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Statistical Strategies Used in Project Empowerment Research. Jessica M. Ketchum Assistant Professor Virginia Commonwealth University. Statistics and Research. Statistics is one of the many tools you will need to conduct quality research.

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statistical strategies used in project empowerment research

Statistical Strategies Used in Project Empowerment Research

Jessica M. Ketchum

Assistant Professor

Virginia Commonwealth University

statistics and research
Statistics and Research
  • Statistics is one of the many tools you will need to conduct quality research.
  • It is essential that you acquire some basic training in statistics to better conduct you research.
    • Analysis
    • Communication
    • Interpretation
    • Limitations
the scientific method
The Scientific Method
  • The scientific method is the process by which scientific information is collected, analyzed, and reported in order to produce unbiased and replicable results in an effort to provide an accurate representation of observable phenomena.
the scientific method4
The Scientific Method
  • Recognized universally as the only truly acceptable way to produce new scientific understanding of the world around us.
  • Based on an empirical approach
    • Decisions and outcomes are based on data
making an observation
Making an Observation
  • First an observation is made of a phenomenon or a group of phenomena.
  • This leads to the formulation of questions or uncertainties that can be answered in a scientifically rigorous way.
  • For example, … one may observe that minorities may have worse employment outcomes 1 year post injury than non-minorities.
formulating a hypothesis
Formulating a Hypothesis
  • Next, a scientific hypothesis is formulated to explain an observation and to make quantitative predictions of new observations.
  • Often hypotheses are generated as a result of extensive background research and literature reviews.
  • May be stated as research hypotheses or statistical hypotheses.
  • The role of the statistician in this step of the SM is to state the hypothesis in a way so that valid conclusions may be drawn and to correctly interpret the results of such conclusions.
designing an experiment
Designing an Experiment
  • An experiment is then designed that will yield the data necessary to validly test an appropriate statistical hypothesis.
  • Improperly designed experiments are the leading cause of invalid results and unjustified conclusions.
  • Well designed experiments ensure the measurement of a phenomenon is both accurate (reliable) and precise (valid).
    • Accuracy refers to the correctness of a measure
    • Precision refers to the consistency of a measure
designing an experiment8
Designing an Experiment
  • The DOE depends on the type of data that needs to be collected to test a specific hypothesis.
  • Data can be collected in a variety of ways
    • The standard is experimentation.
    • A true experimental design is one that randomly assigns subjects to an experimental (treatment) group and a control group.
      • If all other potential factors are controlled, a cause-effect relationship may be tested.
  • A statistician is a critical component for DOE
drawing conclusions
Drawing Conclusions
  • After executing the experiment or research, one would hope to have collected the data necessary to draw conclusions, with some degree of confidence, about the hypotheses that were posed.
  • Often, hypotheses need to be modified and retested with new data and a different design.
  • Results are rarely considered to be conclusive.
    • Results need to be replicated, often a large number of times, before scientific credence is granted them.
traumatic brain injury tbi
Traumatic Brain Injury (TBI)
  • …Dr Arango-Lasprilla spoke to you earlier about the TBI national database…
spinal cord injury sci
Spinal Cord Injury (SCI)
  • The occurrence of an acute traumatic lesion of neural elements in the spinal canal, resulting in temporary or permanent sensory and/or motor deficit.
  • Traumatic cases involve external events that trigger the injury rather than dieases or degeneration.
nidrr nscisc
  • Began in 1975 with funding from NIDRR. Data were collected retrospectively back to 1973 and prospectively since 1975.
  • Internationally recognized as the authoritative source of information about the epidemiology and outcomes in SCI.
national database
National Database
  • Demographic data and information on acute and rehabilitation experiences and treatment outcomes (Form I)
    • 260 variables
  • And during follow-up (Form II) at 1, 2, 5, and every 5 years thereafter.
    • 174 variables
  • Over 30,000 cases from 27 centers with follow-up of up to 30 years.
form i
Form I
  • Demographic characteristics
