How Should Clinicians Interpret Imprecise Trials Assessing Drugs for COVID-19 Patients?

How Should Clinicians Interpret Imprecise Trials Assessing Drugs for COVID-19 Patients?

Publication date: Jun 04, 2020

As the COVID-19 pandemic progresses, researchers are reporting findings of randomized trials comparing standard care with care augmented by experimental drugs. The trials have small sample sizes, so estimates of treatment effects are imprecise. Seeing imprecision, clinicians reading research articles may find it difficult to decide when to treat patients with experimental drugs. Whatever decision criterion one uses, there is always some probability that random variation in trial outcomes will lead to prescribing sub-optimal treatments. A conventional practice when comparing standard care and an innovation is to choose the innovation only if the estimated treatment effect is positive and statistically significant. This practice defers to standard care as the status quo. We argue that clinicians should ignore conventional measures of statistical significance. They should choose treatments that work best in trials, taking side effects into account and recognizing that treatment effects may vary with patient risk factors. To evaluate treatments, we use the concept of near optimality, which jointly considers the probability and magnitude of decision errors. An appealing decision criterion from this perspective is the empirical success rule, which chooses the treatment with the highest observed average patient outcome in the trial. Considering the design of COVID-19 trials, we show that the empirical success rule yields treatment results that are much closer to optimal than those generated by prevailing decision criteria based on hypothesis tests.

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Concepts Keywords
Algorithm Antiviral therapy
Antiviral Therapy Renal replacement therapy
Azithromycin HIV
Binary Pharmaceutical treatments
China Trial protocol
Chloroquine Pharmaceuticals
Clinical Trial Conduct grid search
Clinical Trials Treatment product
Comorbidities Design of experiments
Confidence Intervals Health
Conservative Articles
Covariate Research methods
Covariates Clinical trial
Determinant Statistical hypothesis testing
Dexamethasone Null hypothesis
Gender Simulation
Geneva Trial protocol
Gravity Pdf
Hazard Rate
Healthcare
Heterogeneity
HIV Protease
Hospital
Hydroxychloroquine
Hypothesis Test
Hypothesis Testing
Interferon Beta 1a
Magnitude
Morbidity
Mortality
Mortality Rate
Multiple Comparisons
NIH
Nih
Null Hypothesis
Pairwise Comparison
Pandemic
Pharmaceutical
Pharmaceuticals
Primary Endpoint
Probability
Probability Theory
Protease Inhibitors
Protocol
Randomisation
Randomized Trial
Ritonavir
Solidarity
Stake
Statistical Hypothesis Test
Statistical Significance
Subgroup
Swiss
Symmetric
Symmetry
United Kingdom
Upper Bound
Vln

Semantics

Type Source Name
disease MESH risk factors
drug DRUGBANK Spinosad
disease MESH emergency
drug DRUGBANK Lopinavir
drug DRUGBANK Ritonavir
disease MESH death
drug DRUGBANK Tropicamide
drug DRUGBANK Aspartame
drug DRUGBANK Dexamethasone
drug DRUGBANK Hydroxychloroquine
drug DRUGBANK Azithromycin
drug DRUGBANK Chloroquine
drug DRUGBANK Interferon beta-1a
disease MESH renal
drug DRUGBANK Sodium hydroxide
drug DRUGBANK Carboxyamidotriazole

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