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.

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.

A sequential test for assessing the effectiveness of response strategies during an emerging epidemic.

In epidemiology, the fatality rate is an important indicator of disease severity and has been used to evaluate the effects of new treatments. During an emerging epidemic with limited resources, monitoring the changes in fatality rate can also provide signals on the evaluation of government policies and healthcare quality, which helps to guide public health decision. A statistical test is developed in this paper to detect changes in fatality rate over time during the course of an emerging infectious disease. A major advantage of the proposed test is that it only requires the regularly reported numbers of deaths and recoveries, which meets the actual need as detailed surveillance data are hard to collect during the course of an emerging epidemic especially the deadly infectious diseases with large magnitude. In addition, with the sequential testing procedure, the effective measures can be detected at the earliest possible time to provide guidance to policymakers for swift action. Simulation studies showed that the proposed test performs well and is sensitive in picking up changes in the fatality rate. The test is applied to the 2014-2016 Ebola outbreak in Sierra Leone for illustration.

Analysis of clustered interval-censored data using a class of semiparametric partly linear frailty transformation models.

A flexible class of semiparametric partly linear frailty transformation models is considered for analyzing clustered interval-censored data, which arise naturally in complex diseases and dental research. This class of models features two nonparametric components, resulting in a nonparametric baseline survival function and a potential nonlinear effect of a continuous covariate. The dependence among failure times within a cluster is induced by a shared, unobserved frailty term. A sieve maximum likelihood estimation method based on piecewise linear functions is proposed. The proposed estimators of the regression, dependence, and transformation parameters are shown to be strongly consistent and asymptotically normal, whereas the estimators of the two nonparametric functions are strongly consistent with optimal rates of convergence. An extensive simulation study is conducted to study the finite-sample performance of the proposed estimators. We provide an application to a dental study for illustration.

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.

The English and Chinese language versions of the Short Form 12-item Health Survey are equivalent.

BACKGROUND: The Short Form 12-item Health Survey (SF-12v2) was originally developed in English, but it is also available in Hong Kong (HK) Chinese. While both language versions had their measurement properties well assessed in their respective populations, their measurement invariance in scores has not been examined. Therefore, we aimed to assess their measurement invariance. METHODS: We conducted a cross-sectional study on individuals aged 18 years or older at a university campus. Those who were bilingual in English and Chinese were randomly assigned to self-complete either the standard English or the HK Chinese SF-12v2. Measurement invariance of the two components and eight scales of the SF-12v2 was concluded if the corresponding 90% confidence interval (CI) for the difference between the two language versions entirely fell within the minimal clinically important difference of ± 3 units. Multiple-group confirmatory factor analysis (CFA) was also performed. RESULTS: A total of 1013 participants completed the SF-12v2 (496 in English and 517 in HK Chinese), with a mean age of 22 years (Range 18-58), and 626 participants (62%) were female. There were no significant differences in demographics. Only the physical and mental components and the mental health (MH) scale had their 90% CIs (0.21 to 1.61, - 1.00 to 0.98, and - 0.86 to 2.84, respectively) completely fall within the ± 3 units. The multiple-group CFA showed partial strict invariance. CONCLUSIONS: The English and HK Chinese versions of the SF-12v2 can be used in studies with their two components and MH scores pooled in the analysis.

Estimation of age effect with change-points on survival of cancer patients.

There is a global trend that the average onset age of many human complex diseases is decreasing, and the age of cancer patients becomes more spread out. The age effect on survival is nonlinear in practice and may have one or more important change-points at which the trend of the effect can be very different before and after these threshold ages. Identification of these change-points allows clinical researchers to understand the biologic basis for the complex relation between age and prognosis for optimal prognostic decision. This paper considers estimation of the potentially nonlinear age effect for general partly linear survival models to ensure a valid statistical inference on the treatment effect. A simple and efficient sieve maximum likelihood estimation method that can be implemented easily using standard statistical software is proposed. A data-driven adaptive algorithm to determine the optimal location and the number of knots for the identification of the change-points is suggested. Simulation studies are performed to study the performance of the proposed method. For illustration purpose, the method is applied to a breast cancer data set from the public domain to investigate the effect of onset age on the disease-free survival of the patients. The results revealed that the risk is highest among young patients and young postmenopausal patients, probably because of a change in hormonal environment during a certain phase of menopause.

Longitudinal measurement invariance and explanatory IRT models for adolescents' oral health-related quality of life.

