PharmD Info

Solomon Benny wrote:Steps for conducting any pharmacoeconomic analysis are as follows:
(1) defining the problem,
(2) determining the study's perspective,
(3) determining the alternatives and outcomes,
(4) selecting the appropriate pharmacoeconomic method,
(5) placing monetary values on the outcomes,
(6) identifying study resources,
(7) establishing the probabilities of the outcomes,
(8) applying decision analysis,
(9) discounting costs or performing a sensitivity or incremental cost analysis, and
(10) presenting the results, along with any limitations of the study. By adhering to the analytic steps described, the pharmacist undertaking a pharmacoeconomic evaluation has the greatest likelihood of obtaining valid and useful results.

Can we use the same procedure for sensitivity analysis also?

May be not. Its done at the end to exclude the uncertainty from your study results. If any confounding factors affects your results.

Sherin wrote:1.A sensitivity analysis using only one parameter is termed a one-way sensitivity analysis , while one that simultaneously varies two parameters is a two-way sensitivity analysis

Can you give an example how the sensitivity analysis was done?

Univariate analysis -Univariate analysis applies to data sets that consist of a single variable. If the data set consists of continuous variables, then the measures of interest are the central tendency and spread of the variable.

Naren wrote:Univariate analysis -Univariate analysis applies to data sets that consist of a single variable. If the data set consists of continuous variables, then the measures of interest are the central tendency and spread of the variable.

Can you add some examples for dependent and continuous variables?

A sensitivity analysis determines how different values of an independent variable affect a particular dependent variable under a given set of assumptions. In other words, sensitivity analyses study how various sources of uncertainty in a mathematical model contribute to the model's overall uncertainty. This technique is used within specific boundaries that depend on one or more input variables

For dependent variable example- in pharmaceutical trials, where researchers may administer new medicines to participants to determine the optimal dose. The independent variable is how much medicine the participants receive. If the medicine is an antihistamine, the dependent variable might be the severity of allergy symptoms a participant displays, such as red eyes or sneezing.

@Naren, For an example Age, Sex, Glucose Levels, Gender, Stroke comes under what type of variables?

A sensitivity analysis determines how different values of an independent variable affect a particular dependent variable under a given set of assumptions. In other words, sensitivity analyses study how various sources of uncertainty in a mathematical model contribute to the model's overall uncertainty. This technique is used within specific boundaries that depend on one or more input variables.

1) Examining the changes in results when key variables are varied in an economic model. A cost-effectiveness analysis (CEA) is one of the key tools of economic evaluation. Generally CEA is based on a number of debatable hypothesies, introducing an element of uncertainty. Sensitivity analysis (SA) suggests ways to calculate and evaluate this uncertainty.

2) In order to confirm the influence of such uncertainty, sensitivity analyses are conducted in pharmacoeconomic analyses using models. A sensitivity analysis is a way of examining any change in results due to changes in the parameter values applied within a certain scope.

Multivariate analysis is used to study more complex sets of data than what univariate analysis methods can handle. This type of analysis is almost always performed with software (i.e. SPSS or SAS), as working with even the smallest of data sets can be overwhelming by hand.

Multivariate analysis can reduce the likelihood of Type I errors. Sometimes, univariate analysis is preferred as multivariate techniques can result in difficulty interpreting the results of the test. For example, group differences on a linear combination of dependent variables in MANOVA can be unclear. In addition, multivariate analysis is usually unsuitable for small sets of data.