Ian Marschner Pfizer Australia & NHMRC Clinical Trials Centre

1. Biases in identifying risk factor thresholds: A new look at the "lower-is-better" controversy for cholesterol, blood pressure and other risk factors Ian Marschner Pfizer Australia & NHMRC Clinical Trials Centre

2. Linear Relationship • Relationship between a risk factor and the occurrence of a disease event is essentially linear on an appropriate scale (usually the log-incidence scale) • Existence of a linear relationship suggests a “lower-is-better” approach to risk factor modification

3. Example – Coronary and Vascular events related to cholesterol and blood pressure Mortality per 1000 per year Relative Risk Law & Wald. BMJ 2002.

4. Threshold Relationship • Threshold:pointatwhichapredominantlylinearrelationshipbetweenariskfactorandadiseaseeventbecomeseffectivelyconstant • Existenceofathresholdrelationshipcansuggestlessaggressiveriskfactormodificationstrategiessincemodificationisofnobenefitbeyondacertainpoint

5. Example – CARE Trial Relative risk (log scale) of recurrent coronary event LDL Cholesterol level (mg/dL)

6. J-Curve Relationship • J-curve:predominantlylinearpositiverelationshipbetweenariskfactorandadiseaseeventreversesandbecomesnegative • ExistenceofaJ-curverelationshipcansuggestlessaggressiveriskfactormodificationstrategiessincemodificationisofnobenefitandmayevenbeharmfulbeyondacertainpoint

7. Example – Framingham Study Note: J-curves have also been observed for stroke events

8. Randomised Studies • Randomised studies of intensive versus moderate lowering of cholesterol support lower-is-better e.g. PROVE-IT study:

9. Example – CARE Trial Relative risk (log scale) of recurrent coronary event LDL Cholesterol level (mg/dL)

10. Randomised Studies • Randomised studies of intensive versus moderate lowering of cholesterol support lower-is-better e.g. PROVE-IT study:

11. Conflicting Evidence • Existence of conflicting evidence has complicated the assessment of whether lower-is-better for cholesterol and blood pressure • One explanation for this is that bias has led to spurious non-linear threshold or J-curve relationships in some studies, particularly those on primary or secondary prevention cohorts

12. Explanation for conflicting evidence • Confounding of primary risk factor and residual risk level in primary or secondary prevention studies • E.g. cholesterol level confounded with non-cholesterol risk level • Effect modification often exists between primary risk factor and residual risk level • E.g. risk coronary event increases more quickly with cholesterol when the individual has lower non-cholesterol risk level • Combined effect of confounding and effect modification is a spurious non-linear relationship even when the underlying relationship is a linear lower-is-better one

13. Plan for rest of talk • Provide evidence that there is confounding in primary and secondary prevention studies • Provide evidence that effect modification can exist, particularly in cardiovascular contexts • Explain how the two can combine to produce spurious non-linear relationships that could explain the apparent threshold and J-curve relationships seen in some prior studies

14. Confounding • Inpatientsselectedbecausetheyhavehadapreviousevent(secondaryprevention)orbecausetheyhavenothadapreviousevent(primaryprevention)theriskfactorandtheresidualrisklevelisconfounded • Example1:Inordertohavehadapriorheartattack,patientswithlowcholesterolhavemorenon-cholesterolriskfactors • Example2:Inordernottohavehadapriorheartattack,patientwithhighcholesterolhavelessnon-cholesterolriskfactors

15. Example – Simulation Results Assumptions for simulated population: Incidence of coronary event is related to 8 risk factors (incl. cholesterol) according to a model No relationship between cholesterol and no. of non-cholesterol risk factors in the full population Coronary events simulated according to the risk factor model Primary and secondary prevention sub-populations identified

16. Example – LIPID Trial

17. Effect Modification • EffectModification:Eventrateincreaseslessquicklyastheriskfactorincreasesinpatientswithhigherresidualrisk • Examples:Coronaryandvasculareventrateincreaseslessquicklywithcholesterollevelandbloodpressureinpatientswithhighernon-cholesterolandnon-BPrisklevel

18. ExamplesLIPID: Rate ratio of coronary event for each unit of cholesterolLaw: Rate ratio of coronary event for each unit of cholesterolPSC: Rate ratio of vascular event for each unit of blood pressure

19. Combination of Confounding and Effect Modification • Confounding alone leads to linear attenuation of the risk factor relationship • Strength of association between risk factor and disease event may be under-estimated • Combination of confounding and effect modification leads to non-linear attenuation • Association may appear to be threshold or J-curve

20. Linear Attenuation(confounding only) • Risk level: P = primary risk factor R = residual risk level • Incidence rate: logl(t;P,R) = logl0(t) + aP + bR • Confounding: R = c + dP(d<0) • Apparent relationship: logl(t;S) = logl0*(t) + (a+bd)P • Attenuation: a > a+bd

21. Non cholesterol risk level Hypothetical Effect

22. Non – linear attenuation(confounding and effect modification) • Risk level: P = primary risk factor R = residual risk level • Incidence rate: logl(t;S,NS) = logl0(t) + (a+a0R)P + bR • Confounding: R = c + dP (d<0) • Apparent relationship: logl(t;S) = logl0*(t) + (a+a0c+bd)P + a0dP2 • Attenuation: apparent quadratic relationship

23. Non cholesterol risk level Hypothetical Effect

25. Theoretical Calculations(under the assumption that lower-is-better)

26. Apparent Relationships • Bias can lead to apparent thresholds and J-curves even when the underlying model is linear

27. Adjustment for measurement error (regression dilution) • Measurement error accounts for some but not all of the attenuation

28. Conclusions • Analyses showing an apparent threshold relationship may not be inconsistent with a linear “lower is better” relationship • Aggressive treatment strategies may be warranted despite an apparent threshold or J-curve in the risk factor • Analyses adjusting for residual risk level are crucial and may ameliorate the bias

29. Randomised Studies • Interventionstrategiesarebestbasedonrandomisedtrialscomparing(lessaggressive)threshold-basedinterventionwith(moreaggressive)non-threshold-basedintervention • Example:Despiteearliersuggestionsofacholesterolthreshold,largescaletrialshavenowconfirmedaggressivetreatmentofhighriskpatientsevenatlowercholesterollevels

30. Final Word • Even when we are confident that there is no bias in the risk factor model, assessment of risk factor intervention strategies can be dangerous based solely on risk factor models derived from prospective cohort studies • Degree of improvement in the risk factor may not be a complete “surrogate” for the effect of the intervention • Randomised studies capture the complete effects of the intervention and are therefore preferable for assessing risk factor interventions strategies

31. Randomised Studies • Interventionstrategiesarebestbasedonrandomisedtrialscomparing(lessaggressive)threshold-basedinterventionwith(moreaggressive)non-threshold-basedintervention • Example:Despiteearliersuggestionsofacholesterolthreshold,largescaletrialshavenowconfirmedaggressivetreatmentofhighriskpatientsevenatlowercholesterollevels

32. Example – Simulation Results Assumptions for simulated population: Incidence of coronary event is related to 8 risk factors (incl. cholesterol) according to a model No relationship between cholesterol and no. of non-cholesterol risk factors in the full population Coronary events simulated according to the risk factor model Primary and secondary prevention sub-populations identified