Week 10 - Multi-Level

Question 1

What is the main advantage of pooling estimated treatment effects across clusters over marginal estimation of treatment effects across clusters?

Answer 1

Pooling removes the confounding effects of both observed and unobserved cluster-level confounders.

Question 2

What is the main limitation of pooling estimated treatment effects across clusters?

Answer 2

The cluster sizes needs to be large enough to allow adequate common support of propensity score distributions within clusters.

Question 3

When is marginal estimation of treatment effects across clusters recommended instead of pooling estimated treatment effects across clusters?

Answer 3

When cluster sizes are not large for adequate common support, marginal estimation of the treatment effect across clusters should be used.

Question 4

What is the difference between the propensity score model specification for an individual-level treatment and the propensity score model specification for a cluster-level treatment?

Answer 4

PS model for indv. level requires taking into account both indv. and cluster level covariates. However, PS models for cluster level requires cluster level covariates and higher level.

Question 5

Why is the stable unit treatment value assumption particularly vulnerable to violation when individuals are nested within clusters?

Answer 5

Because of interdependent factors, such as close proximity, regular communication, shared resources, etc…

Question 6

What are two types of models that can be used for propensity score estimation with multilevel data?

Answer 6

• multilevel logistic regression

* logistic regression with fixed cluster effects

Question 7

How can variation in individual-level covariate effects on treatment assignment across clusters be accounted for in propensity score models?

Answer 7

1) If multilevel logistic regression models are used to estimate propensity scores, include random slopes of covariates in the model;
2) If logistic regression with fixed cluster effects is used, include covariate by cluster interactions in the model;
3) Ignore the variation of covariate effects across clusters.

Question 8

What is one advantage of a logistic regression model with fixed cluster effects over a multilevel logistic regression model for propensity score estimation?

Answer 8

1) A LRM removes all confounding due to cluster-level covariates without requiring the inclusion of any cluster-level covariates in the model (Arpino & Mealli, 2011), while the multilevel logistic regression model requires that the research identifies and measures the true confounders at the cluster level.
2) A LRM allows any correlations between individual-level predictors and the fixed cluster effects, while the multilevel logistic regression model assumes that they are uncorrelated.

Question 9

What is one advantage of a multilevel logistic regression model over a logistic regression model with fixed cluster effects for propensity score estimation?

Answer 9

MLR doesn’t suffer from convergence difficulty if the number of clusters is large.

MLR doesn’t suffer if the dataset has many small clusters.

Question 10

What approaches can be used for marginal estimation of treatment effects across clusters that account for cluster effects on the outcome?

Answer 10

• multilevel model with random intercept

* random slope of treatment