Does randomization assert the balance across trial arms? Revisiting Worrall's criticism.

We revisit John Worrall's old but still prominent argument against the view that randomization balances the impact of both known and unknown confounders across the treatment and control arms. We argue that his argument involving indefinitely many possible confounders is at odds with statistical theory as it (1) presumes that the purpose of randomized studies is obtaining perfect point estimates for which perfect balance is needed; (2) mistakes equalizing each confounder with the overall (average) impact of all confounders, and (3) assumes that the joint effect of an infinite series of confounders cannot be bounded. We defend the role of randomization in balancing the impact of confounders across the treatment and control arms by putting forward the statistical sense of the balance claim.

Causal Pluralism in Medicine and its Implications for Clinical Practice.

The existing philosophical views on what is the meaning of causality adequate to medicine are vastly divided. We approach this question and offer two arguments in favor of pluralism regarding concepts of causality. First, we analyze the three main types of research designs (randomized-controlled trials, observational epidemiology and laboratory research).

The failure of drug repurposing for COVID-19 as an effect of excessive hypothesis testing and weak mechanistic evidence.

The current strategy of searching for an effective treatment for COVID-19 relies mainly on repurposing existing therapies developed to target other diseases. Conflicting results have emerged in regard to the efficacy of several tested compounds but later results were negative. The number of conducted and ongoing trials and the urgent need for a treatment pose the risk that false-positive results will be incorrectly interpreted as evidence for treatments' efficacy and a ground for drug approval.

Is meta-analysis of RCTs assessing the efficacy of interventions a reliable source of evidence for therapeutic decisions?

Literature-based meta-analysis is a standard technique applied to pool results of individual studies used in medicine and social sciences. It has been criticized for being too malleable to constrain results, averaging incomparable values, lacking a measure of evidence's strength, and problems with a systematic bias of individual studies. We argue against using literature-based meta-analysis of RCTs to assess treatment efficacy and show that therapeutic decisions based on meta-analytic average are not optimal given the full scope of existing evidence.

Assessing the quality of evidence from epidemiological agent-based models for the COVID-19 pandemic.

Agent-based models (ABMs) are one of the main sources of evidence for decisions regarding mitigation and suppression measures against the spread of SARS-CoV-2. These models have not been previously included in the hierarchy of evidence put forth by the evidence-based medicine movement, which prioritizes those research methods that deliver results less susceptible to the risk of confounding. We point out the need to assess the quality of evidence delivered by ABMs and ask the question of what is the risk that assumptions entertained in ABMs do not include all the key factors and make model predictions susceptible to the problem of confounding.

Agent-based modelling for SARS-CoV-2 epidemic prediction and intervention assessment: A methodological appraisal.

Background: Our purpose is to assess epidemiological agent-based models-or ABMs-of the SARS-CoV-2 pandemic methodologically. The rapid spread of the outbreak requires fast-paced decision-making regarding mitigation measures. However, the evidence for the efficacy of non-pharmaceutical interventions such as imposed social distancing and school or workplace closures is scarce: few observational studies use quasi-experimental research designs, and conducting randomized controlled trials seems infeasible.