Estimation of trajectory of protective efficacy in infectious disease prevention trials using recurrent event times.

In studies of infectious disease prevention, the level of protective efficacy of medicinal products such as vaccines and prophylactic drugs tends to vary over time. Many products require administration of multiple doses at scheduled times, as opposed to one-off or continual intervention. Accurate information on the trajectory of the level of protective efficacy over time facilitates informed clinical recommendations and implementation strategies, for example, with respect to the timing of administration of the doses. Based on concepts from pharmacokinetic and pharmacodynamic modeling, we propose a non-linear function for modeling the trajectory after each dose. The cumulative effect of multiple doses of the products is captured by an additive series of the function. The model has the advantages of parsimony and interpretability, while remaining flexible in capturing features of the trajectories. We incorporate this series into the Andersen-Gill model for analysis of recurrent event time data and compare it with alternative parametric and non-parametric functions. We use data on clinical malaria disease episodes from a trial of four doses of an anti-malarial drug combination for chemoprevention to illustrate, and evaluate the performance of the methods using simulation. The proposed method out-performed the alternatives in the analysis of real data in terms of Akaike and Bayesian Information Criterion. It also accurately captured the features of the protective efficacy trajectory such as the area under curve in simulations. The proposed method has strong potential to enhance the evaluation of disease prevention measures and improve their implementation strategies.

Aryliodonium Salt-Induced Regioselective Access to meta-Substituted Anilines by Arylation of Azoles.

A highly efficient aryliodonium salt-induced regioselective access to meta-substituted anilines by arylation of azoles has been developed under catalyst-free conditions. This efficient transformation provides a facile and scalable approach to a wide range of biologically active N-arylazoles with moderate to high yields. According to the control experiments, two plausible pathways, including a Michael pathway and a free radical coupling pathway, for the reaction were proposed.

Novel 3-arm wait-list controlled trial designs together with mixed-effects analysis improve precision of treatment effect estimators.

Clinical trialists have long been searching for approaches to increase statistical power without increasing sample size. Conventional wait-list controlled (WLC) trials are limited to two trial arms and two or three repeated measurements per person. These features limit statistical power. Furthermore, their analysis is usually based on analysis of covariance or mixed effects modelling, with a focus on estimating treatment effect at one time-period after initiation of therapy. We propose two 3-arm WLC trial designs together with a mixed-effects analysis framework. The designs require three or four repeated measurements per person. The analytic framework defines up to three treatment effect estimands, representing the effects at one to three time-periods after initiation of therapy. The precision (inverse of variance) of the treatment effect estimators in the new and conventional trial designs are analytically derived and evaluated in simulations. The results are interpreted in the context of a cognitive training trial in older people. The proposed designs and analysis methods increase the precision level of treatment effect estimators as compared to conventional designs and analyses. Given a target level of statistical power, the proposed methods require a smaller number of participants per trial than the conventional methods, without necessarily increasing the number of measurements per trial. Furthermore, the proposed analytic framework sheds light on the treatment effects at different times after initiation of therapy, which is not usually considered in conventional WLC trial analysis. In situations that a WLC trial is appropriate, the 3-arm designs are useful alternatives to existing 2-arm designs.

Modelling non-linear patterns of time-varying intervention effects on recurrent events in infectious disease prevention studies.

Protective efficacy of vaccines and pharmaceutical products for prevention of infectious diseases usually vary over time. Information on the trajectory of the level of protection is valuable. We consider a parsimonious, non-linear and non-monotonic function for modelling time-varying intervention effects and compare it with several alternatives. The cumulative effects of multiple doses of intervention over time can be captured by an additive series of the function. We apply it to the Andersen-Gill model for analysis of recurrent time-to-event data. We re-analyze data from a trial of intermittent preventive treatment for malaria to illustrate and evaluate the method by simulation.

Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials.

Stepped-wedge cluster randomized trials (SW-CRTs) are typically analyzed using mixed effects models. The fixed effects model is a useful alternative that controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. In principle, all clusters in SW-CRTs are designed to eventually receive the intervention, but in real-world research, some trials can end with unexposed clusters (clusters that never received the intervention), such as when a trial is terminated early based on interim analysis results. Typically, unexposed clusters are expected to contribute no information to the fixed effects intervention effect estimator and are excluded from fixed effects analyses. In this article we mathematically prove that inclusion of unexposed clusters improves the precision of the fixed effects least squares dummy variable (LSDV) intervention effect estimator, re-analyze data from a recent SW-CRT of a novel palliative care intervention containing an unexposed cluster, and evaluate the methods by simulation. We found that including unexposed clusters improves the precision of the fixed effects LSDV intervention effect estimator in both real and simulated datasets. Our simulations also reveal an increase in power and decrease in root mean square error. These improvements are present even if the assumptions of constant residual variance and period effects are violated. In the case that a SW-CRT concludes with unexposed clusters, these unexposed clusters can be included in the fixed effects LSDV analysis to improve precision, power, and root mean square error.

