Medical Study Analysis Outline and Excerpt

OUTLINE

INTRODUCTION
OBJECTIVES
ACRONYMS

STUDY TYPES

Introduction
Randomized controlled trials

Randomization
Blinding

Procedural studies
Unblinding by treatment effects

Observational studies

Cohort studies
Case-control studies
Advantages
Disadvantages

Confounders
Selection bias
Surveillance bias
Protopathic bias
Reverse causation
Causality

Meta-analysis

Advantages
Disadvantages
Example
Network meta-analysis

STATISTICAL THEORY

The normal distribution
95% Confidence Interval

95% CI and sample size

The p-value
Statistical significance
Multiple comparisons

STUDY DESIGN

Introduction
Study power
Outcome analysis

Intention-to-treat analysis (ITTA)
Per-protocol analysis (PPA)
Patient adherence
When PPA matters
As-treated analysis (ATA)
On-treatment analysis (OTA)
Modified ITTA

Noninferiority outcomes

ABSOLUTE VS RELATIVE RISK

Definitions
NNT / NNH

SENSITIVITY AND SPECIFICITY

Definitions
PPV / NPV
Interplay between the two

OTHER

Recall bias
Reporting bias
Sensitivity analysis
Bayesian theory/inference
Cluster randomization
Pragmatic trials
Propensity score matching

EXCERPT

Doctors order a lot of tests, most of which have a sensitivity and specificity for some condition. On the surface, these terms seem self-explanatory. If a test is sensitive, it's usually positive if a condition is present. If a test is specific, a positive test makes the condition very likely. Most people understand these concepts, but applying the information can sometimes be confusing. To illustrate, I'm going to present you with a clinical scenario.

A very distraught 52-year-old female comes to see you in your clinic. Your colleague ordered a Cologuard test on her, and it came back positive. Someone from your office called her and gave her the results, and since then, she has been unable to sleep and having daily panic attacks because she is afraid she is going to die from colon cancer. She tells you she went to the Cologuard website and read that the test has a sensitivity of 92% and a specificity of 90%. She starts to cry hysterically and says, "That means there is only a 10% chance the test is wrong."

So what do you want to tell her? Does she really have a 90% chance of having colon cancer? Let's break these two measures down and see if we can give her an educated answer.

First, let's tackle sensitivity, defined as the percentage of people with the disease who get a positive result. If a test has high sensitivity, almost everyone with the disease will have a positive test. Cologuard has a sensitivity of 92%, which means only 8% of people with colon cancer will get a negative result (false negatives). High sensitivity tests are most useful when negative because it means the disease is very unlikely. The significance of a positive result depends on the specificity.

Specificity is the percentage of people without the disease who get a negative result. Cologuard has a specificity of 90% which means only 10% of people without colon cancer will get a positive result (false positives). A test with high specificity is most useful when positive because it makes the disease likely. The illustrations below help to explain the relationship. [71]