Covariates and Confounders Flashcards
Define Covariates
Are characteristics of participants that affect the magnitude of the treatment effect in a manner that may be clinically important.
Baseline characteristics of participants that explain part of the variability in outcome
Define Confounders
Variables associated with both intervention and outcome, but are not on the causal pathway.
Confounders can distort the effect of the intervention on the outcome and create bias.
Why/How do Covariates affect clinical trials
Imbalances between trial groups may bias estimate of treatment effect. (overestimate or underestimate)
Differences in treatment effect between subgroups may be clinically important
Why/How do Confounders affect clinical trials
Imbalances between trial groups may bias estimate of treatment effect. (overestimate or underestimate)
e.g. patients taking drug Y were more likely to develop abnormal liver function tests than
patients taking placebo
– On investigation more patients in the treatment group drank alcohol to excess than those in the
placebo group
– On subgroup analysis, stratified by alcohol consumption, there was no difference in liver function
tests between treatment and placebo groups; alcohol was a confounder that had positively biased
the estimate of the safety outcome
Managing covariates and confounders
Reduce the bias of treatment effect:
By Randomisation (stratification of most influential covariates or confounders)
Adjust analysis for covariates or confounders: Stratification or regression
(This Reduces their impact on estimates of treatment effect)
Identify significant covariates: Subgroup analysis (Identify subgroups of participants with different benefits/risks from treatment)
how can covariates and confounders be managed in clinical trials to reduce bias in the estimation of treatment effect
Stratification: Sorting participants into groups e.g. by confounders or covariates
Regression: Adjusted analysis.
Estimate of treatment effect taking into account covariates and confounders
State 2 Stratification methods
Can stratify at randomisation –
or if this fails during analysis
Stratification in analysis:
Investigate effects between intervention and outcome within subgroups
covariate - relationship will persist
confounder - relationship will disappear
Regression
Unadjusted analysis: Estimate of treatment effect with no account taken of covariates or confounders
Adjusted analysis: Estimate of treatment effect taking into account covariates and confounders
*pre-specified to reduce potential bias by ‘fishing’
* Achieved using regression models
– Factors chosen for adjustment
- Strong predictors of outcome e.g. correlated with r>0.50
- Clear ‘large’ imbalance despite randomisation
Rate vs Rate ratio
Rate = cumulative incidence/unit of time
Rate ratio = rate group 1/rate group 2
What is subgroup analysis
A special form of stratification.
Gives information about variation in efficacy/safety that can improve treatment decisions
Hypothesis generating
List the disadvantages of subgroup analysis
- Multiple comparisons
- Underpowered
- Not the primary focus of
randomization so participants may be
unbalanced - Risk of bias e.g. if selected based on
treatment effect - Overinterpreted
List the disadvantages of subgroup analysis and solutions
- Multiple comparisons (limit subgroups, adjust p values)
- Underpowered (pre-plan the subgroups including sample size and randomisation)
- Not the primary focus of
randomization so participants may be
unbalanced - Risk of bias e.g. if selected based on
treatment effect (use biological rationale, pre-specify) - Overinterpreted (Use information to form ideas or
propose associations; not support
conclusions or explain results)
State the methods that can be used to manage covariates and confounders in clinical trial design/analysis to reduce the bias in the estimation of treatment effect.
Randomisation: randomly assigning participants to treatment or control groups, this helps ensure that covariates are balanced across groups, reducing bias.
Stratification: Before randomization, participants are divided into strata based on certain covariates or confounders (e.g., age, disease severity). Randomization is then performed within each stratum to ensure balanced allocation across key covariates.
Subgroup analysis. Identifying significant covariates.
Regression: adjusting for covariates in a regression model. Estimate of treatment effect taking into account covariates and confounders.
State the use of subgroup analysis in clinical trials
Subgroup analysis in clinical trials involves the examination of treatment effects within specific, predefined subsets of participants.
What is a limitation of subgroup analysis in clinical trials.
- Multiple comparisons (multiplicity)
- Underpowered (which can lead to false negatives or false positives)
- Not the primary focus of
randomization so participants may be
unbalanced - Risk of bias e.g. if selected based on
treatment effect - Over-interpreted
