Study Design Design of Studies in STD Research Objectives • Discuss the following study designs – cross sectional – case control – Cohort – Clinical trial • Discuss the by cxu14214

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									Study Design
      Design of Studies in STD Research
Objectives:
• Discuss the following study designs:
  –   cross-sectional
  –   case-control
  –   Cohort
  –   Clinical trial
• Discuss the components of study design:
  – Study Design, population, time frame,
    inclusion/exclusion, sample size, study flow diagram ,
    outcome/predictors/confounders/effect modifiers, plan
    of analysis, efforts to reduce threats to validity,
    strengths/limitations
• Discuss some complicated issues in study
  design
                  Study Designs

 Descriptive           Analytic     Experimental



correlational        case control
                                     clinical trial

 case report/
 case series            cohort
                                    community trial

cross-sectional
        Criteria for
        Causality
•   Biological Credibility
•   Consistency of findings
•   Dose-response
•   Magnitude of the association
•   Time sequence
Cross sectional
     D



     __
     D



     E


     __
     E
Case Control

 E

__       D
E
         __
         D
 E

__
E
    Cohort
             D
             __
E            D


             D
__
E            __
             D
   Phases of a Clinical Trial
• Phase I - safety (pharmacokenetics - to
  determine maximum tolerated dose)
• Phase II - Evidence of a response
• Phase III - Safety, efficacy
• Phase IV - Safety, Acceptability,
  Efficacy
      Study Diagram - Classic
      Randomized Controlled
                                O

                       Int    LTF/C
Eligible Subject
      Pool         R
                       P/SC     O

                              LTF/C
     Study Design - Cross-over


                  E       E
 Study
Eligibles   R

                   C      C
      Hypothesis Testing
• Hypothesis testing involves conducting
  a test of statistical significance and
  quantifying the degree to which
  sampling variability may account for the
  results observed in a particular study
• When designing data collection tools,
  keep in mind your final analysis
Statistical Tests: 2 T-test
   Measures of Association:
  Odds Ratio, Relative Risk
    Objectives should be stated in
       terms of an hypothesis
• Null Hypothesis: There is no difference
  Medication A will have not effect on disease
  progression
• Two tailed Hypothesis: There is some difference
  Medication A will have some effect on disease
  progression
• One tailed Hypothesis: The difference is greater or
  less
   Medication A will reduce deaths due to disease X
  Medication A will increase deaths due to disease X
         Outcome of interest
• Write the research question in advance
• outcome variable:
  – should be measurable in all subjects
  – should be capable of unbiases
    assessments
  – should be ascertained as completely as
    possible
     Response or Outcome
          variables
• You may have outcomes other than
  hard endpoints
• surrogate markers
• quality of life
Follow-up Studies - Survival Analysis

 • This analysis used when subjects are
   entered over a period of time and have
   various lengths of follow-up.
 • Dichotomous endpoints
 • Kaplan Meier or Product Limit
 • Cox Proportional Hazard modeling
     Intent-to-treat Analysis
• For persons who cross-over to the other
  arm. You classify that person into the
  arm they were originally assigned.
• Less biased results than “as treated”
  because you maintain randomization.
• Only works if there is not a lot of
  crossing over very early in the study
   Reasons for withdrawal of
          Subjects
• Ineligibility (misclassification,
  imprisonment, moved)
• Noncompliance (adverse effects of
  intervention, loss of interest, changes in
  underlying conditions, substance usage)
              Measurement
•   Outcome
•   Predictor
•   Confounder
•   Effect modifier
Validity and Reliability


Validity        Reliability


   x                   xx
         Relative Risk
   for a disease exposure
           STD         No STD
Drug use   75          25     100
No drug    25          75     100
use
           100         100      200


           RR = 75/100 = 3.00
                  25/100

           C.I. (2.10 - 4.29)
      Odds Ratio Calculation
                 STD     No STD   Total
Drug use         100     50       150
No Drug use      100     150      250
                 200     200      400




         O.R. = (100*150)     = 3.00
                   (100*50)
Confounding and/or interaction
      (Kleinbaum, Kupper and Morgenstern)
HIV risk perception and self-protective
behaviors among high risk persons in
              community settings
            Patricia Kissinger, Ph.D.(1)
               Nomi Fuchs, MPH (2)
           Catherine Schieffelin, MPH (2)
             Jane Herwehe, MPH (2)
             DeAnn Gruber, MSW (2)