BACKGROUND: Longitudinal invariance is a perquisite for a valid comparison of oral health-related quality of life (OHRQoL) scores over time. Item response theory (IRT) models can assess measurement invariance and allow better estimation of the associations between predictors and latent construct. By extending IRT models, this study aimed to investigate the longitudinal invariance of the two 8-item short forms of the Child Perception Questionnaire (CPQ11-14) regression short form (RSF:8) and item-impact short form (ISF:8) and identify factors associated with adolescents' OHRQoL and its change. METHODS: All students from S1 and S2 (equivalent to US grades 6 and 7) who were born in April 1997 and May 1997 (at age 12) from 45 randomly selected secondary schools were invited to participate in this study and followed up after 3 years. Data on the CPQ11-14 RSF:8 and CPQ11-14 ISF:8, demographics, oral health behavior and status were collected. Explanatory graded response models were fitted to both short forms of the CPQ11-14 data for assessing longitudinal invariance and factors associated with OHRQoL. The Bayesian estimation method - Monte Carlo Markov Chain (MCMC) with Gibbs sampling was adopted for parameter estimation and the credible intervals were used for inference. RESULTS: Data from 649 children at age 12 at baseline and 415 children at age 15 at follow up were analyzed. For the 12 years old children, healthier oral health behavior, better gum status, families with both parents employed and parents' education level were found to be associated with better OHRQoL. Four items among the 2 short forms lacked longitudinal invariance. With statistical adjustment of longitudinal invariance, OHRQoL were found improved in general over the 3 years but no predictor was associated with OHRQoL in follow-up. For those with decreased family income, their OHRQoL had worsened over 3 years. CONCLUSIONS: IRT explanatory analysis enables a more valid identification of the factors associated with OHRQoL and its changes over time. It provides important information to oral healthcare researchers and policymakers.

Semiparametric estimation of time-varying intervention effects using recurrent event data.

We consider the estimation of the optimal interval between doses for interventions such as malaria chemoprevention and vaccine booster doses that are applied intermittently in infectious disease control. A flexible exponential-like function to model the time-varying intervention effect in the framework of Andersen-Gill model for recurrent event time data is considered. The partial likelihood estimation approach is adopted, and a large scale simulation study is carried out to evaluate the performance of the proposed method. A simple guideline for the choice of the optimal interval between successive doses is proposed. The methodology is illustrated with the analysis of data from a malaria chemoprevention trial. Copyright © 2017 John Wiley & Sons, Ltd.

Sample size determination for fold-increase endpoints defined by paired interval-censored data.

Medical studies often define binary end-points by comparing the ratio of a pair of measurements at baseline and end-of-study to a clinically meaningful cut-off. For example, vaccine trials may define a response as at least a four-fold increase in antibody titers from baseline to end-of-study. Accordingly, sample size is determined based on comparisons of proportions. Since the pair of measurements is quantitative, modeling the bivariate cumulative distribution function to estimate the proportion gives more precise results than using dichotomization of data. This is known as the distributional approach to the analysis of proportions. However, this can be complicated by interval-censoring. For example, due to the nature of some laboratory measurement methods, antibody titers are interval-censored. We derive a sample size formula based on the distributional approach for paired interval-censored data. We compare the sample size requirement in detecting an intervention effect using the distributional approach to a conventional approach of dichotomization. Some practical guidance on applying the sample size formula is given.

Estimation of intervention effect using paired interval-censored data with clumping below lower detection limit.

Outcome variables that are semicontinuous with clumping at zero are commonly seen in biomedical research. In addition, the outcome measurement is sometimes subject to interval censoring and a lower detection limit (LDL). This gives rise to interval-censored observations with clumping below the LDL. Level of antibody against influenza virus measured by the hemagglutination inhibition assay is an example. The interval censoring is due to the assay's technical procedure. The clumping below LDL is likely a result of the lack of prior exposure in some individuals such that they either have zero level of antibodies or do not have detectable level of antibodies. Given a pair of such measurements from the same subject at two time points, a binary 'fold-increase' endpoint can be defined according to the ratio of these two measurements, as it often is in vaccine clinical trials. The intervention effect or vaccine immunogenicity can be assessed by comparing the binary endpoint between groups of subjects given different vaccines or placebos. We introduce a two-part random effects model for modeling the paired interval-censored data with clumping below the LDL. Based on the estimated model parameters, we propose to use Monte Carlo approximation for estimation of the 'fold-increase' endpoint and the intervention effect. Bootstrapping is used for variance estimation. The performance of the proposed method is demonstrated by simulation. We analyze antibody data from an influenza vaccine trial for illustration.

A simple and powerful method for the estimation of intervention effects on serological endpoints using paired interval-censored data.

Clinical trials often use a binary "fold increase" endpoint defined according to the ratio of interval-censored measurement at end-of-study to that at baseline. We propose a simple yet principled analytic approach based on the linear mixed-effects model for interval-censored data for the analysis of such paired measurements. Having estimated the model parameters, the risk ratio can be estimated by explicit composite estimand and the variance is estimated using the delta method. The estimation can be implemented using the existing procedures in popular statistical software. We use antibody data from the Chloroquine for Influenza Prevention Trial for illustration.

