Lecture:

Department of Public Health and Informatics

Lecture series

The project title:

The project title is one of the most important features of the protocol because it attracts the attention of the potential reader. It is, therefore, necessary to make it as short and to the point as possible. If we consider two possible examples:

An investigation to evaluate the effect of the Herbst and Twin Block functional appliances on skeletal growth during the treatment of Class II skeletal growth anomalies. A randomized controlled trial.

This title is overlong and states the obvious in a rather wordy way. It goes without saying that because it is the title of a research protocol it is an investigation that will evaluate something. A preferable approach may be:

A randomized trial of Herbst and Twin Block appliances.

The second title comes straight to the point without stating the obvious. It not only attracts the attention of a reader, but it immediately tunes them into the subject matter.

One of most important features because it is the 1st to attract attention of reader

It must be CATSConcise

Accurate

To the point

Short

Introduction:All protocols start with an introduction section. It is a description of the scientific background to the research question and should include the following sections (paragraphs):

a. The importance of the topic

The introduction often begins with a statement of the importance of the research area based on, for example, the number of people who suffer from the disease being studied or the cost that it presents to the health authorities.b. A brief review of current research

The introduction should include a brief review of the landmark studies and important recent ones. It is essential to do a literature search (e.g. Medline, Embase) to be sure you have covered the literature adequately

c. The need for further (your) research

The introduction is not primarily aimed at summarizing present knowledge but must make the case for the need for further (your) research by highlighting the gaps in present knowledge.

d. The broad long-term goals (benefits) of the proposed research

The introduction often concludes with the broad long-term goals (benefits) of the proposed research.Some important points:

1. Introduction should be concise and to the point

2. Preferably 2 to 3 pages of A4 size paper

3. References should be quoted no more than 20

Research question/hypothesisEvery research work must have research question (s)/hypothesis.Research question:Definition: A Research Question is a statement that identifies the phenomenon to be studied

Many studies have more than one research question.

FINER Criteria for a Good Research question:

1. Feasible

Adequate number of subjects

Adequate technical expertise

Affordable in time and money

Manageable in scope

2. Interesting

Getting the answer intrigues the investigator and her/his friends

3. Novel

Confirms, refutes or extends previous findings

Provides new findings

4. Ethical

Amenable to a study that institutional review board will approve

5. Relevant

To scientific knowledge

To clinical and health policy

To future researchExample of research question

For cross sectional descriptive studies:

What is the prevalence of dry eye among the computer users of Dhaka University?

Who are the people mostly affected by conjunctivitis? For case control studies:

Is there any association between computer use and dry eye?

What are the risks of conjunctivitis?

Interventional study:

Does amitryptyline reduce frequency of migraine attack compared with propranolol?

Components of the clinical question

Population - type of person /patient

Intervention (exposure) - type of exposure

Comparisons - type of control

Outcomes - type of outcome

Refining the clinical question: Type of exposure

Are anticoagulant agents useful in

patients who have had a stroke?Type of patient

The well-formulated question

Type of exposure

Type of outcomes

Do anticoagulant agents improve outcomes in

Type of personpatients with acute ischemic strokeType of controlcompared with no treatment?

Hypothesis:Definition:

A hypothesis is an educated guess about how things work.

Many study questions undergo a further transformation into a final and most specific version, termed research hypothesis

When Hypothesis?

Other than descriptive type of cross sectional study all may have a hypothesis

Characteristics of a good hypothesis:

Simple

Specific

Stated in advance

When hypothesis should be formulated?

If any of the following terms appear in the research question:

Greater than

Less than

Causes

Leads to

Compared with

More likely than

Associated with

Related to

Similar to Correlated with

Example of hypothesis:

Chocolate may causes migraine

Bacterial growth may be affected by temperature

Ultra violet light may cause cataract

Types of hypothesis:

Null hypothesis or hypothesis of no difference:

There is no association between the predictor and outcome (e.g. there is no difference in the frequency of dinking well water between subjects who develop peptic ulcer disease and those who do not)

Alternative hypothesis:

The proposition that there is an association (e.g. the frequency of drinking well water is different in subjects who develop peptic ulcer than in those who do not)

In research only alternative hypothesis should be written. The null hypothesis does not need to be written in the dissertation/thesis.

One-and two-sided alternative hypothesis

One sided: specifies the direction of association between predictor and outcome

E.g. drinking well water is more common among subjects who develop peptic ulcers

Two-sided: specifies association; does not specify direction

E.g. Subjects who develop peptic ulcer disease have a different frequency of drinking well water than those who do not.

Simple versus complex hypothesis:Simple hypothesis:

One predictor and one outcome

E.g. A sedentary lifestyle is associated with an increased risk of proteinurea in patients with diabetes

Complex hypothesis:

More than one predictor

(a sedentary lifestyle and alcohol consumption are associated with an increased risk of proteinuria in patients with diabetes) or More than one outcome (alcohol consumption is associated with an increased risk of proteinuria and

neuropathy in patients with diabetes)

Objectives in Research:

Research objectives may be stated as:

1. General objective

2. Specific objectives

3. Ultimate objectives

1. General objective:

It is a short statement of the proposed research

It tells in a summary form what will be done during the study

2. Specific objectives:

Every research work involves several tasks. Statement that tell about each task that will be undertaken during the research work, are termed as specific objectives

3. Ultimate objective:

If we have written the section of rationale well and given justification of undertaking the present study, the ultimate objectives have been covered. However, if we feel the need of writing the ultimate objectives separately, write them in the objectives section.