 (1) Louisiana State University, HIV Outpatient Program
 (2) Children's Hospital - Family Advocacy, Care and
 Education Services (FACES)
               Purpose
• The purpose of this study was to
  examine HIV risk perception and self-
  protective behaviors among high risk
  people in community settings.
                Methods
• Street intercept and in-depth interviews
  were conducted from August 1997 to
  June 1998
• Inclusion:
  – Sexually active people
  – aged 15-35
  – living in six communities of New Orleans
    with the highest gonorrhea rates.
                   Results
• Of 1133 respondents, 97% were African
  American, 37.4% were 15-18 years of age.
• 46.2% reported an HIV risk behavior, 66.5%
  reported condom use, and 69.9% reported ever
  having been tested for HIV.
• Many respondents (39%) perceived themselves to
  be at no risk, but reported engaging in an HIV risk
  behavior
• Adolescents and persons who had been HIV
  tested were most likely to have this discrepancy.
              Results con’t
• Among the 524 persons who reported an HIV
  risk behavior, 19-35 year olds were less likely
  to use condoms and adolescent men were
  less likely to have been HIV tested.
• In-depth interviews revealed diverse reasons
  for failure to perceive oneself at risk and
  failure to be HIV tested including optimistic
  bias, risk group identity, hierarchy of risk and
  fear.
Table 2. Factors associated with a discrepant
           responsea (N=1072)
                     % discrepant   Adjusted
                                    O.R. (95% C.I.)
   Age
    15-18            44.9           1.58 (1.20-2.09)**
    19-35            36.2           1.00

   Gender
    Women            39.2           1.02 ( .79-1.33)
    Men              38.7           1.00

   Used a condom
   last sexual act   42.5           1.16 ( .88-1.52)
     Yes             36.1           1.00
     No

   Been HIV tested
    Yes              39.3           1.37 (1.01-1.85)*
    No               38.3           1.00
Table 3. Factors associated with self protective
         behaviors among persons reporting an HIV
         risk behavior (N=524)
                            Condom use           Ever been HIV
                            Adjusted O.R.        tested
                            (95% C.I.)           Adjusted O.R.
                                                 (95% C.I.)
  Age
   15-18                    1.00                 .16 ( .10- .25)**
   19-35                     .48 ( .32 - .72)** 1.00
  Gender
   Women                     .68 ( .46- 1.01)    1.00
   Men                      1.00                  .34 ( .22 - .52)**
  Self-assessed HIV risk
   Yes                      1.26 ( .65-2.30)      2.01 ( .94-4.29)
   No                       1.00                 1.00
  Assessed partner's risk
   Yes                       .58 ( .31 - 1.10)    .58 (.27-1.24)
   No                       1.00                 1.00
         **p < .01
 Table 4 Association between
  reported risk behavior and
      self-assessed risk
                   Among those Agreement
                   reporting high between self
                   risk behavior reported risk and
                                  assessed risk
                                  K (95% C.I.)
Self-perceived     15.6%          .095 (.057-.135)
at risk
Perceived          19.1%                 .130 (.099-.171)
partner(s)' at
risk

