The Future Glioblastoma Clinical Trials Landscape: Early Phase 0, Window of Opportunity, and Adaptive Phase I-III Studies.

PURPOSE OF REVIEW: Innovative clinical trial designs for glioblastoma (GBM) are needed to expedite drug discovery. Phase 0, window of opportunity, and adaptive designs have been proposed, but their advanced methodologies and underlying biostatistics are not widely known. This review summarizes phase 0, window of opportunity, and adaptive phase I-III clinical trial designs in GBM tailored to physicians. RECENT FINDINGS: Phase 0, window of opportunity, and adaptive trials are now being implemented for GBM. These trials can remove ineffective therapies earlier during drug development and improve trial efficiency. There are two ongoing adaptive platform trials: GBM Adaptive Global Innovative Learning Environment (GBM AGILE) and the INdividualized Screening trial of Innovative GBM Therapy (INSIGhT). The future clinical trials landscape in GBM will increasingly involve phase 0, window of opportunity, and adaptive phase I-III studies. Continued collaboration between physicians and biostatisticians will be critical for implementing these trial designs.

Multi-scale, domain knowledge-guided attention + random forest: a two-stage deep learning-based multi-scale guided attention models to diagnose idiopathic pulmonary fibrosis from computed tomography images.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and usually fatal lung disease of unknown reasons, generally affecting the elderly population. Early diagnosis of IPF is crucial for triaging patients' treatment planning into anti-fibrotic treatment or treatments for other causes of pulmonary fibrosis. However, current IPF diagnosis workflow is complicated and time-consuming, which involves collaborative efforts from radiologists, pathologists, and clinicians and it is largely subject to inter-observer variability. PURPOSE: The purpose of this work is to develop a deep learning-based automated system that can diagnose subjects with IPF among subjects with interstitial lung disease (ILD) using an axial chest computed tomography (CT) scan. This work can potentially enable timely diagnosis decisions and reduce inter-observer variability. METHODS: Our dataset contains CT scans from 349 IPF patients and 529 non-IPF ILD patients. We used 80% of the dataset for training and validation purposes and 20% as the holdout test set. We proposed a two-stage model: at stage one, we built a multi-scale, domain knowledge-guided attention model (MSGA) that encouraged the model to focus on specific areas of interest to enhance model explainability, including both high- and medium-resolution attentions; at stage two, we collected the output from MSGA and constructed a random forest (RF) classifier for patient-level diagnosis, to further boost model accuracy. RF classifier is utilized as a final decision stage since it is interpretable, computationally fast, and can handle correlated variables. Model utility was examined by (1) accuracy, represented by the area under the receiver operating characteristic curve (AUC) with standard deviation (SD), and (2) explainability, illustrated by the visual examination of the estimated attention maps which showed the important areas for model diagnostics. RESULTS: During the training and validation stage, we observe that when we provide no guidance from domain knowledge, the IPF diagnosis model reaches acceptable performance (AUC±SD = 0.93±0.07), but lacks explainability; when including only guided high- or medium-resolution attention, the learned attention maps are not satisfactory; when including both high- and medium-resolution attention, under certain hyperparameter settings, the model reaches the highest AUC among all experiments (AUC±SD = 0.99±0.01) and the estimated attention maps concentrate on the regions of interests for this task. Three best-performing hyperparameter selections according to MSGA were applied to the holdout test set and reached comparable model performance to that of the validation set. CONCLUSIONS: Our results suggest that, for a task with only scan-level labels available, MSGA+RF can utilize the population-level domain knowledge to guide the training of the network, which increases both model accuracy and explainability.

Spatial Two-stage Designs for Phase II Clinical Trials.

