Wednesday, January 11, 2023

Reducing the bias from on the inverse probability of treatment weighting (IPTW) – Healthcare Economist

https://ift.tt/BbTQ85i

When using observational data, assignment to a treatment group is non-random and causal inference may be difficult. One common approach to addressing this is propensity score weighting where the propensity score is the probability that a person is assigned to the treatment arm given their observable characteristics. This propensity is often estimated using a logistic regression of individual characteristics on a binary variable of whether the individual received the treatment or not. Propensity scores are often used that to by applying inverse probability of treatment weighting (IPTW) estimators to obtain treatment effects adjusting for known confounders.

A paper by Xu et al. (2010) shows that using the IPTW approach may lead to an overestimate of the pseudo-sample size and increase the likelihood of a type I error (i.e., rejecting the null hypothesis when it is actually true). The authors claim that robust variance estimators can address this problem but only work well with large sample sizes. Instead, Xu and co-authors proposed using standardized weights in the IPTW as a simple and easy to implement strategy. Here is how this works.

The IPTW approach simply examines the difference between the treated and untreated group after applying the IPTW weighting. Let the frequency that someone is treated be:

where n1 is the number of people treated and N is the total sample size. Let z=1 if the person is treated in the data and z=0 if the person is not treated. Assume that each person has a vector of patient characteristics, X, that impact the likelihood of receiving treatment. Then one calculate the probability of treatment as:

Under standard IPTW, the weights used would be:

Xu and co-authors create a simulation to show that the type 1 error is too high–often 15% to 40%. To correct this, one could use standardized weights (SW) as follows:

The former is used for the treated population (i.e., z=1) and the latter is used in the untreated population (z=0). The authors show that under the standardized weights, the rate of type 1 errors is approximately 5% as intended. In fact, the authors also show that standardized weighting often outperforms robust variance estimators as well for estimating main effects.

For more information, you can read the full article here.


Source link

source https://financetin.com/reducing-the-bias-from-on-the-inverse-probability-of-treatment-weighting-iptw-healthcare-economist/financetin.com

Related Posts:

  • Driving the Insanely Maxed Out Mini GPThe new Mini GP, which will only be sold for one year, is already hard enough to pin down—should you be lucky enough to get your hands on one through a dealer right now. It’s harder to label, written up elsewhere as, in no sh… Read More
  • The 5 Best Helmets To Get For The Active Man On The GoEvery man that leads an active life shares one thing. They need to have a helmet in their life. That way you can go out and worry less about getting into an accident that can cause a head injury. As the workout industry is bi… Read More
  • Purdue to Plead Guilty to Opioid-Related Crimes The company will plead guilty to three counts, including conspiracy to defraud the United States and violating federal anti-kickback laws, the Justice Department officials to the AP. from WebMD Healthhttps://img.webmd… Read More
  • Illness-related fatigue: More than just feeling tiredA common refrain during the COVID-19 pandemic is, “I’m so tired.” After months of adjusted living and anxiety, people are understandably weary. Parents who haven’t had a break from their kids are worn out. Those trying to jug… Read More
  • Scientists Discover New Organs in the Throat If confirmed, these glands could be the first of their kind discovered in about 300 years. Modern anatomy books show only three types of salivary glands, a set near the ears, another below the jaw and a third under the tongu… Read More

0 comments:

Post a Comment