Making SMART decisions in prophylaxis and treatment studies (preprint)

The optimal prophylaxis, and treatment if the prophylaxis fails, for a disease may be best evaluated using a sequential multiple assignment randomised trial (SMART). A SMART is a multi-stage study that randomises a participant to an initial treatment, observes some response to that treatment and then, depending on their observed response, randomises the same participant to an alternative treatment. Response adaptive randomisation may, in some settings, improve the trial participants' outcomes and expedite trial conclusions, compared to fixed randomisation. But 'myopic' response adaptive randomisation strategies, blind to multistage dynamics, may also result in suboptimal treatment assignments. We propose a 'dynamic' response adaptive randomisation strategy based on Q-learning, an approximate dynamic programming algorithm. Q-learning uses stage-wise statistical models and backward induction to incorporate late-stage 'payoffs' (i.e. clinical outcomes) into early-stage 'actions' (i.e. treatments). Our real-world example consists of a COVID-19 prophylaxis and treatment SMART with qualitatively different binary endpoints at each stage. Standard Q-learning does not work with such data because it cannot be used for sequences of binary endpoints. Sequences of qualitatively distinct endpoints may also require different weightings to ensure that the design guides participants to regimens with the highest utility. We describe how a simple decision-theoretic extension to Q-learning can be used to handle sequential binary endpoints with distinct utilities. Using simulation we show that, under a set of binary utilities, the 'dynamic' approach increases expected participant utility compared to the fixed approach, sometimes markedly, for all model parameters, whereas the 'myopic' approach can actually decrease utility.

Sample Pooling is a Viable Strategy for SARS-CoV-2 Detection in Low-Prevalence Settings (preprint)

BACKGROUNDThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has significantly increased demand on laboratory throughput and reagents for nucleic acid extraction and polymerase chain reaction (PCR). Reagent shortages may limit the expansion of testing required to scale back isolation measures. AIMTo investigate the viability of sample pooling as a strategy for increasing test throughput and conserving PCR reagents; to report our early experience with pooling of clinical samples. METHODSA pre-implementation study was performed to assess the sensitivity and theoretical efficiency of two, four, and eight-sample pools in a real-time reverse transcription PCR-based workflow. A standard operating procedure was developed and implemented in two laboratories during periods of peak demand, inclusive of over 29,000 clinical samples processed in our laboratory. RESULTSSensitivity decreased (mean absolute increase in cycle threshold value of 0.6, 2.3, and 3.0 for pools of two, four, and eight samples respectively) and efficiency increased as pool size increased. Gains from pooling diminished at high disease prevalence. Our standard operating procedure was successfully implemented across two laboratories. Increased workflow complexity imparts a higher risk of errors, and requires risk mitigation strategies. Turnaround time for individual samples increased, hence urgent samples should not be pooled. CONCLUSIONSPooling is a viable strategy for high-throughput testing of SARS-CoV-2 in low-prevalence settings.