Loading in 5 sec....

The use of graphical models in multi-dimensional longitudinal dataPowerPoint Presentation

The use of graphical models in multi-dimensional longitudinal data

- 309 Views
- Updated On :

The use of graphical models in multi-dimensional longitudinal data. Volkert Siersma Department of Biostatistics University of Copenhagen IBS Nordic Regional Conference Oslo, June 2-4, 2005. Weight control in type 2 diabetes (T2DM) patients. Diabetes Care in General Practice*:

Related searches for The use of graphical models in multi-dimensional longitudinal data

Download Presentation
## PowerPoint Slideshow about 'The use of graphical models in multi-dimensional longitudinal data' - Melvin

**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

### The use of graphical models in multi-dimensional longitudinal data

Volkert Siersma

Department of Biostatistics

University of Copenhagen

IBS Nordic Regional Conference

Oslo, June 2-4, 2005

Weight control in type 2 diabetes (T2DM) patients longitudinal data

Diabetes Care in General Practice*:

T2DM is an increasingly common illness that is linked to considerable excessive mortality. There are many indications that treatment (…) can postpone the development of diabetic complications. Treatment of T2DM is primarily done in general practice, where the results are not satisfactory.

RCT: Structured vs. Routine care**. 1428 newly diagnosed T2DM patients included among 600 Danish GPs. The structured care group is regularly – every third month – reviewed for a period of about 6 years.

This observed cohort inspires the following discussion.

* http://www.gpract.ku.dk/Ansatte/olivarius.htm#diabetes

** Olivarius, N.d.F., Beck-Nielsen, H., Andreassen, A.H., Horder, M. and Pedersen, P.A. (2001) Randomised controlled trial of structured personal care of type 2 diabetes mellitus. Ann. Intern. Med., 323(7319): 970-975

99kg longitudinal data

3-monthly consultations

- We must control your weight!
- Next time we meet you’ll have:
- …kept current weight.
- …lost x kg.
- ...ah, forget about it.
- Let’s set our next appointment in about 3 months…

Lose 2 kg

3-monthly consultations longitudinal datanext consultation

How do we decide?

97kg

- Very fine, you’ve lost 2 kg!
- Next time we meet you’ll have:
- …kept current weight.
- …lost x kg.
- ...ah, forget about it.
- Let’s set our next appointment in about 3 months…

?

The effect of weight control longitudinal data

55kg

This strategy has to be evaluated to the degree in which certain long-term goals have been fulfilled

Not the effect of a single goal, but the effect of a sequence of goals, a goal setting strategy, has to be evaluated

Markov dynamics longitudinal data

Wt-2

Wt

Wt-1

or:

Wt = f(Wt-1,Wt-2,Gt-1,Gt-2)

Gt-2

Gt-1

In principle a mere simulation engine, but for inference purposes a (graphical) model of some sort is assumed.

Causality longitudinal data

The model, estimated from the data, can be used as a simulation engine to simulate weight development relative to a sequence of goals when the relationships are causal.

Specifically, when there are no unmeasured confounders to the direct relationships with Wt.

Then the do conditional probability, the one used when dictating the goal-setting in a simulation programme, is the same as the see or observed conditional probability, the one we estimate from the data.

Causality longitudinal datacontinued

Wt-2

Wt

Wt-1

?

A variable should be included in the model if:

Gt-2

Gt-1

- It confounds a relation between Wt and another variable

- It has a relation with Wt and is might be used in a strategy

Causality longitudinal datacontinued

Some causality is induced by temporal relations.

Causality of the model can be constructed if the mechanism is well-known.

In behavioural studies, causality has to be introduced by adding the potential confounders to the model. This often leads to large models and may render the model unstable.

Assessing a strategy longitudinal data

Within the model there is no information to determine the goal for the next session beyond the present weight and the weight and goal at the previous session.

Thus, a strategy is a (deterministic) function to determine a goal for the next session from the present weight and the weight and goal at the previous session.

The model for the dynamics is used to simulate the next weight, given the goal and the previous weight.

