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PubMed2006

Subtypes of illicit drug users: a latent class analysis of data from an Australian twin sample.

This article applies methods of latent class analysis (LCA) to data on lifetime illicit drug use in order to determine whether qualitatively distinct classes of illicit drug users can be identified. Self-report data on lifetime illicit drug use (cannabis, stimulants, hallucinogens, sedatives, inhalants, cocaine, opioids and solvents) collected from a sample of 6265 Australian twins (average age 30 years) were analyzed using LCA. Rates of childhood sexual and physical abuse, lifetime alcohol and tobacco dependence, symptoms of illicit drug abuse/dependence and psychiatric comorbidity were compared across classes using multinomial logistic regression. LCA identified a 5-class model: Class 1 (68.5%) had low risks of the use of all drugs except cannabis; Class 2 (17.8%) had moderate risks of the use of all drugs; Class 3 (6.6%) had high rates of cocaine, other stimulant and hallucinogen use but lower risks for the use of sedatives or opioids. Conversely, Class 4 (3.0%) had relatively low risks of cocaine, other stimulant or hallucinogen use but high rates of sedative and opioid use. Finally, Class 5 (4.2%) had uniformly high probabilities for the use of all drugs. Rates of psychiatric comorbidity were highest in the polydrug class although the sedative/opioid class had elevated rates of depression/suicidal behaviors and exposure to childhood abuse. Aggregation of population-level data may obscure important subgroup differences in patterns of illicit drug use and psychiatric comorbidity. Further exploration of a 'self-medicating' subgroup is needed.

Australia↗
PubMed2004

Application of a latent class analysis to empirically define eating disorder phenotypes.

CONTEXT: Diagnostic criteria for eating disorders influence how we recognize, research, and treat eating disorders, and empirically valid phenotypes are required for revealing their genetic bases. OBJECTIVE: To empirically define eating disorder phenotypes. DESIGN: Data regarding eating disorder symptoms and features from 1179 individuals with clinically significant eating disorders were submitted to a latent class analysis. The resulting latent classes were compared on non-eating disorder variables in a series of validation analyses. SETTING: Multinational, collaborative study with cases ascertained through diverse clinical settings (inpatient, outpatient, and community). PARTICIPANTS: Members of affected relative pairs recruited for participation in genetic studies of eating disorders in which probands met DSM-IV-TR criteria for anorexia nervosa (AN) or bulimia nervosa and had at least 1 biological relative with a clinically significant eating disorder. Main Outcome Measure Number and clinical characterization of latent classes. RESULTS: A 4-class solution provided the best fit. Latent class 1 (LC1) resembled restricting AN; LC2, AN and bulimia nervosa with the use of multiple methods of purging; LC3, restricting AN without obsessive-compulsive features; and LC4, bulimia nervosa with self-induced vomiting as the sole form of purging. Biological relatives were significantly likely to belong to the same latent class. Across validation analyses, LC2 demonstrated the highest levels of psychological disturbance, and LC3 demonstrated the lowest. CONCLUSIONS: The presence of obsessive-compulsive features differentiates among individuals with restricting AN. Similarly, the combination of low weight and multiple methods of purging distinguishes among individuals with binge eating and purging behaviors. These results support some of the distinctions drawn within the DSM-IV-TR among eating disorder subtypes, while introducing new features to define phenotypes.

Adolescent↗
PubMed2005

Drug use practices among MDMA/ecstasy users in Ohio: a latent class analysis.

This study describes the drug use practices among 402 recent MDMA (3,4-methelyenedioxymethamphetamine) users recruited in Ohio using respondent-driven sampling. About 64% of the participants were men, 81.6% were white, and the mean age was 20.9 years. Latent class analysis was used to identify subgroups of MDMA users. Use of cocaine, opioids, amphetamines, tranquilizers, inhalants, marijuana, and hallucinogens during the previous 6 months, and days of "drunkenness" in the past 30, were used for classification. A three-class model was preferable and reflected "Limited range," "Moderate range," and "Wide range" drug use patterns. For example, the conditional probability of using opioids during the previous 6 months was .07 in Class 1, .59 in Class 2, and .88 in Class 3. Other substances followed similar patterns. Predictors of class membership were examined in a multinomial logit model in which the "Limited range" Class was treated as the reference group. Participants who were white, younger, and who reported more than 10 occasions of MDMA use were more likely to be in the "Wide range" drug use Class. Latent class analysis is a useful method to help describe and understand variability in polydrug use patterns.

