The road map to problem solving The analysis plan FETP India

The road map to problem solving

The analysis plan

FETP India

Competency to be gained from this lecture

Plan the analysis of a study on the basis of the study objectives

Key areas

• Objectives of the study • Design and indicators• Study parameters• Analysis• Sample size

The ad-hoc approach to conducting an epidemiological study

Before data collection• I want to do a study

I am not clear about the objectives

• I prepare a questionnaire I am not clear about

what information I need

• I collect data I am not clear what I

will use for what

After data collection• I come back with data

I realize they are difficult to analyse

• I analyse the data I realize it is difficult to

interpret the results

• I interpret the results I realize it is difficult to

use them

Sound familiar?

The life cycle of an epidemiological investigation

Identifying data needs

Spelling out the research question

Formulating the study objectives

Planning the analysis

Preparing data collection instruments

Analysing data

Drawing conclusions

Formulating recommendations

Involving the programme

Collecting data

Analysis plan

The analysis plan: A road map to making sense of data

1. Formulate the objectives of the study 2. Choose a design to identify key

indicators3. Identify parameters needed for

indicators 4. Prepare the analysis5. Estimate sample size

Objectives





Objectives

The study objectives

• Formulated in limited number • Sorted out as primary and secondary• Focused• No more than one verb each• Clear about whether:

Hypothesis testing Quantity measuring

• Epidemiological termsObjectives

Estimating versus testing

• Estimating a quantity Use the verb “Estimate” • E.g., Estimate the prevalence of diabetes

• Testing a hypothesis Use the verb “Determine” • E.g., Determine whether a contaminated

well caused an outbreak

Objectives

Good and bad examples of study objectives

• Determine the importance of Kala Azar Estimate the prevalence of Kala Azar in the

community

• Assess vitamin A deficiency and tuberculosis Estimate the effect of vitamin A

supplementation over the cure rate of tuberculosis patients

• Evaluate iodine deficiency and equity Determine whether iodine deficiency is more

common among poorer people Objectives

From testing a hypothesis to estimating a quantity

• Determine whether iodine deficiency is more common among poorer people Hypothesis testing Crude objective, smaller sample size

• Estimate the relative frequency of iodine deficiency among poorer people Quantity estimating More elaborate objective, larger sample size

Objectives





Design and indicators

Elements to consider to choose a study design

• Is the study descriptive or analytical? Is there a need to compare groups? Is there just a need to estimate a frequency?

• Is the outcome (e.g., disease) acute or chronic Need of prevalence data for chronic outcomes Need of incidence data for acute outcomes

• Is the outcome common or rare? Case control design for rare outcomes Cohort / cross sectional designs for common

outcomes


Choosing a study design adapted to the objective to identify the indicator

Example: Estimating the relative frequency of iodine deficiency

among people below poverty line (BPL)

• Elements deducted from the objective: Analytical approach: Compare two groups Chronic condition: Prevalence data Common condition: Survey

• Study design: Analytical cross sectional study

• Indicator: Ratio of prevalence of iodine deficiency among BPL

persons




indicators3. Identify information needed for


Parameters

Identification of information needed to calculate the indicator

• List the indicators that the study will generate Rates, ratio, proportions or quantitative variables

• Example: Measles coverage

• Identify the information elements that will be needed to calculate the indicators Numerators an denominators

• Example: Number of children vaccinated / total children

• Information elements may address: Outcome variable (s) “Covariate”, including

• Potential risk factors• Potential confounders

Parameters

From information element to variables

• Identify the variables that may be used to reflect the information element The information element “Measles vaccination

status” can be assessed by review of cards or interview of the mother

• Choose the best possible variable Review standardized guidelines (e.g., WHO, CDC)

• Plan data collection methods for each variable Observation Interview Laboratory methods

Parameters

Example: Outcome measurement for iodine deficiency study

Information element

Variable

Data collection method to obtain the variable

Past exposure to iodine deficiency

•Goitre •Physical examination

Current iodine intake

•Urine iodine excretion

•Laboratory

Access to iodized salt

•Test of house salt for iodization

•Field spot testParameters

Example: Covariate measurement for iodine deficiency

• Potential risk factors Income

(Validated field methods) Community (e.g., minorities) Caste Education Residence

• Potential confounding factors Age Sex

Parameters





Analysis

Rationale for preparing the data analysis in advance

• Focus on the objectives of the study• Limit multiple comparisons• Avoid comparisons for which the study was

not designed• Ensure that data collected can be analyzed

“Other, specify: _____” kind of data that create minuscule groups that cannot be analyzed

• Save time Filling dummy tables accelerates data analysis

Analysis

Preparing the analysis, stage by stage

• Recoding stage Example: Transform age into age groups

• Descriptive stage Calculate prevalence or incidence

• Analytical stage Univariate, stratified and multivariate analysis Prepare empty (dummy) table shells upfront

• Dichotomize all variables for simple dummy tables– Using the median (e.g., Income > median)– Using a value known to be important (e.g., 200

CD4)Analysis

Example: Initial stage of the analysis of the study on iodine deficiency

according to income• Recoding stage

Create outcome data with laboratory results Recode income data

• Dichotomize quantitative income variable to create a “BPL” Yes/ No variable

• Descriptive stage Calculate prevalence of the thee outcomes

• Goitre, urinary excretion and salt spot test

Adjust confidence intervals for design effect

Analysis

Example: Analytical stage of the analysis of the study on iodine

deficiency according to income

• Univariate analysis Prevalence of three outcomes by age, sex

and residence Prevalence of three outcomes by income

(potentially examine dose response effect)

• Stratified analysis Prevalence of three outcomes by income,

stratified for age, sex and residence

• Multivariate analysis Logistic regression model

Analysis

Dummy table for iodine deficiency study

(Analytical stage) *Prevalence

Prevalence ratio (95% confidence

interval)Exposures Exposed Unexposed

Female sex XX/XX (XX%) XX/XX (XX%) XX (XX-XX)

Muslim XX/XX (XX%) XX/XX (XX%) XX (XX-XX)

Age > median XX/XX (XX%) XX/XX (XX%) XX (XX-XX)

Below poverty line

XX/XX (XX%) XX/XX (XX%) XX (XX-XX)

Schedule caste XX/XX (XX%) XX/XX (XX%) XX (XX-XX)

* All variables dichotomized for the sake of simplicity





Sample size

The analysis plan determines the sample size

• Choose the study design Cohort, case control or survey

• Determine the level Descriptive or analytical

• Common mistake: Designing a descriptive study Trying comparisons for which the sample

size is insufficient

Sample size

Sample size for study on iodine deficiency among people below

poverty• Study design

Analytical cross sectional survey

• Level Analytical Need to:

• Use prevalence ratio for sample size estimation• OR• Use prevalence but multiply final sample size by

two to allow comparisons

Sample size

Take home messages

• Clarify precise, focused objectives• Choose a design to identify the indicator• Know the parameter you want before you

think about how to get information about it • Know where you go with the analysis

The planed analysis drives the data needs and not the reverse

• Deduct your sample size from all of the above

Additional resources on analysis plan

• Dummy tables for field epidemiology• Case study on protocol writing (Scrub

Typhus in Darjeeling, Volume 2)

Documents

The road map to problem solving The analysis plan FETP India