A common endpoint in a single-arm phase II study is tumor response as a binary variable. Two widely used designs for such a study are Simon's two-stage minimax and optimal designs. The minimax design minimizes the maximal sample size and the optimal design minimizes the expected sample size under the null hypothesis. The optimal design generally has the larger total sample size than the minimax design, but its first stage's sample size is smaller than that of the minimax design. The difference in the total sample size between two types of designs can be large and so both designs can be unappealing to investigators. We develop novel designs that compromise on the two optimality criteria and avoid such occurrences using the spatial information on the first stage's required sample size and the total required sample size. We study properties of these spatial designs and show our proposed designs have advantages over Simon's designs and one of its extensions by Lin and Shih. As applications, we construct spatial designs for real-life studies on patients with Hodgkin disease and another study on effect of head and neck cancer on apnea.

Early palliative care: the surprise question and the palliative care screening tool-better together.

OBJECTIVE: The 'surprise question' (SQ) and the palliative care screening tool (PCST) are the common assessment tools in the early identification of patients requiring palliative care. However, the comparison of their prognostic accuracies has not been extensively studied. This study aimed to compare the prognostic accuracy of SQ and PCST in terms of recognising patients nearing end of life (EOL) and those appropriate for palliative care. METHODS: This prospective study used both the SQ and PCST to predict patients' 12-month mortality and identified those appropriate for palliative care. All adult patients admitted to Taipei City Hospital in 2015 were included in this cohort study. The c-statistic value was calculated to indicate the predictive accuracies of the SQ and PCST. RESULTS: Out of 21 109 patients, with a mean age of 62.8 years, 12.4% and 11.1% had a SQ response of 'no' and a PCST score of ≥4, respectively. After controlling for other covariates, an SQ response of 'no' and a PCST score of ≥4 were the independent predictors of 12-month mortality. The c-statistic values of the SQ and PCST at recognising patients in their last year of life were 0.680 and 0.689, respectively. When using a combination of both SQ and PCST in predicting patients' 12-month mortality risk, the predictive value of the c-statistic increased to 0.739 and was significantly higher than either one in isolation (p<0.001). CONCLUSION: A combination of the SQ with PCST has better prognostic accuracy than either one in isolation.

A study design for statistical learning technique to predict radiological progression with an application of idiopathic pulmonary fibrosis using chest CT images.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease characterized by an unpredictable decline in lung function. Predicting IPF progression from the early changes in lung function tests have known to be a challenge due to acute exacerbation. Although it is unpredictable, the neighboring regions of fibrotic reticulation increase during IPF's progression. With this clinical information, quantitative characteristics of high-resolution computed tomography (HRCT) and a statistical learning paradigm, the aim is to build a model to predict IPF progression. DESIGN: A paired set of anonymized 193 HRCT images from IPF subjects with 6-12 month intervals were collected retrospectively. The study was conducted in two parts: (1) Part A collects the ground truth in small regions of interest (ROIs) with labels of "expected to progress" or "expected to be stable" at baseline HRCT and develop a statistical learning model to classify voxels in the ROIs. (2) Part B uses the voxel-level classifier from Part A to produce whole-lung level scores of a single-scan total probability's (STP) baseline. METHODS: Using annotated ROIs from 71 subjects' HRCT scans in Part A, we applied Quantum Particle Swarm Optimization-Random Forest (QPSO-RF) to build the classifier. Then, 122 subjects' HRCT scans were used to test the prediction. Using Spearman rank correlations and survival analyses, we ascertained STP associations with 6-12 month changes in quantitative lung fibrosis and forced vital capacity. CONCLUSION: This study can serve as a reference for collecting ground truth, and developing statistical learning techniques to predict progression in medical imaging.

End-to-end domain knowledge-assisted automatic diagnosis of idiopathic pulmonary fibrosis (IPF) using computed tomography (CT).

