# Presentation: 489829

## Swine Respiratory Disease:Statistical Considerations

**Janice Derr, Ph.D.**
**Center for Veterinary Medicine**
**U.S. Food and Drug Administration**

## Summary

**Consider the statistical analysis at the study design stage **
**Make sure the study has adequate statistical power**
**Make sure the study design will result in a valid statistical analysis**** **

**Notes:**
The summary is also repeated at the end.

## Statistical Aspects of Designs for Field Studies

**Multiple Sites**
**Multiple Pens at Each Site**
**Multiple Pigs Allocated to Each Pen**
**Two Main Options for Allocating Pigs to Experimental Groups and Pens *****(next slide)***
**Evaluation is in the Form “Success/Failure”**

**Notes:**
“Success/Failure” is a dichotomous response.

These factors are options that we consider for a study design.

## Options for Allocating Pigs to Experimental Groups and Pens

**Injectable article**
**Pigs from different experimental groups can be commingled in each pen**
**Article is in feed/water**
**Pigs from different experimental groups need to be in separate pens **

**Notes:**
In the case of an injectable article, the pigs from different experimental groups do not have to be commingled in each pen, but they can be commingled.  It is also possible to have only one experimental group per pen with an injectable article.  

## The Experimental Unit

**The smallest unit to which a treatment is randomized**
***Injectable article:*****  The pig can be the experimental unit**
***Article in feed or water:*****  The pen is the experimental unit**
**The ****pen**** is an important design factor due to ****dependence**** in response within the pen**

**Notes:**
Fourth point:  Regardless of whether the pig or the pen is the experimental unit, the pen is still an important design factor.  
Even in the case of commingled treatments when the pig is the experimental unit, the pen should be included in the analysis model. We will discuss the reasons for this later in the talk when we address the statistical analysis.  

## Allocating Pigs to Pens: Statistical Considerations

***Commingled experimental groups*****:  An equal number of pigs per experimental group per pen is the most efficient design.**
***Separate pens per experimental group*****:  An equal number of pigs per pen is the most efficient design.**
***Both designs:*****  Allow for losses during the study (pigs that will not be in the analysis).**

**Notes:**
An “efficient design” from a statistical perspective means you have made the best use of your resources.  Statistically speaking, you are efficient when you have the smallest standard error possible for testing the effect of treatment, given the resources that you have available (sites, pens, pigs) for the study.

We need to allow for losses during the study because the new statistical methods we can use for analysis are sometimes sensitive to small numbers of animals.  “Allowing for losses” means including more pigs in the pens and in the overall study to allow for the possibility that some of them might not be in the analysis.

## Statisticians Can Provide Input to Questions About Study Design

**How Many Sites?**
**How Many Pens per Site?**
**How Many Pigs per Experimental Group per Site?**
**How Many Pigs per Pen?**
**(Other considerations also enter in to these decisions.)**

**Notes:**
Other considerations that enter into the decision about the number of sites and pigs include the need for substantial evidence and the inferential value of the study. 

We encourage sponsors to discuss these issues with their statisticians and to discuss the study design with CVM.

## Statisticians Need to Know: 

**What is the variation in response among sites? **
**What is the variation in response among pens?  **
**What is the level of dependence among responses from pigs in the same pen?**
**What is the minimum effect size of the test article compared with the negative control that is of interest?**

**Notes:**
Third point:  We may need to look into the level of dependence among responses if we are considering the number of pigs per pen strictly from a statistical perspective.  This is especially important with the new statistical methodology that we can use to analyze data from these studies.

## A Hypothetical Study

_**Objective**_**:  To evaluate the effectiveness of an injectable article in the treatment of Swine Respiratory Disease (SRD).**
**Study was conducted at four sites.**
**320 pigs were enrolled in the study (80/site).**
**Each eligible pig was assigned to either test article or saline injection in a 1:1 ratio.**
**Pigs were commingled in pens, 4 pigs/experimental group/pen (8 pigs total/pen).**
**Several criteria were used to classify the animal as a “Success” or “Failure” after a set period of days.**

**Notes:**
In this hypothetical study, we are defining “success” in terms of a clinical cure.

## Design

**Design**
**Layout **
**for **
**Site 1**

## Hypothetical Results

## Improvements in Statistical Methodology Impact Field Studies in SRD

**In the past, it was difficult to analyze “success/failure” responses from complex study designs. **
**At present, we have improved statistical methodology and software.**
**However, this methodology still has limitations. **

**Notes:**
In the past, it was difficult to represent the entire study design in a statistical model.  Our focus here is the factor of “pen” that permits us to represent the dependence among observations from pigs in the same pen. We’ll call an analysis that represents pigs as independent even though they are grouped in pens as “less ideal.” 
We’ll call an analysis “ideal” if it can represent the entire study design, especially the effect of “pen.” 
The reason that pen is a part of the ideal analysis is because we would like to allow for the possibility of dependence among the responses of pigs within the same pen.  

## Mixed Effects Analysis of Variance for “Success/Failure” Responses 

**Correctly represents a binomial response (such as Success/Failure)**
**Allows for random effect of pen within site**
**Allows for random effect of site**
**Allows for dependence in response among animals in the same pen**
**Does not always run (with small numbers of animals)**

**Notes:**
There are currently some computational limitations to this mixed effects analysis of variance model, especially with small numbers of animals.  When this occurs, we may need to make use of a different analysis approach.  

The term “mixed effects” refers to the ability of the analysis to include both fixed effects (such as the effect of the experimental group) and random effects as shown above.

## Mixed Effects Analysis of Hypothetical Results

## Summary

**Consider the statistical analysis at the study design stage **
**Make sure the study has adequate statistical power**
**Make sure the study design will result in a valid statistical analysis**** **

**Notes:**
The second point:  Statisticians can use information from previous similar studies to estimate power, they may need to run some “worst case” and “best case” simulations.

The third point:  This refers to the sensitivity that software for generalized linear mixed effects model currently has in dealing with small numbers in pens.  You want to make sure you don’t have so few animals per pen that the design is vulnerable to losses or simply can’t be analyzed.  Statisticians can run simulations of the study design -- some “worst case” situations -- to make sure the design is feasible.

It is a good idea to discuss the design and analysis strategies at the protocol stage with CVM.