Download Parametric statistics - Associate professor clinical pharmacology

How to find your way through
the jungle of statistics ...
Carl-Olav Stiller , associate professor
Clinical pharmacology
Karolinska University Hospital - Solna
17176 Stockholm
Tel: 08-5177 3261
[email protected]
Statistikens djungel - grunder
Why do we need statistics
Hypothesis testing
Hypothesis generating
Common pitfalls
Parametric or non-parametric statistics
Independent or dependent observations
Planning of research
Why do we need statistics in
research?
To
To
To
To
test hypothesis
show similarities or differences
analyse correlations
describe findings / data
Differences or similarities ?
Do I want to show differences?
Power analysis Which difference do I want to be able to detect?
Sensitivity and specificity
What can go wrong?
We find a difference which is not true
Alpha problem
We find similarity, but the groups are different t
Beta problem
Hypotes - endpoint
Primary hypotesis
The aim of the study.
Highest evidence
Secondary hypotesis / endpoint
All other tests
Lower evidence
Hypotesis generating
Lack of difference is not the
same as similarity!
If you compare small groups it is hard to
detect any difference.
In order to show similarities the groups
have to have a certain size.
Prior to start of the study you have to
define the interval for similarity.
Sensitivity or specificity
Sensitivity:
May I trust a positive outcome?
What is the likelyhood of a positive outcome
being true / correct ?
Specificity
May I trust a negative outcome?
What is the likelyhood of a negative outcome
being true / correct ?
Parametric or
non- parametric statistics?
Non-parametric statistics: Rank
order: Same, smaller, bigger
Parametric statistics: Based on normal
distribution (Gauss curve)
What kind of data do I have?
 Normal distribution:
Parametric or non-parametric statistics
 Normal distribution with cut off:
Non-parametric statistics preferred
If you use parametric statistics SD gets too low and
your precision seems to be higher than it is.
 Rank order scale:
Non-parametric statistics should be used (is it ?)
 Assement scale:
Non-parametric statistics should be used (is it?)
Rank order scales: examples
Borg scale for excertion: 1-5
 No to maximal excertion
Cardiac failure according to NYHA (New
York heart association) 1-4
Pain intensity – for example migraine headace:
0 – no pain, 1 – some pain, 2 – moderate pain, 3 –
severe pain, 4- very severe pain
Visual analog scale
Pain intensity
0:
No pain
100: Worst imaginable pain
Problem:
 Subjective assessment, everyone has different reference
frames
- 40 for one individual is not the same as 40 for another
 Inter individual variation
 VAS data are often calculated with parametric statistics ”appropiate or not? ”
Combined assessment scales
Depressions skala - Montgomery Åsberg
Olika variabler slås ihop till ett värde
Alzheimer skala - ADAS cog,
Olika förmågor som påverkas vid Alzheimer
skattas och slås ihop
Intelligenskvot
Prestationer i olika test vägs samman
Parametric or
non-parametric statistics ?
Common pit falls:
Non-parametric data are calculated with
parametric statistics
But parametric data may also be calculated
with non-parametric statistics
Control group or test before
treatment and after treatment ?
Test before and after may be useful as
pilot study to generate a hypothesis
”hypotesgenererande”
Control group is ”gold - standard” – better
data and lower risk for false positive
outcome.
Treatment of severe headache with opioids
or NSAIDs i.m. at the emergency department
Harden RN, Gracely RH, Carter T, Warner G The placebo effect in acute headache
management: ketorolac, meperidine,and saline in the emergency department.
Headache 1996 Jun;36(6):352-6
Dependent or independent
observations
Dependent observations
Control before or after treatment
Tissue from different regions of the same
individual
Independent observations
Observations in separate individuals
Common staticaal tests comparing two
or more groups
Parametric statistics
Non-parametric statistics
Two groups
Independent obs.
Unpaired t-test
Dependent obs
Paired t-test
Independent obs.
Mann-Whitney test
Dependent obs.
Wilcoxons test
Three or more groups
Independent obs.
Dependent obs
One-way ANOVA Repeated measures
(analys of variance) ANOVA
+ Tukey – alla par
+ Newman Keuls – alla par
+ Bonferroni – alla par
+ Bonferroni – selekterade par
+ Dunett – mot kontroll
Independent obs.
Kruskall Wallis
Dependent obs.
Friedman test
+ Dunns test
Standard deviation SD
Standard deviation SD
Effect
80
60
40
20
0
Control Drug A
Drug B
Standard error of the mean
SEM = SD /√ n
SEM
Effect
80
60
40
20
0
Control Drug A
Drug B
Confidence interval
 Correct illustration av effect range
95 % konfidens-intervall
Effect
80
60
40
20
0
Control Drug A
Drug B
What is a good clinical study?
Relevant patient population
Sufficient size / power
Clinically relevanta effect outcome
Reference treatment using relevant doses
Double blind / randomised
Sufficient follow up time
Few withdrawals
Common pit falls …..
Preliminary data
Limited number of participants
No control group
Open trial or single blind trials
Beware …..
... Control group with inadequate treatment.
 Second best alternative
 Gold standard
Dose selection of drug and comparator ?
Beware …..
No randomisation
 It is not the treatment, but the group
selection which explains the outcome
Beware ..
Selection criteria
Hard selection: Results may not be
generalisable.
No selection: The treament effect can be
blurred by other aspects.
Beware …..
... Subgroup analysis not planned in
advance
Large number of subgroups analysed
Risk for difference just by chance
Beware…..
Outcome was analysed with unproven
methods
Surrogate outcome
Short follow up
Drop out
Beware …..
... Differences in adverse events were
not analysed
 Rare adverse events are not detected in RCT
Beware …..
... Results are only presented as percent change
and not absolute difference
A large relative change – for example 50 %
decreased may soud impressive, but may be not
important if the risk is low
Summary
Select statistics before you start your
experiment
Analyse your data
Mind pit falls
Good luck