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Transcript of 1 Modeling Change in Health Status: Patterns over Time Susan J. Henly, PhD, RN Methods Director...
1
Modeling Change in Health Status: Patterns over Time
Susan J. Henly, PhD, RNMethods DirectorMinnesota Center for Health Trajectory Research
Seminar: September 24, 2008
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Post-Op Day
876543210
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Pain at bedtime over the 1st post-op week
Busch, S.E. (2002). Sleep patterns following an out-patient surgical procedure. Unpublished MS thesis. Busch, S.E. (2002). Sleep patterns following an out-patient surgical procedure. Unpublished MS thesis. University of Minnesota, Minneapolis. University of Minnesota, Minneapolis.
Why study change in health status?
Within persons, health status varies over time
Accurate description of health status over time is essential to understanding health behaviors and illness responses
Intervention assumes that health status is malleable-- intra-individual change can be predicted and “controlled” (influenced by nursing actions)
Inter-individual differences in intra-individual change can be explained
Some ideas about change
To be or cause to be different
To alter the course of
Naturalistic change
Experimentally induced change
Operationalizing change
Increment: difference on 2 occasions
Rate: speed, velocity, pace
Pattern: form, shape, model
Changing ideas about studying change
Health as a function of time
Purpose
Describe mathematical functions that can be used to model change in health status
Characterize intra-individual change using personalized functions
Recognize that variation in parameters of personalized functions represents inter-individual differences in change
Comment on formulation of hypotheses to explain inter-individual differences in change using parameters of personalized functions
About functions
A function is a rule that maps every point in a defined domain t with one and only one value in its range H
Function rules are defined by their parameters
Functions can be described using equations
Functions can be displayed in tables
Functions can be depicted by their graphs
The general function
Time ► Function ► Health
Hi = fi (t)
For each person, at each point in time, for any given indicator of health status,there is one and only one value for health status.
Time is the primary “predictor” variable
Health status is “outcome”
Health as a function of time shows patterns of change
Each person follows their own pattern: everyone has their own set of parameters
Hi (t): key features
Functions describing change of many kinds
Function Rule Parameters
Constant H (t) = κ κ
Linear H (t) = π0 + π1t π0 , π1
Quadratic H (t) = π0 + π1t + π2t2 π0 , π1 , π2
Polynomial H (t) = πntn + πn-1tn-1+ … + π1t + π0 π0 to πn
Exponential H (t) = α + (ξ – α) exp (ρt) α, ξ, ρ
Sine H (t) = α sin (ωt + θ) + δ α, ω , θ, δ
Piece-wise H (t) = H1 (t), t < tt,
H (t) = H2 (t), t ≥ tt,
Parameters of
H1 and H2
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Change functions
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-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
Time
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Constant
Linear
Quadratic
Polynomial
Exponential
Sine
Piece-wise
Constant functions: Hi (t) = κi
i κi t Hi (t)
1 -.5 -3 -.5
0 -.5
4 -.5
2 5 -2 5
0 5
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3 8 -4 8
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For person i, κi gives the function value at every time t
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Constant functions Hi (t) = κi
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-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
Time
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Linear functions: Hi (t) = π0i + π1i t
i π0i π1i t Hi (t)
1 1.21 .18 -5 0.36
0 1.21
8 2.58
2 1.48 .45 0 1.48
1 1.93
8 5.13
3 4.01 .27 -2 3.48
1 4.28
2 4.55
For person i, π0i gives the function value at t0 (intercept) and π1i gives the rate of change over time (slope). Note that selection of t0 is critical to scientific interpretation of the parameters.
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Linear functionsHi (t) = π0i + π1it
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-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
Time
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Quadratic and higher order polynomial functions
General polynomial form is:
H (t) = πntn + πn-1tn-1+ … + π1t + π0
Quadratic:
H (t) = π2t2 + π1t + π0
Cubic:
H (t) = π3t3 + π2t2+ π1t + π0
And so on with higher order functions of time
Polynomial Equation Grapherhttp://www.math.umn.edu/~garrett/qy/Quintic.html
Exponential functions
Exploring sine functions for periodic change
Variations on the Sine Function
The website is:
http://www.analyzemath.com/trigonometry/sine.htm
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For person i, κi gives the baseline value, π0i gives the function value at the transition point, which is also the intercept in the example and π1i gives the rate of change over time (slope) after the transition. In this example, the time of transition is known. Sometimes, the transition point is itself a parameter to be estimated.
Piece-wise functions (ex)H1i (t) = κi, t < 0H2i (t) = π0i + π1it, t ≥ 0
i κi π0i π1i t Hi (t)
1 3.91 3.91 .30 -1 3.91
2 4.52
4 5.14
2 5.02 5.02 .37 -2 5.02
0 5.02
5 6.88
3 3.54 3.54 .31 0 3.54
3 4.47
7 5.70
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Piece-wise functions (ex)H1i (t) = κi, t < 0H2i (t) = π0i + π1it, t ≥ 0
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-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
Time
Hea
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Statistical models for individual change
Longitudinal data on 3 or more occasions
Sensible metric for time
Theory about change
Graphs of individual cases to identify form of change
Personal parameters estimated to produce smoothed curves for each persons change pattern
Random coefficients in a mixed effects model
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Linear change: variation around the least squares fit line for 3 example persons
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Piece-wise change: variation around the least squares fit line for 3 example persons
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Time
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Theory about change
Longitudinal ( ≥ 3 occasions of observation)
Measurement sensitive to individual change
Statistical models linking intra- and inter-individual change (mixed effects models)
Describing and explaining patterns of change
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Heart soft-touch project
Heart soft-touch outcomes: pain/tension mean comparisons
POD SC AIT t p
1 Mean 3.5 2.4 -2.52 .01
SD 2.6 1.9
2 Mean 2.1 1.3 -2.10 .04
SD 2.0 1.3
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Heart soft-touch outcomes: ITV vs SC
Measurement Time
4.03.53.02.52.01.51.0.50.0-.5-1.0
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Condition
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Heart soft-touch outcomes
SC vs AIT Standard Care Integrative Therapies
Measurement Time
4.03.53.02.52.01.51.0.50.0-.5-1.0
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Time of Observation
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Heart soft-touch outcomes
ITP vs SC SC
Measurement Time
4.03.53.02.52.01.51.0.50.0-.5-1.0
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Condition
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Time of Observation
4.03.53.02.52.01.51.0.50.0-.5-1.0
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Time of Observation
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Nursing practice: people and change
Baby is off to a healthy start.
Patient is going downhill fast.
She recovered quickly after the nurse lifted her spirits.
He had a rocky post-op course.
When he exercised regularly, his glucose levels decreased and stabilized.
She reacted to her husband’s death with an intense sense of depression, but soon returned to her usual sunny self.
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Person
Environment
NursingHealth
TIME
Time for change in the nursing metaparadigm