Agresti/Franklin Statistics, 1 of 56 Section 4.3 What Are Good Ways and Poor Ways to Experiment?

Agresti/Franklin Statistics, 1 of 56

Section 4.3

What Are Good Ways and Poor Ways to Experiment?


An Experiment

Assign each subject (called an experimental unit ) to an experimental condition, called a treatment

Observe the outcome on the response variable

Investigate the association – how the treatment affects the response


Elements of a Good Experiment

Primary treatment of interest

Secondary treatment for comparison

Comparing the primary treatment results to the secondary treatment results help to analyze the effectiveness of the primary treatment


Control Group

Subjects assigned to the secondary treatment are called the control group

The secondary treatment could be a placebo or it could be an actual treatment


Randomization in an Experiment

It is important to randomly assign subjects to the primary treatment and to the secondary (control) treatment

Goals of randomization: • Prevent bias

• Balance the groups on variables that you know affect the response

• Balance the groups on lurking variables that may be unknown to you


Blinding the Study

Subjects should not know which group they have been assigned to – the primary treatment group or the control group

Data collectors and experimenters should also be blind to treatment information


Example: A Study to Assess Antidepressants for Quitting Smoking

Design:

• 429 men and women

• Subjects had smoked 15 cigarettes or more per day for the previous year

• Subjects were highly motivated to quit



Subjects were randomly assigned to one of two groups:• One group took an antidepressant daily

• Second group did not take the antidepressant (this group is called the placebo group)



The study ran for one year

At the end of the year, the study observed whether each subject had successfully abstained from smoking or had relapsed



Results after 1 year:

• Treatment Group: 55.1% were not smoking

• Placebo Group: 42.3% were not smoking

Results after 18 months:• Antidepressant Group: 47.7% not smoking

• Placebo Group: 37.7% not smoking

Results after 2 years:• Antidepressant Group: 41.6% not smoking

• Placebo Group: 40% not smoking



Question to Think About: Are the differences between the two groups statistically significant or are these differences due to ordinary variation?


Section 4.4

What Are Other Ways to Conduct Experimental and Observational

Studies?


Multifactor Experiments

Multifactor Experiments: have more than one categorical explanatory variable (called a factor).


Example: Do Antidepressants and/or Nicotine Patches Help Smokers Quit?


Matched-Pairs Design

Each subject serves as a block

Both treatments are observed for each subject


Example: A Study to Compare an Oral Drug with a Placebo for Treating Migraine Headaches

Subject Drug Placebo

1 Relief No Relief First matched pair

2 Relief Relief

3 No Relief No Relief


Blocks and Block Designs

Block: collection of experimental units that have the same (or similar) values on a key variable

Block Design: identifies blocks before the start of the experiment and assigns subjects to treatments with in those blocks


Experiments vs Observational Studies

An Experiment can measure cause and effect

An observational study can yield useful information when an experiment is not practical

An observational study is a practical way of answering questions that do not involve trying to establish causality


Observational Studies

A well-designed and informative observational study can give the researcher very useful data.

Sample surveys that select subjects randomly are good examples of observational studies.


Random Sampling Schemes

Simple Random Sample: every possible sample has the same chance of selection



Cluster Random Sample: • Divide the population into a large number

of clusters

• Select a sample random sample of the clusters

• Use the subjects in those clusters as the sample



Stratified Random Sample:• Divide the population into separate groups,

called strata

• Select a simple random sample from each strata


Observational Studies

Well-designed observational studies use random sampling schemes


Retrospective and Prospective Studies

Retrospective study: looks into the past

Prospective study: follows its subjects into the future


Case-Control Study

A case-control study is an observational study in which subjects who have a response outcome of interest (the cases) and subjects who have the other response outcome (the controls) are compared on an explanatory variable


Example: Case-Control Study

Response outcome of interest: Lung cancer

• The cases have lung cancer

• The controls did not have lung cancer

The two groups were compared on the explanatory variable:• Whether the subject had been a smoker

Agresti/Franklin Statistics, 1 of 56 Section 4.3 What Are Good Ways and Poor Ways to Experiment?

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