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Failed refutations: further comments on parsimony and likelihood methods and their relationship to Popper's degree of corroboration.

Kluge's (2001, Syst. Biol. 50:322-330) continued arguments that phylogenetic methods based on the statistical principle of likelihood are incompatible with the philosophy of science described by Karl Popper are based on false premises related to Kluge's misrepresentations of Popper's philosophy. Contrary to Kluge's conjectures, likelihood methods are not inherently verificationist; they do not treat every instance of a hypothesis as confirmation of that hypothesis. The historical nature of phylogeny does not preclude phylogenetic hypotheses from being evaluated using the probability of evidence. The low absolute probabilities of hypotheses are irrelevant to the correct interpretation of Popper's concept termed degree of corroboration, which is defined entirely in terms of relative probabilities. Popper did not advocate minimizing background knowledge; in any case, the background knowledge of both parsimony and likelihood methods consists of the general assumption of descent with modification and additional assumptions that are deterministic, concerning which tree is considered most highly corroborated. Although parsimony methods do not assume (in the sense of entailing) that homoplasy is rare, they do assume (in the sense of requiring to obtain a correct phylogenetic inference) certain things about patterns of homoplasy. Both parsimony and likelihood methods assume (in the sense of implying by the manner in which they operate) various things about evolutionary processes, although violation of those assumptions does not always cause the methods to yield incorrect phylogenetic inferences. Test severity is increased by sampling additional relevant characters rather than by character reanalysis, although either interpretation is compatible with the use of phylogenetic likelihood methods. Neither parsimony nor likelihood methods assess test severity (critical evidence) when used to identify a most highly corroborated tree(s) based on a single method or model and a single body of data; however, both classes of methods can be used to perform severe tests. The assumption of descent with modification is insufficient background knowledge to justify cladistic parsimony as a method for assessing degree of corroboration. Invoking equivalency between parsimony methods and likelihood models that assume no common mechanism emphasizes the necessity of additional assumptions, at least some of which are probabilistic in nature. Incongruent characters do not qualify as falsifiers of phylogenetic hypotheses except under extremely unrealistic evolutionary models; therefore, justifications of parsimony methods as falsificationist based on the idea that they minimize the ad hoc dismissal of falsifiers are questionable. Probabilistic concepts such as degree of corroboration and likelihood provide a more appropriate framework for understanding how phylogenetics conforms with Popper's philosophy of science. Likelihood ratio tests do not assume what is at issue but instead are methods for testing hypotheses according to an accepted standard of statistical significance and for incorporating considerations about test severity. These tests are fundamentally similar to Popper's degree of corroboration in being based on the relationship between the probability of the evidence e in the presence versus absence of the hypothesis h, i.e., between p(e|hb) and p(e|b), where b is the background knowledge. Both parsimony and likelihood methods are inductive in that their inferences (particular trees) contain more information than (and therefore do not follow necessarily from) the observations upon which they are based; however, both are deductive in that their conclusions (tree lengths and likelihoods) follow necessarily from their premises (particular trees, observed character state distributions, and evolutionary models). For these and other reasons, phylogenetic likelihood methods are highly compatible with Karl Popper's philosophy of science and offer several advantages over parsimony methods in this context.

Knowledge↗

Slopes of a receiver operating characteristic curve and likelihood ratios for a diagnostic test.

This paper clarifies two important concepts in clinical epidemiology: the slope of a receiver operating characteristic (ROC) curve and the likelihood ratio. It points out that there are three types of slopes in an ROC curve--the tangent at a point on the curve, the slope between the origin and a point on the curve, and the slope between two points on the curve. It also points out that there are three types of likelihood ratios that can be defined for a diagnostic test that produces results on a continuous scale--the likelihood ratio for a particular single test value, the likelihood ratio for a positive test result, and the likelihood ratio for a test result in a particular level or category. It further illustrates mathematically and empirically the following three relations between these various definitions of slopes and likelihood ratios: 1) the tangent at a point on the ROC curve corresponds to the likelihood ratio for a single test value represented by that point; 2) the slope between the origin and a point on the curve corresponds to the positive likelihood ratio using the point as a criterion for positivity; and 3) the slope between two points on the curve corresponds to the likelihood ratio for a test result in a defined level bounded by the two points. The likelihood ratio for a single test value is considered an important parameter for evaluating diagnostic tests, but it is not easily estimable directly from laboratory data because of limited sample size. However, by using ROC analysis, the likelihood ratio for a single test value can be easily measured from the tangent. It is suggested that existing ROC analysis software be revised to provide estimates for tangents at various points on the ROC curve.

