Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Latent Class Analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed1994

The subtyping of schizophrenia in men and women: a latent class analysis.

Latent class analysis on an epidemiologically based series of 447 first contact patients with a broad diagnosis of schizophrenia revealed evidence for two subtypes: a 'neurodevelopmental' type characterized by early onset, poor pre-morbid social adjustment, restricted affect and a male:female ratio of 7:3; and a 'paranoid' type characterized by later onset, persecutory delusions and an almost equal sex ratio. A third 'schizoaffective' subtype, whose existence was less clear cut, was almost entirely confined to females and characterized by dysphoria and persecutory delusions, and had negligible familial risk of schizophrenia. The aetiological, biological and clinical significance of this typology remains to be tested.

Adolescent↗
PubMedDate not supplied

Latent class analysis in chronic disease epidemiology.

Latent class analysis provides a useful framework for the analysis of epidemiological data which may have been mismeasured. In this paper, the latent class model is described in the context of logistic regression with categorical variables, and some examples of its application are provided. In particular, it is shown that adjustment for a misclassified confounding variable can be greatly improved by using the methods presented.

Chronic Disease↗
PubMedDate not supplied

A classification of Scottish infants using latent class analysis.

This paper illustrates the use of latent class analysis to classify 50,000 infants into a small number of classes or case types, as a preliminary to a study of the allocation of neonatal hospital resources throughout Scotland. Information, extracted from a detailed neonatal discharge record, was summarized by 11 clinical and diagnostic catagorical variables. Statistical models incorporating 1 to 6 latent classes were then estimated using the EM algorithm. The 4 class model was chosen because it provided a good description of the data and the resulting classes had a medical interpretation. The factors influencing the choice of model are discussed and goodness of fit tests are presented. The stability of the classes was also investigated using random halves of the data and an earlier comparable data set.

Classification↗
PubMed1990

Latent class analysis of diagnostic agreement.

We describe methods based on latent class analysis for analysis and interpretation of agreement on dichotomous diagnostic ratings. This approach formulates agreement in terms of parameters directly related to diagnostic accuracy and leads to many practical applications, such as estimation of the accuracy of individual ratings and the extent to which accuracy may improve with multiple opinions. We describe refinements in the estimation of parameters for varying panel designs, and apply latent class methods successfully to examples of medical agreement data that include data previously found to be poorly fitted by two-class models. Latent class techniques provide a powerful and flexible set of tools to analyse diagnostic agreement and one should consider them routinely in the analysis of such data.

Algorithms↗
PubMed1994

Latent class analysis of substance abuse patterns.

This chapter discusses use of latent class analysis (LCA) as a tool for identifying substance use patterns in cross-sectional data. LCA serves as an exploratory and data reduction tool that helps clarify the nature of substance use and may provide insight concerning effective prevention strategies. LCA is well suited to categorical data such as typically are collected in substance use research. Use of LCA can be divided into three steps: (1) model comparison and selection, (2) assignment of cases to latent classes, and (3) interpretation of the latent classes. Quantitative indices of model fit may assist model comparison and selection. Latent classes can be interpreted by examining probabilities of substance use in each latent class and by examining differences on exogenous variables. Limitations, extensions, and software for LCA are discussed. An example illustrates use of LCA with actual data collected from a current substance abuse prevention study.

Analysis of Variance↗
PubMed1996

Can we subtype alcoholism? A latent class analysis of data from relatives of alcoholics in a multicenter family study of alcoholism.

We attempt to identify distinctive subtypes of alcoholics using latent class analysis with data from 2551 relatives of alcoholic probands, all participants in the Collaborative Study of the Genetics of Alcoholism. Latent class analysis is a multivariate technique using cross-classified data to identify unobserved ("latent") classes that explain the relationships among observed variables. Data on 37 life-time symptoms of alcohol dependence from 1360 female and 1191 male relatives were analyzed, with a 4 class solution selected as the best fitting among the 2 through 6 class solutions that were examined. We observed the following classes: class 1, nonproblem drinkers (39.6% male, 50% female); class 2, mild alcoholics (persistent desire to stop, tolerance, and blackouts) (31.8% male, 28.7% female); class 3, moderate alcoholics (social, health, and emotional problems) (18.9% male, 14.6% female); and class 4, severely affected alcoholics (withdrawal, inability to stop drinking, craving, health, and emotional problems) (9.7% male, 6.7% female). There was little evidence for the construct of alcohol abuse; endorsement probabilities for abuse symptoms (e.g., arrest and DWIs) were very low for all classes, whereas hazardous use was common among men in class 1. In addition to those in class 3 and class 4, a majority of men in class 2 qualified for DSM-III-R alcohol dependence, suggesting a biomodal distribution of drinkers and alcoholics, with little nondependent problem drinking among men in this high-risk sample. We conclude that, in this sample, alcoholism is not differentiated by symptom profiles but rather lies on a continuum of severity, with the possible exception of withdrawal, which characterized only class 4 individuals.

