R help - Jun 2009 - "time series", "longitudinal data" or "trajectories"

Hi the list

Strictly speaking, this is not a "R" question, but I need the 
information for the
creation of a package. My question is about vocabulary: What is the 
difference between
"time series", "longitudinal data" and
"trajectories"?

Sincerely

Christophe

At 04:02 PM 6/5/2009, Christophe Genolini wrote:
>Hi the list
>
>Strictly speaking, this is not a "R" question, but I need the 
>information for the
>creation of a package. My question is about vocabulary: What is the 
>difference between
>"time series", "longitudinal data" and
"trajectories"?
>
>Sincerely
>
>Christophe
"Longitudinal" data are measurements over long periods of time, often 
at irregular periods, but consistent across subjects.

"Repeated measures" data are replicates at the same point in time, or 
over a short period of time (e.g., laboratory experiments).

"Time series" typically have constant increments of time and 
typically a stochastic character, although this term might be 
considered all-encompassing for all measurements at different times.

"Trajectory" implies a continuous curve in time, as opposed to 
discrete times. "Trajectory" also implies an underlying causal model, 
as it is a term from kinematics.

I hope this helps.

===============================================================Robert A.
LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral at lcfltd.com
Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
824 Timberlake Drive                     Tel: 757-467-0954
Virginia Beach, VA 23464-3239            Fax: 757-467-2947

"Vere scire est per causas scire"

Thanks for yours answers. So if I understand:
 - Trajectories are continuous, the other are discrete.
 - The difference between time series and longitudinal is that time 
series are made at regular time whereas longitudinal are not ?
 - Repeated measures are over a short period of time.


So if I measure the  weight of my patient daily during one week, it will 
be repeated measure ; if I measure it once a week during one year,
it will time series ; if I measure it once a week during one year but 
with some "missing week", it will longitudinal data ? 

Well I guess it is not as simple at that, but is it the idea ?

Christophe

> At 04:02 PM 6/5/2009, Christophe Genolini wrote:
>> Hi the list
>>
>> Strictly speaking, this is not a "R" question, but I need the
>> information for the
>> creation of a package. My question is about vocabulary: What is the 
>> difference between
>> "time series", "longitudinal data" and
"trajectories"?
>>
>> Sincerely
>>
>> Christophe
>
> "Longitudinal" data are measurements over long periods of time,
often
> at irregular periods, but consistent across subjects.
>
> "Repeated measures" data are replicates at the same point in
time, or
> over a short period of time (e.g., laboratory experiments).
>
> "Time series" typically have constant increments of time and
typically
> a stochastic character, although this term might be considered 
> all-encompassing for all measurements at different times.
>
> "Trajectory" implies a continuous curve in time, as opposed to 
> discrete times. "Trajectory" also implies an underlying causal
model,
> as it is a term from kinematics.
>
> I hope this helps.
>
> ===============================================================> Robert
A. LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral at lcfltd.com
> Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
> 824 Timberlake Drive                     Tel: 757-467-0954
> Virginia Beach, VA 23464-3239            Fax: 757-467-2947
>
> "Vere scire est per causas scire"
> ===============================================================>
>

At 04:54 AM 6/6/2009, Christophe Genolini wrote:
>Thanks for yours answers. So if I understand:
>- Trajectories are continuous, the other are discrete.
>- The difference between time series and longitudinal is that time 
>series are made at regular time whereas longitudinal are not ?
>- Repeated measures are over a short period of time.
>
>
>So if I measure the  weight of my patient daily during one week, it 
>will be repeated measure ; if I measure it once a week during one year,
>it will time series ; if I measure it once a week during one year 
>but with some "missing week", it will longitudinal data ?
>Well I guess it is not as simple at that, but is it the idea ?
><snip>
Not exactly.

If you measure weight daily for a week, that is a "time series" 
(equally spaced time measurements over an arbitrary period) and 
"repeated measurements" (multiple measurements on the same subject, 
whether on time or at random or in some other way).

If you measure weight weekly for each week in a year this is a "time 
series" (equally spaced time measurements) and would generally be 
called a "longitudinal" study (measurements over a lengthy enough 
time period that time-related changes are expected).

Missing data are common in "repeated measures" or "longitudinal 
studies". In "longitudinal studies", an additional problem of 
"dropouts" is present, which may be correlated with the unobserved 
measurement (i.e., "missing, not at random" or "non-ignorable, 
non-random"). Also, the long time period of a "longitudinal
study"
may create issues of measurement bias (due to drift in technique or 
clinicians over time) and change in the subject baseline state.

"Time series" is typically used in my experience for measurements 
that have a great degree of regularity (equally-spaced times, few or 
no missing data).

"Trajectory" is a term for "continuous time curve".

Examples:

Study to measure blood pressure measurement fluctuations: N subjects 
measured by M operators every 8 hr during a week. Note there is a 
general expectation of a constant mean value for each subject during 
the period, with probably short-time fluctuations. This would be 
called a "repeated measure" study, Although it could also be called a 
"time series" study, the expectation of no total time period effect 
and the possibility of missing measurements would argue against that 
term. On the other hand, if a posteriori there were little or no 
missing data, and regular time-dependent patterns were observed, its 
name might be shifted to a "time series" study.

Cohort study to measure blood pressure changes over a ten year time 
frame for treated and untreated subjects: There will be significant 
amounts of missing data, dropouts from the study and a long time 
period of observation. This would almost universally be known as a 
"longitudinal" study.

Controlled experiment to measure rates of gelation of batches of 
different gelatins: N lots of gelatin, each measured in solution at 
the same M time periods for viscosity. A continuous underlying 
viscosity vs. time curve is expected (the "trajectory") for each lot. 
Time periods are equal, and there are few missing data. The goal is 
to compare gelation trajectories. This is a "repeated measure" study, 
and might more particularly be characterized as a "time series" study.

When the subjects are living entities, usually the terms "repeated 
measures" or "longitudinal" are used. If measurements are taken
at a
single point in time, the term "cross-sectional" study is used. If 
there is a single response across time, the term "time series" is 
used. If there are multiple responses all measured at the same times 
for the subjects, the term "panel data" is used.

For controlled experiments, the terms "repeated measures" and
"time
series" are common. "Longitudinal" could be used, but generally
is not.



===============================================================Robert A.
LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral at lcfltd.com
Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
824 Timberlake Drive                     Tel: 757-467-0954
Virginia Beach, VA 23464-3239            Fax: 757-467-2947

"Vere scire est per causas scire"