Evaluation of psychometric properties and differential item functioning of 8-item Child Perceptions Questionnaires using item response theory.

BACKGROUND: Four-factor structure of the two 8-item short forms of Child Perceptions Questionnaire CPQ11-14 (RSF:8 and ISF:8) has been confirmed. However, the sum scores are typically reported in practice as a proxy of Oral health-related Quality of Life (OHRQoL), which implied a unidimensional structure. This study first assessed the unidimensionality of 8-item short forms of CPQ11-14. Item response theory (IRT) was employed to offer an alternative and complementary approach of validation and to overcome the limitations of classical test theory assumptions. METHODS: A random sample of 649 12-year-old school children in Hong Kong was analyzed. Unidimensionality of the scale was tested by confirmatory factor analysis (CFA), principle component analysis (PCA) and local dependency (LD) statistic. Graded response model was fitted to the data. Contribution of each item to the scale was assessed by item information function (IIF). Reliability of the scale was assessed by test information function (TIF). Differential item functioning (DIF) across gender was identified by Wald test and expected score functions. RESULTS: Both CPQ11-14 RSF:8 and ISF:8 did not deviate much from the unidimensionality assumption. Results from CFA indicated acceptable fit of the one-factor model. PCA indicated that the first principle component explained >30 % of the total variation with high factor loadings for both RSF:8 and ISF:8. Almost all LD statistic <10 indicated the absence of local dependency. Flat and low IIFs were observed in the oral symptoms items suggesting little contribution of information to the scale and item removal caused little practical impact. Comparing the TIFs, RSF:8 showed slightly better information than ISF:8. In addition to oral symptoms items, the item "Concerned with what other people think" demonstrated a uniform DIF (p < 0.001). The expected score functions were not much different between boys and girls. CONCLUSIONS: Items related to oral symptoms were not informative to OHRQoL and deletion of these items is suggested. The impact of DIF across gender on the overall score was minimal. CPQ11-14 RSF:8 performed slightly better than ISF:8 in measurement precision. The 6-item short forms suggested by IRT validation should be further investigated to ensure their robustness, responsiveness and discriminative performance.

Osteodesis for hallux valgus correction: is it effective?

BACKGROUND: Although the etiology of hallux valgus is contested, in some patients it may be failure of the stabilizing soft tissue structures around the first ray of the foot. Because there is lack of effective soft tissue techniques, osteotomies have become the mainstream surgical approach to compensate for the underlying soft tissue deficiency; osteodesis, a soft tissue nonosteotomy technique, may be a third alternative, but its efficacy is unknown. QUESTIONS/PURPOSES: We asked: (1) Can an osteodesis, a distal soft tissue technique, correct hallux valgus satisfactorily in terms of deformity correction and improvement in American Orthopaedic Foot and Ankle Society (AOFAS) score? (2) Is the effectiveness of an osteodesis affected by the patient's age or deformity severity? (3) What complications are associated with this procedure? METHODS: Between February and October 2010, we performed 126 operations to correct hallux valgus, of which 126 (100%) were osteodeses. Sixty-one patients (110 procedures) (87% of the total number of hallux valgus procedures) were available for followup at a minimum of 12 months (mean, 23 months; range, 12-38 months). This group formed our study cohort. During the study period, the general indications for this approach included failed conservative measures for pain relief and metatarsophalangeal angle greater than 20° or intermetatarsal angle greater than 9°. Intermetatarsal cerclage sutures were used to realign the first metatarsal and postoperative fibrosis was induced surgically between the first and second metatarsals to maintain its alignment. The radiologic first intermetatarsal angle, metatarsophalangeal angle, and medial sesamoid position were measured by Hardy and Clapham's methods for deformity and correction evaluation. Clinical results were assessed by the AOFAS score. RESULTS: The intermetatarsal angle was improved from a preoperative mean of 14° to 7° (p<0.001; Cohen's d=1.8) at followup, the metatarsophalangeal angle from 31° to 18° (p<0.001; Cohen's d=3.1), the medial sesamoid position from position 6 to 3 (p<0.001; Cohen's d=2.4), and AOFAS hallux score from 68 to 96 points (p<0.001). Neither patient age nor deformity severity affected the effectiveness of the osteodesis in correcting all three radiologic parameters; however, the deformities treated in this series generally were mild to moderate (mean intermetatarsal angle, 14°; range, 9°-22°). There were six stress fractures of the second metatarsal (5%), five temporary metatarsophalangeal joint medial subluxations all resolved in one month by the taping-reduction method without surgery, and six metatarsophalangeal joints with reduced dorsiflexion less than 60°. CONCLUSIONS: The osteodesis is a soft tissue nonosteotomy technique, and provided adequate deformity correction and improvement in AOFAS scores for patients with mild to moderate hallux valgus deformities, although a small number of the patients had postoperative stress fractures of the second ray develop. Future prospective studies should compare this technique with osteotomy techniques in terms of effectiveness of the correction, restoration of hallux function, complications, and long-term recurrence. LEVEL OF EVIDENCE: Level IV, therapeutic study. See the Instructions for Authors for a complete description of levels of evidence.