        Kappa .10 (95% C.I. 06-.14) indicating poor reliability
Non-experimental (analytic)
      study designs
• Conducted because of ethics, cost or
  convenience
• Two primary types:
  – Cohort
  – Case-control
             Experimental Designs
• Experiment – a set of observations, conducted under
  controlled circumstances, in which the scientist
  manipulates the conditions to ascertain what effect
  such manipulation has on the observations.
• Ideally only one factor is examined (however,
  biological variation exists)
   – Clinical Trials – (individual in a special environment are
     randomized)
   – Field Trials – (individuals in the community are
     randomized)
   – Community Interventions – (whole communities are
     randomized)
               Field Trials
• Differ from clinical trials in that subjects
  have not yet gotten disease
  – (1955) Salk vaccine for Polio
  – (1975) Vitamin C in preventing the
    common cold)
  – (1982) MRFIT – a field trial of several
    primary preventives of MI (N=12,866 and
    cost $115 million)
Community Intervention and
Cluster Randomized Trials
• Community intervention is an extension of a
  field trial that involves intervention on a
  community-wide basis
  – (eg. Mass media campaigns)
  – (eg. Fluoridated water)
• Cluster randomization - groups of
  participants are randomized. The larger the
  cluster, the less that is accomplished by
  randomizing.
               Study Protocol
•   Rationale and background
•   Objectives
•   Study Design
•   Inclusion/Exclusion
•   Definitions (intervention, measurements,
    adherence)
•   Study Flow chart
•   Sample Size calculation
•   Plan of analysis (interim analysis)
•   Appendices
    – Questionnaires
    – Consent forms
    – Instructions to interviewers
Example of a flow chart for
    randomization
Example of a comparison table to demonstrate
    that randomization was successful
   Incidence vs. Prevalence
• In infectious diseases of short duration,
  incidence may be close to prevalence
• In chronic diseases, prevalence will be
  far greater than incidence
• Monitor disease burden by prevalence
• Monitor efficacy of programs by
  incidence
     Calculate an Incident Rate
      Jan July Jan July Jan July Jan July Jan July Jan time at
      1976 1976 1977 1977 1978 1978 1979 1979 1980 1980 1981 risk
Sub A *----------------------                                     2.0
Sub B        *---------------------------------x                  3.0
Sub C *--------------------------------------------------------- 5.0
Sub D                   *---------------------------------------   4.0
Sub E                         *---------------------------x        2.5
Total Years at risk                                              16.5

* = initiation of study   ID=___cases/___person-years
-- =Time followed
x = development of disease
    Measures of Associaton
• Since clinical trials are prospective and
  the intervention precedes the outcome,
  a relative risk is calculated.
• Covariates and confounders can be
  either controlled for in the design or
  adjusted for in the analysis
    Is PID more common among
         HIV-infected women
• Research Question
• Population
• Inclusion/exclusion
• Study Design
• Type of analysis and Unit of analysis
• What are the predictors, confounders, and
  outcomes of interest
• Findings
• Limitations/Strengths
   Difficulties with this study
• Definition of a case
• Choice a proper control
• Detection bias
    A microbicide to prevent HIV
          among women
• Research Question
• Population
• Inclusion/exclusion
• Study Design
• Type of analysis and Unit of analysis
• What are the predictors, confounders, and
  outcomes of interest
• Findings
• Limitations/Strengths
     Difficulties with this study
•   Ethical dilemma
•   Exposure is altered by study itself
•   Choice of cases and controls
•   Sample size considerations
    An HPV vaccine to prevent
       HPV among women
• Research Question
• Population
• Inclusion/exclusion
• Study Design
• Type of analysis and Unit of analysis
• What are the predictors, confounders, and
  outcomes of interest
• Findings
• Limitations/Strengths
      Difficulties with this study
•   Misclassification bias possible
•   Population to study difficult to find
•   Sample size
•   Generalizability
                Study Design
• Statement of hypothesis
• Population
   – Sampling
   – Inclusion/Exclusion
• Time frame
• Design
• Measurement
   – Predictors
   – Confounders
   – outcome
• Analysis plan
   – Sample size
   – Dummy Tables
   – Analyses to be done
• Efforts to minimize threats to validity
• Strengths and limitations
                  Study Designs

 Descriptive           Analytic     Experimental



correlational        case control
                                     clinical trial

 case report/
 case series            cohort
                                    community trial

cross-sectional
    Confounding

E             D
     C
 E=exposure
C=confounder
 D=disease
  Strategies for Partner
Treatment for STD control
              By
    Patty Kissinger, Ph.D.
                Objectives
•   Background
•   Prior Studies
•   Present Studies
•   Policy implications
           Why treat partners?
• Primary prevention - to break the chain of
  transmission
  – Healthy men don’t access health care
  – Many STDs are asymptomatic
• Secondary prevention - to prevent complications
  of the disease
  – STD infections increase the risk of HIV
  – Recurrence can cause serious health consequences
      Recurrent chlamydia
• Causes PID, ectopic pregnancy,
  infertility and chronic pelvic pain
• Many women are re-infected by an
  untreated partner
• Strategies for partner treatment are
  necessary
    Basic Reproductive Rate of
            Infection
                      (Anderson and May)


