Self-powered SnSx/TiO2 photodetectors (PDs) with dual-band binary response and the applications in imaging and light-encrypted logic gates.

In this work, we report the design and fabrication of self-powered binary response PDs based on II-type heterostructures consisting of SnSx nanoflakes (NFs) and rutile TiO2 nanorod arrays (NRs). The TiO2 NRs effectively block light with wavelengths below 400 nm from reaching SnSx. Under 385 nm light, the photoelectrons in TiO2 recombine with holes in SnSx at the interface due to the energy band bending, resulting in a positive photocurrent. Under 410 nm light, the photoelectrons in SnSx and the photogenerated holes in TiO2 accumulate at the interface, overcoming the interfacial potential barriers induced by the higher Fermi levels of SnSx and inducing a negative photocurrent. Based on the bipolar response, the dual-band imaging capability without external filters and the light-encrypted OR, AND, and NOT logic gates using a single device are demonstrated. This work provides a blueprint for the development of multifunctional self-powered PDs that can simplify system architecture, reduce the energy consumption, and improve accuracy for applications, such as visual systems, light-controlled logic circuits, and encrypted optical communications.

Simultaneity in binary outcome models with an application to employment for couples.

Two of Peter Schmidt's many contributions to econometrics have been to introduce a simultaneous logit model for bivariate binary outcomes and to study estimation of dynamic linear fixed effects panel data models using short panels. In this paper, we study a dynamic panel data version of the bivariate model introduced in Schmidt and Strauss (Econometrica 43:745-755, 1975) that allows for lagged dependent variables and fixed effects as in Ahn and Schmidt (J Econom 68:5-27, 1995). We combine a conditional likelihood approach with a method of moments approach to obtain an estimation strategy for the resulting model. We apply this estimation strategy to a simple model for the intra-household relationship in employment. Our main conclusion is that the within-household dependence in employment differs significantly by the ethnicity composition of the couple even after one allows for unobserved household specific heterogeneity.

A Modified Net Reclassification Improvement Statistic.

The continuous net reclassification improvement (NRI) statistic is a popular model change measure that was developed to assess the incremental value of new factors in a risk prediction model. Two prominent statistical issues identified in the literature call the utility of this measure into question: (1) it is not a proper scoring function and (2) it has a high false positive rate when testing whether new factors contribute to the risk model. For binary response regression models, these subjects are interrogated and a modification of the continuous NRI, guided by the likelihood-based score residual, is proposed to address these issues. Within a nested model framework, the modified NRI may be viewed as a distance measure between two risk models. An application of the modified NRI is illustrated using prostate cancer data.

Saddlepoint approximations to score test statistics in logistic regression for analyzing genome-wide association studies.

We investigate saddlepoint approximations of tail probabilities of the score test statistic in logistic regression for genome-wide association studies. The inaccuracy in the normal approximation of the score test statistic increases with increasing imbalance in the response and with decreasing minor allele counts. Applying saddlepoint approximation methods greatly improve the accuracy, even far out in the tails of the distribution. By using exact results for a simple logistic regression model, as well as simulations for models with nuisance parameters, we compare double saddlepoint methods for computing two-sided P $$ P $$ -values and mid- P $$ P $$ -values. These methods are also compared to a recent single saddlepoint procedure. We investigate the methods further on data from UK Biobank with skin and soft tissue infections as phenotype, using both common and rare variants.

A flexible copula regression model with Bernoulli and Tweedie margins for estimating the effect of spending on mental health.

We develop a flexible two-equation copula model to address endogeneity of medical expenditures in a distribution regression for health. The expenditure margin uses the compound gamma distribution, a special case of the Tweedie family of distributions, to account for a spike at zero and a highly skewed continuous part. An efficient estimation algorithm offers flexible choices of copulae and link functions, including logit, probit and cloglog for the health margin. Our empirical application revisits data from the Rand Health Insurance Experiment. In the joint model, using random insurance plan assignment as instrument for spending, a $1000 increase is estimated to reduce the probability of a low post-program mental health index by 1.9 percentage points. The effect is not statistically significant. Ignoring endogeneity leads to a spurious positive effect estimate.

