Katherine Houghton

Application of latent growth and growth mixture modeling to identify and characterize differential responders to treatment for COPD.

OBJECTIVE To explore the utility of applying growth mixture models (GMMs) in secondary analyses of clinical trials to identify sources of variability in data reported by patients with COPD. METHODS Analyses were performed on data from two 6-month clinical trials comparing indacaterol and open-label tiotropium or blinded salmeterol and the first six months of a 12-month trial comparing indacaterol and blinded formoterol. Latent growth model (LGM) analyses were conducted to explore the response of the SGRQ Symptoms score from baseline to six months and GMM analyses were evaluated as a method to identify latent classes of differential responders. RESULTS Variability in SGRQ Symptom scores was found suggesting subsets of patients with differential response to treatment. GMM analyses found subsets of non-responders in all trials. When the responders were analyzed separately from non-responders, there were increased treatment effects (e.g., symptoms score improvement over six months for whole groups: indacaterol=8-12 units, tiotropium=7 units, salmeterol=9 units, formoterol=11 units. Responder subgroup improvement: indacaterol=9-21 units, tiotropium=7 units, salmeterol=10 units, formoterol=20 units). Responders had significantly different baseline SGRQ Symptom scores, smoking history, age, and mMRC dyspnea scores than non-responders. CONCLUSIONS Patients with COPD represent a heterogeneous population in terms of their reporting of symptoms and response to treatment. GMM analyses are able to identify sub-groups of responders and non-responders. Application of this methodology could be of value on other endpoints in COPD and in other disease areas.

Assessing Changes in Chronic Spontaneous/Idiopathic Urticaria: Comparisons of Patient-Reported Outcomes Using Latent Growth Modeling.

IntroductionAssessing the consequences of chronic spontaneous/idiopathic urticaria (CSU) requires the evaluation of health-related quality of life (HRQoL) associated with the severity of CSU signs and symptoms. It is important to understand how signs, symptoms, and HRQoL change over time in CSU. Evidence is lacking on how closely changes in signs and symptoms of CSU are related to changes in HRQoL. The objective of this study was to assess the correlation between changes in patient-reported outcome measures (PROMs) of signs and symptoms, dermatologic quality of life (QoL), and urticaria-specific QoL.MethodsLatent growth models (LGMs) were applied to longitudinal data from three randomized, Phase 3 clinical trials investigating the efficacy and safety of omalizumab in CSU.ResultsA near-perfect association between changes in signs and symptoms and changes in dermatologic and urticaria-specific QoLs was identified in each clinical trial when using LGMs (correlation coefficient range 0.88–0.92).ConclusionEvidence showed that changes in signs and symptoms are closely related to changes in HRQoL. However, analyses were performed on clinical trial results of an extremely effective treatment; a less effective treatment with much smaller changes over time may not show such close correlations. Results suggest that any of these PROMs may be used to understand changes in CSU.

Identifying Differential Responders and Their Characteristics in Clinical Trials: Innovative Methods for Analyzing Longitudinal Data

OBJECTIVES To present a step-by-step example of the examination of heterogeneity within clinical trial data by using a growth mixture modeling (GMM) approach. METHODS Secondary data from a longitudinal double-blind clinical drug study were used. Patients received enalapril or placebo and were followed for 2 years during the drug component, followed by a 3-year postdrug component. Primary variables of interest were creatinine levels during the drug component and number of hospitalizations in the postdrug component. Latent growth modeling (LGM) methods were used to examine the treatment response variability in the data. GMM methods were applied where substantial variability was found to identify latent (unobserved) subsets of differential responders, using treatment groups as known classes. Post hoc analyses were applied to characterize emergent subgroups. RESULTS LGM methods demonstrated a large variability in creatinine levels. GMM methods identified two subsets of patients for each treatment group. Placebo class 2 (7.0% of the total sample) and enalapril class 2 (8.5%) include individuals whose creatinine levels start at 1.114 mg/dl and 1.108 mg/dl, respectively, and show worsening (slopes: 0.023 and 0.017, respectively). Placebo class 1 (43.1%) and enalapril class 1 (41.4%) individuals start with lower creatinine levels (1.082 and 1.083 mg/dl, respectively) and show very minimal change (0.008 and 0.003, respectively). Post hoc analyses revealed significant differences between placebo/enalapril class 1 and placebo/enalapril class 2 in terms of New York Heart Association functional ability, depression, functional impairment, creatinine levels, mortality, and hospitalizations. CONCLUSIONS GMM methods can identify subsets of differential responders in clinical trial data. This can result in a more accurate understanding of treatment effects.

Differential effects of secukinumab vs. ustekinumab for treatment of psoriasis on quality of life, work productivity and activity impairment: a structural equation modelling analysis

The appearance and lifelong, chronic nature of psoriasis result in considerable burden to patients, such as sleep impairment, depressive symptoms, negative self‐esteem and reduced work productivity.

A (Latent) Class of Their Own: Response Patterns in Trading Off Quantity and Quality of Life in Time Trade-Off Exercises

BACKGROUND Conflicting results regarding associations of time trade-off (TTO) valuations with respondent characteristics have been reported, mostly on the basis of regression analyses. Alternative approaches, such as the latent class analysis (LCA), may add to the further understanding of variations in TTO responses. OBJECTIVES To identify whether subgroups of respondents can be identified on the basis of their responses to TTO exercises and to investigate which respondent characteristics are associated with membership of the identified subgroups. METHODS Members of the Dutch general public, aged 18 to 65 years, completed a Web-based questionnaire concerning sociodemographic characteristics, three TTO exercises valuing health states described using the domains of the EuroQol five-dimensional questionnaire, and preference for quality versus quantity of life. LCA was used to identify patterns in the responses. Predictive variables were included in the final LCA model to identify the particular respondent characteristics that predict subgroup membership. RESULTS The sample consisted of 1067 respondents. Four latent classes were identified in the responses to TTO exercises. Two were high traders, focusing on quality of life and trading off a relatively high number of years. The other two were low traders, focusing on length of life. Predictive analyses revealed significant differences between subgroups in terms of age, sex, subjective life expectancy, and preference for quantity over quality of life. CONCLUSIONS We showed that distinct classes of respondents can be discerned in TTO responses from the general public, distinguishing subgroups of low and high traders. More research in this area should confirm our findings and investigate their implications for health state valuation exercises.