Creatine Kinase↗

Multilevel and continuous pleural fluid pH likelihood ratios for evaluating malignant pleural effusions.

STUDY OBJECTIVE: Expert consensus recommends testing pleural fluid for pH to assist the selection of patients with malignant pleural effusions for pleurodesis. Although published studies report an association between pleural fluid pH and patient outcomes after pleurodesis, clinicians have no definitive information on how to use pH to select patients for pleurodesis. Thus, we quantitatively assessed different methods for deriving likelihood ratios from pleural fluid pH and evaluated the potential role of pH in selecting patients for pleurodesis. DATA SOURCES: MEDLINE, systematic reviews, article reference lists, and contact with primary authors. STUDY SELECTION: Studies that assessed the impact of pleural fluid pH on survival and pleurodesis failure rates among patients with malignant pleural effusions. DATA EXTRACTION: Primary authors provided their data in electronic spreadsheets. DATA SYNTHESIS: Retrieved data sets included survival and pleurodesis failure rates for 417 patients and 433 patients, respectively. Binary, multilevel, and continuous likelihood ratios were calculated to estimate the likelihood of death within 3 months of pleurodesis or pleurodesis failure rates. Values for the likelihood ratios were compared for each of the three strategies, and relative clinical and statistical significance were assessed. Pleural fluid pH had marginal performance for identifying patients with < 3-month anticipated survival; binary likelihood ratios provided as much information as the multilevel and continuous strategies. Likelihood ratios for identifying patients likely to fail pleurodesis were clinically useful. Continuous likelihood ratios provided statistically more information as compared with the multilevel and binary strategies. CONCLUSIONS: Pleural fluid pH has marginal value for estimating death within 3 months of pleurodesis, and binary likelihood ratios (cut point </= 7.20) perform as well as the other strategies assessed. Pleural fluid pH provides more useful information for estimating the likelihood of pleurodesis failure for which continuous likelihood ratios provide the most information as compared with binary or multilevel likelihood ratios.

Data Interpretation, Statistical↗

Bias and efficiency in family-based gene-characterization studies: conditional, prospective, retrospective, and joint likelihoods.

We revisit the usual conditional likelihood for stratum-matched case-control studies and consider three alternatives that may be more appropriate for family-based gene-characterization studies: First, the prospective likelihood, that is, Pr(D/G,A second, the retrospective likelihood, Pr(G/D); and third, the ascertainment-corrected joint likelihood, Pr(D,G/A). These likelihoods provide unbiased estimators of genetic relative risk parameters, as well as population allele frequencies and baseline risks. The parameter estimates based on the retrospective likelihood remain unbiased even when the ascertainment scheme cannot be modeled, as long as ascertainment only depends on families' phenotypes. Despite the need to estimate additional parameters, the prospective, retrospective, and joint likelihoods can lead to considerable gains in efficiency, relative to the conditional likelihood, when estimating genetic relative risk. This is true if baseline risks and allele frequencies can be assumed to be homogeneous. In the presence of heterogeneity, however, the parameter estimates assuming homogeneity can be seriously biased. We discuss the extent of this problem and present a mixed models approach for providing consistent parameter estimates when baseline risks and allele frequencies are heterogeneous. The efficiency gains of the mixed-model prospective, retrospective, and joint likelihoods relative to the efficiency of conditional likelihood are small in the situations presented here.

Alleles↗

Map-likelihood phasing.

