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Six Sigma methodologies use statistical tools used to transform raw data into information. Based on the results, further actions are taken. Statistical tools and related aspects

According to USFDA, a combination product is one composed of any combination of a drug and device; biological product and device; drug and biological product

of Six Sigma methodology comprises about half of Six Sigma. In addition, Six Sigma places a lot of emphasis on graphical interpretation of data collected during the course of m

; or drug, device, and biological product and fixed dose combination would include two or more combinations of drug.

Examples of combination products may in

asurements.

The importance of statistical methods emanate from the fact that many hypotheses can be disproved with sufficient statistical data. The significance of statistical

lude drug-coated devices, drugs packaged with delivery devices in medical kits, and drugs and devices packaged separately but intended to be used together.

ethods in Six Sigma increases with any increase in sample sizes. The statistical methods quantitatively facilitate evaluation of the performance of any process. The purpose of t

here is enormous increase in the number of combination products entering the market in the recent years. Combination products have proven advantages but fixe

is being to tackle the cost of poor quality (COPQ) first, Six Sigma has a broader scope than the traditional cost effectiveness model.

Some Important Statistical Methods In Six

d dose combinations are still in the process of convincing regulatory authority on their advantages over the single ingredient formulations.

Combination pro

Sigma

Variations in processes are measured in terms of deviation from the mean and data falling within the acceptable statistical limits. Graphical representation of this data

ucts have become life saving products for the pharmaceutical companies who doesn’t have many innovative molecules in their product pipeline and have been inc

helps companies to visualize things with greater accuracy. Let us examine a few of the most commonly used Six Sigma statistical methods.

Control Chart

The deviations within th

easingly used in the product life cycle management. Even the companies having product patents are trying to extend their product life cycle through the combi

acceptable limits (upper & lower) are due to common causes. Anything falling beyond the limits is attributable to some specific cause. For example, take the case of writing you

nation products and maximize the revenues. But the companies involved in this practice are overlooking that they are burdening the patients both economically

name ten times. Although there are similarities, you probably won’t be able to find any two signatures being exactly the same. The reason is an inherent variation that produces

and physically. They need to rightly judge the benefits of the combination products and they have to even look at the risks involved when combining the produ

reasonable results within limits and is termed as ‘common cause’. Special causes are those due to forced errors. A control chart has a mathematical mean line in the center and t

ts. Some of the combination products were well accepted by physicians while others suffered. Companies involved in development of combination products are fi

o limit lines. The third component of the Six Sigma control chart is the performance data, which is plotted over time.

You can seek special causes and track common ones through

ding difficulty in defining their combination products and facing various challenges from selecting a combination to marketing it.

Following aspects would a

control charts by looking for:

• One set of data falling beyond the acceptable limits (special cause)

• Greater than 6 data sets climbing or declining steadily within limits

dd to the challenges in developing combination products:

Which markets to tap where the combination products can do fairly well?
Which combination prod

• Eight or more subsequent data sets falling on one side of the mean

• Data falling alternately across the mean line

Interpretation emphasizes seeking out the special cause th

cts are meaningful and rational?
Which therapeutic categories to select?
Which Combinations can address unmet needs of the patients?
Do combin

t brings stability to process variation. The real fun here lies in removing the common cause and induced variations also.

Brainstorming & Affinity Group Tool

Brainstorming gen

tions increase the patient compliance?
What would be the developing cost?
How to tackle the risks encountered during combination product developmen

rates and polishes creative ideas based on the principle that two heads are better than one. An affinity diagram is used to organize & develop brainstorming by fine tuning initi

t?

As combination products don't fit into the traditional categories of drugs, medical devices, or biological products, the USFDA is in the process of devel

l and raw thoughts and removing uncertainties. The advantage of this is that it obviously stimulates for generation of more ideas. The affinity diagram was not originally intend

ping new procedures for reviewing their safety, efficacy and quality.

Professional from academic institutions, pharmaceutical industries, health care indust

ed to be a quality management tool. First devised by Kawakita Jiro, the affinity tool emphasizes the need for sorting and titling the data only at the end. A typical affinity di

y and representatives from various regulatory agencies are working out to design the regulatory requirements for manufacture and sale of combination products

gram organizes the brainstormed ideas on its left panel. On the right side are ideas neatly grouped into affinity sets. Reasons for an idea belonging to a particular set are not

.

As there is an increasing trend of the combination products companies manufacturing such products should be able to tackle the problems involved in the de

given particular importance, but all ideas are clarified. An idea may be present in a single group if it has any resemblance to another.

If there is one thing that summarizes t

elopment. They need to be wiser in analyzing the market trends and the regulatory requirements.

Companies that provide selfless information through particip

e importance of statistical methods in Six Sigma, it can be none better than a saying, famous in Six Sigma circles – “In God we trust, all the rest bring data”. Need we say more

tion in industry events and feedback to regulatory authorities would be able to face the challenges and will be successful in developing combination products

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