Statistical Decision Properties of Imprecise Trials Assessing Covid-19 Drugs

@article{Manski2020StatisticalDP,
  title={Statistical Decision Properties of Imprecise Trials Assessing Covid-19 Drugs},
  author={Charles F. Manski and Aleksey Tetenov},
  journal={NBER Working Paper Series},
  year={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… 

Discovering Treatment Effectiveness Via Median Treatment Effects—Applications to Covid-19 Clinical Trials

Property of median treatment effects as indicators of treatment effectiveness as well as several studies to explore empirically some properties of median-treatment-effect measures of effectiveness are explored.

True COVID-19 mortality rates from administrative data

  • D. Depalo
  • Economics
    Journal of population economics
  • 2020
The narrowest bounds of mortality rates from COVID-19 are between 0.1 and 7.5%, much smaller than the 17.5% discussed in earlier reports, which suggests that the case of Lombardia may not be as special as some argue.

Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald

This paper proposes statistical decision theory as a framework for evaluation of the performance of models in decision making, and considers the common practice of as-if optimization: specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate.

Forming COVID-19 Policy Under Uncertainty

  • C. Manski
  • Economics
    Journal of Benefit-Cost Analysis
  • 2020
Abstract This paper presents my thinking and concerns about formation of COVID-19 policy. Policy formation must cope with substantial uncertainties about the nature of the disease, the dynamics of

References

SHOWING 1-10 OF 12 REFERENCES

Sufficient trial size to inform clinical practice

This paper develops an alternative principle for trial design that aims to directly benefit medical decision making by choosing a sample size that enables implementation of near-optimal treatment rules.

Minimax regret treatment choice with finite samples

A Trial of Lopinavir–Ritonavir in Adults Hospitalized with Severe Covid-19

In hospitalized adult patients with severe Covid-19, no benefit was observed with lopinavir–ritonavir treatment beyond standard care, and future trials in patients withsevere illness may help to confirm or exclude the possibility of a treatment benefit.

Asymptotics for Statistical Treatment Rules

This paper develops asymptotic optimality theory for statistical treatment rules in smooth parametric and semiparametric models. Manski (2000, 2002, 2004) and Dehejia (2005) have argued that the

New tables for multiple comparisons with a control.

The main purpose of the present paper is to give the exact tables for making two-sided comparisons, and a method is given for adjusting the tabulated values to cover the situation where the variance of the control mean is smaller than thevariance of the treatment means.

Statistical treatment rules for heterogeneous populations

An important objective of empirical research on treatment response is to provide decision makers with information useful in choosing treatments. This paper studies minimax-regret treatment choice

Trial Size for Near-Optimal Treatment: Reconsidering MSLT-II

  • The American Statistician
  • 2019

Trial Size for Near-Optimal Treatment: Reconsidering MSLT-II,

  • Tetenov
  • 2019