Published in Biometrics
In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgroups and predicting whether a patient subpopulation might benefit from a particular treatment. Convent...
This article reviews recent advances in causal inference relevant to sociology. We focus on a selective subset of contributions aligning with four broad topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We describe how machine learning, as an ...
This article reviews recent advances in causal inference relevant to sociology. We focus on a selective subset of contributions aligning with four broad topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We describe how machine learning, as an ...
Published in Annual Review of Statistics and its Application
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 representa...
Published in Health economics
Even though prior research has investigated the relationship between same-sex partnership recognition policies and health outcomes, the impact of same-sex marriage laws on sexually transmitted infections has not received much attention. Using state-level panel data from 2000 to 2019, I show that marriage equality legislation decreases the spread of...
Published in Biometrical journal. Biometrische Zeitschrift
The identification and estimation of heterogeneous treatment effects in biomedical clinical trials are challenging, because trials are typically planned to assess the treatment effect in the overall trial population. Nevertheless, the identification of how the treatment effect may vary across subgroups is of major importance for drug development. I...
Published in Clinical trials (London, England)
Secondary analyses of randomized clinical trials often seek to identify subgroups with differential treatment effects. These discoveries can help guide individual treatment decisions based on patient characteristics and identify populations for which additional treatments are needed. Traditional analyses require researchers to pre-specify potential...
Published in World psychiatry : official journal of the World Psychiatric Association (WPA)
It is common experience for practising psychiatrists that individuals with schizophrenia vary markedly in their symptomatic response to antipsychotic medication. What is not clear, however, is whether this variation reflects variability of medication-specific effects (also called "treatment effect heterogeneity"), as opposed to variability of non-s...
Published in Journal of the American Academy of Child and Adolescent Psychiatry
To examine whether anti-Black cultural racism moderates the efficacy of psychotherapy interventions among youth. A subset of studies from a previous meta-analysis of 5 decades of youth psychotherapy randomized controlled trials was analyzed. Studies were published in English between 1963 and 2017 and identified through a systematic search. The 194 ...
Published in Clinical trials (London, England)
Subgroup analyses are frequently used to assess heterogeneity of treatment effects in randomised clinical trials. Inconsistent, improper and incomplete implementation, reporting and interpretation have been identified as ongoing challenges. Further, subgroup analyses were frequently criticised because of unreliable or potentially misleading results...