The recently developed technique of maximum-likelihood density modification [Terwilliger (2000), Acta Cryst. D56, 965-972] allows a calculation of phase probabilities based on the likelihood of the electron-density map to be carried out separately from the calculation of any prior phase probabilities. Here, it is shown that phase-probability distributions calculated from the map-likelihood function alone can be highly accurate and that they show minimal bias towards the phases used to initiate the calculation. Map-likelihood phase probabilities depend upon expected characteristics of the electron-density map, such as a defined solvent region and expected electron-density distributions within the solvent region and the region occupied by a macromolecule. In the simplest case, map-likelihood phase-probability distributions are largely based on the flatness of the solvent region. Though map-likelihood phases can be calculated without prior phase information, they are greatly enhanced by high-quality starting phases. This leads to the technique of prime-and-switch phasing for removing model bias. In prime-and-switch phasing, biased phases such as those from a model are used to prime or initiate map-likelihood phasing, then final phases are obtained from map-likelihood phasing alone. Map-likelihood phasing can be applied in cases with solvent content as low as 30%. Potential applications of map-likelihood phasing include unbiased phase calculation from molecular-replacement models, iterative model building, unbiased electron-density maps for cases where 2F(o) - F(c) or sigma(A)-weighted maps would currently be used, structure validation and ab initio phase determination from solvent masks, non-crystallographic symmetry or other knowledge about expected electron density.

Crystallography, X-Ray↗

Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions.

We continue the theme of previous papers [J. Opt. Soc. Am. A 7, 1266 (1990); 12, 834 (1995)] on objective (task-based) assessment of image quality. We concentrate on signal-detection tasks and figures of merit related to the ROC (receiver operating characteristic) curve. Many different expressions for the area under an ROC curve (AUC) are derived for an arbitrary discriminant function, with different assumptions on what information about the discriminant function is available. In particular, it is shown that AUC can be expressed by a principal-value integral that involves the characteristic functions of the discriminant. Then the discussion is specialized to the ideal observer, defined as one who uses the likelihood ratio (or some monotonic transformation of it, such as its logarithm) as the discriminant function. The properties of the ideal observer are examined from first principles. Several strong constraints on the moments of the likelihood ratio or the log likelihood are derived, and it is shown that the probability density functions for these test statistics are intimately related. In particular, some surprising results are presented for the case in which the log likelihood is normally distributed under one hypothesis. To unify these considerations, a new quantity called the likelihood-generating function is defined. It is shown that all moments of both the likelihood and the log likelihood under both hypotheses can be derived from this one function. Moreover, the AUC can be expressed, to an excellent approximation, in terms of the likelihood-generating function evaluated at the origin. This expression is the leading term in an asymptotic expansion of the AUC; it is exact whenever the likelihood-generating function behaves linearly near the origin. It is also shown that the likelihood-generating function at the origin sets a lower bound on the AUC in all cases.

Area Under Curve↗

Use of the likelihood ratio in the management of the young child with fever.

The febrile infant is a common clinical problem for the primary health care provider. This paper employs the example of a young infant with fever to describe an important epidemiologic concept that is useful in the interpretation of diagnostic data--the likelihood ratio. The likelihood ratio expresses the odds of a given diagnostic test result occurring in a patient with (as opposed to without) the target disorder. Likelihood ratios have three properties that are helpful for clinicians: (1) The likelihoods that make up the likelihood ratio are calculated in a manner similar to sensitivity and specificity and therefore show little variation with change in disease prevalence (unlike predictive values, which change dramatically with disease prevalence). (2) Likelihood ratios can be calculated at several levels of a sign, symptom, or laboratory test. (3) Likelihood ratios can be used to shorten the list of diagnostic possibilities because the pretest "odds" X likelihood ratio = post-test "odds" of a disease. Using likelihood ratios in the practice of primary care medicine should reduce the number of patients with false-positive or false-negative results, sparing some patients needless therapy as well as minimizing the number of patients denied efficacious interventions. Support for likelihood ratios within the primary care medical community will hasten their availability in laboratories of clinical medicine.

Epidemiologic Methods↗

Residence location and likelihood of kidney transplantation.

