Lori E. Dodd

Six months of drug treatment is standard of care for drug-sensitive pulmonary tuberculosis (TB). Understanding the factors determining the length of treatment required for durable cure would allow individualization of treatment durations. We conducted a prospective, randomized, controlled noninferiority trial (PredictTB) of 4 versus 6 months of chemotherapy in patients with pulmonary TB in South Africa and China. Seven hundred and four participants with newly diagnosed, drug-sensitive TB were enrolled and stratified on the basis of radiographic disease characteristics assessed by FDG PET/CT imaging. Participants with less extensive disease ( n = 273) were randomly assigned at week 16 to complete therapy after 4 months or continue receiving treatment for 6 months. This study was stopped early after an interim analysis revealed that patients assigned to the 4-month treatment arm had a higher risk of relapse. Among participants who received 4 months of chemotherapy, 17 of 141 (12.1%) experienced TB-specific unfavorable outcomes compared with only 2 of 132 (1.5%) who completed 6 months of treatment. In the nonrandomized arm that included participants with more extensive disease, only 8 of 248 (3.2%) experienced unfavorable outcomes. Total lung cavity volume and lesion glycolysis at week 16 were associated with the risk of unfavorable outcomes. PET/CT imaging at TB recurrence showed that bacteriological relapses predominantly occurred in active cavities originally present at baseline. Subsequent post hoc automated segmentation of serial PET/CT scans combined with machine learning enabled the classification of participants according to their likelihood of relapse.

Clinical trials conducted during the COVID‐19 pandemic demonstrated the value of adaptive design methods in emerging disease settings, when there can be considerable uncertainty around disease natural history, anticipated endpoint effect sizes and population size. In such settings, there may also be uncertainty regarding the most appropriate primary endpoint. This might lead to an externally‐driven decision to change the primary endpoint during the course of an adaptive trial. If information on the new primary endpoint is already being collected, initially as a secondary endpoint, the trial could continue with a new primary endpoint. In this case it is unclear how statistical inference on the final primary endpoint should be adjusted for interim analyses monitoring the initial primary endpoint so as to control the overall type I error rate as adjusting for monitoring as if this was based on the new endpoint could be conservative whereas failing to make any adjustment could lead to type I error rate inflation if the new and original endpoint are correlated. This paper shows how group‐sequential methods can be modified to control the type I error rate for the analysis of the new primary endpoint irrespective of the true treatment effect on the initial primary endpoint. The method is illustrated using a simulated data example based on a clinical trial of remdesivir in COVID‐19. Construction of critical values for the test of the new primary endpoint require a value for the correlation between this and the initial primary endpoint. We present simulation studies to demonstrate that the type I error rate is controlled when this value is estimated from the data on the two endpoints obtained from the trial.

Motivated by the experience of COVID‐19 trials, we consider clinical trials in the setting of an emerging disease in which the uncertainty of natural disease course and potential treatment effects makes advance specification of a sample size challenging. One approach to such a challenge is to use a group sequential design to allow the trial to stop on the basis of interim analysis results as soon as a conclusion regarding the effectiveness of the treatment under investigation can be reached. As such a trial may be halted before a formal stopping boundary is reached, we consider the final analysis under such a scenario, proposing alternative methods for when the decision to halt the trial is made with or without knowledge of interim analysis results. We address the problems of ensuring that the type I error rate neither exceeds nor falls unnecessarily far below the nominal level. We also propose methods in which there is no maximum sample size, the trial continuing either until the stopping boundary is reached or it is decided to halt the trial.

Early bactericidal activity studies monitor daily sputum bacterial counts in individuals with tuberculosis (TB) for 14 days during experimental drug treatment. The rate of change in sputum bacterial load over time provides an informative, but imperfect, estimate of drug activity and is considered a critical step in development of new TB drugs. In this clinical study, 160 participants with TB received isoniazid, pyrazinamide, or rifampicin, components of first-line chemotherapy, and moxifloxacin individually and in combination. In addition to standard bacterial enumeration in sputum, participants underwent 2-deoxy-2-[¹⁸F]fluoro-d-glucose positron emission tomography and computerized tomography ([¹⁸F]FDG-PET/CT) at the beginning and end of the 14-day drug treatment. Quantitating radiological responses to drug treatment provided comparative single and combination drug activity measures across lung lesion types that correlated more closely with established clinical outcomes when combined with sputum enumeration compared to sputum enumeration alone. Rifampicin and rifampicin-containing drug combinations were most effective in reducing both lung lesion volume measured by CT imaging and lesion-associated inflammation measured by PET imaging. Moxifloxacin was not superior to rifampicin in any measure by PET/CT imaging, consistent with its performance in recent phase 3 clinical trials. PET/CT imaging revealed synergy between isoniazid and pyrazinamide and demonstrated that the activity of pyrazinamide was limited to lung lesion, showing the highest FDG uptake during the first 2 weeks of drug treatment. [¹⁸F]FDG-PET/CT imaging may be useful for measuring the activity of single drugs and drug combinations during evaluation of potential new TB drug regimens before phase 3 trials.

Background: Endpoint choice for randomized controlled trials of treatments for novel coronavirus-induced disease (COVID-19) is complex. Trials must start rapidly to identify treatments that can be used as part of the outbreak response, in the midst of considerable uncertainty and limited information. COVID-19 presentation is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks to over a month and can end in death. While improvement in mortality would provide unquestionable evidence about the clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical, particularly given a multitude of putative therapies to evaluate. Furthermore, patient states in between “cure” and “death” represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly given the variable time course of COVID-19. Outcomes measured at fixed time points, such as a comparison of severity scores between treatment and control at day 14, may risk missing the time of clinical benefit. An endpoint such as time to improvement (or recovery) avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of “recovered” versus “not recovered.” Methods: We evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials. Results: Power for fixed time-point methods depends heavily on the time selected for evaluation. Time-to-event approaches have reasonable statistical power, even when compared with a fixed time-point method evaluated at the optimal time. Discussion: Time-to-event analysis methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses.

Randomized controlled clinical trials are critical for evaluating new experimental therapeutics to treat Ebola virus disease.