PURPOSE: Domain knowledge (DK) acquired from prior studies is important for medical diagnosis. This paper leverages the population-level DK using an optimality design criterion to train a deep learning model in an end-to-end manner. In this study, the problem of interest is at the patient level to diagnose a subject with idiopathic pulmonary fibrosis (IPF) among subjects with interstitial lung disease (ILD) using a computed tomography (CT). IPF diagnosis is a complicated process with multidisciplinary discussion with experts and is subject to interobserver variability, even for experienced radiologists. To this end, we propose a new statistical method to construct a time/memory-efficient IPF diagnosis model using axial chest CT and DK, along with an optimality design criterion via a DK-enhanced loss function of deep learning. METHODS: Four state-of-the-art two-dimensional convolutional neural network (2D-CNN) architectures (MobileNet, VGG16, ResNet-50, and DenseNet-121) and one baseline 2D-CNN are implemented to automatically diagnose IPF among ILD patients. Axial lung CT images are retrospectively acquired from 389 IPF patients and 700 non-IPF ILD patients in five multicenter clinical trials. To enrich the sample size and boost model performance, we sample 20 three-slice samples (triplets) from each CT scan, where these three slices are randomly selected from the top, middle, and bottom of both lungs respectively. Model performance is evaluated using a fivefold cross-validation, where each fold was stratified using a fixed proportion of IPF vs non-IPF. RESULTS: Using DK-enhanced loss function increases the model performance of the baseline CNN model from 0.77 to 0.89 in terms of study-wise accuracy. Four other well-developed models reach satisfactory model performance with an overall accuracy >0.95 but the benefits brought on by the DK-enhanced loss function is not noticeable. CONCLUSIONS: We believe this is the first attempt that (a) uses population-level DK with an optimal design criterion to train deep learning-based diagnostic models in an end-to-end manner and (b) focuses on patient-level IPF diagnosis. Further evaluation of using population-level DK on prospective studies is warranted and is underway.

Prediction of progression in idiopathic pulmonary fibrosis using CT scans at baseline: A quantum particle swarm optimization - Random forest approach.

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive decline in lung function. Natural history of IPF is unknown and the prediction of disease progression at the time of diagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosis of IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictive model for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, there are two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans and their follow-up status; and (b) simultaneously selecting important features from high-dimensional space, and optimizing the prediction performance. We resolved the first challenge by implementing a study design and having an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-up visits. For the second challenge, we integrated the feature selection with prediction by developing an algorithm using a wrapper method that combines quantum particle swarm optimization to select a small number of features with random forest to classify early patterns of progression. We applied our proposed algorithm to analyze anonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields a parsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROI level. These results are superior to other popular feature selections and classification methods, in that our method produces higher accuracy in prediction of progression and more balanced sensitivity and specificity with a smaller number of selected features. Our work is the first approach to show that it is possible to use only baseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence.

Comparison of PREDICTS atherosclerosis biomarker changes after initiation of new treatments in patients with SLE.