Estimation of intervention effects using recurrent event time data in the presence of event dependence and a cured fraction.

Recurrent event data with a fraction of subjects having zero event are often seen in randomized clinical trials. Those with zero event may belong to a cured (or non-susceptible) fraction. Event dependence refers to the situation that a person's past event history affects his future event occurrences. In the presence of event dependence, an intervention may have an impact on the event rate in the non-cured through two pathways-a primary effect directly on the outcome event and a secondary effect mediated through event dependence. The primary effect combined with the secondary effect is the total effect. We propose a frailty mixture model and a two-step estimation procedure for the estimation of the effect of an intervention on the probability of cure and the total effect on event rate in the non-cured. A summary measure of intervention effects is derived. The performance of the proposed model is evaluated by simulation. Data on respiratory exacerbations from a randomized, placebo-controlled trial are re-analyzed for illustration.

Modeling zero-inflated count data using a covariate-dependent random effect model.

In various medical related researches, excessive zeros, which make the standard Poisson regression model inadequate, often exist in count data. We proposed a covariate-dependent random effect model to accommodate the excess zeros and the heterogeneity in the population simultaneously. This work is motivated by a data set from a survey on the dental health status of Hong Kong preschool children where the response variable is the number of decayed, missing, or filled teeth. The random effect has a sound biological interpretation as the overall oral health status or other personal qualities of an individual child that is unobserved and unable to be quantified easily. The overall measure of oral health status, responsible for accommodating the excessive zeros and also the heterogeneity among the children, is covariate dependent. This covariate-dependent random effect model allows one to distinguish whether a potential covariate has an effect on the conceived overall oral health condition of the children, that is, the random effect, or has a direct effect on the magnitude of the counts, or both. We proposed a multiple imputation approach for estimation of the parameters. We discussed the choice of the imputation size. We evaluated the performance of the proposed estimation method through simulation studies, and we applied the model and method to the dental data.

Estimation of summary protective efficacy using a frailty mixture model for recurrent event time data.

Recurrent event time data are common in experimental and observational studies. The analytic strategy needs to consider three issues: within-subject event dependence, between-subject heterogeneity in event rates, and the possibility of a nonsusceptible fraction. Motivated by the need to estimate the summary protective efficacy from recurrent event time data as seen in many infectious disease clinical trials, we propose a two-part frailty mixture model that simultaneously accommodates all the three issues. In terms of vaccine action models, the proposed model is a combination of the 'all-or-none' and the 'leaky' models, and the summary protective efficacy is a unified measure of the vaccine's twofold effects in completely or partially protecting the vaccinated individuals against the study event. The model parameters of interest are estimated using the expectation-maximization algorithm with their respective variances estimated using Louis's formula for the expectation-maximization algorithm. The summary protective efficacy is estimated by a composite estimand with its variance estimated using the delta method. The performance of the proposed estimation approach is investigated by a simulation study. Data from a trial of malaria prophylaxis conducted in Ghana are reanalyzed.

Western mental health training for traditional Chinese medicine practitioners.

OBJECTIVE: To investigate the impact of a Western mental health training course for Traditional Chinese Medicine (TCM) practitioners. METHOD: A combined qualitative and quantitative approach was applied to examine the changes in the TCM practitioners' clinical practice characteristics and attitudes. Focus groups and structured questionnaire surveys were conducted to compare their responses before and after the Course. RESULTS: After a 10-week training course conducted by psychiatrists and family physicians, there were significant changes in confidence of the TCM practitioners for diagnosis (33% being confident before the Course vs. 76% after the Course) and management (24% vs. 55%) of common mental health problems. The causal effects of better classifications to recognition of mental health problems were explained by the qualitative responses. Proportion of TCM practitioners being confident of referring mental health patients to other healthcare professionals doubled from 25% to 50% after the Course. Nonetheless, there was no significant change in percentage of these patients being recommended to Western doctors owing to a lack of formal referral channel. CONCLUSIONS: Western mental health training for TCM practitioners has positive impact on their clinical practice. However, the practical barriers in making referrals highlight the need of closer collaboration between conventional and traditional medicine.