1
|
Liu B, Ji S, Zhu Z. Does higher education matter for health in the UK? SSM Popul Health 2024; 25:101642. [PMID: 38440105 PMCID: PMC10909631 DOI: 10.1016/j.ssmph.2024.101642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 03/06/2024] Open
Abstract
Using six sweeps of data from the 1958 British National Child Development Study (NCDS), we employ a quasi-parametric approach of propensity score matching to estimate the impacts of higher education attainment on a wide range of health-related outcomes for cohorts at ages 33, 42, and 50. The non-pecuniary benefits of higher education on health are substantial. Cohorts with higher levels of education are more likely to report better health, maintain a healthy weight, refrain from smoking, exhibit a lower frequency of alcohol consumption, and are less likely to be obese. The effects on self-reported health, body mass index (BMI), drinking alcohol increase with age, but continuously decrease with smoking frequency. When considering gender heterogeneity, higher education has a more significant effect on BMI and the likelihood of obesity for males, while it has a greater impact on self-reported health, drinking alcohol, and smoking frequencies for females. Furthermore, we find no significant evidence that higher education reduces the likelihood of depression. The results of the Rosenbaum bounds sensitivity analysis suggest that, although our overall results demonstrate robustness, there may still be unobserved hidden bias in the relationship between higher education and self-reported health.
Collapse
Affiliation(s)
| | - Sisi Ji
- Cardiff Business School, Cardiff University, Cardiff, CF10 3EU, UK
| | - Zheyi Zhu
- Cardiff School of Management, Cardiff Metropolitan University, Cardiff, CF5 2YB, UK
| |
Collapse
|
2
|
Heng S, Kang H, Small DS, Fogarty CB. Increasing power for observational studies of aberrant response: An adaptive approach. J R Stat Soc Series B Stat Methodol 2021. [DOI: 10.1111/rssb.12424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Siyu Heng
- University of Pennsylvania Philadelphia PA USA
| | | | | | | |
Collapse
|
3
|
Lee K, Small DS, Dominici F. Discovering Heterogeneous Exposure Effects Using Randomization Inference in Air Pollution Studies. J Am Stat Assoc 2021; 116:569-580. [DOI: 10.1080/01621459.2020.1870476] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Kwonsang Lee
- Department of Statistics, Sungkyunkwan University, Seoul, Republic of Korea
| | - Dylan S. Small
- Department of Statistics, University of Pennsylvania, Philadelphia, PA
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| |
Collapse
|
4
|
Rosenbaum PR. A conditional test with demonstrated insensitivity to unmeasured bias in matched observational studies. Biometrika 2020. [DOI: 10.1093/biomet/asaa032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Summary
In an observational study matched for observed covariates, an association between treatment received and outcome exhibited may indicate not an effect caused by the treatment, but merely some bias in the allocation of treatments to individuals within matched pairs. The evidence that distinguishes moderate biases from causal effects is unevenly dispersed among possible comparisons in an observational study: some comparisons are insensitive to larger biases than others. Intuitively, larger treatment effects tend to be insensitive to larger unmeasured biases, and perhaps matched pairs can be grouped using covariates, doses or response patterns so that groups of pairs with larger treatment effects may be identified. Even if an investigator has a reasoned conjecture about where to look for insensitive comparisons, that conjecture might prove mistaken, or, when not mistaken, it might be received sceptically by other scientists who doubt the conjecture or judge it to be too convenient in light of its success with the data at hand. In this article a test is proposed that searches for insensitive findings over many comparisons, but controls the probability of falsely rejecting a true null hypothesis of no treatment effect in the presence of a bias of specified magnitude. An example is studied in which the test considers many comparisons and locates an interpretable comparison that is insensitive to larger biases than a conventional comparison based on Wilcoxon’s signed rank statistic applied to all pairs. A simulation examines the power of the proposed test. The method is implemented in the R package dstat, which contains the example and reproduces the analysis.
