scholarly journals Evaluating the Proportion of Treatment Effect Explained by a Continuous Surrogate Marker in Logistic or Probit Regression Models

2010 ◽  
Vol 2 (2) ◽  
pp. 229-238 ◽  
Author(s):  
Jie Huang ◽  
Bin Huang
Keyword(s):  
Treatment Effect ◽  
Regression Models ◽  
Surrogate Marker ◽  
Probit Regression

scholarly journals Occupational exposure to foreigners and attitudes towards equal opportunities

2019 ◽  
Vol 8 (3) ◽  
pp. 382-423 ◽  
Author(s):  
Marco Pecoraro ◽  
Didier Ruedin
Keyword(s):  
Labour Market ◽  
Regression Models ◽  
Negative Association ◽  
Job Quality ◽  
Equal Opportunities ◽  
Probit Regression ◽  
Occupational Level ◽  
Positive Attitudes ◽  
The Relationship ◽  
Job Prospects

Abstract We examine the relationship between attitudes to foreigners and the share of foreigners at the occupational level. Using a question on equal opportunities for foreigners from the Swiss Household Panel, ordered probit regression models show a negative association between the share of foreigners in one’s occupation and positive attitudes to foreigners: workers seem to react to competition with foreigners. When we add the occupational unemployment rate, objective pressures in the labour market appear as relevant as contact at the occupational level. Further controlling for occupational heterogeneity establishes that both factors—particularly objective pressures—are probably accounted for by sorting on job quality. We also show that the association between the occupational share of foreigners and attitudes decreases for workers with better job prospects. This implies that workers welcome foreigners to overcome labour market shortages.

scholarly journals Model-free approach to quantifying the proportion of treatment effect explained by a surrogate marker

Biometrika ◽  
2019 ◽  
Vol 107 (1) ◽  
pp. 107-122 ◽  
Author(s):  
Xuan Wang ◽  
Layla Parast ◽  
Lu Tian ◽  
Tianxi Cai
Keyword(s):  
Treatment Effect ◽  
Randomized Clinical Trials ◽  
Randomized Study ◽  
Model Misspecification ◽  
Asymptotic Properties ◽  
Surrogate Marker ◽  
Finite Samples ◽  
Model Free ◽  
Long Term Follow Up

Summary In randomized clinical trials, the primary outcome, $Y$, often requires long-term follow-up and/or is costly to measure. For such settings, it is desirable to use a surrogate marker, $S$, to infer the treatment effect on $Y$, $\Delta$. Identifying such an $S$ and quantifying the proportion of treatment effect on $Y$ explained by the effect on $S$ are thus of great importance. Most existing methods for quantifying the proportion of treatment effect are model based and may yield biased estimates under model misspecification. Recently proposed nonparametric methods require strong assumptions to ensure that the proportion of treatment effect is in the range $[0,1]$. Additionally, optimal use of $S$ to approximate $\Delta$ is especially important when $S$ relates to $Y$ nonlinearly. In this paper we identify an optimal transformation of $S$, $g_{\tiny {\rm{opt}}}(\cdot)$, such that the proportion of treatment effect explained can be inferred based on $g_{\tiny {\rm{opt}}}(S)$. In addition, we provide two novel model-free definitions of proportion of treatment effect explained and simple conditions for ensuring that it lies within $[0,1]$. We provide nonparametric estimation procedures and establish asymptotic properties of the proposed estimators. Simulation studies demonstrate that the proposed methods perform well in finite samples. We illustrate the proposed procedures using a randomized study of HIV patients.

scholarly journals Robust estimation of the proportion of treatment effect explained by surrogate marker information

2015 ◽  
Vol 35 (10) ◽  
pp. 1637-1653 ◽  
Author(s):  
Layla Parast ◽  
Mary M. McDermott ◽  
Lu Tian
Keyword(s):  
Robust Estimation ◽  
Treatment Effect ◽  
Surrogate Marker ◽  
Marker Information

