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BER WORKING PAPER SERIES USE OF PROPENSITY SCORES IN NONLINEAR RESPONSE MODELS: THE CASE FOR HEALTH CARE EXPENDITURES Airbag Base Daniel Pol sky Willard G. Manning Working Paper 14086 http://www.nber.org/papers/w14086NATIONAL

How to fill out use of propensity scores

How to fill out use of propensity scores

1. To fill out use of propensity scores, follow these steps:
2. Define the research question: Determine the objective of your study and what you want to investigate using propensity scores.
3. Identify the treatment and control groups: Clearly define the groups you want to compare and determine the treatment variable.
4. Collect relevant data: Gather all necessary data, including variables such as demographics, exposure, outcomes, and potential confounders.
5. Assess covariates: Identify the potential confounders that may affect the treatment assignment and the outcome. These could include age, gender, socioeconomic status, etc.
6. Estimate propensity scores: Use appropriate statistical methods (e.g., logistic regression) to estimate the propensity scores for each individual in the dataset. Propensity scores represent the likelihood of receiving the treatment based on covariates.
7. Check balance: Assess the balance between treatment and control groups by examining the distribution of propensity scores. Use visualization techniques, such as histograms or boxplots.
8. Match or stratify: Depending on the study design, you can either match individuals with similar propensity scores in the treatment and control groups or stratify the dataset based on propensity score quintiles.
9. Analyze the data: After achieving balance, analyze the outcome variable of interest (e.g., treatment effect) using appropriate statistical methods (e.g., regression models).
10. Interpret the results: Draw conclusions from the analysis and interpret the findings, considering the limitations and assumptions of propensity score analysis.
11. Sensitivity analysis: Perform sensitivity analyses to assess the robustness of the results to potential hidden biases or unmeasured confounders.
12. Report and discuss: Document the entire process, results, limitations, and implications of using propensity scores in your study. Discuss the strengths and weaknesses of this approach.

Who needs use of propensity scores?

1. Propensity scores are useful for researchers, particularly in observational studies, when:
2. - They want to estimate the causal effect of a treatment or intervention in the presence of confounding variables.
3. - Randomized controlled trials are not feasible or ethical.
4. - They aim to balance covariates between treatment and control groups to reduce selection bias.
5. - They want to account for the likelihood of receiving a certain treatment based on observed covariates.
6. - They want to increase comparability between groups and improve the internal validity of their study.
7. - They aim to address potential selection bias and confounding in non-randomized studies.

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