# Presentation: 099878

## Points to ConsiderData Collection and Analysis

- Christy Chuang-Stein, PhD
- Pharmacia Corporation
- PhRMA

## PhRMA

- We are delighted to have the opportunity to provide input.
- We look forward to continuous collaboration with the agency to develop and apply innovative and efficient approaches to collect and analyze safety data during the premarketing phase of drug development.

## Premarketing Safety Database

- It is well accepted that pre-marketing safety database is not capable of detecting rare events.  We need to be clear on what type of events we want this database to have a reasonable chance to observe.  For example, do we want to detect events that occur 1 in 100, or 1 in 1,000?  Only when we lay out the expectation of this database, can we determine the required sample size to meet the objective.

## Chance to Observe SAE(s)

| Event rate | Sample size | Pr(at least 1) | Pr(at least 2) |
| --- | --- | --- | --- |
| 1% | 500 | 0.993 | 0.960 |
| 0.5% | 500 | 0.918 | 0.713 |
|  | 1,000 | 0.993 | 0.960 |
| 0.1% | 1,500 | 0.777 | 0.442 |
|  | 3,000 | 0.950 | 0.801 |
| 0.01% | 6,000 | 0.451 | 0.122 |
|  | 10,000 | 0.632 | 0.264 |
|  | 20,000 | 0.865 | 0.594 |

## The Shadow of Multiplicity

- The concept paper proposes many possible analyses.  We hope it is clear that depending on a particular situation, some analyses will be more relevant than others.
- The multitude of analyses will increase the chance to find a false signal.  Sponsors and the agency need to look at the results in totality, keeping in mind the inflated rate of false positives if we employ statistical procedures that do not adjust for multiplicity.

## Inadequate Power

- One issue concerning “slicing” and “dicing” the data is the limited sample size for subgroup analyses. The false negative rate goes up in the presence of small sample sizes.  So, we need to be realistic what “slicing” and “dicing” can tell us.

## Uniform Approach

- We need to adopt a uniform approach if we are to lump (and possibly split) MedDRA terms for analysis. This is necessary in order to make sensible comparisons among drugs within a class and across classes.
- We need to find a mechanism for the agency and the sponsors to collaborate in a timely fashion, balancing between the need for early planning and that for data-based decisions.

## Pooling and Sampling Fluctuations

- We agree that pooling needs to be based on sound scientific decisions.
- When more data are pooled under such principles, the previously observed event rates can go up or down due to sampling fluctuations.  The fact that a particular rate goes down after more data become available does not necessarily imply the loss of sensitivity or inappropriate pooling as long as we follow the pre-specified pooling strategies.

## Challenges of Missing Data

- Missing data present more a challenge in safety analysis than that in efficacy analysis because exposure duration often has a positive impact on the occurrence of adverse drug reactions.
- To eliminate the exposure factor, one can compare the (quantitative) responses of dropouts at the point of dropping out with those of individuals who continued beyond the point at the pre-defined study periods.
- More research is needed in this area.

## Cumulative Safety Knowledge

- It is important to link all safety data collected during the discovery, development and postmarketing phases together to help us better evaluate the safety of a pharmaceutical product and therefore the benefit/risk balance.
- A decision-tree type of strategy starting with preclinical evaluation throughout the product life cycle will be very helpful to making evidence-based decisions.

## In Summary

- Relevancy to a specific situation.
- Priorities among myriad objectives.
- Clarity in terminologies and definitions.
- Uniformity across drugs.
- Consistency with the benefit/risk principle.
- Transparency in decisions.