Conducting Research 2 Dr Rasha Salama PhD Community Medicine Research • Research is the systematic collection analysis and interpretation of data to answer a certain question or by gwk12915

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									Conducting Research (2)


Dr. Rasha Salama
PhD. Community Medicine
Research
• Research is the systematic
  collection, analysis and
  interpretation of data to answer
  a certain question or solve a
  problem

• It is crucial to follow cascading
  scientific steps when conducting
  one’s research
                 Steps of Scientific Research
Selection of area                           no need for study

Selection of topic                          answers found

Crude research question                      Literature review
                                            no answer


Refined research question

Research hypothesis, goals and objectives

Study design                         Ethical issues

Population & sampling

Variables            confounding             bias

Research tools

Pilot study

Work plan

Collection of data

Data management

Interpretation

Reporting
                  1. Study Design


Descriptive studies                                Analytical studies


 Case report
                              Observational                      Experimental
                                 studies                           studies
  Case serial                                                         Randomized
                                                                    Controlled Clinical
   reports                                                                trials
                      Case-control     Cohort
                                                                      Randomized
Cross-sectional         studies        studies                       Controlled field
    studies                                                               trials

                                                                    Non-randomized
                                                 Retrospective       experiments
                             Prospective
  Ecological                                      (historical)
   studies
How could we select the best Study
design ?

 • Purpose of the study
 • State of existing knowledge (in
   relation to study question)
 • Characteristics of the study
   variables
 • Latency
 • Feasibility
Purpose of the study

• Study of etiology:
   –   Ecologic
   –   Cross-sectional
   –   Case-control
   –   Cohort
   –   Intervention

• Study of therapy:
   – Lab experiments
   – Clinical trials
   – Community intervention
State of existing knowledge (in
relation to study question)
• New idea:
   – Ecologic
   – Cross-sectional
• New hypothesis:
   – Cross-sectional
   – Case-control
• Newly claimed association:
   – Case-control: replication, confirmation
   – Cohort: stronger evidence towards
     causation
• Confirmed association:
   – Experiment/intervention: to prove
     causation
    Characteristics of the study variables
• Very rare exposures: case-control design is
  NOT suitable since it looks for exposure. A very
  large number of subjects is required.

• Very rare disease: cohort design is NOT
  suitable since it looks for outcome. Follow-up
  of a huge number is required.

• Acute disease: prevalence studies are not
  suitable

• Risky exposures: clinical trials are unethical

• Unavailable data: record-based studies are
  not suitable.
Latency
• For diseases with very long
  latency, the costs of
  concurrent cohort studies
  or clinical trials are
  prohibitively high.
Feasibility
•   Time
•   Manpower
•   Equipment
•   Money
    2. Population and Sampling
•    Sampling is the process of selection
     of a number of units from a defined
     study population.

The process of sampling involves:

    1.   Identification of study population
    2.   Determination of sampling population
    3.   Definition of the sampling unit
    4.   Choice of sampling method
    5.   Estimation of the sample size
Identification of study population

• The study or target
  population is the one upon
  which the results of the
  study will be generalized.

• It is crucial that the study
  population is clearly
  defined, since it is the most
  important determinant of
  the sampling population
Determination of sampling population

 • The sampling population is the
   one from which the sample is
   drawn.

 • The definition of the sampling
   population by the investigator is
   governed by two factors:
    – Feasibility: reachable sampling
      population
    – External validity: the ability to
      generalize from the study results to
      the target population.
Definition of the sampling unit

• The definition of the
  sampling unit is done by
  setting:
  – Inclusion criteria
  – Exclusion criteria
  (exclusion criteria are not the
    opposite of inclusion criteria)
Choice of sampling method
• Non probability sampling
• Probability sampling
Non probability sampling:

• Types of non probability
  sampling:
   – Convenience sampling
   – Quota sampling


• Not recommended in medical
  research:
 It is by far the most biases sampling
  procedure as it is not random (not
  everyone in the population has an
  equal chance of being selected to
  participate in the study).
Probability sampling
• “There is a known non-zero
  probability of selection for
  each sampling unit”

