Latent class analysis reveals distinct subgroups of patients based on symptom occurrence and demographic and clinical characteristics

Context Cancer patients experience a broad range of physical and psychological symptoms as a result of their disease and its treatment. On average, these patients report ten unrelieved and co-occurring symptoms. Objectives To determine if subgroups of oncology outpatients receiving active treatment (n=582) could be identified based on their distinct experience with thirteen commonly occurring symptoms; to determine whether these subgroups differed on select demographic, and clinical characteristics; and to determine if these subgroups differed on quality of life (QOL) outcomes. Methods Demographic, clinical, and symptom data from one Australian and two U.S. studies were combined. Latent class analysis (LCA) was used to identify patient subgroups with distinct symptom experiences based on self-report data on symptom occurrence using the Memorial Symptom Assessment Scale (MSAS). Results Four distinct latent classes were identified (i.e., All Low (28.0%), Moderate Physical and Lower Psych (26.3%), Moderate Physical and Higher Psych (25.4%), All High (20.3%)). Age, gender, education, cancer diagnosis, and presence of metastatic disease differentiated among the latent classes. Patients in the All High class had the worst QOL scores. Conclusion Findings from this study confirm the large amount of interindividual variability in the symptom experience of oncology patients. The identification of demographic and clinical characteristics that place patients are risk for a higher symptom burden can be used to guide more aggressive and individualized symptom management interventions.

Susceptibility locus on chromosome 1q23-25 for a schizophrenia subtype resembling deficit schizophrenia identified by latent class analysis

Context: Identifying susceptibility genes for schizophrenia may be complicated by phenotypic heterogeneity, with some evidence suggesting that phenotypic heterogeneity reflects genetic heterogeneity. Objective: To evaluate the heritability and conduct genetic linkage analyses of empirically derived, clinically homogeneous schizophrenia subtypes. Design: Latent class and linkage analysis. Setting: Taiwanese field research centers. Participants: The latent class analysis included 1236 Han Chinese individuals with DSM-IV schizophrenia. These individuals were members of a large affected-sibling-pair sample of schizophrenia (606 ascertained families), original linkage analyses of which detected a maximum logarithm of odds (LOD) of 1.8 (z = 2.88) on chromosome 10q22.3. Main Outcome Measures: Multipoint exponential LOD scores by latent class assignment and parametric heterogeneity LOD scores. Results: Latent class analyses identified 4 classes, with 2 demonstrating familial aggregation. The first (LC2) described a group with severe negative symptoms, disorganization, and pronounced functional impairment, resembling “deficit schizophrenia.” The second (LC3) described a group with minimal functional impairment, mild or absent negative symptoms, and low disorganization. Using the negative/deficit subtype, we detected genome-wide significant linkage to 1q23-25 (LOD = 3.78, empiric genome-wide P = .01). This region was not detected using the DSM-IV schizophrenia diagnosis, but has been strongly implicated in schizophrenia pathogenesis by previous linkage and association studies.Variants in the 1q region may specifically increase risk for a negative/deficit schizophrenia subtype. Alternatively, these results may reflect increased familiality/heritability of the negative class, the presence of multiple 1q schizophrenia risk genes, or a pleiotropic 1q risk locus or loci, with stronger genotype-phenotype correlation with negative/deficit symptoms. Using the second familial latent class, we identified nominally significant linkage to the original 10q peak region. Conclusion: Genetic analyses of heritable, homogeneous phenotypes may improve the power of linkage and association studies of schizophrenia and thus have relevance to the design and analysis of genome-wide association studies.

