Working correlates of protection predict SchuS4-derived-vaccine candidates with improved efficacy against an intracellular bacterium, Francisella tularensis.

Francisella tularensis, the causative agent of tularemia, is classified as Tier 1 Select Agent with bioterrorism potential. The efficacy of the only available vaccine, LVS, is uncertain and it is not licensed in the U.S. Previously, by using an approach generally applicable to intracellular pathogens, we identified working correlates that predict successful vaccination in rodents. Here, we applied these correlates to evaluate a panel of SchuS4-derived live attenuated vaccines, namely SchuS4-ΔclpB, ΔclpB-ΔfupA, ΔclpB-ΔcapB, and ΔclpB-ΔwbtC. We combined in vitro co-cultures to quantify rodent T-cell functions and multivariate regression analyses to predict relative vaccine strength. The predictions were tested by rat vaccination and challenge studies, which demonstrated a clear relationship between the hierarchy of in vitro measurements and in vivo vaccine protection. Thus, these studies demonstrated the potential power a panel of correlates to screen and predict the efficacy of Francisella vaccine candidates, and in vivo studies in Fischer 344 rats confirmed that SchuS4-ΔclpB and ΔclpB-ΔcapB may be better vaccine candidates than LVS.

Detecting participant noncompliance across multiple time points by modeling a longitudinal biomarker.

INTRODUCTION: Participant noncompliance, in which participants do not follow their assigned treatment protocol, has long complicated the interpretation of randomized clinical trials. No gold standard has been identified for detecting noncompliance, but in some trials participants' biomarkers can provide objective information that suggests exposure to non-study treatments. However, existing methods are limited to retrospectively detecting noncompliance at a single time point based on a single biomarker measurement. We propose a novel method that can leverage participants' full biomarker history to detect noncompliance across multiple time points. Conditional on longitudinal biomarker data, our method can estimate the probability of compliance at (1) a single time point of the trial, (2) all time points, and (3) a future time point. METHODS: Across time points, we model the biomarker as a mixture density with (latent) components corresponding to longitudinal patterns of compliance. To estimate the mixture density, we fit mixed effects models for both compliance and the biomarker. We use the mixture density to derive compliance probabilities that condition on the longitudinal biomarker data. We evaluate our compliance probabilities by simulation and apply them to a trial in which current smokers were asked to only smoke low nicotine study cigarettes (Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2). In the simulation, we investigated three different effects of compliance on the biomarker, as well as the effect of misspecification of the covariance structures. We compared probability estimators (1) and (2) to those that ignore the longitudinal correlation in the data according to area under the receiver operating characteristic curve. We evaluated estimator (3) by plotting its calibration lines. For Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, we compared estimators (1) and (3) to a probability estimator of compliance at the last time point that ignores the longitudinal correlation. RESULTS: In the simulation, for both compliance at the last time point and at all time points, conditioning on the longitudinal biomarker data uniformly raised area under the receiver operating characteristic curve across all three compliance effect scenarios. The gains in area under the receiver operating characteristic curve were smaller under misspecification. The calibration lines for the prediction of compliance closely followed 45°, though with additional variability under misspecification. For compliance at the last time point of Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, conditioning on participants' full biomarker history boosted area under the receiver operating characteristic curve by three percentage points. The prediction probabilities somewhat accurately approximated the non-longitudinal compliance probabilities. DISCUSSION: Compared to existing methods that only use a single biomarker measurement, our method can account for the longitudinal correlation in the biomarker and compliance to more accurately identify noncompliant participants. Our method can also use participants' biomarker history to predict compliance at a future time point.

Comparison of an ordinal endpoint to time-to-event, longitudinal, and binary endpoints for use in evaluating treatments for severe influenza requiring hospitalization.

