Statistics you can use: Practical use of statistics in reading medical research literature

1 / 39

# Statistics you can use: Practical use of statistics in reading medical research literature - PowerPoint PPT Presentation

Statistics you can use: Practical use of statistics in reading medical research literature. PAS 610 June 21, 2005 Robert D. Hadley PhD, PA-C. The basics:. “There are three kinds of lies: lies, damned lies, and statistics.” Benjamin Disraeli, British politician (1804 - 1881).

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about ' Statistics you can use: Practical use of statistics in reading medical research literature' - petra-nichols

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### Statistics you can use:Practical use of statistics in reading medical research literature

PAS 610

June 21, 2005

PhD, PA-C

The basics:
• “There are three kinds of lies: lies, damned lies, and statistics.”
• Benjamin Disraeli, British politician (1804 - 1881)
The value of statistics:
• Four economists are going to a meeting on the same train as four statisticians. The economists can\'t help noticing that the statisticians only buy a single ticket, where they bought four. When they inquire, the statisticians say, "Don\'t worry, you\'ll see."
• They get on the train, and when the conductor starts in their car the four statisticians all lock themselves in the WC. When the conductor knocks on the WC door and yells "TICKET", they slide the ticket out under the door, and the conductor stamps it and slides it back. After he\'s gone, the statisticians emerge.
At the station on the way back from the meeting, the economists buy only one ticket, but they can\'t help noticing that the statisticians don\'t buy any. When they inquire, the statisticians say, "Don\'t worry, you\'ll see.“
• As the conductor approaches their car, the economists all pile in the nearest WC and lock the door. One of the statisticians goes and knocks on the door; the economists slide the ticket out. The statisticians take the ticket and lock themselves in the WC at the other end of the car, repeating their maneuver of the previous trip. The economists get thrown off the train.
• Moral: Don\'t use statistical methods you don\'t understand.
“Practical” vs. STA 570
• Ways to represent data
• Sample vs. population
• Ways to compare data
• e.g. Chi-square, Student’s t-test, ANOVA/ ANCOVA, Odds ratios and CI, Cox proportional hazard model, Spearman ranked correlation coefficients, multivariate regression analysis
• Appropriateness of test for the way data were collected
Mean

Median

Quartiles, tertiles, etc.

Mode

Rank

Nominal

Ordinal

Population

Sample

Variance

Standard deviation

Normal distribution

Z-scores, T-scores

Correlation

Parametric vs. Nonparametric

Hypothesis testing

1- vs. 2-tailed

Significance levels

Confidence intervals

Statistical power

Terms: basics
Terms: medical literature-specific
• Intention to treat
• Kaplan-Meier curves
• ROC curves
• Meta-analysis representations
• Odds ratios/Relative Risk
• Risk reduction
• Number needed to treat
• Over what time period?
• For what outcome?
• Number needed to harm
Concepts
• Descriptive vs. inferential statistics
• Type I and II errors

Descriptive

Statistics

Inferential

Statistics

• Includes
• Making inferences
• Hypothesis testing
• Determining

relationships

• Making predictions
• Includes
• Collecting
• Organizing
• Summarizing
• Presenting

data

Type I (alpha)

Incorrectly reject the null hypothesis

Infer that something is significant when it is not

Type II (beta)

Incorrectly accept the null hypothesis

Infer that something is not significant when it really is

Inferential errors

So, which is better to do?

Which way does “intention to treat” skew the inference?

Study design
• Ask the right question in the right way
Statistical power
• Choose the appropriate sample size
Standard deviation and Z-scores

Note: “normal” range for lab tests is ± 2 s.d.

Z and T scores in medicine
• Bone density data are reported as T-scores and Z-scores. T-scores represent the number of SDs from the normal young adult mean bone density values, whereas Z-scores represent the number of SDs from the normal mean value for age- and sex-matched control subjects.
• Results showing Z-scores of −2.0 or lower may suggest a secondary cause of osteoporosis.
Data Representation
• Relative risk, odds ratios, likelihood ratios, hazard ratios
• Odds ratios in meta-analyses
Relative risk

What do unequal CI bars mean?

Meta-analyses
• “Gold standard” is randomized, placebo-controlled, multi-center, double blind clinical trial
• “Platinum standard” is a meta-analysis of multiple “gold standard” trials by different investigators addressing the same question (rarely available)
• Can make use of small studies that by themselves do not achieve statistical significance
Meta-analyses
• How it’s done:
• Search on a specific topic
• Use predefined inclusion/exclusion criteria for studies that relate to topic
• e.g. must be RCT, must measure same specific outcome (like cardiovascular events), etc.
• Combine all studies that meet criteria
• Use statistics appropriate to the way data were gathered in the included studies
• Arrive at a conclusion that was impossible with the individual studies that were included
Other anti-platelet drug
• (Reg. 1)
• Aspirin
• (Reg. 2)
Antiplatelet therapy for CVD

BMJ 2002; 324:71-86

Data Representation
• Kaplan-Meier survivorship, and cumulative incidence of events
• Both are a cumulative measure of something happening
Kaplan-Meier

Bortezomib or High-Dose Dexamethasone for Relapsed Multiple Myeloma

N Engl J Med 2005;352:2487-98

36% RRR nonfatal MI + fatal CHD

(P =.0005)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

ASCOT-LLA: Trial Stopped Nearly 2 Years Early

Atorvastatin 10 mg No. of events: 100

Placebo (diet and exercise only) No. of events: 154

4

3

All patients counseled on

diet and exercise

Cumulative incidence (%)

2

What is approximate NNT for 1 year?

1

0

Years

Sever PS et al. Lancet. 2003;361:1149-1158.

Data Representation
• 2x2, PPV, Chi-Square
• ROC curves
ROC
• Receiver operator characteristic curves
• Radar operators’ ability to distinguish signal from noise
• Higher area under curve (AUC), higher reliability for a given test
• Plot true positives vs. false positives
Data Representation
• Correlation
• many statistical methods
Correlation of clinical data
• Is r=0.16 a strong correlation?
• Can we conclude that CRP and LDL are related?
Box plots (not common)

25th percentile, median and 75th percentile indicated in each box

Other interesting data representation
• Neater than a true scatter plot
• Simple to interpret

Nissen et al, N Engl J Med 2005;352:29-38

An example:
• Peterson RC, Thomas RG, Grandman M, Bennet D, Doody R, Ferris S, et al. Vitamin E and Donepezil for the Treatment of Mild Cognitive Impairment. N Engl J Med 2005;352:2379-88.
• Available at: http://content.nejm.org/cgi/content/full/352/23/2379
Questions:
• What kind of study is this?
• How large is the study?
• What are the inclusion/exclusion criteria?
• What is the outcome measured?
• What is the intervention?
• What are the statistical tests, and are they appropriate?
• What data representations are used?
• Is the result statistically significant?
• Is the result clinically significant?
• How does this knowledge affect my practice?