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Longitudinal data analysis: <=
span
style=3D'position:absolute;top:26.75%;left:5.24%;width:89.88%;height:8.5%'=
>challenges and prospects in public health settings<=
span
style=3D'font-size:91%;mso-special-format:lastCR;display:none'>
Max Petzold
Nordic
School of Public Health &
Karolinska
Institute
Feb
2006
Training Course in Reproductive Health/
Se= xual Health Research
Se= xual Health Research
Ge=
neva
2006
Classical clinical examples
=
Pharmacokinetics
- a substans in Nexium
- a substans in Nexium
WHO rectal Artesmi=
sinins
=
Haemoglobin
levels after antimala=
rials,
Tanzania
Specific characteristics
<=
span
style=3D'mso-special-format:bullet;position:absolute;left:-4.35%'>•=
Repeated
measurements on several objects (individuals, families,
villages)
<=
span
style=3D'mso-special-format:bullet;position:absolute;left:-4.14%'>•=
Normally
the objects are followed over time, e.g. pharmacokineti=
cs, measurements
of performance, baseline/follow up.
•=
Longitudinal analysis
- It is a matter of correctness and richness
Correctness
- uncorrelated and norma=
lly distributed
residuals
Don´t forget model diagnostics!<=
span
lang=3DEN-US style=3D'font-size:91%;mso-special-format:lastCR;display:none=
'>
Richness
- to make inference about
the individual as well as the group
But,
repeated measurements on individuals
Now
increasing with age within an individual
– = Separate individual from group effect
– = Separate individual from group effect
BRAC
- Both classical
longitudinal studies and pre/post control group designs
Targeted intervention for the ultra-poor: does =
it <=
span
lang=3DEN-GB style=3D'font-size:64%'>make any difference in their
health-seeking behaviour?
Syed Masud Ahmed, M= ax Petzold, Zarina Nahar Kabir, Göran Tomson
Syed Masud Ahmed, M= ax Petzold, Zarina Nahar Kabir, Göran Tomson
Intervention ef=
fect:
(38.7-22.7) – (31.8-25.0) =3D 9.20
Table: Proportion seeking ’formal allopathic’ car=
e
But how to calc=
ulate
CIs and control for confounding?
•Utilize effect modification / interaction in=
a regression
•Add confounders, it is regression
•Observe the dependency over time!
•Difficulties when having binary data
Nutrition – Farhana Haseen
•Design: Pre/post intervention control group <=
/span>design
•Measured on family level
•Energy intake as outcome
•Did the intervention make a difference?
=
•Other covariates (confounders)?
Energy intake related to sex?=
<=
span
class=3DBB style=3D'position:absolute;left:-4.43%'>•Phase: 0=3DBase=
line,
1=3DFollow up
<=
span
class=3DBB style=3D'position:absolute;left:-4.43%'>•Stup: 0=3DContr=
ol,
1=3DIntervention
<=
span
class=3DBB style=3D'position:absolute;left:-3.92%'>•Interventiontim=
e:
Interaction Phase*Stup
<=
span
class=3DBB style=3D'position:absolute;left:-4.43%'>•Sex: 0=3DFemale,
1=3DMale
<=
span
style=3D'visibility:hidden'>•
Data collection
•If process, how to measure and how to model
process between measurements?
•When did the event stop? Recurrent events/episodes,
how to count? Varying severeness
<=
span
class=3DBB style=3D'position:absolute;left:-4.43%'>•Recall bias
=
;
<=
span
class=3DBB style=3D'position:absolute;left:-4.43%'>•Varying covaria=
tes
(e.g. education)
Data organisation
•If dates, give start date and date for measurement/survey
•Give all covariates, also shifting
•Wide or long format? Wide is sparse, but long
is what we need. Reshape. Use numbered variable names.=
Be consequent.=
Remember
- both a challenge and a richness
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