Promising zone two-stage design for a single-arm study with binary outcome.

Adaptive designs are increasingly used in clinical trials to assess the effectiveness of new drugs. For a single-arm study with a binary outcome, several adaptive designs were developed by using numerical search algorithms and the conditional power approach. The design based on numerical search algorithms is able to identify the global optimal design, but the computational intensity limits the usage of these designs. The conditional power approach searches for the optimal design without expensive computing time. In addition, promising zone strategy was proposed to move on drug development to the follow-up stages when the interim results are promising. We propose to develop two adaptive designs: One based on the conditional power approach, and the other based on the promising zone strategy. These two designs preserve types I and II error rates. It is preferable to satisfy the monotonic property for adaptive designs: The second stage sample size decreases as the first stage responses go up. We theoretically prove this important property for the two proposed designs. The proposed designs can be easily applied to real trials with limited computing resources.

Response adaptive randomization design for a two-stage study with binary response.

Response adaptive randomization has the potential to treat more participants in better treatments in a trial to benefit participants. We propose optimal response adaptive randomization designs for a two-stage study with binary response, having the smallest expected sample size or the fewest expected number of failures. Equal randomization is used in the first stage, and data from the first stage is used to determine the adaptive sample size ratio in the second stage. In the proposed optimal designs, the type I error rate and the statistical power are calculated from the asymptotic normal distributions. The new designs that minimize the expected number of failures have the advantage over the existing optimal randomized designs to substantially reduce the number of failures.

Regression Discontinuity for Binary Response and Local Maximum Likelihood Estimator to Extrapolate Treatment.

Regression discontinuity is popular in finding treatment/policy effects when the treatment is determined by a continuous variable crossing a cutoff. Typically, a local linear regression (LLR) estimator is used to find the effects. For binary response, however, LLR is not suitable in extrapolating the treatment, as in doubling/tripling the treatment dose/intensity. The reason is that doubling/tripling the LLR estimate can give a number out of the bound [-1, 1], despite that the effect should be a change in probability. We propose local maximum likelihood estimators which overcome these shortcomings, while giving almost the same estimates as the LLR estimator does for the original treatment. A simulation study and an empirical analysis for effects of an income subsidy program on religion demonstrate these points.

A Spiro-MeOTAD/Ga2O3/Si p-i-n Junction Featuring Enhanced Self-Powered Solar-Blind Sensing via Balancing Absorption of Photons and Separation of Photogenerated Carriers.

Solar blind ultraviolet (SBUV) self-powered photodetectors (PDs) have a great number of applications in civil and military exploration. Ga2O3 is a prospective candidate for SBUV detection owing to its reasonable bandgap corresponding to the SBUV waveband. Nevertheless, the previously reported Ga2O3 photovoltaic devices had low photoresponse performance and were still far from the demands of practical application. Herein, we propose an idea of using spiro-MeOTAD (spiro) as the SBUV transparent conductive layer to construct p-i-n PDs (p-spiro/Ga2O3/n-Si). With the aid of double built-in electric fields, the designed p-i-n PDs could operate without any external power source. Furtherly, the influence of spiro thickness on improving the photoelectric performance of devices is investigated in detail and the optimum device is achieved, translating to a peak responsivity of 192 mA/W upon a weak 254 nm light illumination of 2 µW/cm2 at zero bias. In addition, the I-t curve of our PD shows binary response characteristics and a four-stage current response behavior under a small forward bias, and also, its underlying working mechanism is analyzed. In sum, this newly developed device presents great potential for booming the high energy-efficient optoelectronic devices in the short run.

Highly sensitive broadband binary photoresponse in gateless epitaxial graphene on 4H-SiC.

