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Analysis of Vaccine Studies and Causal Inference
Published in Leonhard Held, Niel Hens, Philip O’Neill, Jacco Wallinga, Handbook of Infectious Disease Data Analysis, 2019
An approach as yet underutilized in public health (Bor et al., 2014; Moscoe et al., 2015) is the quasi-experimental design known as the regression discontinuity design (Thistlewaite and Campbell, 1960). In particular, the design might be useful for evaluating vaccines in some situations when randomized studies are not feasible or ethical. The regression discontinuity design allows for causal inference about the effects of interventions when certain conditions hold. The main idea is that an intervention, such as a vaccine, would be administered to a group based on an arbitrary continuous cut-off, such as age. Then we might assume that those just below the cutoff who do not receive vaccine would be comparable to those just above the cutoff who do receive vaccine. The approach has been used widely in economics studies where randomization is often unfeasible. It seems not yet to have been developed in the context of evaluating vaccines. Aronow et al (2016) consider the regression discontinuity design under interference using a local randomization approach, where the causal estimands are related to those presented in Section 8.3.
Effects of innovation and insurance coverage on price elasticity of demand for prescription drugs: some empirical lessons in pharmacoeconomics
Published in Journal of Medical Economics, 2020
In another Medicare study, Kaliski17 posed the question: Does insurance for treatment crowd out prevention? Finding for the affirmative, Kaliski initially developed a regression discontinuity design (i.e. insurance for treatment alone) and supplied evidence from diabetics’ use of insulin. Insulin is expensive, has high coinsurance rates, has no generic version, and is complicated to use. It has been covered for Medicare enrollees only since Part D was added in 2006. Comparing pre- and post-insulin subsidies, the study shows that, prior to 2006, up to 30 percent of female diabetics who used insulin stopped using it once they turned 65 and became eligible for insurance through Medicare. Introduction of more generous insulin subsidies under Part D (post-2006) offset that effect more than one-for-one. This generated savings of up to $487 million per year in foregone costs of care and 4.6 p.p. in foregone heart disease among women.
Bayesian change-point modelling of the effects of 3-points-for-a-win rule in football
Published in Journal of Applied Statistics, 2020
Gebrenegus Ghilagaber, Parfait Munezero
Some researchers have attempted to measure the effect of the reform in various ways and with diverging results. For instance, Brocas and Carrillo [4] analyse the dynamics of the game strategies of teams using game theory and observe that, under the 3pfaw rule, teams tend to play more defensively rather than playing offensive football. Dilger and Geyer [7], on the other hand, apply regression model on data from the German league and conclude that the introduction of the 3pfaw rule has significantly increased the mean goals as well as the proportion of decided matches. Such observation is supported by Moschini [15] who uses a game-theoretic model to investigate the effects of the 3pfaw rules in 35 different countries and concludes that the rule has led to statistically significant increase in the number of expected goals and decrease in the number of drawn matches. In contrast, Hon and Parinduri [12] use regression discontinuity design on data from the German league and find no evidence that 3pfaw makes the games more decisive, increases the number of goals, or decreases goal differences. Skinner and Freeman [21] use Bayesian methods to determine how often the team with greater ability actually wins a football match. Anderson and Sally's [1] analyse various aspects of football games from top-levels leagues. One of their main findings is that it is statistically more valuable to prevent a goal than to score one.
What counts as evidence? Swimming against the tide: Valuing both clinically informed experimentally controlled case series and randomized controlled trials in intervention research
Published in Evidence-Based Communication Assessment and Intervention, 2019
Wendy Best, Wei Ping Sze, Anne Edmundson, Lyndsey Nickels
The goals of this paper are to consider the strengths and limitations of RCTs and single cases/case series in demonstrating effectiveness and/or helping understand the mechanisms of change and establish causal relationships between intervention and outcome. We include examples of intervention studies with adults with aphasia and children with primary speech and language needs, and touch upon interventions with people with other communication impairments. The arguments have strong links with, and applicability to, other health-related intervention research where the needs are complex and heterogeneous (e.g. psychiatry, psychotherapy). We do not, however, provide detail on the specifics of the designs debated, as these are discussed in detail elsewhere (e.g. Ebbels, 2017; Nickels, Best, & Howard, 2015; Thompson, 2006), as are guidelines for reporting these studies (e.g. CONSORT Extension for N-of-1 Trials (CENT; Shamseer et al., 2015); the risk-of-bias in N-of-1 trials (RoBiNT) scale (Tate et al., 2016, 2015); and the template for intervention description and replication (TIDieR; Hoffmann et al., 2014)). We also acknowledge that there are alternative designs, also aiming to establish treatment effectiveness, which are not covered here, including, for example, regression discontinuity design (see, for an example from the SLT/P field, Dyson, Solity, Best, & Hulme, 2018).