Longitudinal Rumble Strips Require a Safety Compromise for Bicycles and Motor Vehicles

This article is part of the Proceedings of the 6th Annual International Cycling Safety Conference held in Davis, California, USA on September 20th through 23rd in the year 2017.<br><br>Paper ID:

Expected value of the final sample size plotted against the true value of log-odds ratio R, when R₁ = R₂ = R₃ = R, when ordinal responses are to be collected and when binary responses are to be collected.

<p>Expected value of the final sample size plotted against the true value of log-odds ratio R, when R₁ = R₂ = R₃ = R, when ordinal responses are to be collected and when binary responses are to be collected.</p

Stopping boundaries for the plot of Z against V.

<p>After the i^th interim analysis the values Z_i and V_i are calculated, and Z_i is plotted against V_i on this figure, i = 1, 2,….</p

GOST: A generic ordinal sequential trial design for a treatment trial in an emerging pandemic

<div><p>Background</p><p>Conducting clinical trials to assess experimental treatments for potentially pandemic infectious diseases is challenging. Since many outbreaks of infectious diseases last only six to eight weeks, there is a need for trial designs that can be implemented rapidly in the face of uncertainty. Outbreaks are sudden and unpredictable and so it is essential that as much planning as possible takes place in advance. Statistical aspects of such trial designs should be evaluated and discussed in readiness for implementation.</p><p>Methodology/Principal findings</p><p>This paper proposes a generic ordinal sequential trial design (GOST) for a randomised clinical trial comparing an experimental treatment for an emerging infectious disease with standard care. The design is intended as an off-the-shelf, ready-to-use robust and flexible option. The primary endpoint is a categorisation of patient outcome according to an ordinal scale. A sequential approach is adopted, stopping as soon as it is clear that the experimental treatment has an advantage or that sufficient advantage is unlikely to be detected. The properties of the design are evaluated using large-sample theory and verified for moderate sized samples using simulation. The trial is powered to detect a generic clinically relevant difference: namely an odds ratio of 2 for better rather than worse outcomes. Total sample sizes (across both treatments) of between 150 and 300 patients prove to be adequate in many cases, but the precise value depends on both the magnitude of the treatment advantage and the nature of the ordinal scale. An advantage of the approach is that any erroneous assumptions made at the design stage about the proportion of patients falling into each outcome category have little effect on the error probabilities of the study, although they can lead to inaccurate forecasts of sample size.</p><p>Conclusions/Significance</p><p>It is important and feasible to pre-determine many of the statistical aspects of an efficient trial design in advance of a disease outbreak. The design can then be tailored to the specific disease under study once its nature is better understood.</p></div

Results of million-fold simulations.

<p>Results of million-fold simulations.</p

Probability of concluding that the experimental treatment is efficacious (E wins) plotted against the true value of the log-odds ratio θ = ln(R).

<p>Probability of concluding that the experimental treatment is efficacious (E wins) plotted against the true value of the log-odds ratio θ = ln(R).</p

Expected value of the final value of the statistic V plotted against the true value of the log-odds ratio θ = log_eR.

<p>Expected value of the final value of the statistic V plotted against the true value of the log-odds ratio θ = log_eR.</p

Scenarios for the evaluation of the trial design.

<p>Scenarios for the evaluation of the trial design.</p

Illustrative plot of Z against V, with stopping boundaries.

<p>The trial stops with the conclusion that the experimental treatment is efficacious at the 11^th interim analysis.</p

Probability of stopping at or before the i^th interim analysis, i = 2, 4, 8, 12, 16, 20; plotted against the true value of the log-odds ratio θ = ln(R).

<p>Probability of stopping at or before the i^th interim analysis, i = 2, 4, 8, 12, 16, 20; plotted against the true value of the log-odds ratio θ = ln(R).</p