/
ABSITE  statistics: the absolute basics ABSITE  statistics: the absolute basics

ABSITE statistics: the absolute basics - PowerPoint Presentation

kittie-lecroy
kittie-lecroy . @kittie-lecroy
Follow
358 views
Uploaded On 2018-11-11

ABSITE statistics: the absolute basics - PPT Presentation

Christian Jones MD FACS Department of Surgery Johns Hopkins School of Medicine stats mean typical average median middle number mode most frequent number normal type of distribution equal about a mean bell curve mean median mode ID: 727795

test bias jones christian bias test christian jones median treatment nonrandom study disease time predictive

Share:

Link:

Embed:

Download Presentation from below link

Download Presentation The PPT/PDF document "ABSITE statistics: the absolute basics" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.


Presentation Transcript

Slide1

ABSITE statistics: the absolute basics

Christian Jones, MD, FACS

Department of Surgery

Johns Hopkins School of MedicineSlide2

stats

mean: typical “average”

median: middle number

mode: most frequent number

normal: type of distribution, equal about a mean, “bell curve”, mean = median = modeSlide3

terms

in

cidence:

in

terval of time

p

revalence:

p

oint in timeSlide4

types of studies

cohort study: prospective, nonrandom assignment to treatment group

case-control study: retrospective, nonrandomSlide5

biases

selection bias: test groups end up being different, e.g., volunteers vs. population

measurement bias: aka system bias, actual measurement is inaccurate, e.g., recall bias

exposure bias: aka treatment bias, treatments not applied equally or as originally planned, e.g., dead patients “withdraw”Slide6

test parameters

sensitivity: how well does the test see the disease?

specificity: how well does the test make sure it really is the disease?

positive predictive value: if the test says yes, how certain can you be that it’s right?

negative predictive value: if the test says no, how certain can you be that it’s right?Slide7

copyright, etc.

This document is entirely the work of

Christian Jones

(

on-call@christianjones.md

) and is hereby released into the public domain, with

no rights reserved

.

This is version 1.1, last modified on 28 January 2016 by

Christian Jones

. Contact the author by email or on

Twitter

(

@jonessurgery) for questions, comments, or concerns.