Bounding Treatment Effects with Contaminated and Censored Data: Assessing the Impact of Early Childbearing on Children

2005 ◽  
Vol 5 (1) ◽  
Author(s):  
Charles H Mullin
Keyword(s):  
Censored Data ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Child Outcomes ◽  
Well Being ◽  
Teenage Mothers ◽  
Age At First Birth ◽  
First Birth ◽  
The Impact

AbstractEmpirical researchers commonly invoke instrumental variable (IV) assumptions to identify treatment effects. This paper considers what can be learned under two specific violations of those assumptions: contaminated and corrupted data. Either of these violations prevents point identification, but sharp bounds of the treatment effect remain feasible. In an applied example, random miscarriages are an IV for women’s age at first birth. However, the inability to separate random miscarriages from behaviorally induced miscarriages (those caused by smoking and drinking) results in a contaminated sample. Furthermore, censored child outcomes produce a corrupted sample. Despite these limitations, the bounds demonstrate that delaying the age at first birth for the current population of non-black teenage mothers reduces their first-born child’s well-being.

Estimating Treatment Effects in the Presence of Noncompliance and Nonresponse: The Generalized Endogenous Treatment Model

2011 ◽  
Vol 19 (2) ◽  
pp. 205-226 ◽  
Author(s):  
Kevin M. Esterling ◽  
Michael A. Neblo ◽  
David M. J. Lazer
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Mean Squared Error ◽  
Principal Stratification ◽  
Squared Error ◽  
Treatment Effect Heterogeneity ◽  
Causal Treatment ◽  
Effect Heterogeneity ◽  
And Control

If ignored, noncompliance with a treatment or nonresponse on outcome measures can bias estimates of treatment effects in a randomized experiment. To identify and estimate causal treatment effects in the case where compliance and response depend on unobservables, we propose the parametric generalized endogenous treatment (GET) model. GET incorporates behavioral responses within an experiment to measure each subject's latent compliance type and identifies causal effects via principal stratification. Using simulation methods and an application to field experimental data, we show GET has a dramatically lower mean squared error for treatment effect estimates than existing approaches to principal stratification that impute, rather than measure, compliance type. In addition, we show that GET allows one to relax and test the instrumental variable exclusion restriction assumption, to test for the presence of treatment effect heterogeneity across a range of compliance types, and to test for treatment ignorability when treatment and control samples are balanced on observable covariates.

THE IMPACT OF EDUCATION ON WOMEN'S PREFERENCES FOR GENDER EQUALITY: EVIDENCE FROM SIERRA LEONE

2018 ◽  
Vol 84 (1) ◽  
pp. 3-40 ◽  
Author(s):  
Colin Cannonier ◽  
Naci Mocan
Keyword(s):  
Sierra Leone ◽  
Well Being ◽  
School Age Children ◽  
Age At First Birth ◽  
Access To Education ◽  
First Marriage ◽  
Age At First Marriage ◽  
Women’S Preferences ◽  
Primary School Age ◽  
The Impact

Abstract:We use data from Sierra Leone where a substantial education program provided increased access to education for primary-school age children but did not benefit children who were older. We exploit the variation in access to the program generated by date of birth and the variation in resources between various districts of the country. We find that an increase in schooling, triggered by the program, has an impact on women's attitudes toward matters that impact women's health and on attitudes regarding violence against women. An increase in education reduces the number of desired children by women and increases their propensity to use modern contraception and to be tested for AIDS. While education makes women more intolerant of practices that conflict with their well-being, increased education has no impact on men's attitudes toward women's well-being. Thus, it is unclear whether the change in attitudes would translate into behavioral changes. Consistent with this finding, education (on this margin) has no impact on women's propensity to get married, their age at first marriage or age at first birth.

scholarly journals Fertility rates and the postponement of first births: a descriptive study with Finnish population data

BMJ Open ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. e026336 ◽  
Author(s):  
Zahra Roustaei ◽  
Sari Räisänen ◽  
Mika Gissler ◽  
Seppo Heinonen
Keyword(s):  
Total Fertility Rate ◽  
Population Data ◽  
Population Based ◽  
Fertility Rate ◽  
Age At First Birth ◽  
Fertility Rates ◽  
First Birth ◽  
Educational Levels ◽  
The Mean ◽  
The Impact