Ultimate objectives describe the idea on the way findings of your study will be useful and beneficial.

Example: The findings of this case control study will help develop an authentic document on different risk factors of red eye in Bangladeshi university student. Such document is so far unavailable in the country.

Use action verb in stating objectives

To assess-----------------------------------

To examine-----------------------------------

To describe-----------------------------------

To explore-----------------------------------

To elucidate-----------------------------------

Literature review:

Sources of information are generally categorized as primary, secondary or tertiary depending on their originality and their proximity to the source or origin.

Primary sources (primary literature): Primary sources are original materials on which other research is based

They are usually the first formal appearance of results in the print or electronic literature

They present information in its original form, neither interpreted nor condensed nor evaluated by other writers.

Primary sources present original thinking; report on discoveries, or share new information.

Some examples of primary sources:

Scientific journal articles

Proceedings of Meetings, Conferences and Symposia

technical reports

dissertations or theses (may also be secondary)

patents

sets of data, such as census statistics

works of literature (such as poems and fiction)

diaries

autobiographies

interviews, surveys and fieldwork

letters and correspondence

speeches

newspaper articles (may also be secondary)

government documents

photographs and works of art

original documents (such as birth certificate or trial transcripts)

Internet communications or e-mail

minutes of meetings

news footage

Secondary Sources (Secondary literature) Secondary sources are less easily defined than primary sources. What some define as a secondary source, others define as a tertiary source.

Nor is it always easy to distinguish primary from secondary sources. A newspaper article is a primary source if it reports events, but a secondary source if it analyses and comments on those events.

In science, secondary sources are those which simplify the process of finding and evaluating the primary literature. More generally, secondary sources describe, interpret, analyse and evaluate the primary sources

comment on and discuss the evidence provided by primary sources

are works, which have one or more steps removed from the event, or information they

Some examples of secondary sources:

bibliographies (may also be tertiary)

biographical works

commentaries

dictionaries and encyclopedias (may also be tertiary)

dissertations or theses (more usually primary)

handbooks and data compilations (may also be tertiary)

history

indexing and abstracting tools used to locate primary & secondary sources (may also be tertiary)

monographs (other than fiction and autobiography)

newspaper and magazine articles (may also be primary)

review articles and literature reviews

treatises

works of criticism and interpretation

Tertiary Sources (Tertiary literature)

This is the most problematic category of all.

Works which list primary and secondary resources in a specific subject area

Works which index, organize and compile citations to, and show you how to use, secondary (and sometimes primary) sources.

Materials in which the information from secondary sources has been "digested" - reformatted and condensed, to put it into a convenient, easy-to-read form.

Some examples of tertiary sources:

almanacs and fact books

bibliographies (may also be secondary)

chronologies

dictionaries and encyclopedias (may also be secondary)

directories

guidebooks, manuals etc

handbooks and data compilations (may also be secondary)

indexing and abstracting tools used to locate primary & secondary sources (may also be secondary)textbooks (may also be secondary)

Study designTypes of studies:

Epidemiological studies are mainly classified as

1. Observational

2. Experimental

1. Observational studies

Characteristics:

Observational studies allow nature to take its course

The investigator measures but does not intervene Have no control over exposures; simply observe what happens to groups of people

Examine associations between risk factors and outcomes

Types:

Two types:

i. Descriptive

ii. Analytical

i. Descriptive study:

Definition:

A descriptive study is limited to a description of the occurrence of a disease in a population and is often the first step in an epidemiological investigation.

Characteristics:

To describe present or past characteristics of persons with a particular outcome

Merely describes, does not analyze

Only one group is studied

No comparison group

No conclusion can be made about the association between exposure and outcome

Type:

1. Case report 2. Case series

3. Cross sectional

4. Survey

5. Archival research

ii. Analytical study:

Definition:

An analytical study goes further by analyzing relationships between health status and other variables.Table: Types of epidemiological study (1)ObservationalExperimental

DescriptiveAnalytical

Case reportEcologicalRCT

Case seriesCross sectionalCluster randomized controlled trials

Cross sectionalCase controlField trial

SurveyCohortCommunity trial

Table: Types of epidemiological study (2)Type of studyAlternative nameUnit of study

Observational studies

Descriptive studies

Analytical studies

EcologicalCorrelationalPopulations

Cross-sectionalPrevalenceIndividuals

Case-controlCase-referenceIndividuals

CohortFollow-upIndividuals

Experimental studiesIntervention studies

Randomized controlled trialsClinical trials Individuals

Cluster randomized controlled trialsGroups

Field trialsHealthy people

Community trialsCommunity intervention studiesCommunities

Descriptive type of study:

1. Case study:

Definition:

Case study refers to the collection and presentation of detailed information about a particular participant or small group, frequently including the accounts of subjects themselves.