Adolescent↗
PubMed2001

Structural asymmetry as a dermatoscopic indicator of malignant melanoma--a latent class analysis of sensitivity and classification errors.

Asymmetry of pigmented skin lesions is an important indicator of possible malignant melanoma and contributes substantially to the diagnosis of melanoma in the dermatoscopic ABCD rule for melanocytic lesions and other algorithms. However, it may be observer dependent. Dermatoscopic asymmetry cannot be assessed objectively and no golden standard of asymmetry diagnosis exists. The aim of this study was to assess the sensitivity of axis (a-) symmetry using latent class analysis. We analysed ratings from four experts in dermatoscopy of 232 pigmented lesions by latent class analysis (LCA). Possible ratings were 'no asymmetry', 'asymmetry in one axis' and 'asymmetry in two axes'. A subset of melanocytic lesions (blue naevi excluded) was analysed. Based on LCA, the asymmetry of the singular lesion was determined. The sensitivity of 'no asymmetry' was 40-77%, 40-70% for one-axis asymmetry, and 77-92% for two-axes asymmetry. Overestimation of asymmetry was more common than underestimation. Melanomas were significantly more asymmetric than pigmented naevi, atypical naevi and papillomas, but not basal cell cancers. Analysis of the melanocytic subset gave similar results. The median asymmetry of malignant melanomas (1.67, interquartile range 1.81-1.99) was higher than for melanocytic naevi. In conclusion, asymmetry and symmetry are important criteria for diagnosing or excluding malignant melanoma using the dermatoscopic ABCD rule, risk stratification and other diagnostic rules. Using LCA, we minimized observer dependence in the assessment of axis (a-) symmetry. LCA, besides conceptualizing the diagnostic process, enables the assignment of lesions to their true diagnostic class.

Basal Cell Carcinoma↗
PubMed2002

Latent class analysis of symptoms associated with chronic fatigue syndrome and fibromyalgia.

BACKGROUND: Chronic fatigue syndrome and fibromyalgia continue to be perplexing conditions of unknown validity. Aetiological and symptomatic heterogeneity is likely and the distinctiveness of these disorders remains unclear. Our aims were to investigate empirically symptomatic heterogeneity in chronic fatigue syndrome and fibromyalgia. METHODS: Latent class analysis was applied to data from 646 patients who met accepted criteria for chronic fatigue syndrome and/or fibromyalgia who were systematically evaluated at a specialist fatigue clinic. Thirty-two symptoms commonly found in chronic fatigue syndrome and fibromyalgia were entered into the latent class analysis. RESULTS: We chose to interpret a four class solution. The classes appeared to differ in a graded fashion (rather than qualitatively) for symptom endorsements, pre-morbid characteristics, and co-morbidity with panic disorder and major depression. CONCLUSIONS: These results were unexpected given the usual assumption of the distinctiveness of chronic fatigue syndrome and fibromyalgia. These results support a conceptualization of chronic fatigue syndrome and fibromyalgia as being characterized by greater similarities than differences.

Adult↗
PubMedDate not supplied

Clinical assessment of infection in nonhealing ulcers analyzed by latent class analysis.