Collapse
Affiliation(s)
- P R Rosenbaum
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A
| |
Collapse
|
5
|
Pimentel SD, Page LC, Lenard M, Keele L. Optimal multilevel matching using network flows: An application to a summer reading intervention. Ann Appl Stat 2018. [DOI: 10.1214/17-aoas1118] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
6
|
Ertefaie A, Small DS, Rosenbaum PR. Quantitative Evaluation of the Trade-Off of Strengthened Instruments and Sample Size in Observational Studies. J Am Stat Assoc 2018. [DOI: 10.1080/01621459.2017.1305275] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Ashkan Ertefaie
- Department of Biostatistics and Computational Biology at the University of Rochester, Rochester, NY
| | - Dylan S. Small
- Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia, PA
| | - Paul R. Rosenbaum
- Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia, PA
| |
Collapse
|
7
|
Lee K, Small DS, Rosenbaum PR. A powerful approach to the study of moderate effect modification in observational studies. Biometrics 2018; 74:1161-1170. [PMID: 29738603 DOI: 10.1111/biom.12884] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 03/01/2018] [Accepted: 03/01/2018] [Indexed: 11/28/2022]
Abstract
Effect modification means the magnitude or stability of a treatment effect varies as a function of an observed covariate. Generally, larger and more stable treatment effects are insensitive to larger biases from unmeasured covariates, so a causal conclusion may be considerably firmer if this pattern is noted if it occurs. We propose a new strategy, called the submax-method, that combines exploratory, and confirmatory efforts to determine whether there is stronger evidence of causality-that is, greater insensitivity to unmeasured confounding-in some subgroups of individuals. It uses the joint distribution of test statistics that split the data in various ways based on certain observed covariates. For L binary covariates, the method splits the population L times into two subpopulations, perhaps first men and women, perhaps then smokers and nonsmokers, computing a test statistic from each subpopulation, and appends the test statistic for the whole population, making <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>2</mml:mn> <mml:mi>L</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn></mml:math> test statistics in total. Although L binary covariates define <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>2</mml:mn> <mml:mi>L</mml:mi></mml:msup> </mml:math> interaction groups, only <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>2</mml:mn> <mml:mi>L</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn></mml:math> tests are performed, and at least <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>L</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn></mml:math> of these tests use at least half of the data. The submax-method achieves the highest design sensitivity and the highest Bahadur efficiency of its component tests. Moreover, the form of the test is sufficiently tractable that its large sample power may be studied analytically. The simulation suggests that the submax method exhibits superior performance, in comparison with an approach using CART, when there is effect modification of moderate size. Using data from the NHANES I epidemiologic follow-up survey, an observational study of the effects of physical activity on survival is used to illustrate the method. The method is implemented in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>R</mml:mi></mml:math> package <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>submax</mml:mi></mml:math> which contains the NHANES example. An online Appendix provides simulation results and further analysis of the example.
Collapse
Affiliation(s)
- Kwonsang Lee
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A
| | - Dylan S Small
- Department of Statistics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A
| | - Paul R Rosenbaum
- Department of Statistics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A
| |
Collapse
|
8
|
Li X, Ding P. General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference. J Am Stat Assoc 2018. [DOI: 10.1080/01621459.2017.1295865] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Xinran Li
- Department of Statistics, Harvard University, Cambridge, MA
| | - Peng Ding
- Department of Statistics, University of California, Berkeley, CA
| |
Collapse
|
9
|
Zubizarreta JR, Keele L. Optimal Multilevel Matching in Clustered Observational Studies: A Case Study of the Effectiveness of Private Schools Under a Large-Scale Voucher System. J Am Stat Assoc 2017. [DOI: 10.1080/01621459.2016.1240683] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- José R. Zubizarreta
- Decision, Risk and Operations Division, and Statistics Department, Columbia University, New York, NY
| | - Luke Keele
- McCourt School of Public Policy and Department of Government, Georgetown University, Washington, DC
| |
Collapse
|
10
|
Rosenbaum PR, Small DS. An adaptive Mantel-Haenszel test for sensitivity analysis in observational studies. Biometrics 2016; 73:422-430. [PMID: 27704529 DOI: 10.1111/biom.12591] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 08/01/2016] [Accepted: 08/01/2016] [Indexed: 11/27/2022]
Abstract
In a sensitivity analysis in an observational study with a binary outcome, is it better to use all of the data or to focus on subgroups that are expected to experience the largest treatment effects? The answer depends on features of the data that may be difficult to anticipate, a trade-off between unknown effect-sizes and known sample sizes. We propose a sensitivity analysis for an adaptive test similar to the Mantel-Haenszel test. The adaptive test performs two highly correlated analyses, one focused analysis using a subgroup, one combined analysis using all of the data, correcting for multiple testing using the joint distribution of the two test statistics. Because the two component tests are highly correlated, this correction for multiple testing is small compared with, for instance, the Bonferroni inequality. The test has the maximum design sensitivity of two component tests. A simulation evaluates the power of a sensitivity analysis using the adaptive test. Two examples are presented. An R package, sensitivity2x2xk, implements the procedure.
Collapse
Affiliation(s)
- Paul R Rosenbaum
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A
| | - Dylan S Small
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A
| |
Collapse
|
11
|
Ritz C, Pilmann Laursen R, Trab Damsgaard C. Simultaneous inference for multilevel linear mixed models-with an application to a large-scale school meal study. J R Stat Soc Ser C Appl Stat 2016. [DOI: 10.1111/rssc.12161] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
12
|
|
13
|
Hansen BB, Rosenbaum PR, Small DS. Clustered Treatment Assignments and Sensitivity to Unmeasured Biases in Observational Studies. J Am Stat Assoc 2014. [DOI: 10.1080/01621459.2013.863157] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
14
|
Zubizarreta JR, Paredes RD, Rosenbaum PR. Matching for balance, pairing for heterogeneity in an observational study of the effectiveness of for-profit and not-for-profit high schools in Chile. Ann Appl Stat 2014. [DOI: 10.1214/13-aoas713] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
15
|
Hsu JY, Small DS, Rosenbaum PR. Effect Modification and Design Sensitivity in Observational Studies. J Am Stat Assoc 2013. [DOI: 10.1080/01621459.2012.742018] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
|