The road map to problem solving

1. The road map to problem solving The analysis planFETP India

2. Competency to be gained from this lecture Plan the analysis of a study on the basis of the study objectives

3. Key areas • Objectives of the study • Design and indicators • Study parameters • Analysis • Sample size

4. 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 The ad-hoc approach to conducting an epidemiological study Sound familiar?

5. Involving the programme Spelling out the research question Formulating recommendations Formulating the study objectives Drawing conclusions Planning the analysis Analysing data Preparing data collection instruments Collecting data The life cycle of an epidemiological investigation Identifying data needs Analysis plan

6. The analysis plan: A road map to making sense of data • Formulate the objectives of the study • Choose a design to identify key indicators • Identify parameters needed for indicators • Prepare the analysis • Estimate sample size Objectives

7. The analysis plan: A road map to making sense of data • Formulate the objectives of the study • Choose a design to identify key indicators • Identify parameters needed for indicators • Prepare the analysis • Estimate sample size Objectives

8. 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 terms Objectives

9. 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

10. 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

11. From testing a hypothesis to estimating a quantity • Determinewhether iodine deficiency is more common among poorer people • Hypothesis testing • Crude objective, smaller sample size • Estimatethe relative frequency of iodine deficiency among poorer people • Quantity estimating • More elaborate objective, larger sample size Objectives

12. The analysis plan: A road map to making sense of data • Formulate the objectives of the study • Choose a design to identify key indicators • Identify parameters needed for indicators • Prepare the analysis • Estimate sample size Design and indicators

13. 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 Design and indicators

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

15. 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 Design and indicators

16. The analysis plan: A road map to making sense of data • Formulate the objectives of the study • Choose a design to identify key indicators • Identify information needed for indicators • Prepare the analysis • Estimate sample size Parameters

17. 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

18. 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

19. Example: Outcome measurement for iodine deficiency study Parameters

20. 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

21. The analysis plan: A road map to making sense of data • Formulate the objectives of the study • Choose a design to identify key indicators • Identify parameters needed for indicators • Prepare the analysis • Estimate sample size Analysis

22. 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

23. 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

24. 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

25. 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

26. Dummy table for iodine deficiency study (Analytical stage) * * All variables dichotomized for the sake of simplicity

27. The analysis plan: A road map to making sense of data • Formulate the objectives of the study • Choose a design to identify key indicators • Identify parameters needed for indicators • Prepare the analysis • Estimate sample size Sample size

28. 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

29. 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

30. 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

31. Additional resources on analysis plan • Dummy tables for field epidemiology • Case study on protocol writing (Scrub Typhus in Darjeeling, Volume 2)