Hydrogen treatment effect on shallow and deep centers in GaSb

A. Y. Polyakov; S. J. Pearton; R. G. Wilson; P. Rai‐Choudhury; R. J. Hillard; X. J. Bao; M. Stam; A. G. Milnes; T. E. Schlesinger; J. Lopata

doi:10.1063/1.107329

Hydrogen treatment effect on shallow and deep centers in GaSb

Applied Physics Letters ◽

10.1063/1.107329 ◽

1992 ◽

Vol 60 (11) ◽

pp. 1318-1320 ◽

Cited By ~ 25

Author(s):

A. Y. Polyakov ◽

S. J. Pearton ◽

R. G. Wilson ◽

P. Rai‐Choudhury ◽

R. J. Hillard ◽

...

Keyword(s):

Treatment Effect ◽

Hydrogen Treatment ◽

Deep Centers

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Characterization of deep centers in AlGaAs/InGaAs/GaAs pseudomorphic HEMT structures grown by molecular beam epitaxy and hydrogen treatment

Journal of Crystal Growth ◽

10.1016/s0022-0248(96)00473-3 ◽

1996 ◽

Vol 169 (4) ◽

pp. 637-642 ◽

Cited By ~ 3

Author(s):

Liwu Lu ◽

Songlin Feng ◽

Jiben Liang ◽

Zhanguo Wang ◽

J. Wang ◽

...

Keyword(s):

Molecular Beam Epitaxy ◽

Molecular Beam ◽

Hydrogen Treatment ◽

Deep Centers

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Natural hydrogen treatment effect during formation of double amorphous silicon-carbide p layer structures producing high-efficiency pin-type amorphous silicon solar cells

Applied Physics Letters ◽

10.1063/1.1853492 ◽

2005 ◽

Vol 86 (3) ◽

pp. 033506 ◽

Cited By ~ 25

Author(s):

Seung Yeop Myong ◽

Koeng Su Lim

Keyword(s):

Silicon Carbide ◽

Solar Cells ◽

Amorphous Silicon ◽

Treatment Effect ◽

High Efficiency ◽

Silicon Solar Cells ◽

Hydrogen Treatment ◽

Amorphous Silicon Carbide ◽

Layer Structures

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The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽

10.1027/1614-2241/a000121 ◽

2017 ◽

Vol 13 (2) ◽

pp. 41-60

Author(s):

Shahab Jolani ◽

Maryam Safarkhani

Keyword(s):

Randomized Controlled Trials ◽

Discrete Time ◽

Treatment Effect ◽

Survival Data ◽

Controlled Trials ◽

Missing Covariates ◽

Indicator Method ◽

Imputation Methods ◽

Randomized Controlled ◽

Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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