Bayesian survival extrapolation for cost-effectiveness analysis: a case study of RELAY for ramucirumab in combination with erlotinib in the treatment of non-small-cell lung cancer.

AIM: Increasing trend for progression-free survival (PFS)-based primary endpoint in oncology has led to lack of mature overall survival (OS) data at the time of approval. To address this evidence gap in economic evaluations, we used a joint Bayesian approach to predict survival outcomes using immature OS data from the RELAY trial. METHODS: Patient data from RELAY and systematic literature review (SLR) of phase 3 randomized clinical trials with hazard ratio (HR) estimates of mature PFS and immature OS were considered. OS and PFS were analyzed individually using a univariate model; bivariate analysis was performed using a joint model based on modified Bayesian normal induced copula estimation model. First, a Bayesian univariate model incorporated informative priors based on predicted HR and acceleration factor for OS and PFS. Second, a Bayesian-based joint model of RELAY PFS and OS data was based on the correlation between PFS and OS established in trials of similar populations. Marginal distribution of PFS was used to estimate the same for OS. RESULTS: Publications (N = 122) of first-line treatments in patients with epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer were identified in the SLR, of which 36 trials were linked to RELAY. Twenty-six trials with HR data were used. The univariate model could predict OS with reduced uncertainty compared with the frequentist approach. In the joint model, the marginal OS distribution borrowed strength from the marginal PFS distribution through the established correlation coefficient. LIMITATIONS: Bayesian approach was successfully used in RELAY analysis but may not be universally applied to oncology trials due to the different associations of OS and PFS and different trial patient populations. CONCLUSIONS: We demonstrated that both the univariate and joint Bayesian models reduced uncertainty in predicting OS compared to frequentist method. The methodology introduced here will have potential applications in clinical decision-making for other oncology trials.

Association between life-course socioeconomic position and inflammatory biomarkers in older age: a nationally representative cohort study in Taiwan.

BACKGROUND: Evidence of an association between low socioeconomic position (SEP) and inflammatory markers is scant. This study aimed to examine how life-course SEP predicted C-reactive protein (CRP) and interleukin (IL-6) in older age from a national cohort. METHODS: We collected data from 1036 participants in the Social Environment and Biomarkers of Aging Study in Taiwan. Four SEP time points, childhood, young adulthood, active professional life, and older age were measured retrospectively. A group-based trajectory analysis method was used to identify the distinct trajectories of life-course SEP, and trajectory group membership was used as the predictor of CRP and IL-6 levels in older age. RESULTS: Three trajectories of life-course SEP were identified within the total sample: Low-Low (36.5%), Low-High (26.8%), and High-High (36.7%). Participants in the High-High group had the lowest levels of CRP and IL-6. Compared with those in the Low-Low group, the participants in the Low-High group had a similar adjusted CRP [-0.032 ln mg/L; 95% confidence interval (CI) - 0.193, 0.128] and IL-6 (0.017 ln pg/mL; 95% CI -0.093, 0.128); the participants in the High-High group had a significantly lower level of adjusted CRP concentration (-0.279 ln mg/L; 95% CI: -0.434, -0.125) and similarly lower IL-6 concentration (-0.129 ln pg/mL; 95% CI -0.236, -0.023) . CONCLUSIONS: Life-course SEP is related to the level of CRP and IL-6 in older age. Our data support the notion that life-course SEP predicts inflammatory markers in older age. Low SEP in childhood is related to elevated inflammatory markers in older age. Even after the transition from low SEP in childhood to high SEP in older age, the risk remains. Further study on SEP and inflammation-related disease is warranted.

Assessing the burden of illness from cervical dystonia using the Toronto Western Spasmodic Torticollis Rating Scale scores and health utility: a meta-analysis of baseline patient-level clinical trial data.

PURPOSE: This study aimed to explore the burden of illness associated with cervical dystonia (CD), including possible demographic and humanistic correlates of baseline disease severity. METHODS: The analysis involved the five multinational randomized, placebo-controlled clinical trials that had evaluated the efficacy and safety of Dysport® in patients with CD, including assessment using the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS). Patient-level TWSTRS scores from the individual studies were meta-analysed to estimate disease severity at baseline. One of the studies had reported Short Form-36 (SF-36) Health Survey quality-of-life measures, and these data were used to investigate whether the severity of CD was associated with humanistic outcomes, as measured by health utility. A generalized regression model was then applied to explore potential correlation between TWSTRS scores and utilities. RESULTS: The estimated pooled mean baseline severity of CD in clinical trial entrants, as measured by TWSTRS score, was 43.23 (95% CI = 39.31-47.15). In general, disease severity was significantly greater in patients aged over 40 years (compared to the reference group aged 18-30 years). However, there was no correlation between disease severity and other demographic characteristics (e.g., weight, height, gender). Higher TWSTRS scores correlated with worse health-related quality of life as perceived by patients and was reflected in health utility (R(2 )= 0.133). CONCLUSIONS: This study was able to define TWSTRS scores in patients with CD in terms of associated utility. This approach could help in capturing the disease's burden through measures that are more tangible than TWSTRS scores to patients, carers, clinicians, and healthcare payers.

