The Rules of Engagement: CTTI Recommendations for Successful Collaborations Between Sponsors and Patient Groups Around Clinical Trials.

OBJECTIVE: To identify the elements necessary for successful collaboration between patient groups and academic and industry sponsors of clinical trials, in order to develop recommendations for best practices for effective patient group engagement. METHODS: In-depth interviews, informed by a previously reported survey, were conducted to identify the fundamentals of successful patient group engagement. Thirty-two respondents from 3 sectors participated: patient groups, academic researchers, and industry. The findings were presented to a multistakeholder group of experts in January 2015. The expert group came to consensus on a set of actionable recommendations for best practices for patient groups and research sponsors. RESULTS: Interview respondents acknowledged that not all patient groups are created equal in terms of what they can contribute to a clinical trial. The most important elements for effective patient group engagement include establishing meaningful partnerships, demonstrating mutual benefits, and collaborating as partners from the planning stage forward. Although there is a growing appreciation by sponsors about the benefits of patient group engagement, there remains some resistance and some uncertainty about how best to engage. Barriers include mismatched expectations and a perception that patient groups lack scientific sophistication and that "wishful thinking" may cloud their recommendations. CONCLUSIONS: Patient groups are developing diverse skillsets and acquiring assets to leverage in order to become collaborators with industry and academia on clinical trials. Growing numbers of research sponsors across the clinical trials enterprise are recognizing the benefits of continuous and meaningful patient group engagement, but there are still mindsets to change, and stakeholders need further guidance on operationalizing a new model of clinical trial conduct.

Estimation of tumour regression and growth rates during treatment in patients with advanced prostate cancer: a retrospective analysis.

BACKGROUND: We applied mathematical models to clinical trial data available at Project Data Sphere LLC (Cary, NC, USA), a non-profit universal access data-sharing warehouse. Our aim was to assess the rates of cancer growth and regression using the comparator groups of eight randomised clinical trials that enrolled patients with metastatic castration-resistant prostate cancer. METHODS: In this retrospective analysis, we used data from eight randomised clinical trials with metastatic castration-resistant prostate cancer to estimate the growth (g) and regression (d) rates of disease burden over time. Rates were obtained by applying mathematical models to prostate-specific antigen levels as the representation of tumour quantity. Rates were compared between study interventions (prednisone, mitoxantrone, and docetaxel) and off-treatment data when on-study treatment had been discontinued to understand disease behaviour during treatment and after discontinuation. Growth (g) was examined for association with a traditional endpoint (overall survival) and for its potential use as an endpoint to reduce sample size in clinical trials. FINDINGS: Estimates for g, d, or both were obtained in 2353 (88%) of 2678 patients with data available for analysis; g differentiated docetaxel (a US Food and Drug Administration-approved therapy) from prednisone and mitoxantrone and was predictive of overall survival in a landmark analysis at 8 months. A simulated sample size analysis, in which g was used as the endpoint, compared docetaxel data with mitoxantrone data and showed that small sample sizes were sufficient to achieve 80% power (16, 47, and 25 patients, respectively, in the three docetaxel comparator groups). Similar results were found when the mitoxantrone data were compared with the prednisone data (41, 39, and 41 patients in the three mitoxantrone comparator groups). Finally, after discontinuation of docetaxel therapy, median tumour growth (g) increased by nearly five times. INTERPRETATION: The application of mathematical models to existing clinical data allowed estimation of rates of growth and regression that provided new insights in metastatic castration-resistant prostate cancer. The availability of clinical data through initiatives such as Project Data Sphere, when combined with innovative modelling techniques, could greatly enhance our understanding of how cancer responds to treatment, and accelerate the productivity of clinical development programmes. FUNDING: None.

Association between dyslipidemia and vascular events in patients treated with statins: report from the UK General Practice Research Database.

OBJECTIVE: A retrospective cohort study was conducted to evaluate the association between low high-density lipoprotein cholesterol (HDL-C) and/or elevated triglycerides (TG) and cardiovascular (CV) and/or cerebrovascular (CB) events among patients with elevated low-density lipoprotein cholesterol (LDL-C) despite statin treatment. METHODS: Patient demographics, clinical characteristics, laboratory data, and CV/CB events, were collected from the UK General Practice Research Database. Abnormal lipid levels were defined using US and European clinical guidelines. The association between the frequency of CV/CB events among patients with HDL-C/TG abnormalities versus patients with isolated low LDL-C was estimated using multivariate Cox proportional hazards regression. RESULTS: Of 19,843 statin-treated patients, 6823 had elevated LDL-C despite therapy for a mean follow-up of 1.99+/-1.06 years. Among these patients, 3115 (45.7%) also had HDL-C/TG abnormalities. A total of 715 patients (10.5%) experienced CV/CB events. In statin-treated patients not at LDL-C goal, the relative risk of a vascular event was 24% higher in patients with HDL-C/TG abnormalities (HR=1.24, 95% CI: 1.06-1.46, p=0.006) than in patients without HDL-C/TG abnormalities. Additional variables that were associated with a significantly increased risk of CV/CB events included age (p<0.0001), gender (p=0.027), and medication possession ratio (p<0.0001), while diabetes mellitus (p<0.0001), hypertension (p<0.0001), 10-year Framingham risk score>30% (p=0.005), statin dose (p<0.0001), and LDL-C level at baseline (p<0.0001) were associated with a significantly decreased risk of CV/CB events. CONCLUSION: Among statin-treated patients with elevated LDL-C from UK clinical practices, reduced HDL-C and/or elevated TGs were associated with a significantly increased relative risk of CV/CB events.

