Conducting Research 2

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Conducting Research (2)

• Dr. Rasha Salama
• PhD. Community Medicine

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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 ones research

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Steps of Scientific Research
no need for study

• Selection of area
• Selection of topic
• Crude research question
• 
  
• 
  no answer
• Refined research question
• Research hypothesis, goals and objectives
• Study design
• Population sampling
• Variables confounding
  bias

answers found
Literature review
Ethical issues
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1. Study Design
Descriptive studies
Analytical studies
Case report
Observational studies
Experimental studies
Case serial reports
Randomized Controlled Clinical trials
Case-control studies
Cohort studies
Randomized Controlled field trials
Cross-sectional studies
Non-randomized experiments
Prospective
Retrospective (historical)
Ecological studies
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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

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Purpose of the study

• Study of etiology
• Ecologic
• Cross-sectional
• Case-control
• Cohort
• Intervention
• Study of therapy
• Lab experiments
• Clinical trials
• Community intervention

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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

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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.

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Latency

• For diseases with very long latency, the costs of
  concurrent cohort studies or clinical trials are
  prohibitively high.

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Feasibility

• Time
• Manpower
• Equipment
• Money

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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
• Identification of study population
• Determination of sampling population
• Definition of the sampling unit
• Choice of sampling method
• Estimation of the sample size

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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

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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.

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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)

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Choice of sampling method

• Non probability sampling
• Probability sampling

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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).

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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

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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.

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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.

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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.

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Multistage sampling
Country
Provinces
Cities
Districts
Households
Person
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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.

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Multi-phase sample
Population
Sample
Test 1
Sub-sample
Test 2
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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

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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.

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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

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III. Level of significance

• Relies on a error or type I error. The maximum
  level of a 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 a error.

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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 ß error or type II error.
• Power is equal to (1- ß), consequently the power
  of the study is increased by decreasing the beta
  error. Thus, sample size is inversely related to
  the level of ß error or directly related to the
  power of the study.

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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

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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

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Research tools

• Most important techniques
• Using available information (records)
• Observation (checklist)
• Self-administered questionnaire
• Interviewing (individual/group)
• Measuring (all lab tests and other investigations)

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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

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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

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Editing Questions Nine Mistakes to Avoid
1. Avoid leading questions 2. Avoid questions
that invite the social desirability bias 3. Avoid
double-barreled questions 4. Avoid long questions
5. Avoid negations 6. Avoid irrelevant
questions 7. Avoid poorly worded response
options 8. Avoid big words 9. Avoid ambiguous
words phrases
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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

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• Sources of errors
• Subject
• Observer
• instrument

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Bias
Design Bias
Information Bias (observer bias)
Interviewer bias
sample bias
Measurement bias (intra and inter obs. Bias)
Study selection bias
Reporting bias
Recall bias
Response bias
Technical bias
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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).

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• 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.

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• b. Admission rate (Berksons) 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.

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• 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.

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• 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.

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• Measurement bias
• Observer variation bias
• Intra-observer variation
• Inter-observer variation
• Subject (biological variation)
• Technical method error variation

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• 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.

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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

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Administering the Research

• Informed consent
• Clear instructions
• Debriefing
• Confidentiality

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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

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• 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

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• 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

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• 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

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6. Interpretation

• Discussion of the results in a way that relates
  data obtained to each other clarifying the
  associations and other findings.

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• 7. Reporting comes next.

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• Thank you