors extracted data from all studies on reliability of f/V(T) as a weaning predictor. RESULTS: Prevalence of successful weaning in studies of f/V(T) revealed significant heterogeneity; mean success rate was 0.75. The heterogeneity and high success rate reflects occurrence of spectrum bias, suggested by the lower value of f/V(T) in subsequent studies than in the original report (77.4 vs. 89.1) and test-referral bias, suggested by lower specificity of f/V(T) in subsequent studies than in the original report (0.52 vs. 0.64). When data from studies in the ACCP Task Force's meta-analysis of studies on f/V(T) were entered into a Bayesian model with pretest probability (prevalence of success) as the operating point, observed posttest probabilities were closely correlated with values predicted by the original report on f/V(T): positive-predictive value r = 0.86 and negative-predictive value r = 0.82. Average sensitivity, the most precise measure of screening-test reliability, was 0.87 +/- 0.14 and average specificity 0.52 +/- 0.26. CONCLUSIONS: Much of the heterogeneity in performance of f/V(T) can be explained by variation in pretest probability of successful outcome, which may be secondary to spectrum and test-referral bias. The average sensitivity of 0.87 indicates that f/V(T) is a reliable screening test for successful weaning.

Bayes Theorem↗

PubMedDate not supplied

Effect of the pretest probability of intrauterine growth retardation on the predictiveness of sonographic estimated fetal weight in detecting IUGR: a clinical application of Bayes' theorem.

Four hundred and five women with singleton pregnancies and fetal age determination by crown-rump length were classified on the basis of their prenatal clinical findings into four risk categories for intrauterine growth retardation (IUGR), defined as a neonatal weight below the 10th percentile of age-dependent birth weight distribution curve. The incidence of IUGR in these four groups were 3.5% (very low risk), 20.6% (low risk), 49.6% (intermediate risk), and 88.0% (high risk). Severe growth retardation (birth weight less than 2.5th percentile) increased from 0% to 76.0% as the incidence of IUGR increased throughout the risk groups. The effect of these pretest risks on the prediction of severe IUGR by sonographic estimated fetal weight (EFW) was evaluated. The positive predictive value of the test, as well as the probability of having a growth-retarded infant after a normal EFW was obtained were considerably higher when the pretest probability of IUGR increased. In the very low risk group, the probability of severe IUGR was negligible regardless of the EFW. When the EFW was less than 10th percentile of our age-dependent EFW curve, the probability of severe IUGR in the other risk groups was high enough to warrant fetal well-being surveillance and/or timely interruption of gestation as appropriate. However, when the pretest probability was high, the risk of severe IUGR in spite of an EFW within the 10th percentile to 90th percentile remained sufficient to require fetal well-being surveillance as well. The study shows that placing ultrasound results in the context of the pretest risk of IUGR may improve clinical decision making in pregnancies complicated by fetal growth retardation.

Bayes Theorem↗

PubMed1971