• Types:
  –   Simple random sampling
  –   Systematic random sampling
  –   Stratified random sampling
  –   Multi-stage random sampling
  –   Cluster sampling
  –   Multi-phase sampling
Simple random sample
• In this method, all subject or
  elements have an equal
  probability of being selected.
  There are two major ways of
  conducting a random sample.
• The first is to consult a random
  number table, and the second
  is to have the computer select
  a random sample.
Systematic random sample
• A systematic sample is
  conducted by randomly
  selecting a first case on a list of
  the population and then
  proceeding every Nth case
  until your sample is selected.
  This is particularly useful if your
  list of the population is long.
• For example, if your list was the
  phone book, it would be easiest
  to start at perhaps the 17th
  person, and then select every
  50th person from that point on.
Stratified sample
• In a stratified sample, we
  sample either proportionately
  or equally to represent various
  strata or subpopulations.
• For example if our strata were
  cities in a country we would
  make sure and sample from
  each of the cities. If our strata
  were gender, we would sample
  both men and women.
Multistage sampling
       Country

     Provinces

       Cities

     Districts

     Households


       Person
Cluster sampling
• In cluster sampling we take a
  random sample of strata and
  then survey every member of
  the group.
• For example, if our strata were
  individuals schools in a city, we
  would randomly select a
  number of schools and then
  test all of the students within
  those schools.
Multi-phase sample

        Population



          Sample          Test 1




         Sub-sample   Test 2
Estimation of the sample size
“how many subjects should be studied?”

• The sample size depends on the
  following factors:

  I. Effect size
  II. Variability of the measurement
  III. Level of significance
  IV. Power of the study
I. Effect size
“magnitude of the difference to be
 detected”

– A large sample size is needed for
  detection of a minute difference.
  Thus, the sample size is inversely
  related to the effect size.
II. Variability of the measurement:

  – The variability of
    measurements is reflected
    by the standard deviation or
    the variance.

  – The higher the standard
    deviation, the larger sample
    size is required. Thus, sample
    size is directly related to the
    SD
 III. Level of significance:


• Relies on α error or type I error. The
  maximum level of α has been
  arbitrarily set to 5% or 0.05.

• Alpha error can be minimized to
  0.01 or even 0.001 but this
  consequently increases the
  sample size. Thus, sample size is
  inversely related to the level of α
  error.
IV. Power of the study:

• The power of the study is the
  probability that it will yield a
  statistically significant result. It is
  related to β error or type II error.

• Power is equal to (1- β),
  consequently the power of the
  study is increased by decreasing
  the beta error. Thus, sample size
  is inversely related to the level
  of β error or directly related to
  the power of the study.
3. Collection of Data
• Data collected are “variables”
• Variables are classified
  according to their:
  – Type:
     • QT (continuous, discrete)
     • QL ( ordinal, nominal)
  – Role in the study:
     • Dependent
     • Independent
  – Relationship with other study
    factors:
     •   Main study variables
     •   Confounding variables
     •   Effect modifiers
     •   Intermediate factors
Methods of collection of data (research
tools)

• Selection of the suitable
  technique depends on:

  –   The availability of information
  –   The type of data
  –   The resources available
  –   The characteristic of the tool
  Research tools
• Most important techniques:

  – Using available information
    (records)
  – Observation (checklist)
  – Self-administered questionnaire
  – Interviewing (individual/group)
  – Measuring (all lab tests and other
    investigations)
Choosing the Format of Your
questionnaire Questions
• Fixed alternative
  – Yes/No
     • Reliable
     • Not powerful
  – Likert
• Open-ended
  – May not be properly answered
  – May be difficult to score
Choosing the Format of Your
Interview
• Unstructured
  – Interviewer bias is a serious problem
  – Data may not be hard to analyze
• Semi-structured
  – Follow-up questions allowed
  – Probably best for pilot studies
• Structured
  – Standardized, reducing interviewer bias
Editing Questions: Nine Mistakes to
Avoid
1. Avoid leading         5. Avoid negations
   questions             6. Avoid irrelevant
2. Avoid questions          questions
   that invite the       7. Avoid poorly
   social desirability      worded
   bias                     response options
3. Avoid double-         8. Avoid big words
   barreled              9. Avoid
   questions                ambiguous
4. Avoid long               words & phrases
   questions
Measurements Errors
• Definition of “error”:
“A false or mistaken result obtained
  in a study or an experiment” John
  last, 2001.

