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Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: Background:: The relationship between darbepoetin alfa and fatigue in chemotherapy-induced anemia (CIA) patients is complex because of patients receiving transfusions and the mediating effect of hemoglobin. Latent growth models (LGMs) were used to examine simultaneously relationships among drug exposure, fatigue outcomes, covariates, and mediating factors. Methods:: Data from four CIA studies (AMG 20010145: small cell lung cancer, n =547; AMG 980297: lung cancer, n =288; AMG 20000161: lymphoproliferative malignancies, n =339; AMG 20030232: non-myeloid malignancies, n =320) were analyzed separately. Patients reported fatigue using the FACT-Fatigue. The effect of darbepoetin alfa on FACT-F changes mediated through hemoglobin changes was examined with LGMs controlling for transfusions, age, sex, baseline ECOG performance status, and health status (EQ-5D VAS). Model fit was assessed using multiple indices including the comparative fit index (CFI). Results:: Darbepoetin alfa increased hemoglobin levels which were associated with decreases in fatigue. Increases in hemoglobin were statistically significantly (p <0.05) related to decreases in fatigue in the studies (AMG 20030145: β̂ =0.28; AMG 980297: β̂ =0.46; AMG 20000161: β̂ =0.59; and AMG 20030232: β̂ =0.39). Darbepoetin alfa statistically significantly increased hemoglobin (AMG 20010145:β̂ =0.50, AMG 980297:β̂ =0.53, AMG 20000161:β̂ =0.47, and AMG 20030232:β̂ =0.30) while controlling for covariates. Model fit was acceptable (CFI≥0.89) in all studies. Conclusions:: Results indicate LGMs may be a valuable statistical method for modeling complex relationships among clinical and patient reported outcomes. A statistically significant effect of darbepoetin alfa on fatigue change through hemoglobin change occurred across four studies, after modeling the effects of transfusions, age, sex, EQ-5D VAS and ECOG. [Copyright &y& Elsevier]

Objective: This study aimed to develop and validate a short, simple, patient-completed instrument for identifying patients with congestion in a 15-day study. Allergic rhinitis (AR) is the most common allergic condition worldwide, with congestion as one of the most salient symptoms. Nevertheless, there is no short screening tool designed specifically to identify congestion that can help patients make decisions about seeking treatment. Methods: Patients (N = 354) received a clinical exam to confirm congestion and assess its possible causes including confirmation of AR. They completed the 13-item draft of the Congestion Quantifier (CQ) and five additional patient-reported outcome instruments. Results: The 13-item draft CQ was reduced to a seven-item version, the CQ7. Internal consistency reliability was 0.93; test–retest reliability = 0.85. Construct validity wasdemonstrated by significant correlations with the Medical Outcomes Study Sleep Scale, the Work Productivity and Activity Impairment Questionnaire—Allergy Specific, and the Positive Affect and Negative Affect Scale Fatigue subscale ( r = 0.23–0.67). The CQ7 can discriminate between controls and patients (AUC > 0.9). Moreover, it can discriminate between different levels of severity of symptoms of AR. A score of 7 provided optimum balance of sensitivity (91%), specificity (86%), and correct classification (90%) for detecting congestion. Conclusion: The CQ7 is reliable, valid, and responsive to differences in severity of nasal congestion. The CQ7 can identify patients with congestion that may need to be evaluated by a clinician. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To compare the relative ability of patient self-assessments and clinician assessments of functional limitations for predicting hospitalizations for older adults with left ventricular dysfunction. Study Design: Latent growth model analyses used secondary data from a clinical drug study, the Studies of Left Ventricular Dysfunction. Self-report and clinician-report data of patients'' functional limitations were collected at baseline, and 6 weeks, 12 months, and 24 months postbaseline to assess the effects of an angiotensin-converting enzyme inhibitor versus placebo. Latent growth model analyses were used to assess the relative effect of baseline ratings and changes in these 2 ratings of patient functional limitations over the initial 2 years of the study (ie, the drug study component) as potential predictors of the number of hospitalizations occurring over 3 subsequent years. Results: Data from 1099 patients aged 65+ years were analyzed. Both baseline patient assessments and changes in patient assessments of functional limitations were significant predictors of the number of subsequent hospitalizations (β = 0.119 and 0.273, respectively). This was not the case for clinician assessments. Moreover, baseline patient and clinician assessments of the patient''s functional limitations were correlated at 0.42, indicating only a modest relationship between the 2 assessments. Conclusions: These results demonstrate the greater utility of patient self-report measures for predicting hospitalizations. Incorporating patient-reported outcomes into clinical practice will expand understanding of the effect of diseases. Assessing changes in patient reports yields important insights into those patients more likely to have multiple admissions for heart failure. [Copyright &y& Elsevier]