Evidence-based sizing of non-inferiority trials using decision models

Abstract Background There are significant challenges to the successful conduct of non-inferiority trials because they require large numbers to demonstrate that an alternative intervention is “not too much worse” than the standard. In this paper, we present a novel strategy for designing non-inferiority trials using an approach for determining the appropriate non-inferiority margin (δ), which explicitly balances the benefits of interventions in the two arms of the study (e.g. lower recurrence rate or better survival) with the burden of interventions (e.g. toxicity, pain), and early and late-term morbidity. Methods We use a decision analytic approach to simulate a trial using a fixed value for the trial outcome of interest (e.g. cancer incidence or recurrence) under the standard intervention (pS) and systematically varying the incidence of the outcome in the alternative intervention (pA). The non-inferiority margin, pA – pS = δ, is reached when the lower event rate of the standard therapy counterbalances the higher event rate but improved morbidity burden of the alternative. We consider the appropriate non-inferiority margin as the tipping point at which the quality-adjusted life-years saved in the two arms are equal. Results Using the European Polyp Surveillance non-inferiority trial as an example, our decision analytic approach suggests an appropriate non-inferiority margin, defined here as the difference between the two study arms in the 10-year risk of being diagnosed with colorectal cancer, of 0.42% rather than the 0.50% used to design the trial. The size of the non-inferiority margin was smaller for higher assumed burden of colonoscopies. Conclusions The example demonstrates that applying our proposed method appears feasible in real-world settings and offers the benefits of more explicit and rigorous quantification of the various considerations relevant for determining a non-inferiority margin and associated trial sample size.https://deepblue.lib.umich.edu/bitstream/2027.42/146777/1/12874_2018_Article_643.pd

Racial variation in the clinical and economic burden of skeletal-related events among elderly men with stage IV metastatic prostate cancer

<p>Prostate cancer (PCa) outcomes vary widely among African American (AA) and non-Hispanic White (NHW) men. The authors investigated racial variation in the incidence of skeletal-related events (SREs) and SRE-related healthcare costs among AA and NHW men, a topic that has received limited attention in the literature. AA and NHW men diagnosed with metastatic PCa were identified from the linked Surveillance, Epidemiology and End Results–Medicare dataset. The sample included 6455 men with metastatic PCa, including 5420 NHW men and 1035 AA men. Approximately 16% experienced SREs during follow-up. AA men were less likely to experience SREs compared with NHW men, controlling for individual characteristics (adjusted odds ratio: 0.79; 95% CI: 0.66– 0.94). The SRE-specific costs were US$35,725 (US$22,190–US$49,260) among AA men and US$25,896 (US$21,669–US$30,123) among NHW men. Although AA men were less likely to experience SREs, there were substantial costs attributable to the treatment of SREs among AA men.</p

An ecological approach to monitor geographic disparities in cancer outcomes.

BackgroundArea-level indices are widely used to assess the impact of socio-environmental characteristics on cancer outcomes. While area-level measures of socioeconomic status (SES) have been previously used in cancer settings, fewer studies have focused on evaluating the impact of area-level health services supply (HSS) characteristics on cancer outcomes. Moreover, there is significant variation in the methods and constructs used to create area-level indices.MethodsIn this study, we introduced a psychometrically-induced, reproducible approach to develop area-level HSS and SES indices. We assessed the utility of these indices in detecting the effects of area-level characteristics on prostate, breast, and lung cancer incidence and stage at diagnosis in the US. The information on county-level SES and HSS characteristics were extracted from US Census, County Business Patterns data and Area Health Resource Files. The Surveillance, Epidemiology, and End Results database was used to identify individuals diagnosed with cancer from 2010 to 2012. SES and HSS indices were developed and linked to 3-year age-adjusted cancer incidence rates. SES and HSS indices empirically summarized the level of employment, education, poverty and income, and the availability of health care facilities and health professionals within counties.ResultsSES and HSS models demonstrated good fit (TLI = 0.98 and 0.96, respectively) and internal consistency (alpha = 0.85 and 0.95, respectively). Increasing SES and HSS were associated with increasing prostate and breast cancer and decreasing lung cancer incidence rates. The results varied by stage at diagnosis and race.ConclusionComposite county-level measures of SES and HSS were effective in ranking counties and detecting gradients in cancer incidence and stage at diagnosis. Thus, these measures provide valuable tools for monitoring geographic disparities in cancer outcomes

Rapporti di lavoro e attività gestoria tra autonomia, subordinazione e nuove proposte

A large proportion of drugs available are of little importance in terms of fulfilling primary healthcare needs. Combination drugs increase the risk of side effects, lead to an ineffective dosage and liability to abuse and may also needlessly increase the cost. Drug combinations make it more difficult to find the causative agent responsible for the adverse reactions. In many cases their stability is doubtful, reducing the efficacy of many preparations. The Fifteenth WHO model list of essential medicines (March 2007) contains only 25 approved fixed dose combinations, whereas in Nepal, there are innumerable examples of irrational drug combinations, which are easily available and can be bought even without a prescription. A system of screening the drug combinations that are already licensed and available in the market is implemented in many developed and developing countries. Rational combinations can be of immense help to the health care system. These combinations may improve the quality of life for many and increase compliance. But irrational fixed dose combination products can be equally harmful

Reflecting on 20 years of breast cancer modeling in CISNET

Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.</p