The distinction between bacterial colonization and infection relies on clinical judgement. Determining sensitivity and specificity of this judgement are problematic as no gold standard exists. Six specialists in wound management independently assessed 120 nonhealing chronic wounds. Sixty-five (54.2%) patients had venous ulcer, 18 (15%) arterial ulcers, 15 (12.5%) ulcerative pyoderma gangraenosum, 12 (10%) neuropathic or pressure ulcers, six (5%) vasculitis ulcers, and four patients had ulcers caused by a primary or metastatic cancer disease. Unrestricted latent class analysis was used for determining sensitivity and specificity in the observer's assessment of hypergranulation, redness, and overall impression of infection. Interrater agreement among observers was determined by restricted latent class analysis. The observers used the diagnoses (redness, hypergranulation, and overall impression of infection with different frequencies (p<0.001, Cochrane's Q test). A two latent class model fitted data. Sensitivity for hypergranulation ranged from 3 to 82%, for redness from 34 to 91% and for overall impression of infection from 37 to 90%. None of the observers were interchangeable. These results indicate that clinical assessment of chronic wounds for the presence of infection are difficult tasks accompanied by great variability and low reliability.

Humans↗
PubMed2005

Determining the sensitivity and specificity of common diagnostic tests for carpal tunnel syndrome using latent class analysis.

BACKGROUND: The accuracy of a diagnostic test used to classify a patient as having disease or being disease-free is a valuable piece of information to be used by the physician when making treatment decisions. If a standard reference test is available, determining the sensitivity and specificity of a new test is straightforward. However, if that reference test is incorrectly assumed to be perfectly sensitive and specific, the errors of the reference test can result in an underestimation of the accuracy of the test being evaluated. Latent class analysis can be applied to determine the sensitivity and specificity of a new test when no standard exists. METHODS: Latent class analysis was used to determine the accuracy rates of three commonly used measures of carpal tunnel syndrome: Tinel's sign, Phalen's test, and the nerve conduction velocity test. Data included 162 wrists from 81 patients seeking treatment for symptoms associated with carpal tunnel syndrome. RESULTS: Tinel's sign and Phalen's test were both highly sensitive (0.97 and 0.92, respectively) and specific (0.91 and 0.88, respectively). The sensitivity and specificity of the nerve conduction velocity test were 0.93 and 0.87, respectively. CONCLUSION: Estimates of the sensitivity and specificity of these common tests for carpal tunnel syndrome support their widespread clinical use.

Adult↗
PubMed2006

Characterizing the course of low back pain: a latent class analysis.

Understanding the course of back pain is important for clinicians and researchers, but analyses of longitudinal data from multiple time points are lacking. A prospective cohort study of consecutive back pain consulters from five general practices in the United Kingdom was carried out between 2001 and 2003 to identify groups defined by their pain pathways. Patients were sent monthly questionnaires for a year. Longitudinal latent class analysis was performed by using pain intensity scores for 342 consulters. Analysis yielded four clusters representing different pathways of back pain. Cluster 1 ("persistent mild"; n = 122) patients had stable, low levels of pain. Patients in cluster 2 ("recovering"; n = 104) started with mild pain, progressing quickly to no pain. Cluster 3 ("severe chronic"; n = 71) patients had permanently high pain. For patients in cluster 4 ("fluctuating"; n = 45), pain varied between mild and high levels. Distinctive patterns for each cluster were maintained throughout follow-up. Clusters showed statistically significant differences in disability, psychological status, and work absence (p < 0.001). This is the first time, to the authors' knowledge, that latent class analysis has been applied to longitudinal data on back pain patients. Identification of four distinct groups of patients improves understanding of the course of back pain and may provide a basis of classification for intervention.

Adult↗
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↗
PubMed2001

Latent class analysis of DSM-IV schizotypal personality disorder criteria in psychiatric patients.

The aim of the study was to evaluate the latent structure of DSM-IV schizotypal personality disorder (SPD) diagnostic criteria. The sample consisted of 564 consecutively admitted inpatients and outpatients. Exploratory latent class analysis identified a four-class model as the best fitting model for DSM-IV SPD criteria. The first of the SPD latent classes was mainly characterized by odd thinking, inappropriate affect, and interpersonal features; the second class by cognitive/perceptual difficulties; the third class by paranoid features; and the fourth class by absence of SPD features. The conditional probability pattern of the fourclass solution could be safely replicated across confounder strata. Unlike previous findings, oddness, aloofness, and social withdrawal, rather than positive symptoms, best characterized SPD even in clinical samples.

