Concept Note Development Course Rajshahi University, Dept of Statistics Day 2 June 29. 2011...

Concept Note Development CourseRajshahi University, Dept of Statistics

Day 2

June 29. 2011

Facilitated by Dorothy Southern and Yushuf Sharker

Centre for Communicable Diseases

ICDDR,B

Day 2 Session 1

Session 1: Research Design and Variables

Time: 9:00 – 10:30

Facilitator: Yushuf Sharker

Objectives

Introduce epidemiological study designs

Variables and their measurements

Precision accuracy and validity of measurements

Research Design and Methods: Two types public health research

Descriptive

To determine the prevalence of CHD in Dhaka city

To determine the diarrhoeal incidence in 2011

Causal

Smoking causes lung disease

Presence of soap near water resources can

reduce the hand contamination

Case reports

Case series

Ecologic studies

Cross-sectional studies

Case-control studies

Cohort studies

Randomized controlled trials

Generate hypotheses

Establish causality

Research Design and Methods


Explore the population through sample survey at a single

time point. The outcome variables are what we like

To describe

To determine

To identify

To compare

To confirm

Cross Sectional (CS) Survey

Observational

Interventional


1. Clearly describe the population (target, study)

2. Inclusion and exclusion criteria (Demographic,

geographic, clinical, …)

3. Information to collect (Variables)

4. Sample size (described later)

5. Data analysis plan

Describing CS study


Limited capacity to document causality (exposure and outcome measured at the same time)

Limitations of CS survey

Disease Not Disease TotalExposed a b a+bNot Exposed

c d c+d

Total a+c b+d N

We cannot describe which causes what from CS survey


Case-Control Study design

Disease Not Disease TotalExposed a b a+bNot Exposed

c d c+d

Total 300 300 600

We fix the number of cases and controls and see whether specific exposure is present either due to by chance


Precise definition of cases and controls

Techniques to ascertain cases and controls to obtain sample from the two population

Matching criteria (for matched case-control study)

Initiatives to reduce selection bias, diagnosis bias etc.

Data collection tools

Limitations: Recall bias, Comparability

Write CC Study design


Cohort study

Disease Not Disease TotalExposed a b 300Not Exposed

c d 300

Total a+c b+d 600

We fix the number of exposed and unexposed and see how many developed disease


Writing cohort study

Clearly describe the population (target, study)

Characteristics of cohort: Inclusion and exclusion criteria (Demographic, geographic, clinical, …) Clear definition of cases and exposures

Follow-up Plan

Time frame

Data analysis plan (Adjusting confounding, …)


Randomized control trial

Gold standard for causal inference

Strong background in favor of RCT (Follow guideline)

Method of treatment allocation (reducing bias)

How the other factors are controlled (Reduce confounding)

Visit WHO Virtual resource Center

http://203.90.70.117/phi/1.Epidemiology.asp




Example from Steve’s Protocol

Handout: study design and method example

Objective 2: Assess the baseline and hand hygiene

behavior and hand contamination with rhinovirus and

fecal bacteria

Any questions

Variables Each study tries to describe the scenario of a disease such as

What is the prevalence of lung cancer Or test a hypothesis, such as:

Smoking causes lung cancer or, Seatbelts prevent death in car accidents.

We must choose specific variables to measure.

We must choose a measurement scale for each variable.

Variable Types

QualitativeNominal

Subjects: Physics, Chemistry, Mathematics

Ordinal Poor, Moderate, Rich

QuantitativeInterval

Temperature; 0°C or 32° F (different zero point)

Ratio Height, Weight (True zero point)

Others Likert

(1) strongly agree, (2) moderately agree, (3) neither agree nor disagree,(4) moderately disagree, or (5) strongly disagree.

Outcome variable

• Lung cancer

Exposure variable

• Smoking

Potential confounders• Sex• Age• Socio-economic status

Demographical Variables

Variables

Hypothesis: Smoking causes lung cancer

Exposure variable Outcome variable

• Smoker/Non-smoker •Lung cancer/No lung cancer

• Smoker/Non-smoker/Ever smoker

•Grade of lung cancer

• Packs per day •Type of lung cancer

• Total pack/years of smoking •Years of survival with cancer

• What brand of cigarettes do you smoke?

•Time of onset of lung cancer

Measurements of variables

Hypothesis: Smoking causes lung cancer

Exposure variable Outcome variable

•Seatbelt Wearer/Non-wearer • Dead/Alive

• Seatbelt Wearer/Non-wearer/Ever Wearer

• Dead/Injured/Uninjured

• How many times a day do you wear a seatbelt?

• Cause of death/type and location of injury

• How many times have you worn a seatbelt in your life?

• Cost of treatment

• What type of car do you drive?