Ratio estimators of intervention effects on event rates in cluster randomized trials.

We consider five asymptotically unbiased estimators of intervention effects on event rates in non-matched and matched-pair cluster randomized trials, including ratio of mean counts r1 , ratio of mean cluster-level event rates r2 , ratio of event rates r3 , double ratio of counts r4 , and double ratio of event rates r5 . In the absence of an indirect effect, they all estimate the direct effect of the intervention. Otherwise, r1 , r2, and r3 estimate the total effect, which comprises the direct and indirect effects, whereas r4 and r5 estimate the direct effect only. We derive the conditions under which each estimator is more precise or powerful than its alternatives. To control bias in studies with a small number of clusters, we propose a set of approximately unbiased estimators. We evaluate their properties by simulation and apply the methods to a trial of seasonal malaria chemoprevention. The approximately unbiased estimators are practically unbiased and their confidence intervals usually have coverage probability close to the nominal level; the asymptotically unbiased estimators perform well when the number of clusters is approximately 32 or more per trial arm. Despite its simplicity, r1 performs comparably with r2 and r3 in trials with a large but realistic number of clusters. When the variability of baseline event rate is large and there is no indirect effect, r4 and r5 tend to offer higher power than r1 , r2, and r3 . We discuss the implications of these findings to the planning and analysis of cluster randomized trials.

Inclusion of unexposed subjects improves the precision and power of self-controlled case series method.

The self-controlled case series is an important method in the studies of the safety of biopharmaceutical products. It uses the conditional Poisson model to make comparison within persons. In models without adjustment for age (or other time-varying covariates), cases who are never exposed to the product do not contribute any information to the estimation. We provide analytic proof and simulation results that the inclusion of unexposed cases in the conditional Poisson model with age adjustment reduces the asymptotic variance of the estimator of the exposure effect and increases power. We re-analysed a vaccine safety dataset to illustrate.

Analysis of incidence and prognosis from 'extreme' case-control designs.

The significant investment in measuring biomarkers has prompted investigators to improve cost-efficiency by sub-sampling in non-standard study designs. For example, investigators studying prognosis may assume that any differences in biomarkers are likely to be most apparent in an extreme sample of the earliest deaths and the longest-surviving controls. Simple logistic regression analysis of such data does not exploit the information available in the survival time, and statistical methods that model the sampling scheme may be more efficient. We derive likelihood equations that reflect the complex sampling scheme in unmatched and matched 'extreme' case-control designs. We investigated the performance and power of the method in simulation experiments, with a range of underlying hazard ratios and study sizes. Our proposed method resulted in hazard ratio estimates close to those obtained from the full cohort. The standard error estimates also performed well when compared with the empirical variance. In an application to a study investigating markers for lethal prostate cancer, an extreme case-control sample of lethal cases and the longest-surviving controls provided estimates of the effect of Gleason score in close agreement with analysis of all the data. By using the information in the sampling design, our method enables efficient and valid estimation of the underlying hazard ratio from a study design that is intuitive and easily implemented.

Study of enhanced low-quality coal oxidative desulphurization and deashing by using HNO3 and microwave pretreatment.

In this paper, the effect of urea-hydrogen peroxide (UHP) solution on desulphurization and demineralization of coal with high sulphur and high ash by using HNO3 and microwave pretreatment was investigated. The oxidation process is strongly dependent on irradiation power and time for microwave pretreatment, UHP concentration, leaching time and temperature of the UHP solution. X-ray diffraction and Fourier transform infrared technique have been performed for the raw and treated coals. Compared with the UHP alone, successive treatments with HNO3 and microwave pretreatment resulted in the significant removal of total sulphur and mineral matter from the coal. The proposed experimental method has the meaning of practical guide to the desulphurization and deashing of coal by microwave.

Two assumptions of the prior event rate ratio approach for controlling confounding can be evaluated by self-controlled case series and dynamic random intercept modeling.