• Types of errors:
   – Systematic error: bias:
   “ an error having a certain magnitude
      and direction repeated with every
      measurement”
   – Random error:
   “ error with no fixed pattern of
      magnitude or direction”
• Sources of errors:
  – Subject
  – Observer
  – instrument
                        Bias


Design                           Information
 Bias                                Bias
                               (observer bias)
                                      Interviewer bias
 sample bias
                                     Measurement bias
 Study selection bias                (intra and inter obs. Bias)
                                      Reporting bias
 Response bias                        Recall bias
                                     Technical bias
Design bias
Selection bias
• Selection bias is a distortion of
  the estimate of effect resulting
  from the manner in which the
  study population is selected.

• This is probably the most
  common type of bias in health
  research, and occurs in
  observational, as well as
  analytical studies (including
  experiments).
a. Prevalence-incidence
  bias
• This type of bias can be introduced
  into a case-control study as a result of
  selective survival among the
  prevalent cases.

• In selecting cases, we are having a
  late look at the disease; if the
  exposure occurred years before, mild
  cases that improved, or severe cases
  that died would have been missed
  and not counted among the cases.
b. Admission rate (Berkson’s) bias
• This type of bias is due to selective factors
  of admission to hospitals, and occurs in
  hospital-based studies.

• The diseased individuals with a second
  disorder, or a complication of the original
  disease, are more likely to be represented
  in a hospital-based sample than other
  members of the general population.

• Differential rates of admission will be
  reflected in biased estimates of the relative
  risks.
• Non-response bias
• This type of bias is due to refusals to
  participate in a study.

• The individuals who do not participate
  are likely to be different from
  individuals who do participate. Non-
  respondents must be compared with
  respondents with regard to key
  exposure and outcome variables in
  order to ascertain the relative degree
  of non-response bias.
• Ascertainment or information
  bias
 Information bias is a distortion in
  the estimate of effect due to
  measurement error or
  misclassification of subjects
  according to one or more
  variables.
• Measurement bias
• Observer variation bias
  – Intra-observer variation
  – Inter-observer variation
• Subject (biological
  variation)
• Technical method error
  variation
• Recall bias
• An error of categorization may occur if
  information on the exposure variable is
  unknown or inaccurate.
• The recall by both cases and controls
  may differ in both amount and accuracy.
  Cases are more likely to recall exposures,
  especially if there has been recent media
  exposure on the potential causes of the
  disease.
• Example: In questioning mothers whose
  recent pregnancies had ended in fetal
  death or malformation (cases), and a
  matched group of mothers whose
  pregnancies had ended normally
  (controls), it was found that 48% of the
  former, but only 20% of the latter reported
  exposure to drugs.
 4. Work plan
 “State in specific steps what exactly
             will be done”

• Method:
  – Listing the activities related to the study
    (planning, implementation, results)
  – Identification of the responsibility for
    each activity
  – Setting time and date for achievement
    of each activity
  – Putting all these elements together in a
    legible form which could be a chart
    (GANNT chart) or a table
  – Budget and any funding agencies
Administering the Research

  •   Informed consent
  •   Clear instructions
  •   Debriefing
  •   Confidentiality
5. Data management
• Data management is the whole
  process of dealing with data
  from the very beginning of the
  study. Data analysis is just the
  last part of it.

• It can be divided into the
  following phases:
   – Preparation of data entry
   – Data entry
   – Data analysis
• Preparation for data entry:
   – Review of questionnaire
     forms
   – Unique I identifier
   – Coding
   – Preparation of master-sheets
     (manual) or spread-sheets
     (computer)
   – Dummy tables
   – Quality control
• Data entry
• Data analysis:
   – Descriptive:
      • Tabular presentation
          – Frequency distribution tables
          – Cross tabulations
      • Graphic presentation:
          –   Bar charts
          –   Pie charts
          –   Line graphs
          –   Others
      • Numeric presentation:
          – Percentages and percentiles
          – Measures of central tendency
          – Measures of dispersion
• Analytic:
  The researcher uses principles of
  biostatistics to test his hypothesis. Detection
  of proper statistical test depends on:

   – The objective of the study:
      • Descriptive
      • Looking for a difference
      • Looking for an association
   – Type of variable:
      • QT
      • QL
   – Distribution of the variable:
      •   Normal
      •   Binomial
      •   Poisson
      •   others
6. Interpretation
• Discussion of the results in a
  way that relates data
  obtained to each other
  clarifying the associations
  and other findings.
7. Reporting comes next.
Thank you

								
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