Adolescent↗
PubMed1990

Latent class analysis of deluded patients.

The internal and external construct validity of 4 severe prognostic psychopathological items is studied in deluded patients. A two-class model of latent structure analysis fits fairly well. One latent class seems to reflect a schizophrenic spectrum while the other class does not include characteristics which are known from one single nosologic entity. Thought disorder is most predictive for schizophrenic class membership, while blunted affect has the least predictive value. This 'schizophrenic' latent class has a fairly high and similar sensitivity to various definitions of schizophrenic disorder. The frequency of the types of delusions differs between the classes.

Affective Symptoms↗
PubMed1998

The structure of psychosis: latent class analysis of probands from the Roscommon Family Study.

BACKGROUND: The nosologic structure of psychotic illness, still influenced as much by historical as empirical perspectives, remains controversial. METHODS: Latent class analysis was applied to detailed symptomatic and outcome assessments of probands (n=343) with broadly defined schizophrenia and affective illness ascertained from a population-based psychiatric registry in Roscommon County, Ireland. First-degree relatives (n=942) were assessed by personal interview and/or review of hospital record. RESULTS: Six classes were found, all of which bore substantial resemblance to current or historical nosologic constructs. In order of decreasing frequency, they were (1) classic schizophrenia, (2) major depression, (3) schizophreniform disorder, (4) bipolar-schizomania, (5) schizodepression, and (6) hebephrenia. These classes differed on many historical and clinical variables not used in the latent class analysis. Compared with relatives of controls, significantly increased rates of major depression were seen in relatives of depressed and schizodepressed probands. Significantly increased rates of bipolar illness were restricted to relatives of bipolar-schizomanic probands. The risks for schizophrenia and schizophrenia spectrum disorders were significantly increased in relatives of all proband classes except major depression. This increase was moderate for bipolar-schizomanic probands, substantial for schizophrenic, schizophreniform, and schizodepressed probands, and marked for hebephrenic probands. CONCLUSIONS: These results suggest a relatively complex typology of psychotic syndromes consistent neither with a unitary model nor with a Kraepelinian dichotomy. The familial vulnerability to psychosis extends across several syndromes, being most pronounced in those with schizophrenialike symptoms. The familial vulnerability to depressive and manic affective illness is somewhat more specific.

Adolescent↗
PubMed1999

The potential of latent class analysis in diagnostic test validation for canine Leishmania infantum infection.

Accuracy assessment of diagnostic tests may be seriously biased if an imperfect reference test is used such as parasitology in the diagnosis of visceral leishmaniasis. We compared classical validity analysis of serological tests for Leishmania infantum with Latent Class Analysis (LCA), to assess whether it circumvented the gold standard problem. Clinical status, three serological tests (IFAT, ELISA and DAT) and parasitological data were recorded for 151 dogs captured in an endemic area. Sensitivity and specificity estimates from the 2x2 contingency tables were broadly corroborated by LCA, but the latter method provided more precise estimates that were robust for the different fitted models. It furthermore yielded a higher prevalence of infection and indicated that parasitology was only 55% sensitive. LCA seems a promising technique for test validation, but caution is required when applying it to sparse data sets. The feasibility and applicability of LCA in infectious disease epidemiology is discussed.

Animals↗
PubMed1996

Latent class analysis in medical research.

In the introduction we give a brief characterization of the usual measures for indicating the quality of diagnostic procedures (sensitivity, specificity and predictive value) and we refer to their relationship to parameters of the latent class model. Different variants of latent class analysis (LCA) for dichotomous data are described in the following: the basic (unconstrained) model, models with parameters fixed to given values and with equality constraints on parameters, multigroup LCA including mixed-group validation, and linear logistic LCA including its relationship to the Rasch model and to the measurement of change in latent subgroups. The problem with the identifiability of latent class models and the possibilities for statistically testing their fit are outlined. The second part refers to latent class models for polytomous data. Special attention is paid to simple variants having fixed and/or equated parameters and to log-linear extension of LCA with its possibility for including on the latent level. Several examples are presented to illustrate typical applications of the model. The paper ends with some warnings that should be taken into consideration by potential users of LCA.

Clinical Trials as Topic↗
PubMed1989

DSM-III major depressive disorder in the community. A latent class analysis of data from the NIMH epidemiologic catchment area programme.

The fit of the structure of DSM-III major depressive disorder to data from two large epidemiological surveys is assessed by latent class analysis. The surveys were conducted at the Baltimore and Raleigh-Durham sites of the National Institute of Mental Health (NIMH) Epidemiologic Catchment Area Program. Three classes are required to fit the data, and the third class bears a strong resemblance to major depressive disorder, although it requires slightly more symptoms to be present than DSM-III. The derived structure replicates successfully for Baltimore and Raleigh-Durham, with a prevalence of the major depression category of 0.9% for both sites.