Given start values (W0 and W1, no goal is set at the first session) a series of weights and weight goals can be simulated.

Assessing a strategy longitudinal datacontinued

- A long-term yield is derived from the simulated weight series. examples include:
- normality: BMI<25 after 10 sessions
- stability: sum of weight differences.

- The process of simulating a weight development and calculating the yield is done several times to get an empirical estimate of the distribution of the yield.
- This distribution can be contrasted to a similarly derived distribution of the yield of a null strategy, i.e. ”no goal set” or indeed any other interesting strategy.

Optimising a strategy longitudinal data

A strategy can be viewed as a function of weight and previous goal with several parameters.

Optimising yield w.r.t. the strategy parameters is a difficult, often high-dimensional, optimisation problem.

Heuristic search methods:

Start with a sensible strategy

Set as current strategy

Evaluate neighbouring strategies

Choose best of these

Repeat until convergence

A collection of generic strategies should be constructed for fast evaluation of intuitive strategies, start values for the optimisation, and base camps for other strategies.

Optimal strategy longitudinal datascope

The simulated weight series and thus a strategy is evaluated conditional on the start values of the process.

An optimal strategy is therefore also only optimal for patients with these start values.

Strategy analysis longitudinal data

- Operationalised optimisation could take the form of a black box on-line data mining exercise.
- Strategy analysis on a more general level is wanted in many cases.
- An overview of the yields of various generic strategies
- An overview of the strategy effect of some sort for the most usual combinations of start values.
- A description or visualisation of some sort of the optimal strategy

Men with 30<BMI<35 at first two post-diagnosis sessions, without heart condition, good HbA1c levels and kidney functioning.

Weight control

Estimated (10.000 simulations) probability of normal body weight (BMI<25) after 5 years (20 sessions)

Men with 30<BMI<35 at first two post-diagnosis sessions, without heart condition, good HbA1c levels and kidney functioning.

Weight control continued

The effect of brute force: B&C

null

0.0098

full

0.1504

min

0.0000

max

0.1999

300 iterations of a simulated annealing instance. Starting from generic strategy A&B

Markov dynamics without heart condition, good HbA1c levels and kidney functioning.baseline covariates

C

Wt-2

Wt

Wt-1

Gt-2

Gt-1

Markov dynamics without heart condition, good HbA1c levels and kidney functioning.time

Wt-2

Wt

Wt-1

dt

Gt-2

Gt-1

Markov dynamics without heart condition, good HbA1c levels and kidney functioning.time framework

t

Wt-2

Wt

Wt-1

dt

Gt-2

Gt-1

Markov dynamics without heart condition, good HbA1c levels and kidney functioning.multivariate outcome

Ht-2

Ht

Ht-1

Wt-2

Wt

Wt-1

Gt-2

Gt-1

Markov dynamics without heart condition, good HbA1c levels and kidney functioning.a chain graph model

Disease markers

Baseline covariates

Disease markers

time t-1

Disease markers

t

dt

Disease markers

t

t-1

t-2

t-k

Treatment indicators

Treatment indicators

Treatment indicators

t-2

t-1

t-k

Chain graph model tools without heart condition, good HbA1c levels and kidney functioning.

- Much of the analysis is the investigation of large chain graph models. Several types of inference are needed.
- Recall our goals: this is not an ordinal variable. Methods are needed to relate partly ordinal variables.
- Finding interactions and including them in the model.
- Relate sets of variables to other sets of variables.

Next time we meet you’ll have:

a) …kept current weight.

b) …lost x kg.

c) ...ah, forget about it.

Sets of ordinal variables can be identified with partly ordinal variables: pseudo gamma

Using graphical models without heart condition, good HbA1c levels and kidney functioning.

- The graphical model serves as a simulation engine.
- Inference on the graphical model is used to check the causality of the relations that are used to simulate the sequences of disease markers and treatment.
- Inference on the graphical model can reveal factors to be included in or excluded from a strategy.
- Examination of interactions can reveal influences of passing time and unrealistic goal setting.

Download Presentation

Connecting to Server..