BACKGROUND: In a universal, public health care system, access to kidney transplantation should not be influenced by residence location. We determined the likelihood of kidney transplantation from deceased donors among Canadian dialysis patients living in 7 geographic regions. Within each region we also determined whether distance from the closest transplant centre was associated with the likelihood of transplantation. METHODS: A random sample of 7034 subjects initiating dialysis in Canada between 1996 and 2000 was studied. We used Cox proportional hazards models to examine the relation between residence location and the likelihood of kidney transplantation from deceased donors over a median period of 2.4 years. RESULTS: There were significant differences in the likelihood of kidney transplantation from deceased donors and predicted waiting times between the different geographic regions. For example, the adjusted relative likelihood of transplantation in Alberta was 3.74 (95% confidence interval [CI] 2.95-4.76) compared with the likelihood in Ontario (p < 0.001). These differences persisted after further adjustment for differences in the rate of deceased organ donation. Within regions, patients who resided 50.1-150 km, 150.1-300 km and more than 300 km from the closest transplant centre had a similar adjusted likelihood of receiving a kidney transplant as those who lived less than 50 km away. INTERPRETATION: The adjusted likelihood of undergoing a kidney transplant from a deceased donor varied substantially between geographic regions in Canada. In contrast, the likelihood of transplantation within regions was not affected by distance from the closest transplant centre.

Aged↗

Likelihood-based disequilibrium mapping for two-marker haplotype data.

We report a theory that gives the sampling distribution of two-marker haplotypes that are linked to a rare disease mutation. The sampling distribution is generated with successive Monte Carlo realizations of the coalescence of the disease mutation having recombination and marker mutation events placed along the lineage. Given a sample of mutation-bearing, two-marker haplotypes, the maximum likelihood estimate of the location of the disease mutation can be calculated from the generated sampling distribution, provided that one knows enough about the population history in order to model it. The two-marker likelihood method is compared to a single-marker likelihood and a composite likelihood. The two-marker maximum likelihood gives smaller confidence intervals for the location of the disease locus than a comparable single-marker maximum likelihood. The composite likelihood can give biased results and the bias increases as the extent of linkage disequilibrium on mutation-bearing chromosomes decreases. Haplotype configurations exist for which the composite likelihood will fail to place the disease locus in the correct marker interval.

Genetic Diseases, Inborn↗

Application of stratum-specific likelihood ratios in mental health screening.

BACKGROUND: The accuracy of a diagnostic procedure is commonly assessed by measuring sensitivity, specificity and positive and negative predictive values. Likelihood ratios provide an alternative method for describing these results, though they are typically reported only for dichotomized outcomes. However, likelihood ratios can also be applied to ordinal or continuous results. METHODS: The present paper discusses the application of stratum-specific likelihood ratios in a primary care setting using the General Health Questionnaire (GHQ-12) and the Symptom Check List 90-R (SCL-90-R). A randomly selected sample (n = 408) of adult outpatients from primary care offices in Düsseldorf was screened using the German versions of the GHQ-12 and the SCL-90-R. RESULTS: Logistic regression analysis indicated that stratum-specific or multilevel likelihood ratios preserve more information than a fixed threshold approach with a single cutoff point. For each test, five clinically useful strata with monotonically increasing stratum-specific likelihood ratios were selected. CONCLUSIONS: Stratum-specific likelihood ratios have enormous practical value, and they are becoming an important way of expressing and comparing the usefulness of different tests. Stratum-specific likelihood ratios reduce the spectrum bias that might arise if only two categories (cases and non-cases) are chosen. Additionally, multilevel likelihood ratios can be used as bedside information to obtain the post-test probability from the pre-test probability of the disorder.

Adult↗

A pseudo-likelihood method for estimating effective population size from temporally spaced samples.

A pseudo maximum likelihood method is proposed to estimate effective population size (Ne) using temporal changes in allele frequencies at multi-allelic loci. The computation is simplified dramatically by (1) approximating the multi-dimensional joint probabilities of all the data by the product of marginal probabilities (hence the name pseudo-likelihood), (2) exploiting the special properties of transition matrix and (3) using a hidden Markov chain algorithm. Simulations show that the pseudo-likelihood method has a similar performance but needs much less computing time and storage compared with the full likelihood method in the case of 3 alleles per locus. Due to computational developments, I was able to assess the performance of the pseudo-likelihood method against the F-statistic method over a wide range of parameters by extensive simulations. It is shown that the pseudo-likelihood method gives more accurate and precise estimates of Ne than the F-statistic method, and the performance difference is mainly due to the presence of rare alleles in the samples. The pseudo-likelihood method is also flexible and can use three or more temporal samples simultaneously to estimate satisfactorily the NeS of each period, or the growth parameters of the population. The accuracy and precision of both methods depend on the ratio of the product of sample size and the number of generations involved to Ne, and the number of independent alleles used. In an application of the pseudo-likelihood method to a large data set of an olive fly population, more precise estimates of Ne are obtained than those from the F-statistic method.