Adult↗
PubMed2000

A latent class analysis of antisocial personality disorder symptom data from a multi-centre family study of alcoholism.

AIMS: To determine if there are subtypes of Antisocial Personality Disorder (ASPD), as manifested by distinctive symptom profiles or by associations with alcohol, other drug dependence or other psychiatric disorders. METHODS: Data on 38 symptoms of ASPD (including childhood conduct disorder) obtained from probands, their relatives and controls (2834 females and 3488 males) recruited for the Collaborative Study on the Genetics of Alcoholism (COGA) were analyzed using latent class analysis. Associations of the resulting latent classes with alcohol dependence (AD) and other psychiatric disorders were examined. FINDINGS: Among women, a 4-class solution was obtained in which conduct disorder and ASPD were found almost exclusively in the most severely affected class with two additional classes with mild and moderate behavior problems also identified. A strong linear trend (p < 0.001) for AD was observed, with each successive class manifesting a higher prevalence than the previous class. Milestones of drinking careers and dependence on other drugs also showed a strong association with class severity. Among men, a 5-class solution was obtained and, like women, the highest prevalence of ASPD (74.6%) was found in the most severely affected class. Somewhat unexpected was the observation that prevalence of AD was equivalent in the two most severe classes. The data for men indicated a class with a milder spectrum of childhood misbehaviors but with an adult ASPD profile--as well as other psychiatric co-morbidity--that was comparable to the most severe class. CONCLUSIONS: Overall, findings from both men and women did not support the existence of subtypes of ASPD, but rather indicated a disorder distributed on a severity spectrum.

Adult↗
PubMed2002

Assessing reliability of categorical substance use measures with latent class analysis.

This article illustrates the use of the latent class model to identify classes of individuals and to assess the psychometric reliability of categorical items. The latent class model is a categorical latent variable model used to identify homogeneous classes of respondents such that class membership accounts for item responses. The assessment of measurement reliability comes directly from the estimates of the model. Although not based on classical test theory, the reliability assessment procedures described here answer the same question-that is, how consistent or dependable is measurement? The goal is to identify reliable indicators of a characteristic by examining measurement error and the inter-relatedness of the items. Methods for estimating the reliability of individual items as well as sets of items are presented. These methods are illustrated with data on cigarette smoking from a national sample of adolescents. By using the procedures described here, researchers are able to determine: (1). which classes of people are measured well and which are not; (2). which items perform well and which do not; and (3). whether items need to be altered or added in order to measure and identify particular classes better.

Adolescent↗
PubMed2006

Disentangling women's responses on complex dietary intake patterns from an Indian cross-sectional survey: a latent class analysis.

OBJECTIVE: To investigate the degree of individual heterogeneity related to complex dietary behaviour and to further examine the associations of different dietary compositions with selected characteristics. DESIGN: Latent class analysis was applied to data from the recent cross-sectional National Family Health Survey that collected information on the intake frequency of selected foods. Different responses regarding intake frequency were condensed into a set of five meaningful latent clusters representing different dietary patterns and these clusters were then labelled based on the reported degree of diet mixing. SETTING: Indian states. Subjects In total, 90,180 women aged 15-49 years. RESULTS: Three clusters were predominantly non-vegetarian and two were vegetarian. A very high or high mixed-diet pattern was observed particularly in the southern and a few north-eastern states. Many women in the very high mixed-diet cluster consumed mostly non-green/leafy vegetables on a daily basis, and fruits and other non-vegetarian diet on a weekly basis. In contrast, those in the low mixed-diet cluster consumed more than three-fifths of the major vegetarian diet ingredients alone on a daily basis. The affluent group that represented the low mixed-diet cluster were primarily vegetarians and those who represented the very high mixed-diet cluster were mostly non-vegetarians. The significant interrelationships of different characteristics highlight not only socio-economic, spatial and cultural disparities related to dietary practices, but also the substantial heterogeneity in diet mixing behaviour. CONCLUSIONS: The results of this study confirmed our hypothesis of heterogeneous dietary behaviour of Indian women and yielded useful policy-oriented results which might be difficult to establish otherwise.