Characteristics:

It is a qualitative descriptive research

It is useful in rare diseases

It may be first to provide clues in identifying a new disease or adverse health effect from an exposure

The case study looks intensely at an individual or small participant pool, drawing conclusions only about that participant or group and only in that specific context.

It provides insights for research questions to be addressed by subsequent, planned studies

It generates hypothesis

Strengths

provide real examples

encourage replication

are generally practical in nature

Provide innovative ideas.

2. Case seriesDefinition:

A case series is a study very similar in structure and form as case reports, but describes a group of cases, instead of a single patient.

This study design may be retrospective or prospective, and usually requires a relatively small sample size (usually 10 or more).

Characteristics:

Retrospective or prospective Usually involves a smaller number of patients

Case series may be consecutive or non-consecutive, depending on whether all cases presenting to the reporting authors over a period of time were included Case series may be confounded by selection bias, which limits statements on the causality of correlations observed It generally involve patients seen over a relatively short time

Case-series studies do not include control subjects

It provides insights for research questions to be addressed by subsequent, planned studies It generates hypothesis

Disadvantages:

Weakest kinds of observational studies

Represent a description of typically unplanned observations

3. Survey:

Assessing public opinion or individual characteristics by the use of questionnaire and sampling methods

Nearly every one has taken part in a survey

e.g. Bangladesh Demographic and Health Survey 2011, Bangladesh Literacy Survey, 20104. Archival research: Study method that examines existing records to obtain data and test hypothesis.

Example: A researcher might study crime statistics in different countries to see if there is a relation between capital punishment and the murder rate.5. Anecdote:

A short account (or narrative) of an interesting or amusing incident, often intended to illustrate or support some point.

Analytical studies

Ecological (correlational) study:Ecology:

Derived from Greek word, Meaning: study of house

The study of the relationships among living organisms and their environment

Human ecology means the study of human groups as influenced by environmental factors, including social and behavioral factors.

Ecological study:Definition:

A study in which the units of analysis are populations or groups of people, rather than individualsCommon types of ecological study are geographical comparisons, time trend analysis or studies of migration.

Example: A study of mortality from lung disease in different cities that are known to have differing levels of air pollution would comprise an ecologic study. The unit of analysis is a city.Look at the association between smoking and lung cancer deaths in different countries. The unit of analysis is a country

Characteristics:

Examine rates of disease in relation to a population-level factor

Population-level factors include summaries of individual population members, environmental measures, and global measures

Study groups are usually identified by place, time, or a combination of the two

Limitations include the ecological fallacy and lack of information on important variables

Ecologic studies generally make use of secondary data that have been collected by the government, some other agency or other investigators.

Advantages include low cost, wide range of exposure levels, and the ability to examine contextual effects on health.

Advantages:

Simple to conduct

Low cost

Wide range of exposure levels

Ability to examine contextual effects on health

Disadvantages:

Inferior to non-ecological designs such as cohort and case-control studies It is susceptible to the ecological fallacy. lack of information on important variables

Usually Hypothesis Generating Lack of adequate data and missing data May not be recorded a group level Within-Group misclassification Confounding

Collinearity

Temporal ambiguity

Ecological fallacy (Rothman and Greenland 1998)Difference between ecological study and cohort study:

Ecological studies can be easily confused with cohort studies, especially if different cohorts are located in different places. In the case of ecological studies there is no information available about the individual members of the populations compared (e.g. comparing several states based on state-wide average air pollution and state-wide average prevalence of respiratory diseases); whereas in a cohort study the data pair exposure/health is known for each individual.

Ecological studyCohort study

No information available about the individual membersInformation on individual members are inevitable

Type of ecologic study:Two type:

1. Ecologic comparison studies (cross sectional ecologic studies)

2. Ecologic trend studies

Ecologic comparison studies (cross sectional ecologic studies):

It involves an assessment of the correlation between exposures rates and disease rates among different groups or populations over the same time period.Usually there are more than 10 groups or populations

Data on disease may include:

1. Incidence rate

2. Prevalence

3. Mortality rates

Examples of exposure data include:

1. Measures of economic development (per capita income and illiteracy rate)

2. Environmental measures (mean ambient temperature, levels of humidity, annual rainfall)

3. Measures of lifestyle (smoking prevalence, mean per capita intake of calories, annual sales of alcohol)

The ecologic trend study:

It involves correlation of changes in exposure and changes in disease over time within the same community, country or other aggregate unit.

Ecologic fallacy:

Definition: The bias that may occur because an association observed between variables on an aggregate level does not necessarily represent the association that exists at the individual level.Cross sectional studySyn: Disease frequency survey, prevalence studyDefinition:

A study that examines the relationship between diseases (or other health related characteristics) and other variables of interest as they exist in a defined population at one particular time.The presence or absence of disease and the presence or absence of the other variables (or, if they are quantitative, their level) are determined in each member of the study population or in a representative sample at one particular time.

