Refined moderation analysis with categorical outcomes in precision medicine.

Moderation analysis is an integral part of precision medicine research. Concerning moderation analysis with categorical outcomes, we start with an interesting observation, which shows that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and the treatment variable in logistic regression models. Hence two estimators of moderating effects can be obtained. We then established the joint asymptotic normality for the two estimators, on which basis refined inference can be made for moderation analysis. The improved precision is helpful in addressing the lack-of-power problem that is common in search of moderators. The above-mentioned results hold for both experimental and observational data. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial on wart treatment.

Vibration training reducing falls in community-living older adults: a pilot randomized controlled trial.

BACKGROUND: Although vibration training has been applied in older adults, it remains unclear if it can reduce falls. AIMS: This pilot randomized-controlled trial aimed to test the effects of an 8-week vibration training program on reducing falls among community-dwelling adults. METHODS: Forty-eight older adults were randomized to two groups: training and control. The training group received three weekly training sessions over eight weeks while the control group maintained their normal lifestyle over the 8-week period. Immediately before (or baseline), following (post-training), and three months after (retest) the 8-week training course, a group of fall risk factors were assessed for all participants. Each participant was also exposed to an unexpected gait-slip on a treadmill during post-training and retest sessions. Their daily-living fall incidence was collected for 12 months after the baseline test. The slip fall was the primary outcome, prospective all-cause falls were the secondary outcome, and fall risk factors acted as the tertiary ones. RESULTS: The vibration training program significantly reduced the risk of slip-falls and improved all fall risk factors immediately after the training course. The training effect may be carried over for three months. The 8-week training program could also lower the number of falls between the baseline test and retest and reduce the recurrent faller rate across the 12 months after the baseline test. DISCUSSION: This study indicates that vibration training might have some effects on fall-related measures in older adults. CONCLUSIONS: An 8-week vibration training program could be effective to reduce falls in older adults. GOV REGISTRATION NUMBER: NCT02694666.

Variable selection in joint frailty models of recurrent and terminal events.

Recurrent event data are commonly encountered in biomedical studies. In many situations, they are subject to an informative terminal event, for example, death. Joint modeling of recurrent and terminal events has attracted substantial recent research interests. On the other hand, there may exist a large number of covariates in such data. How to conduct variable selection for joint frailty proportional hazards models has become a challenge in practical data analysis. We tackle this issue on the basis of the "minimum approximated information criterion" method. The proposed method can be conveniently implemented in SAS Proc NLMIXED for commonly used frailty distributions. Its finite-sample behavior is evaluated through simulation studies. We apply the proposed method to model recurrent opportunistic diseases in the presence of death in an AIDS study.

Multiple moderator meta-analysis using the R-package Meta-CART.

In meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to assess the effectiveness of existing interventions and design new potentially effective interventions. When there are multiple moderators, they may amplify or attenuate each other's effect on treatment effectiveness. However, in most meta-analysis studies, interaction effects are neglected due to the lack of appropriate methods. The method meta-CART was recently proposed to identify interactions between multiple moderators. The analysis result is a tree model in which the studies are partitioned into more homogeneous subgroups by combinations of moderators. This paper describes the R-package metacart, which provides user-friendly functions to conduct meta-CART analyses in R. This package can fit both fixed- and random-effects meta-CART, and can handle dichotomous, categorical, ordinal and continuous moderators. In addition, a new look ahead procedure is presented. The application of the package is illustrated step-by-step using diverse examples.

Random forests of interaction trees for estimating individualized treatment effects in randomized trials.

Assessing heterogeneous treatment effects is a growing interest in advancing precision medicine. Individualized treatment effects (ITEs) play a critical role in such an endeavor. Concerning experimental data collected from randomized trials, we put forward a method, termed random forests of interaction trees (RFIT), for estimating ITE on the basis of interaction trees. To this end, we propose a smooth sigmoid surrogate method, as an alternative to greedy search, to speed up tree construction. The RFIT outperforms the "separate regression" approach in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT are obtained with the infinitesimal jackknife method. We assess and illustrate the use of RFIT via both simulation and the analysis of data from an acupuncture headache trial.

Associations Between Hormonal Biomarkers and Cognitive, Motor, and Language Developmental Status in Very Low Birth Weight Infants.