OBJECTIVE: Patients with SLE have an increased risk of atherosclerosis (ATH) that is not adequately explained by traditional risk factors. We previously described the Predictors of Risk for Elevated Flares, Damage Progression, and Increased Cardiovascular disease in PaTients with SLE (PREDICTS) atherosclerosis-risk panel, which includes proinflammatory HDL (piHDL), leptin, soluble tumour necrosis factor-like weak inducer of apoptosis (sTWEAK) and homocysteine, as well as age and diabetes. A high PREDICTS score confers 28-fold increased odds for future atherosclerosis in SLE. The aim of this study is to determine whether PREDICTS biomarkers are modifiable by common lupus therapies. METHODS: This prospective observational study included SLE subjects started on new lupus treatments. Leptin, sTWEAK, homocysteine and antioxidant function of HDL were measured at baseline (prior to drug initiation), 6 weeks and 12 weeks. RESULTS: 16 subjects started mycophenolate (MMF), 18 azathioprine (AZA) and 25 hydroxychloroquine (HCQ). In MMF-treated subjects, HDL function progressively improved from 2.23 ± 1.32 at baseline to 1.37±0.81 at 6 weeks (p=0.02) and 0.93±0.54 at 12 weeks (p=0.009). sTWEAK levels also improved in MMF-treated subjects from 477.5±447.1 to 290.3±204.6 pg/mL after 12 weeks (p=0.04), but leptin and homocysteine levels were not significantly changed. In HCQ-treated subjects, only HDL function improved from 1.80±1.29 at baseline to 1.03±0.74 after 12 weeks (p=0.05). There were no changes in the AZA group. MMF treatment was still associated with significant improvements in HDL function after accounting for potential confounders such as total prednisone dose and changes in disease activity. Overall, the mean number of high-risk PREDICTS biomarkers at week 12 significantly decreased in the entire group of patients started on a new lupus therapy (2.1±0.9 to 1.8±0.9, p=0.02) and in the MMF-treated group (2.4±0.8 vs 1.8±0.9, p=0.003), but not in the AZA or HCQ groups. In multivariate analysis, the odds of having a high PREDICTS atherosclerosis risk score at 12 weeks were lower with MMF treatment (OR 0.002, 95% CI 0.000 to 0.55, p=0.03). CONCLUSIONS: 12 weeks of MMF therapy improves the overall PREDICTS atherosclerosis biomarker profile. Further studies will determine whether biomarker changes reflect decreases in future cardiovascular events.

A model-based multithreshold method for subgroup identification.

Thresholding variable plays a crucial role in subgroup identification for personalized medicine. Most existing partitioning methods split the sample based on one predictor variable. In this paper, we consider setting the splitting rule from a combination of multivariate predictors, such as the latent factors, principle components, and weighted sum of predictors. Such a subgrouping method may lead to more meaningful partitioning of the population than using a single variable. In addition, our method is based on a change point regression model and thus yields straight forward model-based prediction results. After choosing a particular thresholding variable form, we apply a two-stage multiple change point detection method to determine the subgroups and estimate the regression parameters. We show that our approach can produce two or more subgroups from the multiple change points and identify the true grouping with high probability. In addition, our estimation results enjoy oracle properties. We design a simulation study to compare performances of our proposed and existing methods and apply them to analyze data sets from a Scleroderma trial and a breast cancer study.

Extended two-stage adaptive designs with three target responses for phase II clinical trials.

We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization to find extended two-stage adaptive optimal designs that allow three target response rates for the drug in a phase II trial. Our proposed designs include the celebrated Simon's two-stage design and its extension that allows two target response rates to be specified for the drug. We show that discrete particle swarm optimization not only frequently outperforms greedy algorithms, which are currently used to find such designs when there are only a few parameters; it is also capable of solving design problems posed here with more parameters that greedy algorithms cannot solve. In stage 1 of our proposed designs, futility is quickly assessed and if there are sufficient responders to move to stage 2, one tests one of the three target response rates of the drug, subject to various user-specified testing error rates. Our designs are therefore more flexible and interestingly, do not necessarily require larger expected sample size requirements than two-stage adaptive designs. Using a real adaptive trial for melanoma patients, we show our proposed design requires one half fewer subjects than the implemented design in the study.

Maximin optimal designs for cluster randomized trials.

We consider design issues for cluster randomized trials (CRTs) with a binary outcome where both unit costs and intraclass correlation coefficients (ICCs) in the two arms may be unequal. We first propose a design that maximizes cost efficiency (CE), defined as the ratio of the precision of the efficacy measure to the study cost. Because such designs can be highly sensitive to the unknown ICCs and the anticipated success rates in the two arms, a local strategy based on a single set of best guesses for the ICCs and success rates can be risky. To mitigate this issue, we propose a maximin optimal design that permits ranges of values to be specified for the success rate and the ICC in each arm. We derive maximin optimal designs for three common measures of the efficacy of the intervention, risk difference, relative risk and odds ratio, and study their properties. Using a real cancer control and prevention trial example, we ascertain the efficiency of the widely used balanced design relative to the maximin optimal design and show that the former can be quite inefficient and less robust to mis-specifications of the ICCs and the success rates in the two arms.

