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In order to estimate the effect of the intervention on use of chronic management asthma and COPD medications, we used a combined matching and a difference in difference (DID) regression approach rather than a matching estimator alone to estimate the intervention effect.
To estimate the intervention effect, the interaction term (month X group) was included in the model.
A regression equation was used to estimate the intervention effect of the policy.
Assuming homogeneity, a fixed effects model was applied to estimate the intervention effect in the meta-analysis.
For dichotomous outcomes, logistic regression models will be used to estimate the intervention effect as an OR with 95% CIs.
The second approach to the analysis was a multilevel logistic regression model to estimate the intervention effect.
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26 After matched groups had been constructed, we estimated the intervention effect using a difference-in-difference estimator.
In principle, estimating the intervention effect directly overcomes this problem and finds promising candidates for successful therapeutic intervention more efficiently.
The impact evaluation data are raised over the project duration of 3 years, allowing estimating the intervention's impact on vector densities and human health.
Covariates previously identified as influencing symptom distress in the primary outcome analysis (age, clinical service, working status and baseline SDS-15 score) were adjusted in multivariable analysis to improve the precision of estimating the intervention effect on the two scores.
Therefore both the estimate α (see section Estimating the intervention effect) and its standard error agree with the results obtained from simple imputation when the model is equivalent to assuming 'missing = smoking' or 'LOCF' in the trial arms.
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