Adolescent↗
PubMed2004

Patterns of cigarette smoking among students from 19 colleges and universities in Jiangsu Province, China: a latent class analysis.

Entrance into college may be associated with elevated risk of progression in cigarette use among Chinese students. The purpose of this study was to examine patterns and dynamics of cigarette smoking by age and grade among college students in China. Cross-sectional data from 1845 students (44% females) in 19 colleges and universities in Jiangsu Province were derived from the Chinese Youth Health Risk Behavior Survey in 2001. Data were analyzed using latent class analysis (LCA). Overall 11 mutually exclusive types (latent classes) of cigarette users were derived and their proportions estimated from four variables: lifetime smoking, past 30-day smoking, attempting to quit in the past, and intention to smoke in the future. Two types (resistant never-smokers, self-stopped resistant nonsmokers) were observed across age groups and grades for both males and females while other smoking types varied with gender, age and grade. Advanced cigarette smoking types appeared in older age groups and higher grades. The cross-sectional age and grade trend suggests substantial progression in cigarette smoking among Chinese college students, underscoring the need for prevention among these students. Results also suggest that LCA may be an effective methodology for analyzing behavioral types and their dynamics with a limited number of variables.

Adolescent↗
PubMed2000

Subtyping of juvenile idiopathic arthritis using latent class analysis. British Paediatric Rheumatology Group.

OBJECTIVE: To use statistical techniques to identify underlying subtypes of juvenile idiopathic arthritis (JIA) that best explain the observed relationships of clinical and laboratory variables, and to compare the statistically derived subtypes with those defined by the International League of Associations for Rheumatology (ILAR) criteria and examine them for HLA associations. METHODS: Information on 572 patients diagnosed as having JIA was summarized by 10 clinical and laboratory categorical variables (age at onset, large joint involvement, small joint involvement, polyarthritis, symmetric arthritis, spinal pain, fever, psoriasis, antinuclear antibodies [ANA], and rheumatoid factor). Latent class analysis (LCA) was used to identify underlying ("latent") classes that explained the relationships among the observed variables. Statistical models incorporating 5-8 latent classes were applied to the data. RESULTS: The 7-class model was the most appropriate. Patterns of joint involvement and the presence of ANA were influential in determining latent classes. There was some correspondence between the latent classes and the ILAR categories, but they did not coincide completely. Significant differences between the latent classes were seen for 3 HLA haplotypes (DRB1*04-DQA1*03-DQB1*03, DRB1*13-DQA1*01-DQB1*06, and DRB1*08-DQA1*0401-DQB1*0402). CONCLUSION: LCA provides a novel approach to the task of identifying homogeneous subtypes within the umbrella of JIA. In further work, the identified latent classes will be examined for associations with other candidate genes and for differences in outcome.

Adolescent↗
PubMed2003

Classical and latent class analysis evaluation of sputum polymerase chain reaction and urine antigen testing for diagnosis of pneumococcal pneumonia in adults.

Diagnosis of pneumococcal pneumonia is complicated by the lack of a diagnostic reference standard that is highly sensitive and specific. Latent class analysis (LCA) is a mathematical technique that relates an unobserved ("latent") infection to multiple diagnostic test results by use of a statistical model. We used classical analysis and LCA to evaluate the sensitivity and specificity of blood culture, sputum Gram stain, sputum polymerase chain reaction (PCR), and urine antigen testing for diagnosing pneumococcal pneumonia among 149 adults with community-acquired pneumonia. On the basis of LCA models, sensitivity of autolysin PCR and pneumolysin PCR was 82% and 89%, respectively, but specificity was low, 38% and 27%, respectively. For urine antigen testing, sensitivity was 77%-78%, and specificity was 67%-71%. Results of the LCA models were comparable with those obtained from classical analysis. LCA may be useful for diagnostic test evaluation and for determining the prevalence of pneumococcal infection in epidemiological studies of community-acquired pneumonia and in vaccine efficacy trials.

Adult↗
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↗