Algorithms↗

A solution to the problem of monotone likelihood in Cox regression.

The phenomenon of monotone likelihood is observed in the fitting process of a Cox model if the likelihood converges to a finite value while at least one parameter estimate diverges to +/- infinity. Monotone likelihood primarily occurs in small samples with substantial censoring of survival times and several highly predictive covariates. Previous options to deal with monotone likelihood have been unsatisfactory. The solution we suggest is an adaptation of a procedure by Firth (1993, Biometrika 80, 27-38) originally developed to reduce the bias of maximum likelihood estimates. This procedure produces finite parameter estimates by means of penalized maximum likelihood estimation. Corresponding Wald-type tests and confidence intervals are available, but it is shown that penalized likelihood ratio tests and profile penalized likelihood confidence intervals are often preferable. An empirical study of the suggested procedures confirms satisfactory performance of both estimation and inference. The advantage of the procedure over previous options of analysis is finally exemplified in the analysis of a breast cancer study.

Biometry↗

A likelihood ratio approach to meta-analysis of diagnostic studies.

OBJECTIVE: To develop a clinically and methodologically sound approach to diagnostic meta-analysis. METHODS: Two-step model was used involving four fictitious sets of 10 studies each with varying sensitivity and specificity; this was followed by the application of the method to data from a published systematic review of emergency ultrasound. Multidimensional test characteristics (relating to the detection or exclusion of the condition of interest) were described by likelihood ratio scatterplots and pooled likelihood ratios. Likelihood ratios summarise the ability of a test to revise the prior probability of disease. They can be summarised by established fixed-effects and random-effects methods. RESULTS: Likelihood ratios precisely describe both directions of test performance. By plotting positive against negative likelihood ratios, together with their 95% confidence intervals, a multidimensional forest plot is obtained that can be interpreted in analogy to therapeutic meta-analyses. There are accepted threshold values of positive and negative likelihood ratios (i.e. 10.0 and 0.1) to recommend a test for clinical use. In the matrix space, distinct test characteristics can even be assessed by eyeballing. With regard to data from the real meta-analysis, the suggested high discriminatory power of ultrasound was only partially qualified by likelihood ratios. The positive value confirms the reliability of a positive scan, whereas the negative value questions a normal sonogram. CONCLUSIONS: A full characterisation of test performance requires multidimensional effect measures. Likelihood ratios are recommended descriptors of the two dimensions of diagnostic research evidence and provide a convenient means to visualise and to communicate results as weighted summary estimates of a diagnostic meta-analysis.

Diagnosis↗

Likelihood-based inference for the genetic relative risk based on affected-sibling-pair marker data.

Using genetic marker data from affected sibling pairs, we study likelihood-based linkage analysis under quasi-recessive, quasi-dominant, and general single-locus models. We use an epidemiologic parameterization under a model where the marker locus is closely linked to the putative disease susceptibility gene. This model and parameterization allow inferences about the relative risk associated with the susceptible genotype. We base inferences on approximate likelihoods that focus on the affected siblings in the sibship and, using these likelihoods, we derive closed-form maximum likelihood estimators for model parameters and closed-form likelihood ratio statistics for tests that the relative risk associated with the susceptible genotype is one. Under the general single-locus model, our likelihood ratio test is the same as the iteratively computed triangle test proposed by Holmans (1993, American Journal of Human Genetics 52, 362-374) for the case where marker identity-by-descent is known; our derivation gives a closed form for the test statistic. We present quartiles of the distribution of parameter estimates and critical values for the exact null distribution of our likelihood ratio test statistics; we also give large-sample approximations to their null distributions. We show that the powers of our likelihood ratio tests exceed the powers of more commonly used nonparametric affected-sibling-pair tests when the data meet the inheritance model assumptions used to derive the test; we also show that our tests' powers are robust to violation of model assumptions. We conclude that our model-based inferences provide a practical alternative to more common affected-sibling-pair tests when investigators have some knowledge about the mode of inheritance of a disease and that our methods may sometimes be useful for comparing the genetic relative risk with environmental relative risks.