Zygosity diagnosis in the absence of genotypic data: An approach using latent class analysis

For zygosity diagnosis in the absence of genotypic data, or in the recruitment phase of a twin study where only single twins from same-sex pairs are being screened, or to provide a test for sample duplication leading to the false identification of a dizygotic pair as monozygotic, the appropriate analysis of respondents' answers to questions about zygosity is critical. Using data from a young adult Australian twin cohort (N = 2094 complete pairs and 519 singleton twins from same-sex pairs with complete responses to all zygosity items), we show that application of latent class analysis (LCA), fitting a 2-class model, yields results that show good concordance with traditional methods of zygosity diagnosis, but with certain important advantages. These include the ability, in many cases, to assign zygosity with specified probability on the basis of responses of a single informant (advantageous when one zygosity type is being oversampled); and the ability to quantify the probability of misassignment of zygosity, allowing prioritization of cases for genotyping as well as identification of cases of probable laboratory error. Out of 242 twins (from 121 like-sex pairs) where genotypic data were available for zygosity confirmation, only a single case was identified of incorrect zygosity assignment by the latent class algorithm. Zygosity assignment for that single case was identified by the LCA as uncertain (probability of being a monozygotic twin only 76%), and the co-twin's responses clearly identified the pair as dizygotic (probability of being dizygotic 100%). In the absence of genotypic data, or as a safeguard against sample duplication, application of LCA for zygosity assignment or confirmation is strongly recommended.

Classifying patients by their characteristics and clinical presentations; the use of latent class analysis

In this article, we introduce the general statistical analysis approach known as latent class analysis and discuss some of the issues associated with this type of analysis in practice. Two recent examples from the respiratory health literature are used to highlight the types of research questions that have been addressed using this approach.

Serodiagnosis of Tuberculosis in Asian Elephants (Elephas maximus) in Southern India: A Latent Class Analysis

Background: Mycobacterium tuberculosis, a causative agent of chronic tuberculosis disease, is widespread among some animal species too. There is paucity of information on the distribution, prevalence and true disease status of tuberculosis in Asian elephants (Elephas maximus). The aim of this study was to estimate the sensitivity and specificity of serological tests to diagnose M. tuberculosis infection in captive elephants in southern India while simultaneously estimating sero-prevalence. Methodology/Principal Findings: Health assessment of 600 elephants was carried out and their sera screened with a commercially available rapid serum test. Trunk wash culture of select rapid serum test positive animals yielded no animal positive for M. tuberculosis isolation. Under Indian field conditions where the true disease status is unknown, we used a latent class model to estimate the diagnostic characteristics of an existing (rapid serum test) and new (four in-house ELISA) tests. One hundred and seventy nine sera were randomly selected for screening in the five tests. Diagnostic sensitivities of the four ELISAs were 91.3-97.6% (95% Credible Interval (CI): 74.8-99.9) and diagnostic specificity were 89.6-98.5% (95% CI: 79.4-99.9) based on the model we assumed. We estimate that 53.6% (95% CI: 44.6-62.8) of the samples tested were free from infection with M. tuberculosis and 15.9% (97.5% CI: 9.8 - to 24.0) tested positive on all five tests. Conclusions/Significance: Our results provide evidence for high prevalence of asymptomatic M. tuberculosis infection in Asian elephants in a captive Indian setting. Further validation of these tests would be important in formulating area-specific effective surveillance and control measures.

Vulnerability and multiple deprivation perspectives on economic exclusion in Europe: A latent class analysis

In this paper we address issues relating to vulnerability to economic exclusion and levels of economic exclusion in Europe. We do so by applying latent class models to data from the European Community Household Panel for thirteen countries. This approach allows us to distinguish between vulnerability to economic exclusion and exposure to multiple deprivation at a particular point in time. The results of our analysis confirm that in every country it is possible to distinguish between a vulnerable and a non-vulnerable class. Association between income poverty, life-style deprivation and subjective economic strain is accounted for by allocating individuals to the categories of this latent variable. The size of the vulnerable class varies across countries in line with expectations derived from welfare regime theory. Between class differentiation is weakest in social democratic regimes but otherwise the pattern of differentiation is remarkably similar. The key discriminatory factor is life-style deprivation, followed by income and economic strain. Social class and employment status are powerful predictors of latent class membership in all countries but the strength of these relationships varies across welfare regimes. Individual biography and life events are also related to vulnerability to economic exclusion. However, there is no evidence that they account for any significant part of the socio-economic structuring of vulnerability and no support is found for the hypothesis that social exclusion has come to transcend class boundaries and become a matter of individual biography. However, the extent of socio-economic structuring does vary substantially across welfare regimes. Levels of economic exclusion, in the sense of current exposure to multiple deprivation, also vary systematically by welfare regime and social class. Taking both vulnerability to economic exclusion and levels of exclusion into account suggests that care should be exercised in moving from evidence on the dynamic nature of poverty and economic exclusion to arguments relating to the superiority of selective over universal social policies.

