SAMPLE LEARNING MODULE CONTENT - Department of …



PROBLEM SET ONE: QUESTION 10:

NOTE: IN ADDITION TO HANDING IN A HARD COPY, PLEASE EMAIL COPIES OF YOUR WORK TO: kcobb@stanford.edu

The final part of your homework is to write a brief explanation of a simple statistical concept and to find one or two INTERESTING examples in the medical literature of medical studies where the statistic or concept was applied (to access Stanford’s Lane Medical Library, where many examples are available to you online, goto: ).

You will be contributing content for a web-based easy-to-use program (something like “TurboTax for study design”) that is being developed to help medical researchers design studies (for more information on the project, goto: ). The program leads the user from a specific hypothesis (eg., Does studying statistics at Stanford (exposure) lead to an increase in gray hairs (outcome)?) to an appropriate study design (as we’ve learned in class, the main options are: case-control, cohort, or randomized clinical trial), and through data collection, sample size calculations, and data analysis.

Our class has been solicited to write “learning modules” for statistical concepts that occur in the Sample Size Calculations section. Users can click on learning modules to get more information about a statistical topic if they are unfamiliar with it. Each person in our class will receive 1 of 13 topics we have been given to cover, so there will be some duplication. Feel free to swap topics amongst yourselves.

This will give you a chance to explore the medical literature and to think about how statistics are applied to medical studies. Please be BRIEF and write in the ACTIVE VOICE. These should be as lively and entertaining (while still being accurate, of course) as possible!

Instructions:

We want you to provide 5 short elements:

1) A definition/statement of the problem or issue (see below for an example)

2) An explanation of your statistical topic

3) 1 or 2 examples of a medical study where the topic is applied (and how it is applied). Please cite specific, real studies if possible.

4) Expansion/amplificaiton – if there’s something more you want to add

5) Web resource/s: if you can find any Web resources to refer naïve statisticians to…

Here is an example:

WHAT YOU ARE GIVEN:

Your learning module topic: Types of data

Where the user is coming from: The user has defined a study design (cohort study) and hypothesis, including exposure and outcome variables.

Trigger Question (This is the last thing the user sees before accessing your learning module): Will you compare your exposure groups based on a binary, categorical (other than binary), or a continuous measure of the outcome?

Learning Module Objective: Your job is to help the user to determine whether she will compare the exposure groups in her cohort study using a binary, continuous, or categorical (>2 groups) measure of her outcome variable.

For example, if you are comparing statistics students with humanities students with regards to the amount of gray hair that they accumulate during the winter term, you could either measure gray hair as binary (grew at least one gray hair/grew no gray hairs); categorical (grew a little or no gray hair vs. grew a moderate amount vs. grew a lot of gray hair); or continuous (the number of gray hairs the student grew during the term). The form of the outcome variable will determine the eventual statistical analysis.

WHAT YOU MIGHT WRITE (this example is a little long; try to keep it to 1 page MAXIMUM):

Definition/statement of the problem:

A comparison between exposed and unexposed groups can be based on different types of measurement scales. There are 3 basic classes of measures: Binary, categorical, continuous.

Explanation of the concepts:

1) Binary measures compare 2 or more groups according to presence or absence of an outcome (yes or no). This measure is commonly used to compare the frequency (proportion) of events in 2 or more groups. The odds ratio and the relative risk are examples of compound binary measures.

Example: I hypothesize that palm pilots (exposure) cause brain rot (outcome) in medical students. For this study I will use a binary measure to classify subjects according to whether they own a palm pilot or not (yes/no). Brain rot will also be classified with a binary measure. We will observe students in a class for five minutes on a random day. Those who are sleeping will be deemed to have brain rot and those who are not will be deemed rot-free.

2.) Categorical measures compare 2 or more groups according to 2 or more classifications, such as “No outcome” “Early Stage,” “Advanced Stage”. This type of scale, which has a natural ranking and is sometimes referred to as “ordinal,” is commonly used in studies seeking to establish a dose-response relationship between an exposure and an outcome. Another type of categorical scale is a nominal scale. An example would be classification of subjects according to type of primary cancer (outcome). There is no natural ranking to the categories. The objective is to compare frequency of outcomes in these subgroups without implying a hierarchy of outcome.

Example: Same study, but I would like to know whether palm pilot use is related to severity of brain rot. I will use an ordinal measure of outcome for the in-class diagnosis: alert, nodding, comatose-- and compare the frequency of these outcomes in palm pilot owners and non-owners.

3.) The third type of scale is continuous, or sometimes referred to as “interval.” A continuous measure has some important properties: it usually has a natural 0 and the distances between units on the scale are equal. Some examples are age, systolic blood pressure, body temperature, or a score on a test. This type of measure is often used to compare mean or median values between exposed and unexposed groups.

Example. Same study, but because I cannot find enough alert or nodding subjects, I plan to compare the mean Glasgow Coma score in Palm owners and non-owners.

Expansion/Amplification.

The type of measurement scale used to compare groups depends on the type of data that can be collected and the objectives of the analysis. Binary and categorical measures are relatively easy to obtain and have the advantage of making more definitive statements about an association. Their disadvantage is that they usually require many more subjects than continuous measures to analyze. Continuous measures may be more difficult to obtain, and they may be more difficult to interpret, but they offer more information for a relatively smaller sample size.

For further reading: [some stat web site]

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