BACKGROUND/AIMS: The Food and Drug Administration recommends research into developing well-defined and reliable endpoints to evaluate treatments for severe influenza requiring hospitalization. A novel 6-category ordinal endpoint of patient health status after 7 days that ranges from death to hospital discharge with resumption of normal activities is being used in a randomized placebo-controlled trial of intravenous immunoglobulin (IVIG) for severe influenza (FLU-IVIG). We compare the power of the ordinal endpoint under a proportional odds model to other types of endpoints as a function of various trial parameters. METHODS: We used closed-form analysis and empirical simulation to compare the power of the ordinal endpoint to time-to-event, longitudinal, and binary endpoints. In the simulation setting, we varied the treatment effect and the distribution of the placebo group across the follow-up period with consideration of adjustment for baseline health status. RESULTS: In the analytic setting, ordinal endpoints of high granularity provided greater power than time-to-event endpoints when most patients in the placebo group had either naturally progressed to the category of hospital discharge by day 7 or were far from hospital discharge on day 7. In the simulation setting, adjustment for baseline health status universally raised power for the proportional odds model. Across different placebo group distributions of the ordinal endpoint regardless of adjustment for baseline health status, only time-to-event endpoints yielded higher power than the ordinal endpoint for certain treatment effects. CONCLUSIONS: In this case study, the FLU-IVIG ordinal endpoint provided greater power than time-to-event, binary, and longitudinal endpoints for most scenarios of the treatment effect and placebo group distribution, including the target population studied for FLU-IVIG. The ordinal endpoint was only surpassed by the time-to-event endpoint when many patients in the placebo group were on the cusp of hospital discharge on day 7 and the follow-up period for the time-to-event endpoint was extended to allow for additional events. Our general approach for evaluating the power of several potential endpoints for an influenza trial can be used for designing other influenza trials with different target populations and for other trials in other disease areas.

Statistical testing of baseline differences in sports medicine RCTs: a systematic evaluation.

BACKGROUND/AIM: The CONSORT (Consolidated Standards of Reporting Trials) statement discourages reporting statistical tests of baseline differences between groups in randomised controlled trials (RCTs). However, this practice is still common in many medical fields. Our aim was to determine the prevalence of this practice in leading sports medicine journals. METHODS: We conducted a comprehensive search in Medline through PubMed to identify RCTs published in the years 2005 and 2015 from 10 high-impact sports medicine journals. Two reviewers independently confirmed the trial design and reached consensus on which articles contained statistical tests of baseline differences. RESULTS: Our search strategy identified a total of 324 RCTs, with 85 from the year 2005 and 239 from the year 2015. Overall, 64.8% of studies (95% CI (59.6, 70.0)) reported statistical tests of baseline differences; broken down by year, this percentage was 67.1% in 2005 (95% CI (57.1, 77.1)) and 64.0% in 2015 (95% CI (57.9, 70.1)). CONCLUSIONS: Although discouraged by the CONSORT statement, statistical testing of baseline differences remains highly prevalent in sports medicine RCTs. Statistical testing of baseline differences can mislead authors; for example, by failing to identify meaningful baseline differences in small studies. Journals that ask authors to follow the CONSORT statement guidelines should recognise that many manuscripts are ignoring the recommendation against statistical testing of baseline differences.

Analysis of an ordinal endpoint for use in evaluating treatments for severe influenza requiring hospitalization.

Background/Aims A single best endpoint for evaluating treatments of severe influenza requiring hospitalization has not been identified. A novel six-category ordinal endpoint of patient status is being used in a randomized controlled trial (FLU-Intravenous Immunoglobulin - FLU-IVIG) of intravenous immunoglobulin. We systematically examine four factors regarding the use of this ordinal endpoint that may affect power from fitting a proportional odds model: (1) deviations from the proportional odds assumption which result in the same overall treatment effect as specified in the FLU-IVIG protocol and which result in a diminished overall treatment effect, (2) deviations from the distribution of the placebo group assumed in the FLU-IVIG design, (3) the effect of patient misclassification among the six categories, and (4) the number of categories of the ordinal endpoint. We also consider interactions between the treatment effect (i.e. factor 1) and each other factor. Methods We conducted a Monte Carlo simulation study to assess the effect of each factor. To study factor 1, we developed an algorithm for deriving distributions of the ordinal endpoint in the two treatment groups that deviated from proportional odds while maintaining the same overall treatment effect. For factor 2, we considered placebo group distributions which were more or less skewed than the one specified in the FLU-IVIG protocol by adding or subtracting a constant from the cumulative log odds. To assess factor 3, we added misclassification between adjacent pairs of categories that depend on subjective patient/clinician assessments. For factor 4, we collapsed some categories into single categories. Results Deviations from proportional odds reduced power at most from 80% to 77% given the same overall treatment effect as specified in the FLU-IVIG protocol. Misclassification and collapsing categories can reduce power by over 40 and 10 percentage points, respectively, when they affect categories with many patients and a discernible treatment effect. But collapsing categories that contain no treatment effect can raise power by over 20 percentage points. Differences in the distribution of the placebo group can raise power by over 20 percentage points or reduce power by over 40 percentage points depending on how patients are shifted to portions of the ordinal endpoint with a large treatment effect. Conclusion Provided that the overall treatment effect is maintained, deviations from proportional odds marginally reduce power. However, deviations from proportional odds can modify the effect of misclassification, the number of categories, and the distribution of the placebo group on power. In general, adjacent pairs of categories with many patients should be kept separate to help ensure that power is maintained at the pre-specified level.