Features:

Study at a point (cross section) in time

Easy and economical

Regular cross sectional survey for determining extent of health related problems and sociodemographic characteristics

More common type of research in health science (approximately more than 1/3rd of original articles in major medical journal Inaccurate when studying rare conditions

Subjects are selected without regard to the outcome of interest

Less expensive

They are the best way to determine prevalence

Quick

The principal summary statistic of cross sectional studies is the odds ratio

Weaker evidence of causality than cohort studies

Advantages:

Easy to conduct

Simple in term of design, and calculation

Less time consuming

Cheapest

Ability to establish relationship between exposure and outcome

Suitable for problem identification

Gives idea about the disease prevalence

Associations can be studied

[Cross-sectional studies can also be used for examining associations, although the choice of which variables to label as predictors and which as outcomes depends on the cause-and-effect hypotheses of the investigator rather than on the study design]

Disease

YesNo

Risk

FactorYesAB

NoCD

Disadvantages:

We cannot come to the conclusion about the disease aetiology

We cannot follow up the cases

Very weak analytical power

We cannot find out the incidence of the disease

Example:

Javadi MA, Katibeh M, Rafati N, Dehghan MH, Zayeri F, Yaseri M, et al. 2009, Prevalence of diabetic retinopathy in Tehran province: a population-based study BMC Ophthalmology vol. 9, pp. 12

Findings:

Screened patients: 7989

Diabetic patients: 759 (9.5%)

Of them, 639 patients (84.2%) underwent eye examination

Five patients (0.7%) with media opacity were excluded

Examined patients with diabetes: 634

Diabetic retinopathy: 240

Standardized prevalence of retinopathy: 37%

non proliferative 27.3%

proliferative diabetic retinopathy 9.6%

The prevalence of any type of visual impairment (BCVA < 20/60) in patients with PDR was remarkably higher than that in patients without PDR (18.5% vs. 7.0%, P = 0.002, OR = 2.09, 95% CI: 1.02- 4.26).

Case control study:Syn: Case comparison study, case compeer study, case history study, case referent study, retrospective study

Definition: The observational epidemiologic study of persons with the disease (or other outcome variable) of interest and a suitable control (comparison, reference) group of persons without the disease.Case control study can be called retrospective because it starts after the onset of disease and looks back to the postulated causal factors.

SHAPE \* MERGEFORMAT

Advantages:

Cheap, easy and quick studies

Rapid and less time consuming

Small sample size is sufficient

Suitable for rare diseases (e.g. macular edema)

Multiple exposures can be examined

No ethical problem

Risk factor can be identified

Disadvantages:

Control selection difficult

Subject to bias (Any systemic error in which there is tendency to produce result in own favour)

We can not measure incidence

Multiple outcomes cannot be studied

Do not establish risks and rates directly

Not suitable for rare exposure

Example:

Leske MC, Chylack LT, Jr, Wu SY, The Lens Opacities Case-Control Study Group, 1991, The Lens Opacities Case-Control Study: Risk Factors for Cataract, Arch Ophthalmol.vol. 109, no. 2, pp. 244-51.

Objective:

To evaluated risk factors for age-related nuclear, cortical, posterior subcapsular, and mixed cataracts

Participants: 1380

Age range: 40-79

Type of cataract:

Posterior subcapsular only, 72 patients;

Nuclear only, 137 patients;

Cortical only, 290 patients;

Mixed cataract, 446 patients; and

Controls, 435 patients.

Statistical analysis: Logistic regression analyses

Low education increased risk (odds ratio [OR]= 1.46)

Regular use of multivitamin supplements decreased risk (OR =0.63) for all cataract types.

Dietary intake of riboflavin, vitamins C, E, and carotene, which have antioxidant potential, was protective for cortical, nuclear, and mixed cataract;

Diabetes increased risk of posterior subcapsular, cortical, and mixed cataracts (OR =1.56).

Oral steroid therapy increased posterior subcapsular cataract risk (OR = 5.83). Females (OR =1.51) and nonwhites (OR = 2.03) were at increased risk only for cortical cataract.

Risk factors for nuclear cataract were a nonprofessional occupation (OR =1.96), current smoking (OR = 1.68), body mass index (OR = 0.76), and occupational exposure to sunlight (OR =0.61).

Cohort studies

Syn: longitudinal study, follow-up study, incidence study, concurrent study, prospective studyDefinition:A study in which two or more groups of individuals those are free of disease and those differ according to the extent of exposure to a factor of interest, are followed over a period of time to see how their exposures affect their outcomes.

In terms of time

Prospective >90%

Retrospective lung cancer)

Can be used where randomization is not possible

Magnitude of a risk factors effect can be quantified

Multiple outcomes can be studied (smoking > lung cancer, COPD, larynx cancer)

Disadvantages:

Large sample size

Losses to follow up

Exposure may be distorted

Ethical problem (you are morally responsible because you are not advising him that do not smoke because it will harm you)

High cost

Long time

Not suitable for rare diseases

Not suitable for diseases with long-latency

Unexpected environmental changes may influence the association

Nonresponse, migration and loss-to-follow-up biases Hawthorne effect Confounding variables are the major problem in analyzing cohort studies.The Hawthorne effect

Definition: The Hawthorne effect is referring to the tendency of some people to work harder and perform better when they are participants in an experiment. Individuals may change their behavior due to the attention they are receiving from researchers rather than because of any manipulation of independent variables.

Definition: The Hawthorne effect is a form of reactivity whereby subjects improve or modify an aspect of their behavior being experimentally measured simply in response to the fact that they are being studied, not in response to any particular experimental manipulation.