BACKGROUND: Male infants are more prone to health problems and developmental delays than female infants. OBJECTIVES: On the basis of theories of gender differences in brain development and social relationships, we explored associations between testosterone and cortisol levels with infant cognitive, motor, and language development ("infant development") in very low birth weight (VLBW) infants, controlling for mother-infant interactions, characteristics of mothers and infants, and days of saliva collection after birth. METHODS: A total of 62 mother-VLBW infant pairs were recruited from the newborn intensive care unit of a tertiary medical center in the Southeast United States. Data were collected through infant medical record review, biochemical measurement, observation of mother-infant interactions, and standard questionnaires. Infant development was assessed at 6 months corrected age (CA), and mother-infant interactions were observed at 3 and 6 months CA. RESULTS: General linear regression with separate analyses for each infant gender showed that high testosterone levels were positively associated with language development of male infants after controlling for mother-infant interactions and other covariates, whereas high cortisol levels were negatively associated with motor development of female infants after controlling for mother-infant interactions. CONCLUSIONS: Steroid hormonal levels may well be more fundamental factors for assessing infant development than infant gender or mother-infant interactions at 6 months CA.

Association of Temporal Variations in Staffing With Hospital-Acquired Pressure Injury in Military Hospitals.

To more precisely evaluate the effects of nurse staffing on hospital-acquired pressure injury (HAPI) development, data on nursing care hours per patient day (NCHPPD), nursing skill mix, patient turnover (i.e., admissions, transfers, and discharges), and patient acuity were merged with patient information from pressure injury prevalence surveys that were collected annually for the Military Nursing Outcomes Database (MilNOD) project. The MilNOD included staffing and adverse events from 56 medical-surgical, stepdown, and critical care units in 13 military hospitals over a 4-year-period. Data on 1,643 patients were analyzed with Cox proportional hazards models and generalized estimating equations. Staffing was not associated with pressure injuries in stepdown or critical care patients. However, among the 1,104 medical-surgical patients, higher licensed practical nurse (LPN) nursing care hours per patient day (NCHPPD) 3 days and 1 week prior to the HAPI discovery date were associated with fewer HAPI (HR 0.27, p < .001), after controlling for patient age, Braden mobility score, and albumin level. Neither total staff number, nor RN NCHPPD, nor the proportion of staff who were RNs (RN skill mix) were associated with HAPI. These findings suggest that on military medical-surgical units, LPNs play a major role in HAPI prevention. Although the national trend in acute care is to staff hospital units with more RNs and patient care technicians, and fewer LPNs, hospitals should reconsider LPNs as valuable members of the nursing care team. © 2016 Wiley Periodicals, Inc.

Sparse estimation of Cox proportional hazards models via approximated information criteria.

We propose a new sparse estimation method for Cox (1972) proportional hazards models by optimizing an approximated information criterion. The main idea involves approximation of the ℓ0 norm with a continuous or smooth unit dent function. The proposed method bridges the best subset selection and regularization by borrowing strength from both. It mimics the best subset selection using a penalized likelihood approach yet with no need of a tuning parameter. We further reformulate the problem with a reparameterization step so that it reduces to one unconstrained nonconvex yet smooth programming problem, which can be solved efficiently as in computing the maximum partial likelihood estimator (MPLE). Furthermore, the reparameterization tactic yields an additional advantage in terms of circumventing postselection inference. The oracle property of the proposed method is established. Both simulated experiments and empirical examples are provided for assessment and illustration.

Subgroup Identification in Personalized Treatment of Alcohol Dependence.

BACKGROUND: Identification of patient subgroups to enhance treatment effects is an important topic in personalized (or tailored) alcohol treatment. Recently, several recursive partitioning methods have been proposed to identify subgroups benefiting from treatment. These novel data mining methods help to address the limitations of traditional regression-based methods that focus on interactions. METHODS: We propose an exploratory approach, using recursive partitioning methods, for example, interaction trees (IT) and virtual twins (VT), to flexibly identify subgroups in which the treatment effect is likely to be large. We apply these tree-based methods to a pharmacogenetic trial of ondansetron. RESULTS: Our methods identified several subgroups based on patients' genetic and other prognostic covariates. Among the 251 subjects with complete genotype information, the IT method identified 118 with specific genetic and other prognostic factors, resulting in a 17.2% decrease in the percentage of heavy drinking days (PHDD). The VT method identified 88 subjects with a 21.8% decrease in PHDD. Overall, the VT subgroup achieved a good balance between the treatment effect and the group size. CONCLUSIONS: A data mining approach is proposed as a valid exploratory method to identify a sufficiently large subgroup of subjects that is likely to receive benefit from treatment in an alcohol dependence pharmacotherapy trial. Our results provide new insights into the heterogeneous nature of alcohol dependence and could help clinicians to tailor treatment to the biological profile of individual patients, thereby achieving better treatment outcomes.

