From study objectives to analysis plan

1. From study objectives to analysis plan Helen Maguire

2. don’t get bogged down -or stuck …

3. its logical

4. the keys to successful research • get the research question crystal clear • write a clear outline (concept paper) • involve stakeholders • talk to people • revise again and again • get the research question crystal clear

5. avoid X • too ambitious/ unfocussed • unsound unscientific basis for hypothesis • not clear what impact the findings will have in the field .. • no clear plan of next steps • not ethical • lacking appropriate expertise

6. 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?

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

8. analysis plan: road map epietmobile • choose a design to identify key indicators • identify parameters (variables) needed for indicators • prepare the analysis • estimate sample size Objectives

9. analysis plan: road map • choose a design to identify key indicators • identify parameters needed for indicators • prepare the analysis • estimate sample size Design and indicators

10. what sort of designs do you know? • what do you need to consider to help you decide which one to use?

11. choosing a suitable study design

13. what to consider when choosing a study design • is the study descriptive or analytical? • are you comparing groups? • are you estimating a frequency? • is the outcome (e.g., disease) acute or chronic • prevalence data for chronic disease • incidence data for acute outcomes • is it common or rare? • case control for rare outcomes • cohort / cross sectional for common outcomes Design and indicators

14. testing a hypothesis estimating a quantity • determinewhether hepatitis C is more common in people who inject drugs (PWID) • hypothesis testing • crude objective, smaller sample size • estimatethe relative frequency of hepatitis C infection in PWID vs others • quantity estimating • more elaborate objective, larger sample size Objectives

15. clearly identify the study population • this is different from the sample you will study

16. examples - what design? • who is most likely to get wound infection after appendicectomy? • what lifestyle factors are associated with acquiring hepatitis C? • what experiences do patients with rare disease such as MDR TB have using NHS services?

17. what design • who is most likely to get wound infection after appendicectomy • cohort study • what lifestyle factors are associated with acquiring hepatitis C • case-control study comparing those with hep C and those without • what experiences do patients with rare disease such as MDR TB have using NHS services • qualitative study or survey with open ended questions

18. analysis plan: road map • choose a design to identify key indicators • identify variables needed for indicators • prepare the analysis • estimate sample size Parameters

19. what indicators ? • who is most likely to get wound infection after appendicectomy • cohort study • what lifestyle factors are associated with acquiring hepatitis C • case-control study comparing those with hep C and those without • what experiences do patients with rare disease such as MDR TB have using NHS services • qualitative study or survey with open ended questions

20. estimating the relative frequency of meningococcal carriage in children of parents who smoke vs others • … from the objectives: • analytical approach: compare two groups • chronic condition: prevalence data • common condition: survey • study design: • analytical cross sectional study • indicator: • ratio of prevalence of meningococcal carriage among children of smokers vs others Design and indicators

21. what’s needed to calculate the indicator? • list the indicators that the study will generate • proportion with carriage for categorical variables, prevalence rate, prevalence ratios • remember: • outcome variable(s) • “covariates” including • potential risk factors • potential confounders • identify the information needed to calculate the indicators • numerators and denominators • example: number carrying / total children Parameters

22. ....from indicators to variables • identify variables that enable you to get your indicator • information “meningococcal C vaccination status” can be collected by review of cards or interview of the mother • choose the best variable • review standardized guidelines (e.g., WHO, CDC) • e.g. how to measure smoking • plan data collection methods for each variable • record review • interview • observation • laboratory data Parameters

23. covariate measurement for meningococcal carriage study among children of smokers vs others • potential risk factors • income(validated field methods) • ethnic group • education • area of residence • vaccination against Men C • potential confounding factors • age • sex Parameters

24. analysis plan road map • choose a design to identify key indicators • identify parameters needed for indicators • prepare the analysis • estimate sample size Analysis

25. rationale for preparing the data analysis in advance • focus on the objectives of the study • avoid multiple comparisons • avoid comparisons for which the study was not designed ….(tempting as it is..) • ensure data collected can be analyzed • “Other, specify: _____” ?? .. groups that cannot be analyzed • save time • filling dummy tables speeds data analysis Analysis

26. a word about coding

27. a word about coding • binary coding (usually): • “1” is yes • “0” is no • gender (usually): • “1” is male • “0” or “2” is female • age (or any categorical ordered var.) • by percentiles • by common sense

29. preparing the analysis, stage by stage • recoding stage • example: age into age groups • descriptive stage • calculate prevalence or incidence • analytical stage • univariate, stratified and multivariable analysis • prepare empty (dummy) tables (shells) now Analysis

30. initial stage of analysis meningococcal carriage according to smoking status • recoding stage • create outcome data with laboratory results -carriage Yes/No • recode smoking data dichotomize quantitative smoking variable (how many do you smoke a day (0,1,2,3…20)) -smoke Yes/ No • descriptive stage • calculate prevalence of meningococcal carriage Analysis

31. analytical stage meningococcal carriage according to smoking status of parent • univariate analysis • prevalence of outcome by age, sex and residence • prevalence of outcome by smoking (potentially examine dose response effect) • stratified analysis • prevalence of outcome by smoking, stratified for age, sex and residence • multivariable analysis (adjusted risk/rate ratio) • logistic regression model • binomial regression Analysis

32. dummy table for meningococcal carriage study – proportion carriage by factor (analytical stage) * *variables dichotomized

33. analysis plan: road map • choose a design to identify key indicators • identify parameters needed for indicators • prepare the analysis • estimate sample size Sample size

34. 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 • making comparisons for which the sample size is insufficient Sample size

35. sample size for study on meningococcal carriage • study design • analytical cross sectional survey • level • analytical • need to • use prevalence ratio for sample size estimation Sample size

38. take home messages • clarify again - precise focussed objectives • choose a design - identify the indicator • know the parameter (variable) you want before you think about how to get information about it • know where you go with the analysis • the planned analysis drives the data needs and not the reverse • work out sample size from all of the above

39. thanks for your attention