Adjustment methods of Schottky barrier height in one- and two-dimensional semiconductor devices

The Schottky contact which is a crucial interface between semiconductors and metals is becoming increasingly significant in nano-semiconductor devices. A Schottky barrier, also known as the energy barrier, controls the depletion width and carrier transport across the metal-semiconductor interface. Controlling or adjusting Schottky barrier height (SBH) has always been a vital issue in the successful operation of any semiconductor device. This review provides a comprehensive overview of the static and dynamic adjustment methods of SBH, with a particular focus on the recent advancements in nano-semiconductor devices. These methods encompass the work function of the metals, interface gap states, surface modification, image-lowering effect, external electric field, light illumination, and piezotronic effect. We also discuss strategies to overcome the Fermi-level pinning effect caused by interface gap states, including van der Waals contact and 1D edge metal contact. Finally, this review concludes with future perspectives in this field.

A cautionary tale: an evaluation of the performance of treatment switching adjustment methods in a real world case study

BACKGROUND

Treatment switching in randomised controlled trials (RCTs) is a problem for health technology assessment when substantial proportions of patients switch onto effective treatments that would not be available in standard clinical practice. Often statistical methods are used to adjust for switching: these can be applied in different ways, and performance has been assessed in simulation studies, but not in real-world case studies. We assessed the performance of adjustment methods described in National Institute for Health and Care Excellence Decision Support Unit Technical Support Document 16, applying them to an RCT comparing panitumumab to best supportive care (BSC) in colorectal cancer, in which 76% of patients randomised to BSC switched onto panitumumab. The RCT resulted in intention-to-treat hazard ratios (HR) for overall survival (OS) of 1.00 (95% confidence interval [CI] 0.82-1.22) for all patients, and 0.99 (95% CI 0.75-1.29) for patients with wild-type KRAS (Kirsten rat sarcoma virus).

METHODS

We tested several applications of inverse probability of censoring weights (IPCW), rank preserving structural failure time models (RPSFTM) and simple and complex two-stage estimation (TSE) to estimate treatment effects that would have been observed if BSC patients had not switched onto panitumumab. To assess the performance of these analyses we ascertained the true effectiveness of panitumumab based on: (i) subsequent RCTs of panitumumab that disallowed treatment switching; (ii) studies of cetuximab that disallowed treatment switching, (iii) analyses demonstrating that only patients with wild-type KRAS benefit from panitumumab. These sources suggest the true OS HR for panitumumab is 0.76-0.77 (95% CI 0.60-0.98) for all patients, and 0.55-0.73 (95% CI 0.41-0.93) for patients with wild-type KRAS.

RESULTS

Some applications of IPCW and TSE provided treatment effect estimates that closely matched the point-estimates and CIs of the expected truths. However, other applications produced estimates towards the boundaries of the expected truths, with some TSE applications producing estimates that lay outside the expected true confidence intervals. The RPSFTM performed relatively poorly, with all applications providing treatment effect estimates close to 1, often with extremely wide confidence intervals.

CONCLUSIONS

Adjustment analyses may provide unreliable results. How each method is applied must be scrutinised to assess reliability.

Placebo in the Relapsing-Remitting Multiple Sclerosis CLARITY Randomized Controlled Trial and CLARITY Extension Using Treatment Switching Adjustment Methods

OBJECTIVES

Treatment switching adjustment methods are often used to adjust for switching in oncology randomized controlled trials (RCTs). In this exploratory analysis, we apply these methods to adjust for treatment changes in the setting of an RCT followed by an extension study in relapsing-remitting multiple sclerosis.

METHODS

The CLARITY trial evaluated cladribine tablets versus placebo over 96 weeks. In the 96-week CLARITY Extension, patients who received placebo in CLARITY received cladribine tablets; patients who received cladribine tablets in CLARITY were re-randomized to placebo or cladribine tablets. End points were time to first qualifying relapse (FQR) and time to 3- and 6-month confirmed disability progression (3mCDP, 6mCDP). We aimed to compare the effectiveness of cladribine tablets with placebo over CLARITY and the extension. The rank-preserving structural failure time model (RPSFTM) and iterative parameter estimation (IPE) were used to estimate what would have happened if patients had received placebo in CLARITY and the extension versus patients that received cladribine tablets and switched to placebo. To gauge whether treatment effect waned after the 96 weeks of CLARITY, we compared hazard ratios (HRs) from the adjustment analysis with HRs from CLARITY.

RESULTS

The RPSFTM resulted in an HR of 0.48 [95% confidence interval (CI) 0.36-0.62] for FQR, 0.62 (95% CI 0.46-0.84) for 3mCDP and 0.62 (95% CI 0.44-0.88) for 6mCDP. IPE algorithm results were similar. CLARITY HRs were 0.44 (95% CI 0.34-0.58), 0.60 (95% CI 0.41-0.87) and 0.58 (95% CI 0.40-0.83) for FQR, 3mCDP and 6mCDP, respectively.

CONCLUSIONS

Treatment switching adjustment methods are applicable in non-oncology settings. Adjusted CLARITY plus CLARITY Extension HRs were similar to the CLARITY HRs, demonstrating significant treatment benefits associated with cladribine tablets versus placebo.

FUNDING

EMD Serono, Inc. (a business of Merck KGaA, Darmstadt, Germany).

"Linearity assessment methods for sex steroid hormones and carrier proteins among men in the National Health and Nutrition Examination Survey (NHANES III)"

INTRODUCTION

It has been hypothesized that racial disparities among several diseases are explained by differences in testosterone (T), 17-β estradiol (E), sex hormone binding globulin (SHBG) and albumin (A) levels, yet epidemiologic results have been mixed. Statistical advice regarding appropriate adjustment methods for carrier proteins of sex steroid hormones in the literature is scant. Therefore, we investigated different adjustment methods for carrier proteins.

METHODS

Data for 1496 men, >17 years from the Third National Health and Nutrition Examination Survey (NHANES III) 1988-91 were used to analyze linearity between sex hormones and carrier proteins by examining correlation, plots, and regression models. The statistical importance of age, body mass index (BMI), and race/ethnicity were examined for changes in results by the adjustment method.

RESULTS

T was weakly correlated with SHBG and A (r-squared, 0.25, 0.13, respectively) and E was weakly negatively correlated with A (p<0.0001), but not SHBG (p<0.1799). Based on the model residual plots and r-squared, the categorical model performed better than linear models. Regression coefficients for age, BMI, and race/ethnicity groups using quotient (e.g., T/A and E/A) models differed from continuous and categorical models.

CONCLUSION

Choosing an appropriate adjustment for carrier proteins is important to prevent bias in analyses and inconsistency in findings across studies. Linearity between variables should not be assumed when adjusting models, and should be conducted and reported. An independent categorical carrier protein variable is recommended in analysis exploring factors predicting sex hormone levels, although statistical testing should always be employed.