Sun protection practices and sun exposure among children with a parental history of melanoma.

BACKGROUND: First-degree relatives of melanoma survivors have a substantially higher lifetime risk for melanoma than individuals with no family history. Exposure to ultraviolet radiation (UVR) is the primary modifiable risk factor for the disease. Reducing UV exposure through sun protection may be particularly important for children with a parental history of melanoma. Nonetheless, limited prior research has investigated sun protection practices and sun exposure among these children. METHODS: The California Cancer Registry was used to identify melanoma survivors eligible to participate in a survey to assess their children's sun protection practices and sun exposure. The survey was administered by mail, telephone, or web to Latino and non-Latino white melanoma survivors with at least one child (0-17 years; N = 324). RESULTS: Sun exposure was high and the rate of sunburn was equivalent to or higher than estimates from average-risk populations. Use of sun protection was suboptimal. Latino children were less likely to wear sunscreen and hats and more likely to wear sunglasses, although these differences disappeared in adjusted analyses. Increasing age of the child was associated with lower sun protection and higher risk for sunburn, whereas higher objective risk for melanoma predicted improved sun protection and a higher risk for sunburns. Perception of high barriers to sun protection was the strongest modifiable correlate of sun protection. CONCLUSIONS: Interventions to improve sun protection and reduce sun exposure and sunburns in high-risk children are needed. IMPACT: Intervening in high-risk populations may help reduce the burden of melanoma in the United States.

Effect of implant support for prostheses on electromyographic activity of masseter muscle and jaw movement in patients after mandibular fibula free flap reconstruction.

PURPOSE: Dental implants are used to stabilize, support, and retain prostheses in the mandible following fibula free flap reconstruction. A previous longitudinal prospective study showed that an implant-supported prosthesis (IP) provided additional improvement in masticatory performance compared to a conventional prosthesis (CP). Therefore, in this paper, the impact of implant retention and support of mandibular prostheses on neuromuscular function is reported via a within-subject analysis. MATERIALS AND METHODS: Forty-six participants were enrolled in the study. Prosthetic treatment with a CP was completed in 33 subjects following oromandibular resection and fibula free flap reconstruction. Twenty-five subjects completed evaluation of the CP after an adaptation period. Standardized masticatory tests with peanuts were given to subjects on the defect and nondefect chewing sides. Electromyography (EMG) of masseter muscles and jaw movement was performed and recorded simultaneously in 19 of these subjects. IP treatment was then completed in 16 of these subjects, and 15 of them participated in the IP evaluation after an adaptation period. Of these 15 subjects, 13 completed EMG and jaw movement recordings for both CP and IP. RESULTS: EMG activity of the defect-side masseter muscle increased significantly from CP to IP conditions when chewing on either side, but no significant change was found for nondefect-side muscle activity. Jaw movement parameters showed no significant changes from CP to IP. CONCLUSION: In patients restored with mandibular fibula free flap reconstruction, implant support for mandibular prostheses has the benefit of permitting greater muscle effort on the defect side, irrespective of the side on which the bolus is being chewed. The impact of an IP on jaw movements is limited.

A panel of biomarkers is associated with increased risk of the presence and progression of atherosclerosis in women with systemic lupus erythematosus.