ObjectivesWe described the trend of fertility rates, age-specific fertility rates and associated factors in Finland over a 30-year period.DesignA descriptive population-based register study.SettingFertility data, including age at first birth, childlessness and educational levels were gathered from the Finnish Medical Birth Register and Statistics Finland.ParticipantsAll 1 792 792 live births from 1987 to 2016 in Finland.Main outcome measuresCompleted fertility rate, total fertility rate and age-specific fertility rate.ResultsThe total fertility rate of Finnish women fluctuated substantially from 1987 to 2016. Since 2010, the total fertility rate has gradually declined and reached the lowest during the study period in 2016: 1.57 children per woman. The mean maternal age at first birth rose by 2.5 years from 26.5 years in 1987 to 29 years in 2016. The proportion of childless women at the age of 50 years increased from 13.6% in 1989 to 19.6% in 2016. By considering the impact of postponement and childlessness, the effect on total fertility rates was between −0.01 and −0.12 points. Since 1987, the distribution of birth has declined for women under the age of 29 and increased for women aged 30 or more. However, start of childbearing after the age of 30 years was related to the completed fertility rate of less than two children per woman. The difference in completed fertility rate across educational groups was small.ConclusionsPostponement of first births was followed by decline in completed fertility rate. Increasing rate of childlessness, besides the mean age at first birth, was an important determinant for declined fertility rates, but the relation between women’s educational levels and the completed fertility rate was relatively weak.

Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint

2021 ◽  
pp. 096228022110558
Author(s):  
Steven D Lauzon ◽  
Wenle Zhao ◽  
Paul J Nietert ◽  
Jody D Ciolino ◽  
Michael D Hill ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Logistic Regression ◽  
Selection Bias ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Simulation Studies ◽  
Binary Outcome ◽  
Treatment Allocation ◽  
Analysis Phase ◽  
The Impact

Minimization is among the most common methods for controlling baseline covariate imbalance at the randomization phase of clinical trials. Previous studies have found that minimization does not preserve allocation randomness as well as other methods, such as minimal sufficient balance, making it more vulnerable to allocation predictability and selection bias. Additionally, minimization has been shown in simulation studies to inadequately control serious covariate imbalances when modest biased coin probabilities (≤0.65) are used. This current study extends the investigation of randomization methods to the analysis phase, comparing the impact of treatment allocation methods on power and bias in estimating treatment effects on a binary outcome using logistic regression. Power and bias in the estimation of treatment effect was found to be comparable across complete randomization, minimization, and minimal sufficient balance in unadjusted analyses. Further, minimal sufficient balance was found to have the most modest impact on power and the least bias in covariate-adjusted analyses. The minimal sufficient balance method is recommended for use in clinical trials as an alternative to minimization when covariate-adaptive subject randomization takes place.

Treatment Effect Accounting for Network Changes

2020 ◽  
pp. 1-25 ◽  
Author(s):  
Margherita Comola ◽  
Silvia Prina
Keyword(s):  
Treatment Effect ◽  
Peer Effects ◽  
Instrumental Variable ◽  
Estimation Procedure ◽  
Response Model ◽  
Informal Networks ◽  
Savings Accounts ◽  
Network Change ◽  
Before And After ◽  
The Impact

Networks may rewire in response to interventions. We propose a measure of the treatment effect when an intervention affects the structure of a social network. We develop a treatment-response model that incorporates dynamic peer effects and provide its identification conditions and the associated instrumental-variable strategy. We illustrate our estimation procedure using a panel dataset containing information on a financial network before and after a field experiment that randomized access to savings accounts. Results show that neglecting the network change results in underestimation of the impact of the intervention and the role played by informal networks through which the intervention diffuses.

scholarly journals Age at First Birth and Fathers' Subsequent Health: Evidence From Sibling and Twin Models

2007 ◽  
Vol 3 (2) ◽  
pp. 104-115 ◽  
Author(s):  
Tetyana Pudrovska ◽  
Deborah Carr
Keyword(s):  
Early Life ◽  
Well Being ◽  
The United States ◽  
Men’S Health ◽  
Men's Health ◽  
Age At First Birth ◽  
First Birth ◽  
Linear Effect ◽  
Independent Observations ◽  
Family Models