Logistic versus hierarchical modeling: an analysis of a statewide inpatient sample.

BACKGROUND: Although logistic regression is traditionally used to calculate hospital standardized mortality ratio (HSMR), it ignores the hierarchical structure of the data that can exist within a given database. Hierarchical models allow examination of the effect of data clustering on outcomes. STUDY DESIGN: Traditional logistic regression and random intercepts fixed slopes hierarchical models were fitted to a dataset of patients hospitalized between 2005 and 2007 in Massachusetts. We compared the observed to expected (O/E) in-hospital death ratios between the 2 modeling techniques, a restricted HSMR using only those diagnosis models that converged in both methods and a full hybrid HSMR using a combination of the hierarchical diagnosis models when they converge, plus the remaining diagnoses using standard logistic regression models. RESULTS: We restricted the analysis to the 36 diagnoses accounting for 80% of in-hospital deaths nationally, based on 1,043,813 admissions (59 hospitals). A failure of the hierarchical models to converge in 15 of 36 diagnosis groups hindered full HSMR comparisons. A restricted HSMR, derived from a dataset based on the 21 diagnosis groups that converged (552,933 admissions) showed very high correlation (Pearson r = 0.99). Both traditional logistic regression and hierarchical model identified 12 statistical outliers in common, 7 with high O/E values and 5 with low O/E values. In addition, the multilevel analysis identified 5 additional unique high outliers and 1 additional unique low outlier, and the conventional model identified 2 additional unique low outliers. CONCLUSIONS: Similar results were obtained from the 2 modeling techniques in terms of O/E ratios. However, because a hierarchical model is associated with convergence problems, traditional logistic regression remains our recommended procedure for computing HSMRs.

Trustful societies, trustful individuals, and health: An analysis of self-rated health and social trust using the World Value Survey.

This study analyses the relationships between self-rated health and both individual and mean national social trust, focusing on a variant of Wilkinson's hypothesis that individuals will be less healthy the greater the lack of social cohesion in a country. It employs multilevel modelling on World Values Survey data across 69 countries with a total sample of 160,436 individuals. The results show that self-rated health are positively linked to social trust at both country and individual levels after controlling for individual socio-demographic and income variables plus individual social trust; increased trust is associated with better health. Moreover, this analysis of social trust gives some insight into distinctive results for the former Soviet Bloc countries, which have high reported levels of poor health, alongside the Scandinavian countries which have high levels of trust and better health situations. Our results support and extend the Wilkinson hypothesis that the level of trust, an indicator of social cohesion, is predictive of individuals' health.

Global variations in health: evaluating Wilkinson's income inequality hypothesis using the World Values Survey.

This international comparative study analyses individual-level data derived from the World Values Survey to evaluate Wilkinson's [(1996). Unhealthy societies: The afflictions of inequality. London: Routledge; (1998). Mortality and distribution of income. Low relative income affects mortality [letter; comment]. British Medical Journal, 316, 1611-1612] income inequality hypothesis regarding variations in health status. Random-coefficient, multilevel modelling provides a direct test of Wilkinson's hypothesis using micro-data on individuals and macro-data on income inequalities analysed simultaneously. This overcomes the ecological fallacy that has troubled previous research into links between individual self-rated health, individual income, country income and income inequality data. Logic regression analysis reveals that there are substantial differences between countries in self-rated health after taking account of age and gender, and individual income has a clear effect in that poorer people report experiencing worse health. The Wilkinson hypothesis is not supported, however, since there is no significant relationship between health and income inequality when individual factors are taken into account. Substantial differences between countries remain even after taking account of micro- and macro-variables; in particular the former communist countries report high levels of poor health.

Compositional and contextual approaches to the study of health behaviour and outcomes: using multi-level modelling to evaluate Wilkinson's income inequality hypothesis.

Much research into health behaviour and outcomes involves evaluating compositional and contextual hypotheses: the former suggest that behaviour/outcomes are a function of the individual's characteristics alone, whereas the latter argue for the importance of contextual/environmental influences. Wilkinson has presented a contextual argument relating inter-country variations in mortality rates to income inequalities; Gravelle has countered this arguing that Wilkinson's findings are a statistical artefact and that a compositional approach, relating mortality to individual income, is sufficient. Discriminating between these two cases requires a methodology combining the two approaches. Multi-level modelling is proposed and applied to two data sets. The results sustain Gravelle's case, emphasising the role of compositional rather than contextual variables in accounting for inter-country variations in health status.