OBJECTIVES: The prior event rate ratio (PERR) is a recently developed approach for controlling confounding by measured and unmeasured covariates in real-world evidence research and observational studies. Despite its rising popularity in studies of safety and effectiveness of biopharmaceutical products, there is no guidance on how to empirically evaluate its model assumptions. We propose two methods to evaluate two of the assumptions required by the PERR, specifically, the assumptions that occurrence of outcome events does not alter the likelihood of receiving treatment, and that earlier event rate does not affect later event rate. STUDY DESIGN AND SETTING: We propose using self-controlled case series (SCCS) and dynamic random intercept modeling (DRIM), respectively, to evaluate the two aforementioned assumptions. A nonmathematical introduction of the methods and their application to evaluate the assumptions are provided. We illustrate the evaluation with secondary analysis of deidentified data on pneumococcal vaccination and clinical pneumonia in The Gambia, West Africa. RESULTS: SCCS analysis of data on 12,901 vaccinated Gambian infants did not reject the assumption of clinical pneumonia episodes had no influence on the likelihood of pneumococcal vaccination. DRIM analysis of 14,325 infants with a total of 1719 episodes of clinical pneumonia did not reject the assumption of earlier episodes of clinical pneumonia had no influence on later incidence of the disease. CONCLUSION: The SCCS and DRIM methods can facilitate appropriate use of the PERR approach to control confounding. PLAIN LANGUAGE SUMMARY: The prior event rate ratio is a promising approach for analysis of real-world data and observational studies. We propose two statistical methods to evaluate the validity of two assumptions it is based on. They can facilitate appropriate use of the prior even rate ratio.

Preparation of ZnO nanoparticles modified with silane coupling-agents to fabricate anti-UV Poly(vinyl chloride) films.

The uses of inorganic metal oxide as ultraviolet (UV) absorbers have potential to increase the production of UV protection and can also overcome the disadvantages of organic molecules. In this article, we report an effective technique to fabricating polyvinyl chloride (PVC) films with well UV shielding efficiency. Surface modification of zinc oxide (ZnO) nanoparticles (NPs) with different silane coupling-agents were achieved, and through solution casting technique dispersed within the PVC matrix. Infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA) and UV spectrophotometer were applied to study the structures, dispersions, and optical properties. The results showed that the functionalized ZnO NPs could be well dispersed in PVC and endow the polymer composite films with significantly improved anti-UV capability. The facile processing and obtained properties of PVC composites have shown potential for low cost and environmentally sustainable applications in the UV protection field.

Mixture cure model with random effects for clustered interval-censored survival data.

The mixture cure model is an effective tool for analysis of survival data with a cure fraction. This approach integrates the logistic regression model for the proportion of cured subjects and the survival model (either the Cox proportional hazards or accelerated failure time model) for uncured subjects. Methods based on the mixture cure model have been extensively investigated in the literature for data with exact failure/censoring times. In this paper, we propose a mixture cure modeling procedure for analyzing clustered and interval-censored survival time data by incorporating random effects in both the logistic regression and PH regression components. Under the generalized linear mixed model framework, we develop the REML estimation for the parameters, as well as an iterative algorithm for estimation of the survival function for interval-censored data. The estimation procedure is implemented via an EM algorithm. A simulation study is conducted to evaluate the performance of the proposed method in various practical situations. To demonstrate its usefulness, we apply the proposed method to analyze the interval-censored relapse time data from a smoking cessation study whose subjects were recruited from 51 zip code regions in the southeastern corner of Minnesota.

Estimation of the primary, secondary and composite effects of malaria vaccines using data on multiple clinical malaria episodes.