Due to weak light-matter interaction, standard chemical vapor deposition (CVD)/exfoliated single-layer graphene-based photodetectors show low photoresponsivity (on the order of mA/W). However, epitaxial graphene (EG) offers a more viable approach for obtaining devices with good photoresponsivity. EG on 4H-SiC also hosts an interfacial buffer layer (IBL), which is the source of electron carriers applicable to quantum optoelectronic devices. We utilize these properties to demonstrate a gate-free, planar EG/4H-SiC-based device that enables us to observe the positive photoresponse for (405-532) nm and negative photoresponse for (632-980) nm laser excitation. The broadband binary photoresponse mainly originates from the energy band alignment of the IBL/EG interface and the highly sensitive work function of the EG. We find that the photoresponsivity of the device is > 10 A/W under 405 nm of power density 7.96 mW/cm2 at 1 V applied bias, which is three orders of magnitude greater than the obtained values of CVD/exfoliated graphene and higher than the required value for practical applications. These results path the way for selective light-triggered logic devices based on EG and can open a new window for broadband photodetection.

Copula-based robust optimal block designs.

Blocking is often used to reduce known variability in designed experiments by collecting together homogeneous experimental units. A common modeling assumption for such experiments is that responses from units within a block are dependent. Accounting for such dependencies in both the design of the experiment and the modeling of the resulting data when the response is not normally distributed can be challenging, particularly in terms of the computation required to find an optimal design. The application of copulas and marginal modeling provides a computationally efficient approach for estimating population-average treatment effects. Motivated by an experiment from materials testing, we develop and demonstrate designs with blocks of size two using copula models. Such designs are also important in applications ranging from microarray experiments to experiments on human eyes or limbs with naturally occurring blocks of size two. We present a methodology for design selection, make comparisons to existing approaches in the literature, and assess the robustness of the designs to modeling assumptions.

Non-inferiority tests for binary endpoints with variable margins.

Non-inferiority comparison between binary response rates of test and reference treatments is often performed in clinical studies. The most common approach to assess non-inferiority is to compare the difference between the estimated response rates with some margin. Previous methods use a variety of margins, including fixed margin, step-wise constant margin, and piece-wise smooth margin, where the latter two are functions of the reference response rate. The fixed margin approach assumes that the margin can be determined from historical trials with the consistent difference between the reference treatment and placebo, which may not be available. The step-wise constant margin approach suffers discontinuity in the power function which can cause trouble in sample size determination. Furthermore, many methods ignore the variability in margins dependent on the estimated reference response rate, leading to poor type I error control and power function approximation. In this study, we propose a variable margin approach to overcome the difficulties in fixed and step-wise constant margin approaches. We discuss several test statistics and evaluate their performance through simulation studies.

The objective function controversy for group testing: Much ado about nothing?

Group testing is an indispensable tool for laboratories when testing high volumes of clinical specimens for infectious diseases. An important decision that needs to be made prior to implementation is determining what group sizes to use. In best practice, an objective function is chosen and then minimized to determine an optimal set of these group sizes, known as the optimal testing configuration (OTC). There are a few options for objective functions, and they differ based on how the expected number of tests, assay characteristics, and testing constraints are taken into account. These varied options have led to a recent controversy in the literature regarding which of two different objective functions is better. In our paper, we examine these objective functions over a number of realistic situations for infectious disease testing. We show that this controversy may be much ado about nothing because the OTCs and corresponding results (eg, number of tests and accuracy) are largely the same for standard testing algorithms in a wide variety of situations.

Calculation of confidence intervals for a finite population size.

