Survivor-complier effects in the presence of selection on treatment, with application to a study of prompt ICU admission

Pre-treatment selection or censoring (`selection on treatment') can occur when two treatment levels are compared ignoring the third option of neither treatment, in `censoring by death' settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estimating a new survivor-complier causal effect. Although this effect is generally not identified under standard IV assumptions, it is possible to construct sharp bounds. We derive these bounds and give a corresponding data-driven sensitivity analysis, along with nonparametric yet efficient estimation methods. Importantly, our approach allows for high-dimensional confounding adjustment, and valid inference even after employing machine learning. Incorporating covariates can tighten bounds dramatically, especially when they are strong predictors of the selection process. We apply the methods in a UK cohort study of critical care patients to examine the mortality effects of prompt admission to the intensive care unit, using ICU bed availability as an instrument

The Effect of Minimum Wages on Labor Market Outcomes: County-Level Estimates from the Restaurant-and-Bar Sector

We use county-level data on employment and earnings in the restaurant-and-bar sector to evaluate the impact of minimum wage changes on low-wage labor markets. Our empirical approach is similar to the literature that has used state-level panel data to estimate minimum-wage impacts, with the difference that we focus on a particular sector rather than demographic group. Our estimated models are consistent with a simple competitive model of the restaurant-and-bar labor market in which supply-and-demand factors affect both the equilibrium outcome and the probability that a minimum wage will be binding in any given time period. Our evidence does not suggest that minimum wages reduce employment in the overall restaurant-and-bar sector, after controls for trends in sector employment at the county level are incorporated in the model. Employment in this sector appears to exhibit a downward long-term trend in states that have increased their minimum wages relative to states that have not, thereby predisposing fixed-effects estimates towards finding negative employment effects.

The Effect of Minimum Wages on Wages and Employment: County-Level Estimates for the United States

We use county-level data on employment and earnings in the restaurant-and-bar sector to evaluate the impact of minimum wage changes on low-wage labor markets. Our empirical approach is similar to the literature that has used state-level panel data to estimate minimum-wage impacts, with the difference that we focus on a particular sector rather than demographic group. Our estimated models are consistent with a simple competitive model of the restaurant-and-bar labor market in which supply-and-demand factors affect both the equilibrium outcome and the probability that a minimum wage will be binding in any given time period. Our evidence does not suggest that minimum wages reduce employment in the overall restaurant-and-bar sector, after controls for trends in sector employment at the county level are incorporated in the model. Employment in this sector appears to exhibit a downward long-term trend in states that have increased their minimum wages relative to states that have not, thereby predisposing fixed-effects estimates towards finding negative employment effects.county-level data, wages and employment, minimum wages, spatial trends

A mixed model approach for structured hazard regression

The classical Cox proportional hazards model is a benchmark approach to analyze continuous survival times in the presence of covariate information. In a number of applications, there is a need to relax one or more of its inherent assumptions, such as linearity of the predictor or the proportional hazards property. Also, one is often interested in jointly estimating the baseline hazard together with covariate effects or one may wish to add a spatial component for spatially correlated survival data. We propose an extended Cox model, where the (log-)baseline hazard is weakly parameterized using penalized splines and the usual linear predictor is replaced by a structured additive predictor incorporating nonlinear effects of continuous covariates and further time scales, spatial effects, frailty components, and more complex interactions. Inclusion of time-varying coefficients leads to models that relax the proportional hazards assumption. Nonlinear and time-varying effects are modelled through penalized splines, and spatial components are treated as correlated random effects following either a Markov random field or a stationary Gaussian random field. All model components, including smoothing parameters, are specified within a unified framework and are estimated simultaneously based on mixed model methodology. The estimation procedure for such general mixed hazard regression models is derived using penalized likelihood for regression coefficients and (approximate) marginal likelihood for smoothing parameters. Performance of the proposed method is studied through simulation and an application to leukemia survival data in Northwest England