Regression Discontinuity for Causal Effect Estimation in Epidemiology

Oldenburg, Catherine E.; Moscoe, Ellen; Bärnighausen, Till

doi:10.1007/s40471-016-0080-x

Cited by 54 publications

(73 citation statements)

References 38 publications

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“…Our results were obtained using a regression discontinuity design, a quasi-experimental study design that enables causal inference without the strong assumptions required in most observational studies [ 26 – 28 , 35 , 40 , 43 , 59 – 61 ]. So long as values of CD4 measurements are not systematically manipulated by patients or providers, random variability in measured CD4 counts guarantees that patients will be similar (in expectation) in a small range on either side of the 350-cells/μl eligibility threshold [ 30 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

“…Our analytic strategy, which was based on a preexisting, single, well-known clinical practice threshold, followed best practices for the conduct and reporting of regression discontinuity designs [ 37 , 38 , 40 , 43 – 45 ]. Our primary analysis tested the null hypothesis, determined a priori, that immediate (rather than deferred) ART eligibility would have no effect on retention in care.…”

Section: Methodsmentioning

confidence: 99%

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Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa

et al. 2017

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BackgroundLoss to follow-up is high among HIV patients not yet receiving antiretroviral therapy (ART). Clinical trials have demonstrated the clinical efficacy of early ART; however, these trials may miss an important real-world consequence of providing ART at diagnosis: its impact on retention in care.Methods and findingsWe examined the effect of immediate (versus deferred) ART on retention in care using a regression discontinuity design. The analysis included all patients (N = 11,306) entering clinical HIV care with a first CD4 count between 12 August 2011 and 31 December 2012 in a public-sector HIV care and treatment program in rural South Africa. Patients were assigned to immediate versus deferred ART eligibility, as determined by a CD4 count < 350 cells/μl, per South African national guidelines. Patients referred to pre-ART care were instructed to return every 6 months for CD4 monitoring. Patients initiated on ART were instructed to return at 6 and 12 months post-initiation and annually thereafter for CD4 and viral load monitoring. We assessed retention in HIV care at 12 months, as measured by the presence of a clinic visit, lab test, or ART initiation 6 to 18 months after initial CD4 test. Differences in retention between patients presenting with CD4 counts just above versus just below the 350-cells/μl threshold were estimated using local linear regression models with a data-driven bandwidth and with the algorithm for selecting the bandwidth chosen ex ante. Among patients with CD4 counts close to the 350-cells/μl threshold, having an ART-eligible CD4 count (<350 cells/μl) was associated with higher 12-month retention than not having an ART-eligible CD4 count (50% versus 32%), an intention-to-treat risk difference of 18 percentage points (95% CI 11 to 23; p < 0.001). The decision to start ART was determined by CD4 count for one in four patients (25%) presenting close to the eligibility threshold (95% CI 20% to 31%; p < 0.001). In this subpopulation, having an ART-eligible CD4 count was associated with higher 12-month retention than not having an ART-eligible CD4 count (91% versus 21%), a complier causal risk difference of 70 percentage points (95% CI 42 to 98; p < 0.001). The major limitations of the study are the potential for limited generalizability, the potential for outcome misclassification, and the absence of data on longer-term health outcomes.ConclusionsPatients who were eligible for immediate ART had dramatically higher retention in HIV care than patients who just missed the CD4-count eligibility cutoff. The clinical and population health benefits of offering immediate ART regardless of CD4 count may be larger than suggested by clinical trials.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa

et al. 2017

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“…This analysis uses a regression discontinuity design to investigate the effect of selective secondary schooling on health [34,35]. In such a design, allocation to an exposure depends on the value of a continuous variable (the forcing variable).…”