Are poker players all the same? : latent class analysis

Poker is the gambling game that is currently gaining the most in popularity. However, there is little information on poker players' characteristics and risk factors. Furthermore, the first studies described poker players, often recruited in universities, as an homogeneous group who played in only one of the modes (land based or on the Internet). This study aims to identify, through latent class analyses, poker player subgroups. A convenience sample of 258 adult poker players was recruited across Quebec during special events or through advertising in various media. Participants filled out a series of questionnaires (Canadian Problem Gambling Index, Beck Depression, Beck Anxiety, erroneous belief and alcohol/drug consumption). The latent class analysis suggests that there are three classes of poker players. Class I (recreational poker players) includes those who have the lowest probability of engaging intensively in different game modes. Participants in class II (Internet poker players) all play poker on the Internet. This class includes the highest proportion of players who consider themselves experts or professionals. They make a living in part or in whole from poker. Class III (multiform players) includes participants with the broadest variety of poker patterns. This group is complex: these players are positioned halfway between professional and recreational players. Results indicate that poker players are not an homogeneous group identified simply on the basis of the form of poker played. The specific characteristics associated with each subgroup points to vulnerabilities that could potentially be targeted for preventive interventions.

Nonsuicidal Self-Injury and Suicidal Behavior: A Latent Class Analysis among Young Adults

Although there is a general consensus among researchers that engagement in nonsuicidal self-injury (NSSI) is associated with increased risk for suicidal behavior, little attention has been given to whether suicidal risk varies among individuals engaging in NSSI. To identify individuals with a history of NSSI who are most at risk for suicidal behavior, we examined individual variability in both NSSI and suicidal behavior among a sample of young adults with a history of NSSI (N = 439, Mage = 19.1). Participants completed self-report measures assessing NSSI, suicidal behavior, and psychosocial adjustment (e.g., depressive symptoms, daily hassles). We conducted a latent class analysis using several characteristics of NSSI and suicidal behaviors as class indicators. Three subgroups of individuals were identified: 1) an infrequent NSSI/not high risk for suicidal behavior group, 2) a frequent NSSI/not high risk for suicidal behavior group, and 3) a frequent NSSI/high risk for suicidal behavior group. Follow-up analyses indicated that individuals in the ‘frequent NSSI/high risk for suicidal behavior’ group met the clinical-cut off score for high suicidal risk and reported significantly greater levels of suicidal ideation, attempts, and risk for future suicidal behavior as compared to the other two classes. Thus, this study is the first to identity variability in suicidal risk among individuals engaging in frequent and multiple methods of NSSI. Class 3 was also differentiated by higher levels of psychosocial impairment relative to the other two classes, as well as a comparison group of non-injuring young adults. Results underscore the importance of assessing individual differences in NSSI characteristics, as well as psychosocial impairment, when assessing risk for suicidal behavior.

Profiles of language development in pre-school children : a longitudinal latent class analysis of data from the early language in Victoria study

Background Pre-school language impairment is common and greatly reduces educational performance. Population attempts to identify children who would benefit from appropriately timed intervention might be improved by greater knowledge about the typical profiles of language development. Specifically, this could be used to help with the early identification of children who will be impaired on school entry.

Methods This study applied longitudinal latent class analysis to assessments at 8, 12, 24, 36 and 48 months on 1113 children from a population-based study, in order to identify classes exhibiting distinct communicative developmental profiles.

Results Five substantive classes were identified: Typical, i.e. development in the typical range at each age; Precocious (late), i.e. typical development in infancy followed by high probabilities of precocity from 24 months onwards; Impaired (early), i.e. high probabilities of impairment up to 12 months followed by typical language development thereafter; Impaired (late), i.e. typical development in infancy but impairment from 24 months onwards; Precocious (early), i.e. high probabilities of precocity in early life followed by typical language by 48 months. The entropy statistic (0.84) suggested classes were fairly well defined, although there was a non-trivial degree of uncertainty in classification of children. That half of the Impaired (late) class was expected to have typical language at 4 years and 6% of the numerically large Typical class was expected to be impaired at 4 years illustrates this. Characteristics indicative of social advantage were more commonly found in the classes with improving profiles.