Catchment Area, Health↗
PubMed1999

Latent class analysis applied to patterns of fetal sonographic abnormalities: definition of phenotypes associated with aneuploidy.

The aim of the present study was to generate different latent variables that classify the major chromosome aneuploidies using frequency and patterns of fetal sonographic abnormalities in a large database. A total of 1867 fetuses with sonographic abnormalities recorded in a database at New England Medical Center from January 1995 to March 1998 were available for the statistical analysis. Included within this group were 61 aneuploid fetuses, including 11 with 45,X, 30 with trisomy 21, 14 with trisomy 18 and 6 with trisomy 13, 40 structural malformations and/or sonographic markers were detected in these 61 aneuploid fetuses. The ability of malformations and sonographic markers to generate different groups of phenotypes was evaluated by means of latent class analysis, using the 61 affected cases. Four different classes were generated with the hypothetical assumption that each of them could satisfactorily identify a respective fetal aneuploidy represented in the study group. Among 40 fetal malformations and/or sonographic markers, the most important findings in generating specific karyotypic groups were cystic hygroma (class 1), duodenal atresia (class 2), holoprosencephaly (class 3) and omphalocele (class 4), respectively. Accuracy of the classification was 72 per cent for Turner syndrome (class 1), 74 per cent for Down syndrome (classes 1 and 2), 88 per cent for trisomy 13 (class 3) and 93 per cent for trisomy 18. The frequency of associated malformations detected sonographically can help to define a phenotype that is likely to be representative of a specific aneuploidy. Before the definitive karyotype is available or, in cases in which patients refuse an invasive prenatal diagnostic procedure, this may improve antenatal clinical management.

Aneuploidy↗
PubMed1998

Latent class analysis of temperance board registrations in Swedish male-male twin pairs born 1902 to 1949: searching for subtypes of alcoholism.

BACKGROUND: Alcoholism is clinically heterogeneous. We have attempted to identify and validate subtypes of broadly defined alcoholism. METHODS: Latent class analysis (LCA) was applied to data on the number, age at onset and reasons for temperance board registration (TBR) in all male-male twin pairs of known zygosity born in Sweden from 1902-1949. RESULTS: Of the five classes identified, two were relatively common: single-cause registrant-drunk (SCR-D); and early-onset multiple-cause registrant (EO-MCR). In contrast to the SCR-D class, the EO-MCR class was characterized by: (i) earlier age at first TBR; (ii) higher number of TBRs; (iii) TBRs for drunk driving and alcohol-related crimes; (iv) much higher risk for alcohol-related imprisonment and hospitalization; (v) higher levels of neuroticism and novelty-seeking; and (vi) much greater risk for TBR in co-twins. In twin pairs concordant for TBR, concordance for LCA-derived class assignment far exceeded chance expectation, more so in monozygotic than in dizygotic pairs. CONCLUSIONS: Alcoholism is aetiologically as well as clinically heterogeneous. The two most common subtypes identified in these analyses bear substantial but imperfect resemblance to previously proposed typologies.

Alcoholism↗
PubMed1998

Latent class analysis of lifetime depressive symptoms in the national comorbidity survey.

OBJECTIVE: Although clinical trials have documented the importance of identifying individuals with major depression with atypical features, there are fewer epidemiological data. In a prior report, the authors used latent class analysis (LCA) to identify a distinctive atypical depressive subtype; they sought to replicate that finding in the current study. METHOD: Using the National Comorbidity Survey data, the authors applied LCA to 14 DSM-III-R major depressive symptoms in the participants' lifetime worst episodes (N=2,836). Validators of class membership included depressive disorder characteristics, syndrome consequences, demography, comorbidity, personality/attitudes, and parental psychiatric history. RESULTS: The best-fitting LCA solution had six classes. Four were combinations of atypicality and severity: severe atypical, mild atypical, severe typical, and mild typical. Syndrome severity (severe atypical and typical versus mild atypical and typical classes) was associated with a pronounced pattern of more and longer episodes, worse syndrome consequences, increased psychiatric comorbidity, more deviant personality and attitudes, and parental alcohol/drug use disorder. Syndrome atypicality (severe and mild atypical versus severe and mild typical classes) was associated with decreased syndrome consequences, comorbid conduct disorder and social phobia, higher interpersonal dependency and lower self-esteem, and parental alcohol/drug use disorder. CONCLUSIONS: As in prior reports, the atypical subtype of depression can be identified in epidemiological samples and, like typical depression, exists in mild and severe variants. Atypical depressive subtypes were characterized by several distinctive features. However, the correspondence between epidemiologically derived typologies of atypical depression and DSM-IV major depression with atypical features is not yet known.