Using a sample of 540 siblings and twins from the National Survey of Midlife Development in the United States, this study examines the relationship between the age at which men become biological fathers and their subsequent health. The analysis includes both between-family models that treat brothers as independent observations and within-family models that account for unobserved genetic and early-life environmental endowments shared by brothers within families. Findings indicate that age at first birth has a positive, linear effect on men's health, and this relationship is not explained by the confounding influences of unobserved early-life characteristics. However, the effect of age at first birth on fathers' health is explained by men's socioeconomic and family statuses. Whereas most research linking birth timing to specific diseases focuses narrowly on biological mechanisms among mothers, this study demonstrates the importance of reproductive decisions for men's health and well-being.

scholarly journals Selection Bias When Using Instrumental Variable Methods to Compare Two Treatments But More Than Two Treatments Are Available

2016 ◽  
Vol 12 (1) ◽  
pp. 219-232 ◽  
Author(s):  
Ashkan Ertefaie ◽  
Dylan Small ◽  
James Flory ◽  
Sean Hennessy
Keyword(s):  
Selection Bias ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Health Improvement ◽  
Effect Estimate ◽  
Diabetic Patients ◽  
Data Set ◽  
The Health Improvement Network ◽  
Theoretical Evidence

Abstract Instrumental variable (IV) methods are widely used to adjust for the bias in estimating treatment effects caused by unmeasured confounders in observational studies. It is common that a comparison between two treatments is focused on and that only subjects receiving one of these two treatments are considered in the analysis even though more than two treatments are available. In this paper, we provide empirical and theoretical evidence that the IV methods may result in biased treatment effects if applied on a data set in which subjects are preselected based on their received treatments. We frame this as a selection bias problem and propose a procedure that identifies the treatment effect of interest as a function of a vector of sensitivity parameters. We also list assumptions under which analyzing the preselected data does not lead to a biased treatment effect estimate. The performance of the proposed method is examined using simulation studies. We applied our method on The Health Improvement Network (THIN) database to estimate the comparative effect of metformin and sulfonylureas on weight gain among diabetic patients.

scholarly journals Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness

2020 ◽  
Vol 8 (1) ◽  
pp. 182-208
Author(s):  
Nick Huntington-Klein
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Weighted Average ◽  
Small Sample ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Heterogeneous Effects ◽  
The Impact ◽  
Local Average

AbstractIn Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may vary across the sample. In this case, IV produces a local average treatment effect (LATE), and if monotonicity does not hold, then no effect of interest is identified. In this paper, I calculate the weighted average of treatment effects that is identified under general first-stage effect heterogeneity, which is generally not the average treatment effect among those affected by the instrument. I then describe a simple set of data-driven approaches to modeling variation in the effect of the instrument. These approaches identify a Super-Local Average Treatment Effect (SLATE) that weights treatment effects by the corresponding instrument effect more heavily than LATE. Even when first-stage heterogeneity is poorly modeled, these approaches considerably reduce the impact of small-sample bias compared to standard IV and unbiased weak-instrument IV methods, and can also make results more robust to violations of monotonicity. In application to a published study with a strong instrument, the preferred approach reduces error by about 19% in small (N ≈ 1, 000) subsamples, and by about 13% in larger (N ≈ 33, 000) subsamples.

Adverse Childhood Experiences, Early and Nonmarital Fertility, and Women’s Health at Midlife

2019 ◽  
Vol 60 (3) ◽  
pp. 309-325 ◽  
Author(s):  
Kristi Williams ◽  
Brian Karl Finch
Keyword(s):  
Women’S Health ◽  
Women's Health ◽  
Adverse Childhood Experiences ◽  
Family Policy ◽  
Later Life ◽  
Well Being ◽  
Age At First Birth ◽  
First Birth ◽  
Childhood Experiences ◽  
Adverse Childhood

Adverse childhood experiences (ACEs) have powerful consequences for health and well-being throughout the life course. We draw on evidence that exposure to ACEs shapes developmental processes central to emotional regulation, impulsivity, and the formation of secure intimate ties to posit that ACEs shape the timing and context of childbearing, which in turn partially mediate the well-established effect of ACEs on women’s later-life health. Analysis of 25 years of nationally representative panel data from the National Longitudinal Study of Youth (NLSY79; n = 3,893) indicates that adverse childhood experiences predict earlier age at first birth and greater odds of having a nonmarital first birth. Age and marital status at first birth partially mediate the effect of ACEs on women’s health at midlife. Implications for public health and family policy aimed at improving maternal and child well-being are discussed.

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