Section: Methodsmentioning

confidence: 99%

“…The difference in outcome due to selective schooling is then the difference of the intercepts at the cut-point. To account for the fuzziness of the score-assignment relationship, we estimated the effect of selective schooling as the ratio of the effect given the probability of attendance (equivalent to local average treatment effect estimation in randomised controlled trials) [35,36]. To do so, we used a two-stage least squares method, where the first stage was a model of the probability of attending a selective school contingent on exam score, the cut-points and their interaction.…”

Section: Primary Modelmentioning

confidence: 99%

The long-term health effects of attending a selective school: a natural experiment

Butler

Black

Craig

et al. 2020

BMC Med

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Background: Education is widely associated with better physical and mental health, but isolating its causal effect is difficult because education is linked with many socioeconomic advantages. One way to isolate education's effect is to consider environments where similar students are assigned to different educational experiences based on objective criteria. Here we measure the health effects of assignment to selective schooling based on test score, a widely debated educational policy. Methods: In 1960s Britain, children were assigned to secondary schools via a test taken at age 11. We used regression discontinuity analysis to measure health differences in 5039 people who were separated into selective and non-selective schools this way. We measured selective schooling's effect on six outcomes: mid-life self-reports of health, mental health, and life limitation due to health, as well as chronic disease burden derived from hospital records in mid-life and later life, and the likelihood of dying prematurely. The analysis plan was accepted as a registered report while we were blind to the health outcome data. Results: Effect estimates for selective schooling were as follows: self-reported health, 0.1 worse on a 4-point scale (95%CI − 0.2 to 0); mental health, 0.2 worse on a 16-point scale (− 0.5 to 0.1); likelihood of life limitation due to health, 5 percentage points higher (− 1 to 10); mid-life chronic disease diagnoses, 3 fewer/100 people (− 9 to + 4); late-life chronic disease diagnoses, 9 more/100 people (− 3 to + 20); and risk of dying before age 60, no difference (− 2 to 3 percentage points). Extensive sensitivity analyses gave estimates consistent with these results. In summary, effects ranged from 0.10-0.15 standard deviations worse for self-reported health, and from 0.02 standard deviations better to 0.07 worse for records-derived health. However, they were too imprecise to allow the conclusion that selective schooling was detrimental. Conclusions: We found that people who attended selective secondary school had more advantaged economic backgrounds, higher IQs, higher likelihood of getting a university degree, and better health. However, we did not find that selective schooling itself improved health. This lack of a positive influence of selective secondary schooling on health was consistent despite varying a wide range of model assumptions. Background Education is associated with physical and mental health and life expectancy in countries around the world, and the role of education in improving health is a focus of diverse research and global policy [1-6]. However, there is vigorous debate about the causal effects of educational reforms. Because children from socioeconomically advantaged families are more likely to attend betterresourced schools, be educated longer, and gain more qualificatio ns, the association between education and health may simply reflect background advantages rather than effects of educational policy itself [4, 7-9].

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“…Next, although it is not possible to directly test for differences in unobservable characteristics, we follow Lee and Lemieux (2010) and Oldenburg et al (2016) and examine whether the density of the assignment variable (the relative household water use efficiency) is continuous. If households exert control over the assignment, there should be a discontinuity in this density at the threshold.…”

Section: Regression Discontinuity Designmentioning

confidence: 99%

Learning to live within your (water) budget: Evidence from allocation-based rates

Pérez-Urdiales

Baerenklau

2019

Resource and Energy Economics

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As allocation-based rates (ABR) become more widely adopted in the residential water sector, it is important to determine whether consumers interpret the water budget as a target for water use, or if they simply respond to the increasing block design. This paper exploits a natural experiment created when ABR were adopted by a California water district, that allows us to identify households with high water use as a treatment group. We find evidence of heterogeneous customer responses, and policy-induced behavioral changes that imply customers adjust their target level of water use based on the information signal provided by the water budget.

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Regression Discontinuity for Causal Effect Estimation in Epidemiology

Cited by 54 publications

References 38 publications

Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa

Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa

The long-term health effects of attending a selective school: a natural experiment

Learning to live within your (water) budget: Evidence from allocation-based rates

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