Historical perspective: The term was coined in 1950 by Henry A. Landsberger when analysing older experiments from 1924-1932 at the Hawthorne Works (a Western Electric factory outside Chicago). Hawthorne Works had commissioned a study to see if its workers would become more productive in higher or lower levels of light. The workers' productivity seemed to improve when changes were made and slumped when the study was concluded. It was suggested that the productivity gain was due to the motivational effect of the interest being shown in them. Although illumination research of workplace lighting formed the basis of the Hawthorne effect, other changes such as maintaining clean work stations, clearing floors of obstacles, and even relocating workstations resulted in increased productivity for short periods. Thus the term is used to identify any type of short-lived increase in productivity.

Types:1. Prospective

2. Retrospective (historical cohort study)

1. Prospective cohort study

Definition:

A group of people is chosen who do not have the outcome of interest (for example, myocardial infarction). The investigator then measures a variety of variables that might be relevant to the development of the condition. Over a period of time the people in the sample are observed to see whether they develop the outcome of interest (that is, myocardial infarction).

2. Retrospective cohort:

Example:Prospective Cohort study:

Lindblad BE, Hakansson M, Philipson B, Wolk A, 2007, Alcohol Consumption and Risk of Cataract Extraction: A Prospective Cohort Study of Women, vol. 114, no. 4, pp. 680-85

Purpose

To investigate the association between alcohol consumption and the risk of cataract extractionDesign

Population-based prospective cohort study

Participants

34,713 women participating in the Swedish Mammography Cohort; Age: 49 to 83 years

Data collection tool: a self-administered questionnaire about alcohol, smoking, and other lifestyle factors

Completion year: 1997

Results

Duration of follow up 84 months

Incident cases of age related cataract extraction: 3587

Compared with never drinkers, the relative risk of cataract extraction among current drinkers was 1.11 (95% confidence interval [CI] 1.021.21) after adjustment for age and other potential risk factors.

In multivariate analysis, an increment of 13 g alcohol intake per day was associated with a 7% increased risk of cataract extraction (relative risk, 1.07; 95% CI 1.021.12)

World famous cohorts:

British Doctors' Cohort Study Framingham Heart Study

EPIC Study: European Prospective Investigation

HYPERLINK "http://www.nus-cme.org.sg/FamousCS.html" \l "3" \t "_blank" into Cancer and Nutrition

Nurses' Health Study

Physicians' Health Study

Women's Health Initiative

Retrospective cohort study:

Study subjects: Children vaccinated against varicellaBorn after 1993; they all followed up through 1999

Two centers in USA (A and B)

Information was obtained from automated vaccination, clinic, hospital discharge, and pharmacy records.

CharacteristicsArea AArea B

Total population80,5848,181

Breakthrough26897

Risk factors

Asthma--

Inhaled steroids prescribed at any time--

Oral steroids prescribed before vaccination--

Oral steroids prescribed 3 month after vaccinationaRR=2.4aRR=2.8

Vaccine given before 15 months of ageaRR=1.4

Varicella vaccination followed MMR vaccine within 28 daysaRR=3.1

Verstraeten T, Jumaan AO, Mullooly JP, Seward JF, Izurieta HS, DeStefano F. A Retrospective Cohort Study of the Association of Varicella Vaccine Failure With Asthma, Steroid Use, Age at Vaccination, and Measles-Mumps-Rubella Vaccination. Pediatrics 2003;112;e98-e103

Case reportsCase seriesCross sectional studiesEcological studiesCase controlCohort

SubjectsSingle case>1; exact number can varySeveral subjects assessed individuallyAggregate group of individualsTwo groups of subjectsTwo groups of subjects

CharacteristicsDescribes unusual feature of a caseA group of cases; no control grouppoint-in-time picture of health status/ health-related behaviouroften studied by geographical areaOne group has disease of interest and other is disease-freeOne group is exposed to risk factor of interest and other is non-exposed

OrientationPresentPresentPresentPresentPastFuture

Time factorQuickQuickQuickQuickQuickTime consuming

CostInexpensiveInexpensiveInexpensiveInexpensiveInexpensiveExpensive

Follow up periodNoneNoneNoneNoneOften prolonged

PurposeGenerate hypothesisGenerate hypothesisGenerate hypothesisGenerate hypothesisTest hypothesisTest hypothesis

Cause effect relationshipCannot be interpreted Cannot be interpretedCannot be interpretedCannot be interpretedCan be suggestedCan be interpreted

Measure of disease frequency and riskNoneNonePrevalence,

AssociationCorrelationOdds ratioPrevalence, Incidence,

Relative risk,

Attributable risk

Potential problems--Cannot assess seasonal variation Ecological fallacyRecall and selection biasAttrition

RemarksCan study multiple causes of disease; useful for studying rare diseasesCan study multiple outcomes of a single exposure

Experimental study:

Type:

1. Clinical trials

2. Field trials

3. Community trials

Clinical trials

Randomized

Non randomized

Pre-test/post-test study (before-after studies)

Quasi-experimental study

Different name of clinical trial: Randomized Clinical trial

Randomized Drug trial

Randomized control trial

Commonly used Design in clinical trial:1. Parallel design

2. Cross-over designs

3. Cluster randomized trial

4. Factorial trial

Parallel design

Cross over design

Example:Silaste ML, 2003, Dietary effects on antioxidants, oxidised LDL and homocysteine,[Thesis] Department of Internal Medicine, University of Oulu, Finland

Objective: To investigate the influence of modifications in the vegetable, berry, and fruit intake and dietary fat on the plasma concentrations of antioxidants, lipids, lipoprotein(a), oxidised LDL, folate, and homocysteine.