    • Age, sex, race/ethnicity, Hispanic origin, English primary language, marital status, residence, zip code, living with, level of education, employment, Veteran of US Military Forces
  • Injury characteristics
    • Date of injury, ICD-9 external causes of injury code, nature of injury, vertebral injury, associated injury, spinal surgery, halo device, TLSO at rehab discharge
  • Neurologic Exam Findings
    • Category of Neurologic Impairment, ASIA impairment scale, anal sensory and motor levels, level of preserved neurologic function
  • Functional Independence Measure (FIM) Motor scale
  • “Costs” of treatment/services
    • Length of stay, charges, payor source
form ii
Form II
  • Changes in demographics
    • Residence, marital status, education, employment
  • Rehospitalization (how long and why)
  • Self perceived health status
  • FIM
  • SWLS
  • Craig Handicap Assessment and Reporting Techniques (CHART) – Short Form
  • Patient Health Questionnaire – Brief Version
  • Alcohol Use
  • Assistive Technology
  • Neurologic exam findings (year 1 or 2 follow-up only)
an example
An Example
  • Observation: Employment outcomes are worse for minorities than non minorities at one year post TBI.
  • How could we measure employment outcomes?
    • Proportion of persons w/ TBI employed TBI out all persons w/ TBI at 1 yr post injury.
    • separately for minorities and non-minorities.
  • Research hypothesis: Employment rates are lower for minorities than non-minorities at 1 yr post TBI.
  • Statistical hypothesis: The proportion of employed minorities with TBI is lower than the proportion of employed non-minorities with TBI at 1 yr post injury.
design an experiment
Design an Experiment
  • Can we create a true experimental design?
    • Cannot randomize people to minority and non-minority groups.
    • Cannot establish a true cause-effect relationship.
  • Need to measure employment rates for minorities and non-minorities at 1 yr post TBI.
    • Use the TBIMS national database.
from 2006
From 2006…
  • Employment Rates 1 yr post TBI:
    • Non-Minorities: 901/2638 = 0.342 = 34.2%
    • Minorities: 196/1173 = 0.167 = 16.7%
  • The odds of being employed at 1 yr follow-up is 2.6 time greater for non-minorities than minorities.
  • Looks compelling…
  • But are these disparities really just due to minority status???
  • Are there other factors that may confound the relationship between minority status and employment rates?
  • A confounding factor is one that correlates with both the dependent and the independent variable.
  • Studies need to control for these factors to understand the true relationship between the independent and dependent variables.
  • For example, Minorities were less likely than Non-minorities to be employed at injury (57.1% vs 66.8%) and pre-injury employment status is highly related to post-injury employment status.
  • Can you think of any other potential confounding factors???
  • We considered:
    • Sex, pre-injury marital status, pre-injury employment status, pre-injury level of education, cause of injury, Age, DRS at rehab discharge, FIM at rehab discharge, GCS score, and PTA.
final model
Final Model
  • Found that the odds of being employed at 1 yr follow-up were 2.17 times greater for non-minorities than minorities
    • When controlling for pre-injury employment status, DRS at discharge, Age, marital status, sex, level of education, and cause of injury.
    • Arango-Lasprilla, J.C., Ketchum, J.M., Williams, K., Kreutzer, J.S., Marquez de la Plata, C.D., O’Neil-Pirozzi, T.M., and Wehman, P. (2008). Racial Differences in Employment Outcomes after Traumatic Brain Injury. Archives of Physical Medicine and Rehabilitation 89 (5):988 – 995.
upcoming project
Upcoming Project
  • What is the rate of unemployment for Hispanics and Blacks with disability (TBI) compared to Whites at 1, 2, and 5 years post-injury?
  • How do the rates of unemployment post-TBI injury among the three groups (Whites, Blacks and Hispanics) change over time (1-5 years post-injury)?
specific hypotheses
Specific Hypotheses
  • After adjusting for demographic and injury characteristics:
    • There will be significant differences among Caucasians, Hispanics, and African Americans in unemployment rates at 1, 2, and 5 years post TBI
    • Unemployment rates will significantly change over time for each race/ethnicity group.
  • Specifically, African Americans and Hispanics will show greater rates of unemployment post-injury as compared to Caucasians at all follow-up years and there will be significant decreases in unemployment rates over time for all three races/ethnicities (1-5 years) post-injury.
    • We do not expect the decline in unemployment rates to differ across the three race/ethnicity groups.
getting involved
Getting Involved
  • If you would like to get involved with this research project or you have your own research question you would like to address using the TBI or SCI national databases, please contact myself or Dr. Arango-Lasprilla