Modeling change in health status patterns over time
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Modeling Change in Health Status: Patterns over Time . Susan J. Henly, PhD, RN Methods Director Minnesota Center for Health Trajectory Research Seminar: September 24, 2008. Pain at bedtime over the 1 st post-op week.

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Modeling Change in Health Status: Patterns over Time

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Modeling Change in Health Status: Patterns over Time

Susan J. Henly, PhD, RN

Methods Director

Minnesota Center for Health Trajectory Research

Seminar: September 24, 2008


Pain at bedtime

over the 1st post-op week

Busch, S.E. (2002). Sleep patterns following an out-patient surgical procedure. Unpublished MS thesis. 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

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.


Hi (t): key features

  • 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


Functions describing change of many kinds


Change functions


Constant functions: Hi (t) = κi

For person i,

κigives the function value at every time t


Constant functions

Hi (t) = κi


Linear functions: Hi (t) = π0i + π1i t

For person i,

π0igives 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.


Linear functionsHi (t) = π0i + π1it


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 Grapher

http://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


Piece-wise functions (ex)

H1i (t) = κi, t < 0

H2i (t) = π0i + π1it, t ≥ 0

For person i,

κi gives the baseline value, π0igives 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 < 0

H2i (t) = π0i + π1it, t ≥ 0


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


Linear change: variation around the least squares fit line for 3 example persons


Piece-wise change: variation around the least squares fit line for 3 example persons


Describing and explaining patterns of change

  • Theory about change

  • Longitudinal ( ≥ 3 occasions of observation)

  • Measurement sensitive to individual change

  • Statistical models linking intra- and inter-individual change (mixed effects models)


Heart soft-touch project


Heart soft-touch outcomes: pain/tension mean comparisons


Heart soft-touch outcomes: ITV vs SC


Heart soft-touch outcomes


Heart soft-touch outcomes

ITPvs SC

ITP


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.


Person

TIME

Health

Nursing

Environment

Time for change

in the nursing metaparadigm


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