OBJECTIVE: An increased frequency of atherosclerosis (ATH) in systemic lupus erythematosus (SLE) is well-documented but not fully explained by the presence of traditional cardiac risk factors. Several nontraditional biomarkers, including proinflammatory high-density lipoprotein (piHDL) and leptin, have been individually associated with subclinical ATH in SLE. The aim of this study was to examine whether these and other biomarkers can be combined into a risk profile, the Predictors of Risk for Elevated Flares, Damage Progression, and Increased Cardiovascular Disease in Patients with SLE (PREDICTS), that could be used to better predict future progression of ATH. METHODS: In total, 210 patients with SLE and 100 age-matched healthy control subjects (all women) participated in this prospective cohort study. The longitudinal presence of carotid plaque and intima-media thickness (IMT) were measured at baseline and followup (mean ± SD 29.6 ± 9.7 months). RESULTS: At followup, carotid plaque was present in 29% of SLE patients. Factors significantly associated with plaque, determined using Salford Predictive Modeling and multivariate analysis, included age ≥48 years (odds ratio [OR] 4.1, P = 0.002), high piHDL function (OR 9.1, P < 0.001), leptin levels ≥34 ng/dl (OR 7.3, P = 0.001), plasma soluble TWEAK levels ≥373 pg/ml (OR 28.8, P = 0.004), and history of diabetes (OR 61.8, P < 0.001). Homocysteine levels ≥12 µmoles/liter were also a predictor. However, no single variable demonstrated an ideal combination of good negative predictive values (NPVs), positive predictive values (PPVs), sensitivity, and specificity. A high-risk PREDICTS profile was defined as ≥3 positive biomarkers or ≥1 positive biomarker plus a history of diabetes; for high-risk SLE patients, the PPV was 64%, NPV was 94%, sensitivity was 89%, and specificity was 79%. In multivariate analysis, SLE patients with the high-risk profile had 28-fold increased odds for the longitudinal presence of plaque (P < 0.001) and increased progression of IMT (P < 0.001). CONCLUSION: A high-risk PREDICTS score confers 28-fold increased odds of the presence of any current, progressive, or acquired carotid plaque, both in patients with SLE and in control subjects, and is significantly associated with higher rates of IMT progression.

Novel Statistical Designs for Phase I/II and Phase II Clinical Trials With Dose-Finding Objectives.

In modern drug development, there has been an increasing interest in adaptive clinical trials-research designs that allow judicious modification of certain aspects of an ongoing clinical trial based on prespecified criteria according to accumulating data to achieve predetermined experimental objectives. A particularly important application of adaptive designs is in phase I and II stages of drug development. Many novel adaptive designs have been proposed in the context of phase I oncology trials of cytotoxic agents where acceptable toxicity frequently translates into therapeutic response. However, an assessment of efficacy measurements based on biomarkers in early development is also very important. The current paper gives an overview of adaptive designs for early development studies that utilize efficacy measurements in design adaptation rules. These include seamless phase I/II designs, where efficacy and safety considerations are both incorporated in dose-finding objectives, and phase II dose-response studies, which typically aim at establishing a dose-response relationship with respect to some efficacy outcome and at identifying the most promising doses to be tested in subsequent confirmatory trials. The authors discuss statistical, logistical, and regulatory aspects of these designs and provide perspectives on their applications in modern clinical trials.

Forward selection two sample binomial test.

Fisher's exact test (FET) is a conditional method that is frequently used to analyze data in a 2 × 2 table for small samples. This test is conservative and attempts have been made to modify the test to make it less conservative. For example, Crans and Shuster (2008) proposed adding more points in the rejection region to make the test more powerful. We provide another way to modify the test to make it less conservative by using two independent binomial distributions as the reference distribution for the test statistic. We compare our new test with several methods and show that our test has advantages over existing methods in terms of control of the type 1 and type 2 errors. We reanalyze results from an oncology trial using our proposed method and our software which is freely available to the reader.

Sorting multiple classes in multi-dimensional ROC analysis: parametric and nonparametric approaches.

In large-scale data analysis, such as in a microarray study to identify the most differentially expressed genes, diagnostic tests are frequently used to classify and predict subjects into their different categories. Frequently, these categories do not have an intrinsic natural order even though the quantitative test results have a relative order. As identifying the correct order for a proper definition of accuracy measures is important for a high-dimensional receiver operating characteristic (ROC) analysis, we propose rigorous and automated approaches to sort out the multiple categories using simple summary statistics such as means and relative effects. We discuss the hypervolume under the ROC manifold (HUM), its dependence on the order of the test results and the minimum acceptable HUM values in a general multi-category classification problem. Using a leukemia data set and a liver cancer data set, we show how our approaches provide accurate screening results when we have a large number of tests.

