Communication of treatment rankings obtained from network meta-­analysis using data visualization

The number of published network meta-analysis (NMA) reports has increased substantially in recent years. NMAs combine direct and indirect evidence and enable comparisons between all relevant treatment options for a given disease, even when some treatments have not been directly compared with each other. In the absence of randomized trials comparing all treatment options to each other, NMAs address important information needs of patients and clinicians about the comparative effectiveness of treatment alternatives. NMA results may be difficult to communicate and interpret effectively given the large volume of complex information generated on multiple alternative treatments with multiple benefit and harm outcomes. For example, NMA comparing 5 treatments result in 10 pair-wise comparisons; if results are available for 3 benefit and 3 harm outcomes, decision makers are faced with 60 sets of results. Identifying the best treatment option to initiate therapy is, thus, not straightforward. Although several graphical and tabular displays exist to report the pertinent results of NMAs, existing reporting guidelines differ in their recommendations. Consequently, there is significant variation in the current way NMA findings are reported and presented..

Does industry sponsorship bias research findings?

A new study reveals that the findings obtained from industry sponsored studies for widely prescribed cholesterol drugs are similar in magnitude as those in non-industry sources. Huseyin Naci, LSE Health research fellow and co-author of the study, explains further

Evidence of comparative efficacy should have a formal role in European drug approvals

Despite methodological concerns, comparative efficacy evidence should be required at the time of drug approval, says Corinna Sorenson and colleagues, to allow patients, clinicians, and other healthcare decision makers to determine whether a new drug is superior, equivalent, or inferior to its existing alternatives

History bias, study design, and the unfulfilled promise of pay-for-performance policies in health care

Recently, PCD published a longer, related article, “How Do You Know Which Health Care Effectiveness Research You Can Trust? A Guide for the Perplexed,” that was translational in nature. The article used simple graphs and easy-to-understand text—in five case studies—to illustrate how powerful biases, combined with weak study designs that cannot control for them, can yield untrustworthy research on several widespread interventions: influenza vaccination policy, health information technology, drug safety, prevention of childhood obesity and hospital safety (“mortality reduction”) programs. Since this was not a formal methods paper, the target audiences were policy makers, journalists, trainees, and the public. The primary goal was to understand how weak or strong study designs are likely to fail or succeed in controlling for these pervasive biases. At the start of that article, we promised to add to these case examples of common biases and research designs to show why “caution is needed in understanding and accepting the results of research that may have profound and long-lasting effects on health policy and clinical practice.” In this sixth case study, the authors revisit one of the most common and virulent biases, threats of history. Studies can mislead policymakers and clinicians because they fail to control for history, which represents pre-existing or co-occurring changes in study outcomes that were happening with or without the intervention. The policy in this case, pay-for-performance (PfP, see below), is extremely sensitive to this powerful bias because medical practice is always changing as a result of factors unrelated to a policy, such as widespread media or national guidelines supporting a life-saving treatment, e.g., beta blockers for acute MI (1). Without investigating and visualizing outcomes over time before and after a policy or intervention, it is likely that the investigators will attribute such ongoing changes to “effects” of the quality improvement policy, resulting in millions of dollars of waste implementing ineffective PfP policies worldwide

Negotiating drug prices in the US - lessons from Europe

In the US, spending on prescription drugs increased from $30 billion in 1980 to$335 billion in 2018, which is mainly associated with the high prices of brand-name drugs.1 Prices are substantially higher compared with European countries.2 One explanation for this price differential is that, unlike in the US, national health authorities in Europe negotiate new drug prices with manufacturers. The US Inflation Reduction Act, which was signed into law in August 2022, empowers the US Secretary of Health and Human Services to negotiate the prices for a limited number of brand-name drugs, with the greatest spending within Medicare’s Part B (which covers clinician-administered drugs) and Part D (which covers retail drugs) programs.3 The bill stipulates that drug pricing negotiations must consider policies, such as whether a drug represents a therapeutic advance or fulfills an unmet medical need.3,4 Therefore, policy makers in the US will need to address 2 questions as a matter of priority: How should therapeutic advance (which can also be referred to as therapeutic value) be assessed? How should evidence on therapeutic advance be used to negotiate drug prices? Lessons can be learned from European countries (such as England, France, Germany, and Switzerland) that have a long track record in evaluating drug value and using this information to secure lower price

Specialty drugs: a distinctly American phenomenon

Various stakeholders in the pharmaceutical supply chain assign the specialty label to drugs on the basis of a combination of several unrelated factors, including whether a drug treats a rare condition. But the single most common feature of specialty drugs is high cost

Generating comparative data on clinical benefits and harms of statins to inform prescribing decisions: evidence from network meta-analyses

Background and Importance: comparative evidence generated using systematic reviews and meta-analyses can form the basis of high quality prescribing decisions in clinical practice. Such evidence is imperative when choosing a first-line treatment among multiple alternatives, particularly in the United States where there is no single national authority responsible for providing practice guidelines for prescribers. Objective: Using cholesterol-lowering statins as a case study, this thesis set out to evaluate the comparative clinical benefits and harms of statins for the prevention of coronary heart disease. Novelty and Empirical Contribution: The empirical work presented in this thesis was based on a systematic review and network meta-analysis, for the first time combining the placebo-controlled and active-comparator trials of statins. Using 184 randomized trials including 260,630 individuals with or without cardiovascular disease, this thesis makes four major contributions to the literature on the comparative effectiveness and safety of statins, showing the following:(1) cholesterol-lowering effects of statins are less pronounced than suggested by the previous reviews; (2) statins potentially differ in terms of their comparative effects on clinically meaningful benefit outcomes, which are not fully explained by their cholesterol-lowering effects;(3) harms associated with statins are rare; still, some statins are safer than others; and (4) unlike previous findings in the literature, there is no evidence of industry sponsorship bias affecting the trials of statins. Implications for Clinical Practice: Although there are statistically detectable and clinically relevant differences among individual statins, the empirical work presented in this thesis does not conclusively identify a clear winner among statins that should be favored in clinical practice. Future&Research Directions: The potential mechanisms underlying the observed differences between individual statins should be investigated in future studies. Policy Relevance: The findings presented in this thesis suggest that statin prescribing patterns over the past decade – and in particular atorvastatin’s exceptional sales performance despite its equivalence to simvastatin – are not supported by the current best evidence. A proposed policy option is to raise the bar for market entry of new drugs by requiring comparative evidence at the time of approval decisions. Network meta-analysis methods can be used at the United States Food and Drug Administration setting, thereby making comparative evidence available before prescribing patterns are established