Research questions:

Does a diet high in common vegetables, berries, citrus fruit, and poly unsaturated fatty acid (PUFA) enhance the plasma concentrations of carotenoids, vitamin C, and vitamin E?

Do the modifications in the dietary intake of vegetables, berries, and fruit influence the plasma concentrations of lipids, lipoproteins, and oxidised LDL?

How do the dietary modifications and gene polymorphisms affect the serum paraoxonase-1 activity?

Does a high intake of natural folate from food increase the serum folate concentration and decrease the plasma tHcy concentration?

Do the common gene polymorphisms alter the dietary response of plasma tHcy concentration?

Study design: Randomized control trial cross over design

Methodology:The intervention consisted of

Baseline diet: 2 weeks

Two diet periods (low and high vegetable diets) 5 weeks each

Wash-out period in between: 3 weeks

An important feature of the study was a crossover design, in which each individual served as her own control.

The order of the study diets was randomly assigned for each subject.

Cluster Randomized trails Some time individual randomization impossible

Have to randomize community

Example:MacIntyre CR, Cauchemez S, Dwyer DE, Seale H, Cheung P, Browne G, 2009, Face Mask Use and Control of Respiratory Virus Transmission in Households, Emerging Infectious Diseases vol. 15, no. 2 DOI: 10.3201/eid1502.081167, available from: www.cdc.gov/eid

Justification of their study:

During an influenza pandemic:

Supplies of antiviral drugs may be limited, and

There will be unavoidable delays in the production of a matched pandemic vaccine

For new or emerging respiratory virus infections, no pharmaceutical interventions may be available.

Even with seasonal influenza, widespread oseltamivir resistance in influenza virus A (H1N1) strains have recently been reported

Objective:

To describe the efficacy of face mask use for preventing spread of influenza-like infection.

Intervention:

Group 1: Control (no face mask)

Group 2: Surgical mask intervention

Group 3: P2 mask intervention Result:

Masks may play an important role in reducing transmission

Figure: Flow diagram of recruitment for the prospective, cluster-randomized trial, Sydney, New South Wales, Australia, 2006 and 2007 winter influenza seasons.

Factorial trials Proposed by R.A. Fisher in the 1920s

Developed in the 1940s for use in industrial experiments

This design is used to assess simultaneously the effects of main treatments and one or several other factors.

In a specific case of two treatments A and B if one wished to assess simultaneously the effects in subjects who are:

Given neither A nor B

Given A alone and not B

Given B alone and not A

Given both A and BThe simplest factorial experiment, a 22 design

Factorial design: 2 by 2 type

Drug A

Level A=0 A=1

B B=0 None A alone

B=1 B alone A & B both

DREAM Study (Diabetes reduction assessment with Ramipril and Rosiglitazone Medication)Factorial design

RosiglitazonePlacebo

RamiprilRamipril+ RosiglitazoneRamipril+ Placebo

PlaceboRosiglitazone+ placeboPlacebo+ Placebo

Start: May 2001 Follow 3 to 5 years

Final Results: 2005/2006

Before-After studies

Quasi-experimental designs No randomization

No intervention in control group

Community Trial

Study Designs (all)

Validity ranking Types of study design

Highest

Randomized clinical trial

Prospective cohort study

Retrospective cohort study

Nested case-control study

Time-series analysis

Cross-sectional study

Ecologic study

Cluster analysis

Case study

Lowest Anecdote

Clinical development of a drug:Phase of trial Subject No. of subjects Duration of study Purpose Study procedure

Phase I Healthy Volunteers/target disease (Ca, HIV) 20-80 Up to 1 month Metabolic and pharmacological actionMaximally tolerable dose Single, ascending dose tiers, Unblinding, Uncontrolled

Phase II Patients with target disease 200-300 Several months Safety, efficacy pharmacokineticsEstablishes minimumand maximum effective doseRandomizationDouble blindCompare with a placebo

Phase III Patients with target disease Several hundred to thousands Several years Obtain additional information regarding Safety, efficacy RandomizationBlindingCompare withcurrently available Drug

Phase IVPatientsThousandsOngoingPost marketingMonitor ongoing safety in large populationsUncontrolledObservational

Blinding Blinding, also called masking If the outcome can conceivably be affected by patient or investigator expectations, then blinding is important.

Types of Blinding Single Blind: The patient is blind

Double Blind: The patient and the investigator are blind

Triple Blind: The patient, investigator and data-cleanup people are blind. The statistician can only be partially blinded since he/she has to know which patients are in the same treatment group.

Sampling Probability sampling

Simple random sampling

Stratified random sampling

Systematic random sampling

Cluster sampling

Multistage sampling

Nonprobablity sampling

Judgment or purposive sampling

Quota sampling Convenient sampling Snowball Sampling

Snowball sampling is a method in which a researcher identifies one member of some population of interest, speaks to him/her, and then asks that person to identify others in the population that the researcher might speak to. This person is then asked to refer the researcher to yet another person, and so on.