Association of Maternal and Infant Salivary Testosterone and Cortisol and Infant Gender With Mother-Infant Interaction in Very-Low-Birthweight Infants.

Male very-low-birthweight (VLBW) infants are more prone than females to health and developmental problems and less positive mother-infant interactions. Because gender differences in brain development and social relationships suggest hormonal influences on quality of mother-infant interaction, the authors explored the associations of maternal and infant salivary testosterone and cortisol levels with mother-infant interactions in the sample as a whole and by gender, after controlling for covariates. Data were collected prospectively from 62 mothers and their VLBW infants through infant record review, maternal interview, biochemical measurement of both mothers and infants, and observation of mother-infant interactions at 40 weeks postmenstrual age and at three and six months corrected age. Infants' positive interactions increased and mothers' decreased from three to six months. In generalized estimating equation (GEE) analyses, after controlling for covariates, higher maternal testosterone and infant cortisol were associated with more positive and more frequent maternal interactive behaviors. In GEE analyses by infant gender, after controlling for covariates, effects of maternal and infant hormone levels became more significant, especially on infants' interactive behaviors. Based on these preliminary findings, among VLBW infants, males with high testosterone are expected to have less positive mother-infant interactions than males with low testosterone or female infants.

Predictors of attrition among rural breast cancer survivors.

Attrition can jeopardize both internal and external validity. The goal of this secondary analysis was to examine predictors of attrition using baseline data of 432 participants in the Rural Breast Cancer Survivors study. Attrition predictors were conceptualized based on demographic, social, cancer treatment, physical health, and mental health characteristics. Baseline measures were selected using this conceptualization. Bivariate tests of association, discrete-time Cox regression models and recursive partitioning techniques were used in analysis. Results showed that 100 participants (23%) dropped out by Month 12. Non-linear tree analyses showed that poor mental health and lack of health insurance were significant predictors of attrition. Findings contribute to future research efforts to reduce research attrition among rural underserved populations.

Sleep quality, stress, caregiver burden, and quality of life in maternal caregivers of young children with bronchopulmonary dysplasia.

Little is known about the influence of sleep quality, stress, and caregiver burden on quality of life in maternal caregivers of young children with bronchopulmonary dysplasia (BPD). In 61 maternal caregivers (mean age 29.59 years) of young children with BPD (mean age 13.93 months), caregivers reported sleeping a mean of 5.8 hours, and significant correlations were found between sleep quality and depressive symptoms and stress, as well as an inverse correlation with quality of life. Sleep quality was found to be the most significant predictor of quality of life in maternal caregivers.

Using item response theory models to evaluate the Practice Environment Scale.

BACKGROUND AND PURPOSE: The purpose of this article is to introduce different types of item response theory models and to demonstrate their usefulness by evaluating the Practice Environment Scale. METHODS: Item response theory models such as constrained and unconstrained graded response model, partial credit model, Rasch model, and one-parameter logistic model are demonstrated. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) indices are used as model selection criterion. RESULTS: The unconstrained graded response and partial credit models indicated the best fit for the data. Almost all items in the instrument performed well. CONCLUSIONS: Although most of the items strongly measure the construct, there are a few items that could be eliminated without substantially altering the instrument. The analysis revealed that the instrument may function differently when administered to different unit types.

Enhanced electromagnetic wave absorption of bacterial cellulose/ reduced graphene oxide aerogel by eco-friendly in-situ construction.

Electromagnetic wave absorption materials (EWAMs) have become an effective means to address electromagnetic (EM) radiation and enhance stealth technology, among which aerogels are valued for their lightweight nature and excellent designability. This study utilized environmentally friendly preparation and in-situ reduction techniques to fabricate bacterial cellulose (BC) / reduced graphene oxide (RGO) aerogels, achieving tailored EM wave loss capabilities by controlling the reduction time of ascorbic acid. Benefitting from the effects of freeze-casting, BC winding, hydrogen bond, and RGO layers coupling, the aerogel maintains their original structure after reduction and exhibits satisfactory EM wave absorption. The minimum reflection loss (RLmin) is -38.52 dB, with an effective absorption bandwidth (EAB) of 6.68 GHz and a maximum radar cross section (RCS) reduction of 44.69 dBsm. Additionally, the aerogel's lightweight (a low density of 9.03 mg/cm3) and outstanding thermal insulation properties enable it to adapt to complex conditions. Thus, the study provides a novel approach for the construction of industrialized and sustainable RGO-based EWAMs.

