Post on 21-May-2020
Session 1: Defining the research question and
selecting endpoints
Iden%fying the research ques%on…
l Important to have a clear question before starting to design your study
l This will allow you to make the most appropriate decisions around:
- The study population - The choice of study design - The method of collecting data - The primary outcome of interest - The main exposure/predictors of interest - The number of patients to be recruited
Research ques%on shouId be:
l Clear
l Unambiguous
l Measurable
l Of clinical/biological relevance
l Realistic with the resources available
Iden%fying the research ques%on…
QUESTION: Are we treating our HIV-infected patients adequately?
Is this a clearly defined question?
Iden%fying the research ques%on…
QUESTION: Are we treating our HIV-infected patients adequately?
How do we define ‘treatment’? - HAART? - PCP prophylaxis? - General medical care?
Iden%fying the research ques%on…
QUESTION: Are we treating our HIV-infected patients adequately?
How do we define ‘adequate’? - Increase in CD4 count? - Viral load suppression? - Improvement in clinical outcome? - Improvement in survival? - Some other measure?
Iden%fying the research ques%on…
QUESTION: Are we treating our HIV-infected patients adequately?
What patient group are we interested in? - All newly diagnosed patients? - All patients under established follow-up? - All patients on ART? - Those with co-infection or comorbidities? - Those with low CD4 counts?
Iden%fying the research ques%on…
QUESTION: Are we treating our HIV-infected patients adequately?
REVISED QUESTION: What proportion of patients starting antiretroviral therapy (ART) achieve viral load suppression at 12 months after starting first-line ART?
Choosing an appropriate study design
l The research questions that can be addressed in any study will depend on the study design
l Whilst some designs may offer benefits in terms of costs, time and administrative effort, studies that are quick and cheap to perform will generally provide less robust evidence
l Research question SHOULD determine choice of study design – in practice, lack of time and/or resources usually plays a major part in decision…
Choosing an appropriate study design
l RCT?
l Cohort?
l Case-control study?
l Cross-sectional study?
REVISED QUESTION: What proportion of patients starting antiretroviral therapy (ART) achieve viral load suppression at 12 months after starting first-line ART?
Study endpoints
l A well defined study endpoint should: - Be defined in advance - Address the primary aim of the study - Have biological/clinical relevance - Be appropriate for the population included in the study
l Well defined study endpoints are equally important for all study designs, whether RCTs or observational studies
Possible endpoints
l Clinical endpoints - New AIDS events, death, CDC stage B events
l Virological endpoints - VL <50 copies/ml at specific timepoints, absolute change
in VL, area under the curve for VL
l Immunological endpoints - CD4>200 cells/mm3, CD4 increase >100 cells/mm3
l Other endpoints - Treatment switches, adherence, quality of life, toxicity
l Composite endpoints - Time to loss of virological response (TLOVR)
Toxicity endpoints
l Toxicity endpoints may be based on clinical symptoms and/or laboratory data
l Need to be aware of possible biases when interpreting results from such studies, particularly if observational studies:
- Irregular/infrequent laboratory monitoring - Selective laboratory monitoring - Between-clinic assay variability - Clinic differences in monitoring policies - Bias due to confounding
Example – Primary endpoint
l We wish to compare the efficacy of cART in drug users compared to non-drug users in previously antiretroviral-naïve adults in an observational study
l Is this a good primary endpoint?
“New AIDS-‐defining event or death”
Example – Primary endpoint
l We wish to compare the efficacy of cART in drug users compared to non-drug users in previously antiretroviral-naïve adults in an observational study
l Is this a good primary endpoint?
“Remaining on treatment at one year”
Example – Primary endpoint
l We wish to compare the efficacy of cART in drug users compared to non-drug users in previously antiretroviral-naïve adults in an observational study
l Is this a good primary endpoint?
“Achieving virological suppression”
Example – Primary endpoint
l We wish to compare the efficacy of cART in drug users compared to non-drug users in previously antiretroviral-naïve adults in an observational study
l Is this a good primary endpoint?
“Achieving a viral load <50 copies/ml one year aCer starDng cART”
Classification of endpoints
l Myocardial infarction (D:A:D Study) Diagnosis based on established algorithm adapted from standardized criteria that included cardiac pain, cardiac enzyme or troponin levels, ECG readings, and autopsy results if available. Nonfatal MIs not associated with clinical symptoms are not included
l CoDe (Causes of Death initiative) Used for coding causes of death, designed specifically for HIV-positive individuals
l Chronic kidney disease (e.g. EuroSIDA) i) eGFR <60 mL/min/1.73m2 if baseline eGFR >60 60 ii) 25% decline from baseline value if baseline eGFR <60
Main exposures/predictor of interest
l Like the primary endpoint, the main predictors should ideally be defined in advance
l Most randomised controlled trials consider one or two main predictors. However, in a cohort study there is more flexibility to consider a number of factors
l The exposures should be clearly defined and explained
Example – main exposure/predictors
l We wish to compare the efficacy of cART in drug users compared to non-drug users in previously antiretroviral-naïve adults in an observational study
l Is this a good measure of the primary exposure?
“Individual reports having ever taken drugs”
Example – main exposure/predictors
l We wish to compare the efficacy of cART in drug users compared to non-drug users in previously antiretroviral-naïve adults in an observational study
l Is this a good measure of the primary exposure?
“Individual reports having ever injected illegal substances”
Example – main exposure/predictors
l We wish to compare the efficacy of cART in drug users compared to non-drug users in previously antiretroviral-naïve adults in an observational study
l Is this a good measure of the primary exposure?
“Individual reports current (at start of cART) injecDng drug use”
Confounders
l Confounding is a particular issue in observational studies (rarely an issue in RCTs)
l Occurs when a factor exists that is associated with both the exposure and outcome of interest
l Although can never be certain that all confounding has been accounted for, important to collect information on known confounders
l Possible to adjust for potential confounders using multivariable models, provided they are known and accurately measured
Example – Confounders
l When considering the impact of social class on the occurrence of cardiovascular disease, smoking is a potential confounder
Social class CVD
Example – Confounders
l When considering the impact of social class on the occurrence of cardiovascular disease, smoking is a potential confounder
Social class
Smoking status
CVD
Example – Confounders
l We wish to compare the efficacy of cART in drug users compared to non-drug users in previously antiretroviral-naïve adults in an observational study
l Can you think of any potential confounders?
Example – Asking the right ques%on
l Our research question may therefore become:
l Study design: Observational cohort study
l Study population: Previously antiretroviral-naïve HIV-positive adults
l Primary endpoints: VL<50 copies/ml after 1 year of cART
l Potential confounders/effect modifiers: HBV and HCV status, social class, pre-cART CD4 count, gender, age…
Summary
l Important to have a clear research quesDon before starDng to design your study – this will then determine choice of study design and study endpoints
l Endpoints require careful thought – where possible, they should be pre-‐defined in study protocol, should address the main aim of the study and be of relevance for the populaDon of interest
l Always be aware of the potenDal for confounding in observaDonal studies