Comments An investigation into the relationship between statins and cancer using population-based data Jennifer C. Melvin*, Hans Garmo*†, Rhian Daniel‡, Thurkaa Shanmugalingam*, Pär Stattin§, Christel Häggström§, Sarah Rudman¶, Lars Holmberg*†,††‡‡ and Mieke Van Hemelrijck* *Cancer Epidemiology Group, Division of Cancer Studies, School of Medicine, King's College London, ‡Centre for Statistical Methodology and Department of Medical Statistics, London School of Hygiene and Tropical Medicine, ¶ Department of Oncology, Guy's and St Thomas' NHS Foundation Trust, London, UK, †Regional Cancer Centre, Uppsala/Örebro, ††Department of Surgical Sciences and ‡‡Regional Oncologic Centre, Uppsala University, Uppsala, and §Department of Surgical and Perioperative Sciences, Urology and Andrology, Umeå University, Umeå, Sweden
Introduction Recent studies suggest statins reduce risk of fatal cancer by inhibiting 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, a key enzyme for cholesterol synthesis [1]. For prostate cancer, there are inconsistent and often incomparable results due to differences in study design and timing of statin use (i.e. before/after diagnosis).
Fig. 1 All patients were selected from the National Prostate Cancer Registry (NPCR) of Sweden, and all covariate information was retrieved from PCBaSe 2.0.
Men diagnosed with prostate cancer 1/1/2006 – 31/12/2009 (n=36 965)
Existing studies are observational, with ensuing limitations of confounding and bias. Therefore, application of causal inference methods to observational data to explore potential protective effects of statin use and prostate cancer-death is of interest. These methods mimic randomised clinical trials (RCTs) and reduce limitations of traditional statistical analyses [2]. Here, we aimed to identify whether statins given after prostate cancer diagnosis have an effect on prostate cancer-death using causal inference methods for a large population-based study.
Missing risk stage, low risk, or metastatic disease ( n=15 969)
Aged ≥90 years (n=240)
Prescribed statins before diagnosis (n=5771)
Dead within a month (n=59)
Patients and Methods PCBaSe Sweden is based on the National Prostate Cancer Register (NPCR) and includes data on tumour characteristics and primary treatment [3]. We estimated the effect of statins on prostate cancer-death in the presence of time-dependent confounders (TDCs) using causal inference methods (Fig. 1 and Table 1). These methods have been described in detail elsewhere [4]. Causal Inference Methods Briefly, inverse probability weights (IPW) estimation of a marginal structural model (MSM) are used to account for TDCs. Apart from lowering cholesterol levels, statins may also reduce risk of (prostate cancer) death by approximating other lifestyle © 2014 The Authors BJU International © 2014 BJU International | doi:10.1111/bju.12935,1297771 Published by John Wiley & Sons Ltd. www.bjui.org
Men included to study (n=14 926)
characteristics (e.g. diet). Furthermore, the probability of exposure to statins is associated with the decision to measure cholesterol, which may be associated with characteristics influencing prostate cancer-death (e.g. socio-demographic factors or disease stage). For instance, men on androgen-deprivation therapy (ADT) may have a higher prescription rate of statins due to increased risk of metabolic syndrome [5].
BJU Int 2015; 116: 681–686 wileyonlinelibrary.com
Comments
Table 1 Illustration of differences between the ‘ideal’ clinical trial, and the one which we have emulated here. Trial characteristics Ideal Statin naive Intermediate prostate cancer risk
Baseline cholesterol
50/50 split in both trial arms Knowledge of compliance to treatment (i.e. statins)
Difficulties encountered Emulated
Prescribed Drug Register (PDR) to identify all statin naive men (at time of diagnosis). National Prostate Cancer Register (NPCR) of Sweden to identify men diagnosed with intermediate-risk prostate cancer. Based on assumption that statin naive men had normal cholesterol levels. Observational data does not guarantee even distribution between trial arms. Per-protocol/intention-to-treat
Cholesterol levels (ongoing)
We did not have this information.
PSA levels (ongoing) to monitor disease severity
ADT initiation was included as a proxy variable because PSA levels were not available.
PDR captures all outpatient prescriptions with details of dose and duration from July 2005. NPCR captures >96% of all newly diagnosed, biopsy confirmed prostate cancers registered in Sweden. All men diagnosed between January 2006 and December 2009 were included. Not ideal because men with higher cholesterol levels could see an increased benefit with regards to prostate cancer prognosis as a result of a greater percentage decrease in cholesterol levels. IPW was used to inflate under-represented sections of sample populations. Particularly, without cholesterol levels we have no option but to assume compliance (and essentially effectiveness) to statin prescriptions, a generally invalid method. We attempted to account for this through the use of information on anti-hypertensive, anti-diabetic, and anti-obesity drugs as well as obesity related hospital visits as a proxy for hyperlipidaemia. Prostate cancer severity was an important confounder (OR for ADT initiation and prostate cancer death: 7.93 (95% CI 5.82–10.80)), therefore a more accurate measure of severity itself would be highly desirable.
