Interrupted time series (ITS) analysis is a valuable study design for evaluating the
effectiveness of population-level health interventions that have been implemented at a …

Population health interventions are essential to reduce health inequalities and tackle other
public health priorities, but they are not always amenable to experimental manipulation …

Interrupted time series analysis differs from most other intervention study designs in that it
involves a before-after comparison within a single population, rather than a comparison with …

In this article, I introduce the itsa command, which performs interrupted time-series analysis
for single-and multiple-group comparisons. In an interrupted time-series analysis, an …

Time series data are a collection of chronological observations which are generated by
several domains such as medical and financial fields. Over the years, different tasks such as …

Abstract Difference-in-difference (DD) methods are a common strategy for evaluating the
effects of policies or programs that are instituted at a particular point in time, such as the …

Difference-in-differences (DID) analysis is used widely to estimate the causal effects of
health policies and interventions. A critical assumption in DID is “parallel trends”: that pre …

Our current health research enterprise is painstakingly slow and cumbersome, and its
results seldom translate into practice. The slow pace of health research contributes to …

Objective Interrupted time series (ITS) designs are robust quasi-experimental designs
commonly used to evaluate the impact of interventions and programs implemented in …

Background Conditional cash transfer (CCT) programs provide poor families with cash
conditional on investments in health and education. Brazil's Bolsa Família program began in …