Conclusions Developmental profiles show that some pre-schoolers' language is characterized by periods of accelerated development, slow development and catch-up growth. Given the uncertainty in classifying children into these profiles, use of this knowledge for identifying children who will be impaired on school entry is not straightforward. The findings do, however, indicate greater need for language enrichment programmes among disadvantaged children.

The value of impulsivity to define subgroups of addicted individuals differing in personality dysfunction, craving, psychosocial adjustment, and wellbeing: A latent class analysis

High impulsivity is common to substance and gambling addictions. Despite these commonalities, there is still substantial heterogeneity on impulsivity levels within these diagnostic groups, and variations in impulsive levels predict higher severity of symptoms and poorer outcomes. We addressed the question of whether impulsivity scores can yield empirically driven subgroups of addicted individuals that will exhibit different clinical presentations and outcomes. We applied latent class analysis (LCA) to trait (UPPS-P impulsive behavior scale) and cognitive impulsivity (Stroop and d2 tests) scores in three predominantly male addiction diagnostic groups: Cocaine with Personality Disorders, Cocaine Non-comorbid, and Gambling and analyzed the usefulness of the resulting subgroups to differentiate personality beliefs and relevant outcomes: Craving, psychosocial adjustment, and quality of life. In accordance with impulsivity scores, the three addiction diagnostic groups are best represented as two separate classes: Class 1 characterized by greater trait impulsivity and poorer cognitive impulsivity performance and Class 2 characterized by lower trait impulsivity and better cognitive impulsivity performance. The two empirically derived classes showed significant differences on personality features and outcome variables (Class 1 exhibited greater personality dysfunction and worse clinical outcomes), whereas conventional diagnostic groups showed non-significant differences on most of these measures. Trait and cognitive impulsivity scores differentiate subgroups of addicted individuals with more versus less severe personality features and clinical outcomes.

Outcomes of antiretroviral therapy in the Swiss HIV Cohort Study: latent class analysis

An in-depth understanding of the different groups that make up the HIV-infected population should inform prevention and care. Using latent class analysis (LCA) we identified seven groups with similar socio-demographic and behavioral characteristics at enrolment in the Swiss HIV Cohort Study: older gay men, younger gay men, older heterosexual men, injection drug users, single migrants, migrant women in partnerships and heterosexual men and women. Outcomes of combination antiretroviral therapy (ART) were analyzed in 1,633 patients starting ART. Compared to older gay men, the probability of a virologic response to ART was reduced in single migrants, in older heterosexual men and in IDUs. Loss to follow-up was higher in single migrants and IDUs, and mortality was increased in older heterosexual men and IDUs. Socio-behavioral groups identified by LCA allow insights above what can be gleaned from traditional transmission groups, and may identify patients who could benefit from targeted interventions.

Distinguishing phenotypes of childhood wheeze and cough using latent class analysis

Airway disease in childhood comprises a heterogeneous group of disorders. Attempts to distinguish different phenotypes have generally considered few disease dimensions. The present study examines phenotypes of childhood wheeze and chronic cough, by fitting a statistical model to data representing multiple disease dimensions. From a population-based, longitudinal cohort study of 1,650 preschool children, 319 with parent-reported wheeze or chronic cough were included. Phenotypes were identified by latent class analysis using data on symptoms, skin-prick tests, lung function and airway responsiveness from two preschool surveys. These phenotypes were then compared with respect to outcome at school age. The model distinguished three phenotypes of wheeze and two phenotypes of chronic cough. Subsequent wheeze, chronic cough and inhaler use at school age differed clearly between the five phenotypes. The wheeze phenotypes shared features with previously described entities and partly reconciled discrepancies between existing sets of phenotype labels. This novel, multidimensional approach has the potential to identify clinically relevant phenotypes, not only in paediatric disorders but also in adult obstructive airway diseases, where phenotype definition is an equally important issue.