The final step of the MPS analyses uses the
IPTW weights multiplied by the sample weights to weight an OLS regression predicting the six health indicators.
Prior to
IPTW, treated patients trended toward more improvement after 1 year, but with "unimpressive absolute values."
With
IPTW adjustment made, the differences in pain -related comorbidities went away.
Therefore, to correct for self-selection bias, we used
IPTW (Hirano and Imbens 2001) as a probability weight in logistic regression models with covariance control for unbalanced treatment-control samples.
With
IPTW adjustments made, the differences in pain-related comorbidities went away.
the exposure-outcome association is estimated in a regression model that is weighted using the
IPTWs.
When using
IPTW, the results indicated that the effect of in-home care was more significant for gains in development than was the effect of center care.
Inverse probabilities of treatment weights (
IPTWs) were created on the basis of the inverse of the predicted probability of a patient receiving the treatment that was actually received (the above-mentioned ESA dose categories), given the baseline and time-varying covariates.
When the
IPTW adjustment was included as a covariate in the model, the adjusted HR s for the risk of CV death and MI were 0.41 (95% CI : 0.21-0.63, P = 0.029) and 0.29 (95% CI : 0.08-0.92, P = 0.037), respectively [Table 3].
Zero-truncated negative binomial models with
IPTW were used to compare utilization among rural and urban children with at least one encounter for PT/OT, psychiatry/psychology, or other outpatient service.
We did this by performing
IPTW analyses comparing postdischarge cost and utilization between patients with and without an MRSA infection during an initial hospitalization.
IPTW were calculated as the inverse of the propensity score.
