This document discusses sensitivity analysis, which determines the impact of independent variables on dependent variables under given assumptions. There are two main approaches: local sensitivity analysis, which uses derivatives at a single point; and global sensitivity analysis, which uses Monte Carlo techniques across a range of samples. Additional techniques discussed include differential sensitivity analysis, one-at-a-time sensitivity measures, factorial analysis, and regression analysis. Sensitivity analysis is useful for forecasting outcomes if scenarios differ from predictions and assessing risks by analyzing output dependencies on inputs.

1. PES
MODERN COLLEGE OF PHARMACY (Ladies)
MOSHI, PUNE
Savitribai Phule Pune University
M.PHARM
Semester- II
By
Mrs. Sneha Patil
Assistant Professor
Dept. of Pharmaceutics
Sensitive analysis

2. Content
1. Introduction
2. Types
3. Other techniques
4. Reference

3. 1. Introduction
 Sensitive analysis is a technique to determine the impact of
independent variable on a particular dependent variable under a
given set of assumptions
 Its usage will depend on one or more input variables within the
specific boundaries
 This is also known as what-if analysis
 There are two main approaches for analyzing sensitivity
1. Local Sensitive Analysis
2. GlobalSensitive Analysis
Local Sensitive Analysis Sensitive Analysis

4. 2. Types of analyzing sensitivity
1. Local sensitive analysis
 This analysis is based on the numerical or analytical derivative based,
As the derivatives are taken at a single point, this is called local
 This method is suitable for simple cost functions but not for complex
models
 This is a one-at-a-time (OAT) technique, which analyzes the impact of
one parameter on the cost function, keeping other parameters fixed
2. Global sensitive analysis
 This is the second approach which is often implemented using Monte
Carlo technique and uses a global set of samples to explore the
design

5. 3. Various other techniques
3. Differential Sensitivity Analysis
 It is a direct method which involves solving temporal sensitivity
analysis to simple partial derivatives
 While this technique is computationally efficient, the task of solving
equations is intense
4. One at a time sensitive measure
 This is often referred to as local analysis because it is an indicator
only for the point estimates discussed and not for the entire
distribution
5. Factorial Analysis
 This includes selecting the specified number of samples for a
particular parameter and then running the combination model

6. Various other techniques continue…..
6. Regression Analysis
 This is a comprehensive method of obtaining responses for complex
models.
7. Subjective sensitive analysis
 This is a subjective, qualitative, simple and easy method to exclude
input parameters
 Indicating the sensitivity of the simulation to the model values is the
key application of the sensitive analysis
 Sensitive analysis is a way to forecast a decision's outcome if a
scenario turns out to be different from the main predictions
 It helps in assessing the risk of strategy, identifying and analyzing the
dependency of the output on the input values

7. 4. Reference
 Karri V V S Narayana Reddy, K Gowthamarajan, Arun Radhakrishnan,
Arun Radhakrishnan. A Textbook of Computer aided drug
development. S. vikas and company, PV Publication; 2021 edition; Pg.
no. 273, 274