A hierarchical step-model for causation of bias-evaluating cancer treatment with epidemiological methods

Figures & data

Table I.  Misclassification: Predicted alteration of effect-measures by type of measuring error and effect-measure.

Measurement Error:	Effect-Measure:	Influence on Effect-Measure:
Decreased sensitivity in measuring outcome events that does NOT vary with treatment (exposure)	Absolute risk (difference between incidences of two groups)	Deviation towards the null (0.0)
	Relative risk (ratio of incidences across two groups)	None
Decreased specificity in measuring outcome events that does NOT vary with treatment (exposure)	Absolute risk	None
	Relative risk	Deviation towards the null (1.0)
Error in measuring outcome events that vary with treatment (exposure)	Absolute or relative risks	Unpredictable
Error in measuring treatment (exposure) that does NOT vary with outcome events	Relative risk	Deviation towards the null (1.0)
Error in measuring treatment (exposure) that varies with outcome events	Absolute or relative risks	Unpredictable

Table II.  Remedies for increasing validity before and during a study, as well as suggestions for validation post-study completion.

	Increase/Evaluate validity in:
Step of Hierarchical Model:	Study Design Phase:	Study Completion Phase:	Post-Study Completion Phase:
Step 1: Confounding	Randomly allocate treatments (exposure) between subjects or groups	Protect randomness of allocation	Confirm that random allocation was done stringently
	Utilize “Before-After” comparison between subjects or groups	Protect definition of the treatment (exposure) groups	Confirm that all factors causally related to outcome were measured as potential confounders
	Match cohorts subject-by-subject or group-by-group	Apply sham treatment thoroughly	Examine the effect-measure change (magnitude and direction) after adjustment for each potential confounder; ensure changes appear appropriate
	Include a sham treatment (placebo) group	Record day of randomization	Confirm that sham treatment accounted for all factors related to outcome in those treated (besides the investigated treatment (exposure))
	Define start of observation period based on moment of random allocation
	Define start of observation period based on introduction of variable related to true onset of disease	Consistently record factors related to true onset of disease	Confirm that observation of the hazard curve for outcome did not vary between the contrasted groups
	Define targeted person-time independent of outcome
Step 2: Misrepresentation	“Blind” subjects and investigators	Avoid loss of follow-up to attrition when completing a cohort study	Examine the size of attrition or non-participation and confirm that neither spuriously affect the effect-measure
		Avoid non-participation among cases or controls when completing a case-control study
	Select controls that reflect distribution of exposure (treatment) in the targeted person-time	Include all person-time observed during investigation of the association of interest when completing a pooled analysis
		Include all person-time published concerning the association of interest when completing a literature compilation
Step 3: Misclassification	“Blind” subjects and investigators	Confirm “blindness” of individuals	Determine if measuring errors of outcome vary with treatment (exposure)
	Construct measuring means to diminish measuring errors	Work to diminish all measuring errors	Determine if measuring errors of treatment vary with outcome
Step 4: Analytic Adjustment	Ensure information is collected on potential confounders	Choose appropriate statistical modeling techniques	Determine whether statistical model used has an appropriate goodness-of-fit
	Ensure information is collected on covariates that depict varying attrition or measuring errors	Include in statistical model only potential confounders and covariates depicting misrepresentation of misclassification	Determine whether appropriate potential confounders or other covariates were used during analytic adjustments
	Ensure information is collected on variables related to the true-onset of the outcome
	Ensure that measuring errors of confounders or covariates are small or that they do not vary with treatment or outcome group	Stratify only for potential confounders or covariates depicting “misrepresentation of misclassification”	Determine whether or not errors were made in the analysis
	Choose appropriate statistical models and techniques.	Make no errors

Table III.  Validity-influencing characteristics of some errors introduced in each of the four steps defined by the hierarchical step model.

Step in Hierarchical Model:	Error Introduced:	Characteristics of Error:
Step 1: Confounding	Bias due to presence of true risk factors of the outcome in the targeted person-time leading to inadequate comparisons in targeted person-time	•Magnitude and direction of alteration of effect-measure depend upon: association between:1. the association between the confounder and the treatment2. the association between the confounder and the outcome•Magnitude of alteration is always weaker than effect of confounder alone•Direction of alteration is always the same as that of confounder alone
Step 2: Misrepresentation	Bias due to misrepresentation of targeted person-time	•Alteration of effect-measure is unpredictable•Maximal alteration can be calculated
Step 3: Misclassification	Bias due to measuring errors within observed person-time	•Magnitude and predictability of alteration of effect-measure depend upon:1. sensitivity and specificity in assessing subject treatment and outcome2. whether or not error in measuring treatment or outcome varies across treatment and outcome groups3. type of effect measure being calculated (ratio or difference)
Step 4: Analytic Adjustment	Bias due to statistical analyses (by calculation errors or “adjustment” aimed at eliminating bias)	•Magnitude and predictability of alteration of effect-measure depend upon:1. statistical techniques2. occurrence of errors in measuring confounders or variables indicating misrepresentation or misclassification3. whether or not covariates used in stratification or modeling are chosen appropriately