Are You Looking for Madame or Maman? Role Playing the French Professor and the Mother in Academia

Since becoming a parent, the last six years have been the most exhilarating and exhausting in the personal and professional spheres of my life. My heart was brimming with love following the birth of my first daughter while my brain was sending me stress signals to begin preparing for a tenure-track position in French at a small liberal arts college the next month. After the birth of my second daughter, and a year marked by injury, illness, and applying for tenure, I began to feel a growing sense of urgency to connect with other academic mothers.In this article, I share my personal journey as a female academic and mother with the aim of contributing to a wider discussion about maternal health and parenting in the academy. I reflect on the tensions originating from the roles I inhabit as both professor and mother—roles that have appeared to be at odds with one another from my job search through the tenure process. I have come to realize that I am happiest when I am able to see the various facets of my identity overlap in ways that invite knowledge and experience to nurture each other. I have sought to make my dual roles as professor and parent visible to my students by narrating various experiences raising my daughters in a bilingual home, by bringing my daughters regularly to campus, and by living in France together as a family while working with study abroad programs

A Review of Generalizability and Transportability

When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and limitations for estimating causal effects in a target population. Estimates from randomized data may have internal validity but are often not representative of the target population. Observational data may better reflect the target population, and hence be more likely to have external validity, but are subject to potential bias due to unmeasured confounding. While much of the causal inference literature has focused on addressing internal validity bias, both internal and external validity are necessary for unbiased estimates in a target population. This paper presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations.Comment: 30 pages, 3 figure

A Note on Risk Prediction for Case-Control Studies

We introduce a new method for prediction in case-control study designs, which is a simple extension of the work by van der Laan (2008). Case-control samples are biased since the proportion of cases in the sample is not the same as the population of interest. The case-control weighting for prediction proposed in this paper relies on knowledge of the true incidence probability P(Y=1) to eliminate the bias of the sampling design. In many practical settings, case-control weighting will outperform an existing method for prediction, intercept adjustment

Causal Inference for Nested Case-Control Studies using Targeted Maximum Likelihood Estimation

A nested case-control study is conducted within a well-defined cohort arising out of a population of interest. This design is often used in epidemiology to reduce the costs associated with collecting data on the full cohort; however, the case control sample within the cohort is a biased sample. Methods for analyzing case-control studies have largely focused on logistic regression models that provide conditional and not marginal causal estimates of the odds ratio. We previously developed a Case-Control Weighted Targeted Maximum Likelihood Estimation (TMLE) procedure for case-control study designs, which relies on the prevalence probability q0. We propose the use of Case-Control Weighted TMLE in nested case-control samples, with either known q0 or q0 estimated from the full cohort. We show that this procedure is efficient for a reduced data structure, the data structure where covariate information is not collected or available on non-case-control subjects, and recognize that it is not fully efficient for the full data. However, in many common scenarios, the full data is not available, thus our procedure is maximally efficient for the data given. For statistical inference, we view the nested case-control sample as a missing data problem (Robins et al., 1994). Case-Control Weighted TMLE on the reduced data structure is illustrated in simulations for cohorts with and without right censoring and also effect modification in randomized controlled trials

Conditional Cross-Design Synthesis Estimators for Generalizability in Medicaid

While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of interest. However, few generalizability approaches exist for estimating causal quantities in a target population when the target population is not well-represented by a randomized study but is reflected when additionally incorporating observational data. To generalize to a target population represented by a union of these data, we propose a class of novel conditional cross-design synthesis estimators that combine randomized and observational data, while addressing their respective biases. The estimators include outcome regression, propensity weighting, and double robust approaches. All use the covariate overlap between the randomized and observational data to remove potential unmeasured confounding bias. We apply these methods to estimate the causal effect of managed care plans on health care spending among Medicaid beneficiaries in New York City.Comment: 25 pages, 4 figures; supplement of 31 pages, 12 figures, and 4 table