Disease Susceptibility↗

Likelihood ratios: a real improvement for clinical decision making?

The concept of likelihood ratio has been advocated for several years as one of the better means to evaluate diagnostic tests and as a practical and valuable tool in clinical decision making. In this paper we review the basic concepts underlying the evaluation of diagnostic tests and we explore the properties and usefulness of both positive and negative likelihood ratios compared with sensitivity and specificity. Particular attention is given to the use of likelihood ratios in the clinical setting. Likelihood ratios have three main advantages: they are intuitive, they simplify the predictive value calculation and the overall evaluation of sequential testing. Disadvantages are the non-linearity and the necessity to recalculate probabilities in odds. Although they summarize the information contained in sensitivity and specificity, these characteristics are still necessary for certain clinical decisions. Since likelihood ratios have been promoted among physicians and medical students, we discuss examples of inappropriate use and misunderstandings in the medical literature: the frequent omission of confidence intervals, the choice of cut-off points based on likelihood ratios for positive test results only and the confusion between likelihood ratios for ranges and those for cut-off points.

Decision Support Techniques↗

Refining clinical diagnosis with likelihood ratios.

Likelihood ratios can refine clinical diagnosis on the basis of signs and symptoms; however, they are underused for patients' care. A likelihood ratio is the percentage of ill people with a given test result divided by the percentage of well individuals with the same result. Ideally, abnormal test results should be much more typical in ill individuals than in those who are well (high likelihood ratio) and normal test results should be most frequent in well people than in sick people (low likelihood ratio). Likelihood ratios near unity have little effect on decision-making; by contrast, high or low ratios can greatly shift the clinician's estimate of the probability of disease. Likelihood ratios can be calculated not only for dichotomous (positive or negative) tests but also for tests with multiple levels of results, such as creatine kinase or ventilation-perfusion scans. When combined with an accurate clinical diagnosis, likelihood ratios from ancillary tests improve diagnostic accuracy in a synergistic manner.

Diagnosis↗

Maximum-likelihood estimation of relatedness.

Relatedness between individuals is central to many studies in genetics and population biology. A variety of estimators have been developed to enable molecular marker data to quantify relatedness. Despite this, no effort has been given to characterize the traditional maximum-likelihood estimator in relation to the remainder. This article quantifies its statistical performance under a range of biologically relevant sampling conditions. Under the same range of conditions, the statistical performance of five other commonly used estimators of relatedness is quantified. Comparison among these estimators indicates that the traditional maximum-likelihood estimator exhibits a lower standard error under essentially all conditions. Only for very large amounts of genetic information do most of the other estimators approach the likelihood estimator. However, the likelihood estimator is more biased than any of the others, especially when the amount of genetic information is low or the actual relationship being estimated is near the boundary of the parameter space. Even under these conditions, the amount of bias can be greatly reduced, potentially to biologically irrelevant levels, with suitable genetic sampling. Additionally, the likelihood estimator generally exhibits the lowest root mean-square error, an indication that the bias in fact is quite small. Alternative estimators restricted to yield only biologically interpretable estimates exhibit lower standard errors and greater bias than do unrestricted ones, but generally do not improve over the maximum-likelihood estimator and in some cases exhibit even greater bias. Although some nonlikelihood estimators exhibit better performance with respect to specific metrics under some conditions, none approach the high level of performance exhibited by the likelihood estimator across all conditions and all metrics of performance.

Alleles↗

Maximum-likelihood density modification.

A likelihood-based approach to density modification is developed that can be applied to a wide variety of cases where some information about the electron density at various points in the unit cell is available. The key to the approach consists of developing likelihood functions that represent the probability that a particular value of electron density is consistent with prior expectations for the electron density at that point in the unit cell. These likelihood functions are then combined with likelihood functions based on experimental observations and with others containing any prior knowledge about structure factors to form a combined likelihood function for each structure factor. A simple and general approach to maximizing the combined likelihood function is developed. It is found that this likelihood-based approach yields greater phase improvement in model and real test cases than either conventional solvent flattening and histogram matching or a recent reciprocal-space solvent-flattening procedure [Terwilliger (1999), Acta Cryst. D55, 1863-1871].

Crystallography, X-Ray↗