Adolescent↗
PubMed1999

Latent class analysis permits unbiased estimates of the validity of DAT for the diagnosis of visceral leishmaniasis.

BACKGROUND: Substantial uncertainty surrounds the specificity of the Direct Agglutination Test (DAT) for visceral leishmaniasis (VL) in clinical suspects, since no good gold standard exists for unequivocally identifying diseased subjects. We explored the Latent Class Analysis (LCA) modelling technique to circumvent this problem. PATIENTS AND METHODS: Data on 149 clinical suspects recruited in 1993-96 during a multicentre study in Sudan were re-examined. Clinical data, lymph node and bone marrow aspirate and DAT results were available. IFAT was performed in 1997 on stored filter paper blood of 80 individuals. Classical Validity Analysis (CVA) in a 2 x 2 contingency table with parasitology as a gold standard was compared with the parameter estimates produced by the best fitting LCA model. RESULTS: The sensitivity estimates of DAT produced by CVA (98% (89%-100%)) were almost exactly reproduced by LCA. The specificity estimates by LCA were substantially higher than those obtained in CVA. Specificity of DAT depended, however, on whether the subject was treated for VL before. In subjects without prior treatment, CVA estimated DAT specificity at 68% (56%-79%), whereas LCA estimated it at 85% (63%-100%). CONCLUSION: LCA modelling proved a useful tool, as it gave consistent estimates of test characteristics and allowed for control of confounding factors and interaction effects. Since VL is a life-threatening disease for which expensive but effective and safe treatment exists, a clinical suspect in an endemic area should be treated on the basis of a positive DAT result.

Adolescent↗
PubMed1991

Latent class analysis of organic aspects of obsessive-compulsive disorder in children and adolescents.

Organic aspects of obsessive-compulsive disorder (OCD) have previously been described and hypotheses of biological etiology have been suggested. Sixty-one patients, 8-17 years of age, who fulfilled the DSM-III criteria for OCD in a review of the records were compared with 117 matched control patients for organic features. The indicators chosen for an organic concept were neurological signs, more than mild electroencephalographic abnormality, specific developmental disorder and attention deficit, and their defining property of an organic concept was confirmed by latent class analysis. Neurological signs was the most sensitive and specific indicator. Significantly fewer OCD children than control patients were assigned to the organic class. Almost all the types of obsessive-compulsive symptoms were more related to the non-organic class. Such extroverted symptoms as behavioral problems and loss of temper were significantly more frequent in patients assigned to the latent organic class, whereas symptoms of phobia and depressive mood were more often present in patients belonging to the nonorganic class. No difference was found between OCD patients and controls as to frequency of birth complications. The findings do not support the evidence of OCD having signs of major cerebral disturbance found by conventional neuropediatric methods.

Adjustment Disorders↗
PubMed1992

Observer homogeneity in the histologic diagnosis of Helicobacter pylori. Latent class analysis, kappa coefficient, and repeat frequency.

Four pathologists independently examined 82 antral mucosal biopsy specimens for the presence of Helicobacter pylori and indicated whether their assessments were certain. The pathologists made a positive diagnosis in from 56% to 84% of the specimens (significant heterogeneity, p < 0.01). The frequency of uncertain diagnoses was from 4% to 20% (p < 0.01). Uncertain statements occurred more frequently among negative than among positive diagnoses. For the six pairs of observers the kappa coefficients were between 0.39 and 0.82. By a latent class analysis measures of diagnostic accuracy were calculated comparing the observers' assessments with an estimated consensus diagnosis. The predictive values of a positive diagnosis ranged from 0.70 to 1.00. By calculation of repeat frequencies--that is, the probability that an observer's statement was confirmed by another observer--it became evident that uncertain statements were less frequently (61%) confirmed than were certain ones (85%). It is concluded that observer homogeneity is only moderate with regard to the histologic diagnosis of H. pylori, which should be considered both in daily clinical routine and in scientific studies. Disagreement between observers was associated with negative diagnoses, presumably because the pathologists felt more uncertain in these cases.

Adult↗
PubMedDate not supplied

The value of latent class analysis in medical diagnosis.

Assessment of the value of diagnostic indicators such as symptoms and laboratory tests results from calculation of the sensitivity and specificity of the indicators. Knowledge of the rate of occurrence of the disease allows for additional calculations of the error rates in using an indicator. These calculations are accurate only when the data on which they are based are reliable. If the diagnosis, which is used as the criterion for computing the sensitivity and specificity, is not accurate, then the resulting calculations will be in error. We show how a statistical method, latent class analysis, allows for the estimation of the characteristics of indicators even when an accurate diagnosis is unavailable. In addition, the method deals with several indicators at once, and provides a way to combine the information from all the indicators to make a diagnosis.

Biometry↗