Sponsorship bias in oncology cost effectiveness analysis.

OBJECTIVES: Cost effectiveness analysis (CEA) has been increasingly used to inform cancer treatment coverage policy making worldwide. The primary objective of this study was to assess the association between industry sponsorship and CEA results in oncology. STUDY DESIGN AND SETTING: All CEAs in oncology used incremental cost per quality-adjusted life year (QALY) as health effect identified from the Tufts CEA Registry since 1976 was analyzed. Descriptive analyses were performed to present and compare the characteristics of CEA funded by industry and non-industry. Robust logistic regression was performed to assess the relationship between the industry sponsorship and cost effective conclusion over a wide range of threshold values. RESULTS: A total of 1537 CEAs in oncology published from 1976 to 2021 were included. There were 387 (25.2%) with the industry sponsorship. CEAs sponsored by the industry were more likely to report ICERs below $50,000/QALY (adjusted odds ratio (OR), 1.91, 95% confidence interval (CI), 1.45-2.51, P < 0.001), $100,000/QALY (2.74, 1.98-3.79, P < 0.001), and $150,000/QALY (3.53, 2.37-5.27, P < 0.001) than studies without industry sponsorship. CONCLUSIONS: Our study suggests that there has been a significant sponsorship bias in CEAs in oncology. This bias could have a profound implication on drug pricing and coverage policy making.

Sponsorship Bias in Clinical Trials in the Dental Application of Probiotics: A Meta-Epidemiological Study.

Many experimental and clinical trials have investigated the dental application of probiotics, although the evidence concerning the effects of probiotic supplements is conflicting. We aimed to examine whether sponsorship in trials about dental applications of probiotics is associated with biased estimates of treatment effects. Overall, 13 meta-analyses involving 48 randomized controlled trials (23 with high risk of sponsorship bias, 25 with low risk) with continuous outcomes were included. Effect sizes were calculated from differences in means of first reported continuous outcomes, divided by the pooled standard deviation. For each meta-analysis, the difference in standardized mean differences between high-risk and low-risk trials was estimated by random effects meta-regression. Differences in standardized mean differences (DSMDs) were then calculated via meta-analyses in a random effects meta-analysis model. A combined DSMD of greater than zero indicated that high-risk trials showed more significant treatment effects than low-risk trials. The results show that trials with a high risk of sponsorship bias showed more significant intervention effects than did low-risk trials (combined DSMD, 0.06; 95% confidence interval, 0.3 to 0.9; p < 0.001), with low heterogeneity among meta-analyses (I2 = 0%; between-meta-analyses variance τ2 = 0.00). Based on our study, high-risk clinical trials with continuous outcomes reported more favorable intervention effects than did low-risk trials in general.

How to Assess the Epistemic Wrongness of Sponsorship Bias? The Case of Manufactured Certainty.

Although the impact of so-called "sponsorship bias" has been the subject of increased attention in the philosophy of science, what exactly constitutes its epistemic wrongness is still debated. In this paper, I will argue that neither evidential accounts nor social-epistemological accounts can fully account for the epistemic wrongness of sponsorship bias, but there are good reasons to prefer social-epistemological to evidential accounts. I will defend this claim by examining how both accounts deal with a paradigm case from medical epistemology, recently discussed in a paper by Bennett Holman. I will argue that evidential accounts cannot adequately capture cases of sponsorship bias that involve the manufacturing of certainty because of their neutrality with respect to the role of non-epistemic values in scientific practice. If my argument holds, it further highlights the importance of integrating social and ethical concerns into epistemological analysis, especially in applied contexts. One can only properly grasp sponsorship bias as an epistemological problem if one resists the methodological tendency to analyze social, ethical, and epistemological issues in isolation from each other.

Exploring the Influence of Alcohol Industry Funding in Observational Studies on Moderate Alcohol Consumption and Health.

Funding of research by industry in general can lead to sponsorship bias. The aim of the current study was to conduct an initial exploration of the impact of sponsorship bias in observational alcohol research by focusing on a broad spectrum of health outcomes. The purpose was to determine whether the outcome depended on funding source. We focused on moderate alcohol consumption and used meta-analyses that are the basis of several international alcohol guidelines. These meta-analyses included observational studies that investigated the association of alcohol consumption with 14 different health outcomes, including all-cause mortality, several cardiovascular diseases and cancers, dementia, and type 2 diabetes. Subgroup analyses and metaregressions were conducted to investigate the association between moderate alcohol consumption and the risk of different health outcomes, comparing findings of studies funded by the alcohol industry, ones not funded by the alcohol industry, and studies with an unknown funding source. A total of 386 observational studies were included. Twenty-one studies (5.4%) were funded by the alcohol industry, 309 studies (80.1%) were not funded by the alcohol industry, and for the remaining 56 studies (14.5%) the funding source was unknown. Subgroup analyses and metaregressions did not show an effect of funding source on the association between moderate alcohol intake and different health outcomes. In conclusion, only a small proportion of observational studies in meta-analyses, referred to by several international alcohol guidelines, are funded by the alcohol industry. Based on this selection of observational studies the association between moderate alcohol consumption and different health outcomes does not seem to be related to funding source.

Using network meta-analysis to evaluate the existence of small-study effects in a network of interventions.

Suggested methods for exploring the presence of small-study effects in a meta-analysis and the possibility of publication bias are associated with important limitations. When a meta-analysis comprises only a few studies, funnel plots are difficult to interpret, and regression-based approaches to test and account for small-study effects have low power. Assuming that the cause of funnel plot asymmetry is likely to affect an entire research field rather than only a particular comparison of interventions, we suggest that network meta-regression is employed to account for small-study effects in a set of related meta-analyses. We present several possible models for the direction and distribution of small-study effects and we describe the methods by re-analysing two published networks. Copyright © 2012 John Wiley & Sons, Ltd.