• Long-term disabilities/complications

Measurements of variablesHypothesis: Seatbelts prevent death in car accidents

Measurements of variables

Relevance, Precision and Accuracy of measurement

Relevance: The variable I have chosen actually helps me test the hypothesis.

Precision: If I measure the same thing multiple times, I get the same (or a very similar) result

Accuracy: My measurement matches the true value of the variable.

Validity: The "best available approximation to the truth or falsity of a given inference, proposition or conclusion.“- Cook and Campbell (1979)

Any questions

Day 2 Session 2

Session-2: Statistical Conclusion Validity

Time: 11:00 – 13:00

Facilitator: Yushuf Sharker

Objectives

Data collection instruments

Sample size calculation

Data analysis plan

Statistical conclusion validity

Data Collection Instruments

Clearly describe your instruments and establish their efficiencies

If you are confused, plan for piloting

How did you plan to reduce the non-response and response errors

Your plan to ensure data consistency at field level How data will be stored How your data will be shared with total data

attributes

Data Collection Tools

Commonly used instruments in PIDVS Questionnaire Structured observation Spot observation Smart Soap Infra red camera AQM device Laboratory test of samples Secondary resources

Sampling method

Clearly define/describe your

Target population Study population Sampling frame Sampling unit Sampling frame is not available, how

did you ensure randomness

Sampling method

Description Sampling plan

Clearly describe how you are going to select your study participants from your study population

Random Sampling SRS, Stratified SRS, Systematic, Cluster,

Multistage …

Non-Probability Sampling Judgment sampling, Selective group,

Snowball Sampling, Social Network

Sample Size and Power Calculation

Sample size depends on

• Hypothesis

• Study design

• Sampling design

• Outcome variable and their measurement scale

• Statistics of interest

• Precision/effect size of statistics



• For prevalence estimation, how may sample unit is required to get the assumed if the assumption is really true.

• For causality, Assume a measure of association. Then determine the required sample size of your population setup to get the level of association if that truly exists.



• Most of the time researchers use the sampling distribution of the statistics for estimating the sample size

• For some extraordinary cases such as, if the statistics does not hold the usual assumption of distribution, researchers use simulation based estimation of sample size


Example

Problem: The investigator plans a case-control study of whether a history of herpes simplex is associated with lip cancer. A brief pilot study finds that about 30% of persons without lip cancer have had herpes simplex. The investigator is interested in detecting, with alpha = 0.025 and power = 90%, whether the odds ratio for lip cancer associated with herpes simplex infection is 2.5 or more. How many subjects will be required to perform the CC study?


Power analysis

• There are some uncertainties in the assumptions

• You don’t have sufficient sampling unit

• You have limited time and resources

Provide a justification of lower the sample size Provide a justification of lower the sample size using power analysisusing power analysis


Example: Power analysis

Null prevalence .5

Reality is greater than that level

Sample size required 29 to detect the difference if this is true

.6.7

.8.9

1P

ow

er

.7 .8 .9 1Alternatives


Example: Complicated power analysis

• We have a recode of 30% recovery of Nipah infection without any drug

• Researcher interested to introduce chloroquine to treat all Nipah patients this season

• The drug can reduce the death to 50%

• How may patient is required to take the decision that chloroquine works


Example: Complicated power analysis

Death Not Death TotalNot Treated 70 30 100Not Treated Random Random ?

Handout: Sample size estimation and power analysis

Sample size calculationHow to write

First clearly state your assumption about the

population (based on previous study would be the best

assumption)

Null hypothesis

Alternative Hypothesis

Describe the expected effect size

Declare the expected power and confidence level

Sample size calculationHow to write

Use the sample size estimation method suitable for

the study design

Describe the adjustment for non-response or

independency or loss

If required, justify the sample size will provide

uniformly sufficient power for all possible alternatives.

(Power analysis)

Any questions

Data analysis plan

Describe how you are going to analyze your data focusing on the following

Data preparation plan Keeping track on your incoming data Quality control of data Recording and checking missing and unusual values Recoding for example: Defining pneumonia with

multiple symptom (according to definition) Data reconstruction from multiple databases for

secondary resources

Data analysis plan

Describe how you are going to analyze your data focusing on the following

Models

Techniques for estimation and test

Clearly define or refer for some new techniques

Handout: study design and method example

Secondary data analysis

How to approach

Secondary data saves your time and cost in research Ensure that the data required for your study is available and you already have the access Explore the codebook of those data/datasets What types of processing is required How different types of data will be combined for the study

Secondary data analysis

How to approach

Describe the models Describe the techniquesGeneralizability of finding based on the data Describe the limitations and how you will overcome

To use secondary data you must provide a justification of how your targeted data is useful for your study

Any questions

Statistical Conclusion Validity

How your conclusion from the data are reasonable or credible Two types of error is possible in a study

Finding no relationship when there is one (or, "missing the needle in the haystack")