BACKGROUND: An effective malaria vaccine affects the risk of malaria directly, through the vaccine-induced immune response (the primary effect), and indirectly, as a consequence of reduced exposure to malaria infection and disease, leading to slower acquisition of natural immunity (the secondary effect). The beneficial primary effect may be offset by a negative secondary effect, resulting in a smaller or nil composite effect. Reports of malaria vaccine trials usually present only the composite effect. We aimed to demonstrate how the primary and secondary effects can also be estimated from trial data. METHODS: We propose an enhancement to the conditional frailty model for the estimation of primary effect using data on disease episodes. We use the Andersen-Gill model to estimate the composite effect. We consider taking the ratio of the hazard ratios to estimate the secondary effect. We used directed acyclic graphs and data from a randomized trial of the RTS,S/AS02 malaria vaccine to illustrate the problems and solutions. Time-varying effects were estimated by partitioning the follow-up into four time periods. RESULTS: The primary effect estimates from our proposed model were consistently stronger than the conditional frailty model in the existing literature. The primary effect of the vaccine was consistently stronger than the composite effect across all time periods. Both the primary and composite effects were stronger in the first three months, with hazard ratios (95% confidence interval) 0.62 (0.49-0.79) and 0.68 (0.54-0.84), respectively; the hazard ratios weakened over time. The secondary effect appeared mild, with hazard ratio 1.09 (1.02-1.16) in the first three months. CONCLUSIONS: The proposed analytic strategy facilitates a more comprehensive interpretation of trial data on multiple disease episodes. The RTS,S/AS02 vaccine had modest primary and secondary effects that waned over time, but the composite effect in preventing clinical malaria remained positive up to the end of the study. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov NCT00197041.

Statistical inference in matched case-control studies of recurrent events.

BACKGROUND: The concurrent sampling design was developed for case-control studies of recurrent events. It involves matching for time. Standard conditional logistic-regression (CLR) analysis ignores the dependence among recurrent events. Existing methods for clustered observations for CLR do not fit the complex data structure arising from the concurrent sampling design. METHODS: We propose to break the matches, apply unconditional logistic regression with adjustment for time in quintiles and residual time within each quintile, and use a robust standard error for observations clustered within persons. We conducted extensive simulation to evaluate this approach and compared it with methods based on CLR. We analysed data from a study of childhood pneumonia to illustrate the methods. RESULTS: The proposed method and CLR methods gave very similar point estimates of association and showed little bias. The proposed method produced confidence intervals that achieved the target level of coverage probability, whereas the CLR methods did not, except when disease incidence was low. CONCLUSIONS: The proposed method is suitable for the analysis of case-control studies with recurrent events.

Bias control in the analysis of case-control studies with incidence density sampling.

BACKGROUND: Previous simulation studies of the case-control study design using incidence density sampling, which required individual matching for time, showed biased estimates of association from conditional logistic regression (CLR) analysis; however, the reason for this is unknown. Separately, in the analysis of case-control studies using the exclusive sampling design, it has been shown that unconditional logistic regression (ULR) with adjustment for an individually matched binary factor can give unbiased estimates. The validity of this analytic approach in incidence density sampling needs evaluation. METHODS: In extensive simulations using incidence density sampling, we evaluated various analytic methods: CLR with and without a bias-reduction method, ULR with adjustment for time in quintiles (and residual time within quintiles) and ULR with adjustment for matched sets and bias reduction. We re-analysed a case-control study of Haemophilus influenzae type B vaccine using these methods. RESULTS: We found that the bias in the CLR analysis from previous studies was due to sparse data bias. It can be controlled by the bias-reduction method for CLR or by increasing the number of cases and/or controls. ULR with adjustment for time in quintiles usually gave results highly comparable to CLR, despite breaking the matches. Further adjustment for residual time trends was needed in the case of time-varying effects. ULR with adjustment for matched sets tended to perform poorly despite bias reduction. CONCLUSIONS: Studies using incidence density sampling may be analysed by either ULR with adjustment for time or CLR, possibly with bias reduction.

Kawasaki Disease following administration of 13-valent pneumococcal conjugate vaccine in young children.

Kawasaki disease (KD) is a systemic vasculitis mainly affecting young children and the leading cause of acquired heart disease in developed countries. We performed a self-controlled case series analysis to investigate the association between PCV13 and KD. All hospitalized KD cases <2 y old from our hospital in Singapore from 2010 to 2014 were included. Complete KD cases were classified based on the definitions of the American Heart Association. During the study period, 288 KD cases were identified. A total of 21 KD cases (12 were classified as Complete KD) had date of onset within the risk interval of day 1 to day 28 post PCV13. The age-adjusted Relative Incidence (RI) for KD following PCV13 dose 1, dose 2 and dose 3 were 1.40 (95%CI, 0.72 to 2.71), 1.23 (95% CI, 0.62 to 2.44) and 0.34 (95% CI, 0.08 to 1.40) respectively. There were seven Complete KD cases with onset during the risk interval after dose 1 of PCV13 (age-adjusted RI 2.59, 95%confidence interval (CI), 1.16 to 5.81). We did not detect a significant increased risk for overall KD among PCV13 recipients. However, a significant association between PCV13 and Complete KD was noted following receipt of the first dose of PCV13.