For any estimate of response, confidence intervals are important as they help quantify a plausible range of values for the population response. However, there may be instances in clinical research when the population size is finite, but we wish to take a sample from the population and make inference from this sample. Instances where you can have a fixed population size include when undertaking a clinical audit of patient records or in a clinical trial a researcher could be checking for transcription errors against patient notes. In this paper, we describe how confidence interval calculations can be calculated for a finite population. These confidence intervals are narrower than confidence intervals from population samples. For the extreme case of when a 100% sample from the population is taken, there is no error and the calculation is the population response. The methods in the paper are described using a case study from clinical data management.

The sum of standardized residuals: Goodness-of-fit test for binary response models.

We propose a new goodness-of-fit statistic for evaluating generalized linear models with binary responses on the basis of the sum of standardized residuals. We derive the asymptotic distribution of the sum of standardized residuals statistic and argue that, despite its relative simplicity, it typically outperforms many of the more sophisticated currently used goodness-of-fit statistics.

Model-free tests of equality in binary data under an incomplete block design.

Using Prescott's model-free approach, we develop an asymptotic procedure and an exact procedure for testing equality between treatments with binary responses under an incomplete block crossover design. We employ Monte Carlo simulation and note that these test procedures can not only perform well in small-sample cases but also outperform the corresponding test procedures accounting for only patients with discordant responses published elsewhere. We use the data taken as a part of the crossover trial comparing two different doses of an analgesic with placebo for the relief of primary dysmenorrhea to illustrate the use of test procedures discussed here.

On logistic regression analysis of dichotomized responses.

We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd.

Influence of gene copy number on self-regulated gene expression.

Using an analytically solvable stochastic model, we study the properties of a simple genetic circuit consisting of multiple copies of a self-regulating gene. We analyse how the variation in gene copy number and the mutations changing the auto-regulation strength affect the steady-state distribution of protein concentration. We predict that one-reporter assay, an experimental method where the extrinsic noise level is inferred from the comparison of expression variance of a single and duplicated reporter gene, may give an incorrect estimation of the extrinsic noise contribution when applied to self-regulating genes. We also show that an imperfect duplication of an auto-activated gene, changing the regulation strength of one of the copies, may lead to a hybrid, binary+graded response of these genes to external signal. The analysis of relative changes in mean gene expression before and after duplication suggests that evolutionary accumulation of gene duplications may, at a given mean burst size, non-trivially depend on the inherent noisiness of a given gene, quantified by the inverse of the maximal mean frequency of bursts. Moreover, we find that the dependence of gene expression noise on gene copy number and auto-regulation strength may qualitatively differ, e.g. in monotonicity, depending on whether the noise is measured by Fano factor or coefficient of variation. Thus, experimentally-based hypotheses linking gene expression noise and evolutionary optimisation in the context of gene copy number variation may be ambiguous as they are dependent on the particular function chosen to quantify noise.

Variable selection for binary spatial regression: Penalized quasi-likelihood approach.

We consider the problem of selecting covariates in a spatial regression model when the response is binary. Penalized likelihood-based approach is proved to be effective for both variable selection and estimation simultaneously. In the context of a spatially dependent binary variable, an uniquely interpretable likelihood is not available, rather a quasi-likelihood might be more suitable. We develop a penalized quasi-likelihood with spatial dependence for simultaneous variable selection and parameter estimation along with an efficient computational algorithm. The theoretical properties including asymptotic normality and consistency are studied under increasing domain asymptotics framework. An extensive simulation study is conducted to validate the methodology. Real data examples are provided for illustration and applicability. Although theoretical justification has not been made, we also investigate empirical performance of the proposed penalized quasi-likelihood approach for spatial count data to explore suitability of this method to a general exponential family of distributions.

Instrument Assisted Regression for Errors in Variables Models with Binary Response.

We study errors-in-variables problems when the response is binary and instrumental variables are available. We construct consistent estimators through taking advantage of the prediction relation between the unobservable variables and the instruments. The asymptotic properties of the new estimator are established, and illustrated through simulation studies. We also demonstrate that the method can be readily generalized to generalized linear models and beyond. The usefulness of the method is illustrated through a real data example.