Snowball sampling is very good for cases where members of a special population are difficult to locate. For example, Selection of beggars has no frame work. This can be best done by asking an initial group of beggars to supply the names of other beggars they come across. Selection of street sex workers also can be made following this network approachSimple random sampling

Lottery method

Table of random numbers

Computer random number generator

Stratified sampling

Total sample 100

Data will be collected from

Dhaka

75

Rajshahi

12

Khulna

5

Barisal

5

Chittagong

3

Systematic sampling

Population size (N=500)

Decide sample size (n=100)

Calculate interval size (interval 5)

Cluster sampling

Divide population into clusters

Clusters: Random selection

Include all patients within sample clusterSuppose that we need a random sample of n=200 households from a population of N=8000 households of a city. Since there does not exist any good list of the households, it would be a difficult job to sample the individual households. It would be at the same time too expensive to prepare such a list. Instead, we can obtain a sample of blocks by dividing the entire area into a number of blocks and then selecting 200/8000 =2.5% of the blocks. Suppose we make 80 blocks each with 100 households. Then 2.5% of 80 blocks implies 80*2.5%=2 sample blocks. These 2 blocks contain 200 households. These households located within the boundaries of the sample blocks comprise the sample.Multistage cluster sampling

Upzilla (3 out of 6)

Union (for each upzilla select 2 union)

Village (for each union select 5 village)

Go to village, randomize people

The national Iodine deficiency disorders surveys of 1993, 1999 and 2004 conducted under the joint collaboration of UNICEF and Dhaka University adopted a multi-stage cluster sampling procedure. In 1999 survey, the whole country was divided into three ecological zones viz. flood prone, hilly and plain. Initially, thana were selected as primary sampling units from each of the zones and from the selected thana, mouza were selected as the secondary sampling units. At the third and final stage, households were selected and all members of the selected households were subjected to detailed interview. Difference between cluster sampling and stratified sampling 1. In cluster sampling the cluster is treated as the sampling unit so analysis is done on a population of clusters (at least in the first stage). In stratified sampling, the analysis is done on elements within strata. 2. In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected clusters are studied. 3. The main objective of cluster sampling is to reduce costs by increasing sampling efficiencyCluster Vs Stratified Sampling

Cluster Sampling

When natural groupings are evident in a statistical population, cluster sampling technique is used.

Cluster sampling can be opted if the group consists of homogeneous members.

The advantages of cluster sampling over other sampling methods is, it is cheaper as compared to the other methods.

The main disadvantage of cluster sampling is, it introduces higher sampling error. This sampling error can be represented as design effect.

Stratified Sampling

Stratified sampling is a method where in, the member of a group are grouped into relatively homogeneous groups.

For heterogeneous members in the groups, stratified sampling is a good option.

The advantages of stratified sampling are, this method ignores the irrelevant ones and focuses on the crucial sub populations. Another advantage is, with this method, for different sub populations, you can opt for different sampling techniques. This sampling method also helps in improving the efficiency and accuracy of the estimation. This sampling method allows greater balancing of statistical power of tests.

The disadvantages of this sampling method are, it requires choice of relevant stratification variables which can be tough at times. When there are homogeneous subgroups, it is not much useful. Its implementation is expensive. If not provided with accurate information about the population, then an error may be introduced

Figure: Cluster sampling

Figure: Cluster sampling

Statistics

Definition: Statistics may be defined as the science and art of collection, organization, analysis, interpretation and presentation of numerical data from which a definite conclusion can be made.

C collection

O organization

A analysis

I interpretation

P presentation

Parameter: Character of population

Statistic: Character of sample

Biostatistics:

It deals with data relating to biological aspects of human being.

Biostatistics is much broader term and includes the vital statistics.

E. g. Marriage, divorce birth, death etc.

Data: Data is a set of values recorded on one or more observational units.

Variable:

Definition:

A variable is a characteristic of a person, object or phenomenon that can take on a different value.

Types of variable

A variable can be classified in a number of ways.

A. The causal relationship

B. The design of the study

C. The unit of measurement

From the viewpoint of causation:

In studies that attempt to investigate a causal relationship or association, four sets of variables may operate:

1. Changes variables, which are responsible for bringing about change in a phenomenon

2. Outcome variables, which are the effects of a change variable;

3. Variables which affect the link between cause-and-effect variables;

4. Connecting or linking variables, which in certain situations are necessary to complete the relationship between cause-and-effect variables.

Connecting or linking variables (4)

Change variables (1)Outcome variable (2)

Variables that affect the relationships (3)

In research terminology:

Change variables are called: independent variablesOutcome/effect variables are called: dependent variablesThe unmeasured variable affecting the cause-and-effect relationship are called extraneous variablesThe variables that link a cause-and-effect relationship are called intervening variables.