Threats of this error1.Less precise measures of disease and exposures2.poor reliability of treatment implementation3.There are so may uncontrolled factors active4.Heterogenity of respondents


Finding a relationship when there is not

Possible reasons for this error

1.Fishing and error rate (Mostly happens for secondary data)

Violation of assumptions require for testing can lead to either of the errors


While writing the data analysis plan be careful on following assumptions

•Randomness•Independence •Identical distribution•Sample size

Violation of these assumption posses a threat about your conclusion and so propose some robust method

Avoid multiple test in a single data/use Bonferroni corrections

In multistage sampling we are selecting sampling unit from different stage, outcomes of sample units becomes similar within each stage. Research must consider this dependency in their data analysis plan for valid inference


Any questions

Day 2 Session 3

Session 3: Logistics

Time: 14:30 – 15:30

Facilitator: Dorothy Southern

Developing a Timeline and Budget

Objective

•To explore cross-cutting issues that could affect the timing and budget of a study.

•To draft a feasible timeline and budget

Cross-cutting issues

• Overall context of the study population and study site– Rural vs. urban– Site/hospital-based vs. population based

• Number of staff to be trained for data collection– Consider contract status, sickness, drop out,

maternity leave, replacement plan• Transportation

– Consider area, distance, transport options• Accommodation

– Per diem, or lodging

Developing a Gantt Chart for timeframe

Think about:• Your institution’s Internal Review Board (IRB)• Selecting and hiring staff• Ordering special equipment or materials• Pre-testing data collection tools in the field – Data collection…..do your math!

• How many respondents can you reach? • How many samples can you collect?

• Date analysis…ongoing cumulative process, or?• Knowledge translation…dissemination

meetings, manuscript writing

TimelineActivities

Time line Year

2012

1 2 3 4 5 6 7 8 9 10 11 12

IRBApproval

X

Ordering materials &equipment

X

Staff selection & hiring & training

X X

Data collection X X X X

Data analysis X X X X X

Dissemination meetings

X

Manuscript writing

X X X

Developing a Budget

• A budget is a plan, expressed in quantitative and monetary terms, related to research study activities that will be implemented within a specific period of time.

• Always include a budget in every concept note. Funding decisions are made by managers who need to have an idea of the cost of study to decide if they are interested

What is a budget justification?

• A budget justification is an explanation of the costs listed on the budget in paragraph form.

• It provides the rationale for the amount budgeted and explains to the reader how you plan to use the resources reflected in the budget.

• An example would be of explaining how many personnel you have listed in your budget. You could write…– The principal investigator will be charged at 50 %

of her salary to oversee the implementation of the study. There will be three field research attendants to carry out data collection at 100% salary.

Factors to be considered for budgeting:

• Goals and objectives

• Activities and timeframe

• Management plan

• Administrative guidelines

• Assumptions

• Flexibility

• Consolidation

The main characteristics and purpose of a budget include:

Planning for implementation of activities Prepared in advance Quantitative and/or monetary units

Relates to objectives or goals Projecting an cost/expense against each activity Time bound

Long-term strategy for success Monitoring the financial output against activities Shows overspend in an area Allows for adjustments

Before starting the budgetBefore starting the budget

Group Discussion

Need to have clear understanding of:

Guidelines from donor agencies/organizations Special requirements and restrictions

Requirement from your organization Your budget needs

Types of Costs

Direct costs

Salary, fringe benefits, travel, equipment, lab supplies, etc

Are easily associated with a specific project

Indirect costs

Also known as Overhead costs (OH) or Facilities & Administration costs (F&A)

Utilities, admin support, general office supplies

Can be associated with many other sponsored projects or cannot be easily associated with a specific project

Practical Tips

• A low-cost study is much easier to get funded.

• High costs are a primary reason why funding agencies reject studies.

• But, because of unforeseen expenses and complications, some flexibility in funding makes it easier to conduct the study, so you can achieve the study objectives without asking for more funding.

• In general aim for a budget that will solidly meet the objectives of the study.

• For first draft of concept note, develop a complete general budget in Microsoft Excel, based upon experience with other studies.

• Think about all the cross-cutting issues, then include all important items in your study site/population context

• Do not include a detailed budget at this stage. If the funding agency is interested after seeing the estimated cost, specifics can be discussed later.

• One strategy is to take the best budget estimate, add 25% to it, and add one line in the concept note that says, ‘Estimated budget is $ X.’– 25%, because a first draft budget consistently

fails to account for unexpected expenses that only become clear as the research study is developed completely

• This strategy minimizes the risk that subsequent drafts of the funding proposal will demand a drastically higher budget.

• This helps maintain your credibility with the funding agency.