Urinary fatty acid biomarkers for prostate cancer detection.

The lack of accuracy in the current prostate specific antigen (PSA) test for prostate cancer (PCa) screening causes around 60-75% of unnecessary prostate biopsies. Therefore, alternative diagnostic methods that have better accuracy and can prevent over-diagnosis of PCa are needed. Researchers have examined various potential biomarkers for PCa, and of those fatty acids (FAs) markers have received special attention due to their role in cancer metabolomics. It has been noted that PCa metabolism prefers FAs over glucose substrates for continued rapid proliferation. Hence, we proposed using a urinary FAs based model as a non-invasive alternative for PCa detection. Urine samples collected from 334 biopsy-designated PCa positive and 232 biopsy-designated PCa negative subjects were analyzed for FAs and lipid related compounds by stir bar sorptive extraction coupled with gas chromatography/mass spectrometry (SBSE-GC/MS). The dataset was split into the training (70%) and testing (30%) sets to develop and validate logit models and repeated for 100 runs of random data partitioning. Over the 100 runs, we confirmed the stability of the models and obtained optimal tuning parameters for developing the final FA based model. A PSA model using the values of the patients' PSA test results was constructed with the same cohort for the purpose of comparing the performances of the FA model against PSA test. The FA final model selected 20 FAs and rendered an AUC of 0.71 (95% CI = 0.67-0.75, sensitivity = 0.48, and specificity = 0.83). In comparison, the PSA model performed with an AUC of 0.51 (95% CI = 0.46-0.66, sensitivity = 0.44, and specificity = 0.71). The study supports the potential use of urinary FAs as a stable and non-invasive alternative test for PCa diagnosis.

Urinary volatile organic compounds in prostate cancer biopsy pathologic risk stratification using logistic regression and multivariate analysis models.

Prostate cancer (PCa) is the second leading cause of cancer-related death in American men after lung cancer. The current PCa diagnostic method, the serum prostate-specific antigen (PSA) test, is not specific, thus, alternatives are needed to avoid unnecessary biopsies and over-diagnosis of clinically insignificant PCa. To explore the application of metabolomics in such effort, urine samples were collected from 386 male adults aged 44-93 years, including 247 patients with biopsy-proven PCa and 139 with biopsy-proven negative results. The PCa-positive group was further subdivided into two groups: low-grade (ISUP Grade Group = 1; n = 139) and intermediate/high-grade (ISUP Grade Group ≥ 2; n = 108). Volatile organic compounds (VOCs) in urine were extracted by stir bar sorptive extraction (SBSE) and analyzed using thermal desorption with gas chromatography and mass spectrometry (GC-MS). We used machine learning tools to develop and evaluate models for PCa diagnosis and prognosis. In total, 22,538 VOCs were identified in the urine samples. With regularized logistic regression, our model for PCa diagnosis yielded an area under the curve (AUC) of 0.99 and 0.88 for the training and testing sets respectively. Furthermore, the model for differentiating between low-grade and intermediate/high-grade PCa yielded an average AUC of 0.78 based on a repeated test-sample approach for cross-validation. These novel methods using urinary VOCs and logistic regression were developed to fill gaps in PCa screening and assessment of PCa grades prior to biopsy. Our study findings provide a promising alternative or adjunct to current PCa screening and diagnostic methods to better target patients for biopsy and mitigate the challenges associated with over-diagnosis and over-treatment of PCa.

A revision of the quality of life-breast cancer survivors (QOL-BCS) instrument.

The importance of quality of life (QOL) measurement among breast cancer survivors (BCS) is well-recognized by researchers and clinicians. With data from 427 rural BCS, the authors used a combination of clinical expertise and statistical analysis to revise a 48-item measure of QOL specific to BCS. The revised 15-item measure showed adequate psychometric properties and provides the basis for a brief yet comprehensive multidimensional measure of QOL applicable to most BCS. Future work includes fine-tuning the measurement model and examining its generalizability among non-rural BCS.

Refined moderation analysis with binary outcomes in precision medicine research.

Moderation analysis for evaluating differential treatment effects serves as the bedrock of precision medicine, which is of growing interest in many fields. In the analysis of data with binary outcomes, we observe an interesting symmetry property concerning the ratio of odds ratios, which suggests that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and treatment variable in logistic regression models. We then obtain refined inference on moderating effects by rearranging data and combining two models into one via a generalized estimating equation approach. The improved efficiency is helpful in addressing the lack-of-power problem that is common in the search for important moderators. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial on wart treatment.