Ro=  D c
Ro is the basic reproductive rate of
     infection

   is the transmission coefficient


D   is the infectious period


c    is the mean rate of sexual partner
    change
        Sexual Networks


X            X
                     X
    X
         X               X
                 X
Methods of Partner Treatment
• Partner referral
• Partner notification
• Patient delivered partner treatment
     Problems with Partner
           Referral
• Studies of chlamydia demonstrate that
  only 25-40% of named male partners
  were treated.
• Partners
  – not told
  – refuse to come for testing/Rx
     Problems with Partner
          Notification
• Confidentiality
• Expensive and time consuming
  – Almost 800,000 cases of chlamydia and
    400,000 cases of gonorrhea were reported
    in the US in 2001
• Not all partners are named
• Hard to find partners
       Problems with Partner
             Treatment
• Safety
   – Allergies
   – Pregnant women
• Liability
   – Physician
   – Nurses
   – Institutions
• Fear of uncontrolled antibiotic use
   – Fear of selling medication
   – Fear of stocking up on medicine
 Empirical data in favor of PDPM
• Retrospective cohort in New Orleans (Kissinger et al.,
  Sex Trans Inf 1998; 74:331-333)
• Correlational in Sweden (Ramsted et al 1991; 2:116-
  118)
• Cross-sectional in San Francisco (Hammer et al.
  National STD Conf 2000; Wisconsin)
• Cross-sectional in Washington (Golden et al STD
  1999; 26:543-547).
• Randomized trial in Uganda (Nuwaha et al. STD
  2001; 105-110
    Multi-centered Trial – Infertility
         Prevention Program
• 1787 women aged 14-34
• Eight cities
• Randomized to PDPM versus PR
• Tested at 1 and 4 months using LCR or
  PCR
• Given 1 gm azithromycin
• Outcome was recurrence
    Second Study - Prospective Study


Strategy     (n/N)     %    RR     95%CI        p value


Partner
referral    (108/726) 15     1        --          --

 Patient
delivered   (87/728)   12   0.8   (0.62-1.05)    0.102
        Issues with the study
•   Loss-to-follow-up
•   Low power
•   Persistence versus recurrence
•   Powder form of medication
   PDPM seems reasonable
• At the time of treatment for their own
  chlamydial infection, a majority of
  women have a partner who remains
  untreated (Golden, 2001)
• Most patients with STDS prefer to notify
  the partner themselves (Golden, 2001)
• Men generally perceive practical
  obstacles to obtaining treatment
 (Fortenberry, 1997)
   Present Studies PA0008 –
Female trichomonas trial and male
          urethritis study
• Testing three methods: partner referral,
  booklet referral, PDM
• Male urethritis – quasi-experimental
  – Delgado
• Female trichomonas – randomized trial
  – 01 Family Planning
• Baseline and follow-up visit
  – ACASI interviews
  – STD testing
Booklet referral
   Patient Delivered Partner
           Medicine
• For Trichomonas (1 gram of
  metronidazole)
• For Male urethritis (1 sachet of
  azithromycin 1gram sachet and 1 dose
  of cefixime 400 mg orally)
• Directly observed medication for index
      Outcomes measures
• How many partners are treated (index
  patient-report)
• How many partners show up to clinic
  saying that they have been referred by
  an index partner
• Recurrence rates
  – InPouch
  – BD urines
                 Trichomonas
            Referral   Booklet   PDM
Patients    64         61        61
Partners    68         68        73
Ratio       1.06       1.11      1.20
Follow-up   66.2       85.3      75.3
rate
Desired     113        113       113
% of        56.6       54.0      54.0
desired
enrolled
             Male urethritis
            Referral   Booklet   PDM
Partners    282        237       207
Index       141        121       111
Ratio       2.0        1.96      1.86
Follow-up   66.9       81.0      64.0
rate*
Desired     182        182       182
% of        77.5       66.5      61.0
desired
enrolled
         Interim Analysis

Partner took the ARM 1   ARM 2          ARM 3
medicine
Male Urethritis 36.8     45.1*          77.0**
Trichomonas    73.9      60.3           90.1*




                                 *P<0.05, **P<0.01
    Policy implementation issues
•   More evidence?
•   Practice protection
•   Need to educate
•   Financial support

								
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