Any questions

Day 2 Session 4

Session 4: Ethics, Gender and Knowledge Translation

Time: 16:00 – 17:00 PM

Facilitator: Dorothy Southern

Objective

To explore bigger picture issues that are crucial to funding success

1.Ethics/Bioethics

2.Mainstreaming Gender into Research

3.Collaboration

4.Knowledge Translation

What is Ethics/Bioethics

Ethics is a set of moral principles, values, duties and obligations. It includes the principle of conduct that governs an individual or a group.

Bioethics is the moral principles, values, duties and obligations related to carrying out any research activity with human subjects, irrespective of the nature of the research.

dorothy

Yushuf....I need the information from Jaynal about ethics dealing with statistical issues.

Why bioethics is important?

• We conduct research on public health issues that affect the lives of people in Bangladesh and in other similar settings

• In public health research our study participants are the most important stakeholders

• A study can not generate valid quality data unless the researcher can obtain full cooperation from these stakeholders

Bioethical Principles

Respect for Persons

Beneficence

Justice

Respect for Persons

• Informed consent:

– Voluntariness: no coercion– Freedom to withdraw– Information: risks, benefits, alternatives

• Additional protections for vulnerable– Children– Pregnant women– Prisoners

Beneficence

Maximize possible benefits, minimize potential harm

Emphasis on safety—avoid unnecessary risks if at all possible

Risk to subjects requires justification

Privacy and confidentiality

Justice

• Fair procedures for recruitment

• Subjects should not be ‘included’ or ‘excluded’ because of their vulnerability

• Benefits of research should be fairly distributed

• Participation in research is associated with a favorable balance of potential benefits and harms

Do you have your Ethics Certificate?

• NIH guidelines http://phrp.nihtraining.com

• CITI guidelines www.citiprogram.org– Free– Easy and interactive

http://phrp.nihtraining.com/

http://www.citiprogram.org/

Mainstreaming Gender into Research

What is gender?

Gender refers to the socially constructed roles played by women and men that are assigned on the basis of their sex.

Gender roles are behavioural patterns expected from women and men that are reinforced by society and culture.

Gender roles are usually specific to a given area and time

Both a strategy and a process for making women’s, as well as men’s, health concerns and experiences an integral dimension in the design, implementation, monitoring and evaluation of a research study.

What is mainstreaming gender in health research?

Why is mainstreaming gender important?

• Gender is a research theme that cuts across all areas of scientific research.

• Many agencies and organizations have a ‘gender’ focus in their strategic plans

• Many funding sources require a gender dimension be included in the protocol

Does gender affect the research question being considered?

• Use the gender analysis framework to guide your research

• Gender analysis can help:– identify where gender differences are – design studies to bridge the gaps in knowledge

regarding men and women’s health

How to incorporate gender into specific areas of the concept note?

Background:•Explore the research topic by including noted differences by sex, gender and diversity (ethnicity, SES)

Research Design and Methods:•Plan data collection and analysis to include stratification by sex, gender and diversity.

Collaboration

• Explore the feasibility of internal and external collaboration

What is Knowledge Translation ?

A dynamic and iterative process that includes:

Synthesis, dissemination, exchange and ethically sound application of the results of a research study

Why is knowledge translation important?

It can alert funding agencies/donors about how your results (evidence) can be used to impact programs and policy if shared with an appropriate audience.

Evidence can:• convince the target audience of the urgency of the current problem• inform adoption of a preferred alternative or course of action • Increase demand from policymakers for evidence and data• serve as an impetus for action •

-

How to share results with stakeholders

• Who needs to know?• Government of Bangladesh/Policy makers• Practitioners• Academics

• How will you share?• Dissemination meeting• Training workshop• Collaborative arrangement• International conference • Publications:

– working papers– internal (institutional) publication– international publications

Audience Communication Objective(s) Format of Information Means of Dissemination

Level Audience Communication Objectives

Format of Information

Means of Dissemination

Sub-district/District

Distributors of safe water and hygiene products (for-profit, NGO, and government)

Explain managerial implications of the research and how to achieve increased distribution.

Marketing materials in Bangla

•NGO groups•Private sector distributors •Briefings by local government•Stakeholder meetings

Regional and National Distributors of safe water and hygiene products (for-profit, NGO, and government)

Share managerial/political implications of the research and how to achieve increased distribution

Policy Brief Marketing materials in Bangla and English

•Stakeholder dissemination meetings • Web site • Publications

National and Global • Donors and policy-makers

•Academic

•Academic

•Share implications of the research and how findings apply to broader marketing issues related to health outcomes (e.g., bed nets).

•Explain implications of the research on decision-making by the poor.

•Permits replication and facilitate additional research

•Policy Brief

• Research article

•Provide documentation of methods and data

•Web site •Publications•Conference presentations

• Conference and academic presentations • Refereed journal publications

•Website

Any questions?

Thank you!

Concept Note Development Course Rajshahi University, Dept of Statistics Day 2 June 29. 2011...

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