Proportion of treatment effect (PTE) explained by a surrogate marker

2003 ◽  
Vol 22 (22) ◽  
pp. 3449-3459 ◽  
Author(s):  
Cong Chen ◽  
Hongwei Wang ◽  
Steven M. Snapinn
Keyword(s):  
Treatment Effect ◽  
Surrogate Marker

Fingolimod effect on brain volume loss independently contributes to its effect on disability

2015 ◽  
Vol 21 (7) ◽  
pp. 916-924 ◽  
Author(s):  
MP Sormani ◽  
N De Stefano ◽  
G Francis ◽  
T Sprenger ◽  
P Chin ◽  
...  
Keyword(s):  
Volume Change ◽  
Treatment Effect ◽  
Brain Volume ◽  
Surrogate Marker ◽  
Lower Percentage ◽  
Volume Loss ◽  
Disability Progression ◽  
T2 Lesions ◽  
Brain Volume Loss ◽  
Relapsing Remitting

Background: Brain volume loss occurs in patients with relapsing–remitting MS. Fingolimod reduced brain volume loss in three phase 3 studies. Objective: To evaluate whether the effect of fingolimod on disability progression was mediated by its effects on MRI lesions, relapses or brain volume loss, and the extent of this effect. Methods: Patients (992/1272; 78%) from the FTY720 Research Evaluating Effects of Daily Oral Therapy in Multiple Sclerosis (FREEDOMS) study were analyzed. Month-24 percentage brain volume change, month-12 MRI-active lesions and relapse were assessed. The Prentice criteria were used to test surrogate marker validity. The proportion of treatment effect on disability progression explained by each marker was calculated. Results: Two-year disability progression was associated with active T2 lesions (OR = 1.24; p = 0.001) and more relapses during year 1 (OR = 2.90; p < 0.001) and lower percentage brain volume change over two years (OR = 0.78; p < 0.001). Treatment effect on active T2 lesions, relapses and percentage brain volume change explained 46%, 60% and 23% of the fingolimod effect on disability. Multivariate analysis showed the number of relapses during year 1 (OR = 2.62; p < 0.001) and yearly percentage brain volume change over two years (OR = 0.85; p = 0.009) were independent predictors of disability progression, together explaining 73% of fingolimod effect on disability. Conclusions: The treatment effect on relapses and, to a lesser extent, brain volume loss were both predictors of treatment effect on disability; combining these predictors better explained the effect on disability than either factor alone.

scholarly journals Pengaruh Faktor Sosial Ekonomi terhadap Perkawinan Muslim di Indonesia

2020 ◽  
Vol 6 (8) ◽  
pp. 1674
Author(s):  
Fauzia Aqilla Fadhil ◽  
Ilmiawan Auwalin
Keyword(s):  
Family Life ◽  
Regression Models ◽  
Probability Model ◽  
Muslim Women ◽  
Sample Population ◽  
Probit Regression ◽  
Linear Probability Model ◽  
Logit Regression ◽  
Indonesian Family Life Survey ◽  
Linear Probability

This study aims to find out what factors that affect a Muslim's decision to get married. This study uses the data from the Indonesian Family Life Survey (IFLS) with a quantitative approach using 83% of the sample population in Indonesia covering approximately 30,000 people taken in 13 of the 27 provinces in Indonesia. This study was analyzed using Linear Probability Model (LPM) regression, Logit regression and Probit regression. The data in this study were processed using STATA MP software. According to the results of data using three regression models, the factors that affect the decision of each individual in Indonesia in general to marry are gender, religion, age, education and occupation. The factors that affect each individual Muslim in Indonesia to make a decision to marry are gender, age, education and occupation. Then, for women in Indonesia in general, the factors that affect the decision to get married are religion, age, and occupation. Last but not least, for Muslim women, the factors that affect the decision to marry is age and occupation.Keywords: Socio-Economy, Muslim Marriage, Marital Decision

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