Body size and human energy requirements: reduced mass-specific resting energy expenditure in tall adults.

Two observations favor the presence of a lower mass-specific resting energy expenditure (REE/weight) in taller adult humans: an earlier report of height (H)-related differences in relative body composition; and a combined model based on Quetelet and Kleiber's classic equations suggesting that REE/weight proportional, variantH(-0.5). This study tested the hypothesis stating that mass-specific REE scales negatively to height with a secondary aim exploration of related associations between height, weight (W), surface area (SA), and REE. Two independent data sets (n = 344 and 884) were evaluated, both with REE measured by indirect calorimetry and the smaller of the two including fat estimates by dual-energy X-ray absorptiometry. Results support Quetelet's equation (W proportional, variantH(2)), but Kleiber's equation approached the interspecific mammal form (REE proportional, variantW(0.75)) only after adding adiposity measures to weight and age as REE predictors. REE/weight scaled as H( approximately (-0.5)) in support of the hypothesis with P values ranging from 0.17 to <0.001. REE and SA both scaled as H( approximately 1.5), and REE/SA was nonsignificantly correlated with height in all groups. These observations suggest that adiposity needs to be considered when evaluating the intraspecific scaling of REE to weight; that relative to their weight, taller subjects require a lower energy intake for replacing resting heat losses than shorter subjects; that fasting endurance, approximated as fat mass/REE, increases as H(0.5); and that thermal balance is maintained independent of stature by evident stable associations between resting heat production and capacity of external heat release. These observations have implications for the modeling of adult human energy requirements and associate with anthropological concepts founded on body size.

Scaling of human body composition to stature: new insights into body mass index.

BACKGROUND: Although Quetelet first reported in 1835 that adult weight scales to the square of stature, limited or no information is available on how anatomical body compartments, including adipose tissue (AT), scale to height. OBJECTIVE: We examined the critical underlying assumptions of adiposity-body mass index (BMI) relations and extended these analyses to major anatomical compartments: skeletal muscle (SM), bone, residual mass, weight (AT+SM+bone), AT-free mass, and organs (liver, brain). DESIGN: This was a cross-sectional analysis of 2 body-composition databases: one including magnetic resonance imaging and dual-energy X-ray absorptiometry (DXA) estimates of evaluated components in adults (total n=411; organs=76) and the other a larger DXA database (n=1346) that included related estimates of fat, fat-free mass, and bone mineral mass. RESULTS: Weight, primary lean components (SM, residual mass, AT-free mass, and fat-free mass), and liver scaled to height with powers of approximately 2 (all P<0.001); bone and bone mineral mass scaled to height with powers >2 (2.31-2.48), and the fraction of weight as bone mineral mass was significantly (P<0.001) correlated with height in women. AT scaled weakly to height with powers of approximately 2, and adiposity was independent of height. Brain mass scaled to height with a power of 0.83 (P=0.04) in men and nonsignificantly in women; the fraction of weight as brain was inversely related to height in women (P=0.002). CONCLUSIONS: These observations suggest that short and tall subjects with equivalent BMIs have similar but not identical body composition, provide new insights into earlier BMI-related observations and thus establish a foundation for height-normalized indexes, and create an analytic framework for future studies.

Why do obese patients not lose more weight when treated with low-calorie diets? A mechanistic perspective.

Maximal weight loss observed in low-calorie diet (LCD) studies tends to be small, and the mechanisms leading to this low treatment efficacy have not been clarified. Less-than-expected weight loss with LCDs can arise from an increase in fractional energy absorption (FEA), adaptations in energy expenditure, or incomplete patient diet adherence. We systematically reviewed studies of FEA and total energy expenditure (TEE) in obese patients undergoing weight loss with LCDs and in patients with reduced obesity (RO), respectively. This information was used to support an energy balance model that was then applied to examine patient adherence to prescribed LCD treatment programs. In the limited available literature, FEA was unchanged from baseline in short-term (<12 wk) treatment studies with LCDs; no long-term (>or=26 wk) studies were found. Review of doubly labeled water and respiratory chamber studies identified 10 reports of TEE in RO patients (n = 150) with long-term weight loss. These patients, who were weight stable, had a TEE almost identical to measured or predicted values in never-obese subjects (weighted mean difference: 1.3%; range: -1.7-8.5%). Modeling of energy balance, as supported by reviewed FEA and TEE studies, suggests that obese subjects participating in LCD programs have a weight loss less than half of that predicted. The small maximal weight loss observed with LCD treatments thus is likely not due to gastrointestinal adaptations but may be attributed, by deduction, to difficulties with patient adherence or, to a lesser degree, to metabolic adaptations induced by negative energy balance that are not captured by the current models.