Making correct decisions is of paramount importance in clinical medicine and health-related disciplines. Randomized clinical trials are considered the gold-standard type of study for the assessment of the efficacy of a treatment. However, conducting a randomized clinical trial is not always possible; observational studies should be conducted, instead. For lack of randomization in observational studies, there may be a priori differences in the distributions of certain variables (e.g., age, race, and quality of health care services) between the study groups that may result in a biased estimate of the outcome of interest. Risk adjustment methods are used to account for these a priori differences and find an unbiased measure of the treatment effect. The method involves several steps including defining the outcome of interest and identifying its potential outcome predictors. Then, we need to operationalize the selected risk factors and construct a statistical model or other methods of adjustment.This will result in a more accurate (less biased) estimate of the treatment effect.

Making correct decisions is of paramount importance in clinical medicine and health-related disciplines. Randomized clinical trials are considered the gold-standard type of study for the assessment of the efficacy of a treatment. However, conducting a randomized clinical trial is not always possible; observational studies should be conducted, instead. For lack of randomization in observational studies, there may be a priori differences in the distributions of certain variables (e.g., age, race, and quality of health care services) between the study groups that may result in a biased estimate of the outcome of interest. Risk adjustment methods are used to account for these a priori differences and find an unbiased measure of the treatment effect. The method involves several steps including defining the outcome of interest and identifying its potential outcome predictors. Then, we need to operationalize the selected risk factors and construct a statistical model or other methods of adjustment.This will result in a more accurate (less biased) estimate of the treatment effect.

Making correct decisions is of paramount importance in clinical medicine and health-related disciplines. Randomized clinical trials are considered the gold-standard type of study for the assessment of the efficacy of a treatment. However, conducting a randomized clinical trial is not always possible; observational studies should be conducted, instead. For lack of randomization in observational studies, there may be a priori differences in the distributions of certain variables (e.g., age, race, and quality of health care services) between the study groups that may result in a biased estimate of the outcome of interest. Risk adjustment methods are used to account for these a priori differences and find an unbiased measure of the treatment effect. The method involves several steps including defining the outcome of interest and identifying its potential outcome predictors. Then, we need to operationalize the selected risk factors and construct a statistical model or other methods of adjustment.This will result in a more accurate (less biased) estimate of the treatment effect.

Making correct decisions is of paramount importance in clinical medicine and health-related disciplines. Randomized clinical trials are considered the gold-standard type of study for the assessment of the efficacy of a treatment. However, conducting a randomized clinical trial is not always possible; observational studies should be conducted, instead. For lack of randomization in observational studies, there may be a priori differences in the distributions of certain variables (e.g., age, race, and quality of health care services) between the study groups that may result in a biased estimate of the outcome of interest. Risk adjustment methods are used to account for these a priori differences and find an unbiased measure of the treatment effect. The method involves several steps including defining the outcome of interest and identifying its potential outcome predictors. Then, we need to operationalize the selected risk factors and construct a statistical model or other methods of adjustment.This will result in a more accurate (less biased) estimate of the treatment effect.