Hence:

1. Independent variable:

The cause supposed to be responsible for brining about change(s) in a phenomenon or situation

2. Dependent variable: The outcome of the change(s) brought about by introduction of an independent variable

3. Extraneous variable: These factors, not measured in the study, may increase or decrease the magnitude or strength of the relationship between independent and dependent variables

4. Intervening variable:

SYN. Intermediate variable

Sometimes called the confounding variable

It links the independent and dependent variables

Example: Smoking causes lung cancer

Change variables (1)

Figure: Independent, dependent and extraneous variables in a causal relationship

Figure: Independent, dependent, extraneous and intervening variables From the viewpoint of the study design:

In controlled experiments the independent (cause) variable may be introduced or manipulated either by the researcher.

In these situations there are two types of variables:

1. Active variables

2. Attribute variables

Active variables: Those variables that can be manipulated, changed or controlled

Attributed variables: Those variables that cannot be manipulated, changed or controlled

Table: Active and attribute variables

Study intervention

Different treatment models

Experimental intervention

Program serviceStudy population

Age

Gender

Level of motivation

Attitudes

Religions

Active variablesAttribute variables

A researcher can manipulateA researcher cannot manipulate

From the viewpoint of the unit of measurementVariables:

Type:

1. Qualitative or categorical2. Quantitative

Nature:

1. Discrete

2. Continuous

Scale:

1. Nominal

2. Ordinal

3. Interval

4. Ratio

Qualitative Variable: Variables that take non-numeric narrative values are called Qualitative, or Categorical variable E.g. Gender which takes the non-numeric narrative value of Male or Female.Two types:

a) Dichotomous:

a. Only two categories

b. Yes/no, good/bad, rich/poor

b) Polytomous

a. More than two categories

b. Religion (Christian, Hindu, Muslim), Socioeconomic condition (lower, mid and upper)Quantitative Variable:

Variables that take numerical values are called Quantitative variable. This variable has two characteristic namely discrete and continuous.

Discrete: A variable that takes only isolated or absolute values is called discrete e.g. parity, family size, pulse rate etc.

Continuous: When a variable can assume any value within a meaningful range or continuum it is called continuous, for example, age, height, weight etc.

Scale of measurement:

Proposed by S S Stevens in 1946a) Nominal or classificatory scale

b) Ordinal or ranking scale

c) Interval scale

d) Ratio scalea) Nominal variable (scale)Definition: A nominal variable consists of named categories, with no implied order among the categories.

Properties: Observations of a qualitative variable can only be classified and counted. There is no natural order (for example- Gender). Data categories are represented by labels or names Even when the labels are numerically coded, the data categories have no logical order.

e.g.

Gender: 1. Male2. Female

Marital Status: 1. Unmarried2. Married3. Divorced4. Widow

b) Ordinal or ranking scale

Definition: An ordinal variable consists of ordered categories, where the differences between cannot be considered to be equal.

Properties:

The categories are distinct, mutually exclusive and exhaustive

The categories are possible to be ranked or order

The distance or difference from one category to the other category is not necessarily constantVariables with an ordered series

e.g.

Mild

Moderate

Severe

What Does Mutually Exclusive Mean?A statistical term used to describe a situation where the occurrence of one event is not influenced or causedby another event. In addition, it is impossible for mutually exclusive events to occur at the same time.

Mutually Exclusive Events

Two events are mutually exclusive if they cannot occur at the same time (i.e., they have no outcomes in common).Non-Mutually Exclusive Events

Two events are non-mutually exclusive if they have one or more outcomes in common.

In the Venn Diagram above, the probabilities of events A and B are represented by two disjoint sets (i.e., they have no elements in common).In the Venn Diagram above, the probabilities of events A and B are represented by two intersecting sets (i.e., they have some elements in common).

Interval variables:

Definition: An interval variables has equal distances between values, but the zero point is arbitraryProperties:

These are equally spaced variables, e.g. temperature

Zero has meaning

The data classifications are mutually exclusive and exhaustive

The data can be meaningfully ranked or ordered

Distance between attributes does have meaning. For example, when we measure temperature (in Fahrenheit), the distance from 30-40 is same as distance from 70-80. (e.g. 30-40 =70-80)

The ratio between number in the scale is not, however, necessarily the same as that between the amounts of the attribute. A room at 200C is not twice as hot as one at 100C.

Example: Temperature (Celsius, Fahrenheit), IQ, SAT score, PHTemperature, expressed in F or C, is not a ratio variable. A temperature of 0.0 on either of those scales does not mean 'no temperature'. However, temperature in degrees Kelvin in a ratio variable, as 0.0 degrees Kelvin really does mean 'no temperature'.

Ratio variables (scale):

Definition: A ratio variables has equal intervals between values and a meaningful zero point

Properties:

Data classifications are ordered according to the amount of the characteristics they possess.

Equal differences in the characteristics are represented by equal differences in the numbers assigned to the classifications.

The zero means absence and

Ratio between the 2 numbers is meaningful.

Example: age, Temperature (Kelvin)Difference:

NominalOrdinalIntervalRatio

Frequency distributionYesYesYesYes

Median and percentilesNoYesYesYes

Add or subtractNoNoYesYes

Mean, standard deviation, standard error of the meanNoNoYesYes

Ratio, or coefficient of variationNoNoNoYes

Flow chart regarding variables:

Variable based on threshold values:

A particular group of derived variables are those based on threshold values of a measured variable.

Derived variableOriginal variables

LBW

Yes