Application of fractional factorial designs to study drug combinations.

Herpes simplex virus type 1 (HSV-1) is known to cause diseases of various severities. There is increasing interest to find drug combinations to treat HSV-1 by reducing drug resistance and cytotoxicity. Drug combinations offer potentially higher efficacy and lower individual drug dosage. In this paper, we report a new application of fractional factorial designs to investigate a biological system with HSV-1 and six antiviral drugs, namely, interferon alpha, interferon beta, interferon gamma, ribavirin, acyclovir, and tumor necrosis factor alpha. We show how the sequential use of two-level and three-level fractional factorial designs can screen for important drugs and drug interactions, as well as determine potential optimal drug dosages through the use of contour plots. Our initial experiment using a two-level fractional factorial design suggests that there is model inadequacy and that drug dosages should be reduced. A follow-up experiment using a blocked three-level fractional factorial design indicates that tumor necrosis factor alpha has little effect and that HSV-1 infection can be suppressed effectively by using the right combination of the other five antiviral drugs. These observations have practical implications in the understanding of antiviral drug mechanism that can result in better design of antiviral drug therapy.

Theoretically informed correlates of hepatitis B knowledge among four Asian groups: the health behavior framework.

BACKGROUND: Few studies have examined theoretically informed constructs related to hepatitis B (HBV) testing, and comparisons across studies are challenging due to lack of uniformity in constructs assessed. The present analysis examined relationships among Health Behavior Framework factors across four Asian American groups to advance the development of theory-based interventions for HBV testing in at-risk populations. METHODS: Data were collected from 2007-2010 as part of baseline surveys during four intervention trials promoting HBV testing among Vietnamese-, Hmong-, Korean- and Cambodian-Americans (n = 1,735). Health Behavior Framework constructs assessed included: awareness of HBV, knowledge of transmission routes, perceived susceptibility, perceived severity, doctor recommendation, stigma of HBV infection, and perceived efficacy of testing. Within each group we assessed associations between our intermediate outcome of knowledge of HBV transmission and other constructs, to assess the concurrent validity of our model and instruments. RESULTS: While the absolute levels for Health Behavior Framework factors varied across groups, relationships between knowledge and other factors were generally consistent. This suggests similarities rather than differences with respect to posited drivers of HBV-related behavior. DISCUSSION: Our findings indicate that Health Behavior Framework constructs are applicable to diverse ethnic groups and provide preliminary evidence for the construct validity of the Health Behavior Framework.

Comparison of methods for estimating the intraclass correlation coefficient for binary responses in cancer prevention cluster randomized trials.

The intraclass correlation coefficient (ICC) is a fundamental parameter of interest in cluster randomized trials as it can greatly affect statistical power. We compare common methods of estimating the ICC in cluster randomized trials with binary outcomes, with a specific focus on their application to community-based cancer prevention trials with primary outcome of self-reported cancer screening. Using three real data sets from cancer screening intervention trials with different numbers and types of clusters and cluster sizes, we obtained point estimates and 95% confidence intervals for the ICC using five methods: the analysis of variance estimator, the Fleiss-Cuzick estimator, the Pearson estimator, an estimator based on generalized estimating equations and an estimator from a random intercept logistic regression model. We compared estimates of the ICC for the overall sample and by study condition. Our results show that ICC estimates from different methods can be quite different, although confidence intervals generally overlap. The ICC varied substantially by study condition in two studies, suggesting that the common practice of assuming a common ICC across all clusters in the trial is questionable. A simulation study confirmed pitfalls of erroneously assuming a common ICC. Investigators should consider using sample size and analysis methods that allow the ICC to vary by study condition.