Estimates of the association between statins and prostate cancer-death may be biased due to a combination of different components associated with cholesterol levels and/or disease stage/ongoing prostate cancer management. Thus, serum cholesterol may affect future prescription of statins as well as prostate cancer-death, an example of confounding by indication. Statin prescriptions before diagnosis affect serum cholesterol levels and thus represent a TDC affected by the exposure variable itself. Standard statistical methods handle TDCs, but do not allow study of exposure effect on the outcome transmitted via this TDC. Hence, IPW and MSMs are required as they relax the assumption that TDCs cannot be affected by previous exposure [2].
Table 2 Odds ratios (ORs) and 95% CIs for all three models.
Thus, IPW is an alternative to conducting a RCT. IPW allows for creation of a pseudo-population in which neither treatment nor study continuation are influenced by measured subject-specific characteristics. More specifically, it inflates the weight of under-represented sections of the sampled population so that the data becomes representative of a RCT, with equal distribution of patient characteristics between drug and placebo groups. Restricting the analysis of observational data to individuals with complete data, without re-weighting, would introduce bias when missingness is not completely at random. As a result, the analysis of observational data mimics a pseudo-population free of confounding and dropout.
indicate sizeable confounding, and reducing variability via truncation effectively blocks the mechanism by which MSMs deal with confounding. Thus, truncation masked the failure of IPW to adequately account for confounding, but did not resolve the issue. Hence, our results highlight the need for a clinical trial.
Results A protective effect of statins on prostate cancer-death was seen initially, but the estimates were not significant when TDCs were included (Table 2). However, the IPWs were extremely variable, even after truncation [6]. Extreme weights
682
© 2014 The Authors BJU International © 2014 BJU International
Model Baseline adjusted/unweighted* MSM using IPW† MSM with truncated weights‡
IPW range
OR (95% CI)
– 0.0019–13 574.00 0.035–13.16
0.62 (0.51–0.75) 0.89 (0.69–1.14) 0.86 (0.81–0.91)
*Adjusted for baseline characteristics (region, education, civil status, initial treatment, stage of disease, mode of detection, year of diagnosis, country of birth, Charlson comorbidity index) with time-dependent confounders entered as time-updated covariates; †baseline characteristics, anti-hypertensive drugs, anti-diabetic drugs, anti-obesity drugs and related hospital visits, 5-α reductase inhibitors, and initiation of ADT; ‡as above, but with the top and bottom 1% of IPW truncated to reduce variability.
Discussion A protective effect of statins was seen in the first model, which disappeared when a weighted model accounting for TDC was performed. However, this may reflect that statins are less likely to be prescribed to men with severe prostate cancer than those with less aggressive disease, as hyperlipidaemia may not be a major concern when prostate cancer is aggressive. The protective effect of statins disappeared in the weighted model where characteristics of prostate cancer severity were handled more sophisticatedly. After IPW truncation, the association reverted to resemble that observed originally. However,
Comments
extreme variability of the weights, even after truncation, questions whether these findings can be meaningfully interpreted. Most studies do not distinguish between effects of statins administered before or after cancer diagnosis, resulting in bias by indication [6]. Prostate cancer diagnosed after statins may be different, as disease severity could be affected, so it becomes difficult to elucidate their effect on prostate cancer-death if this is not clearly distinguished. However, a more recent observational study found statins used after diagnosis reduced the risk of prostate cancer-death, with the effect more pronounced in those exposed before diagnosis [7]. Disease severity may have an effect on prescription of statins. For example, hyperlipidaemia may not be the primary cause for concern when in the presence of advanced prostate cancer. Time-dependent assessment of severity, as recommended by the Prostate Cancer Clinical Trials Working Group (i.e. repeated measures of PSA) [8], is required to handle this bias. Lacking this information, time spent on ADT was used as a proxy for severity instead. The resulting weighted model did not show an association between statins and prostate cancer-death (Table 1). Apart from disease severity, data on other potential important confounders are needed. Evidence suggests cholesterol is associated with prostate cancer-death and may be underlying the link between statins and cancer [1]. Even if the effect of cholesterol on cancer-specific death is small, serum lipid levels must be accounted for. Otherwise, we assume serum lipids do not have an effect on cancer-specific death, which contradicts pre-clinical studies.
population-based data. As yet no published studies present information on serum cholesterol levels and disease severity in a single setting. These findings show that well-defined clinical trials are needed before an effect of a (e.g. lipid-lowering) drug on cancer-specific mortality can be claimed, and research into drugs in relation to diseases other than their intended purpose in observational settings should be interpreted cautiously.
Acknowledgments The authors would like to thank Andrea Rotnitzky for her expert guidance and participation in the discussions.
Conflict of Interest None declared.
References 1 2 3
4
5
While traditional epidemiological approaches allow for time-dependent exposures and TDCs, they fail to give causally interpretable estimates when confounders themselves are influenced by previous measures of the exposure, as is the case here. An underlying assumption of IPW estimation is the positivity assumption: all treatment arms of the clinical trial are possible in the observational setting, meaning no subject has zero probability of entering either arm based on the covariates to be adjusted for. This is violated as patients with normal cholesterol levels would rarely be prescribed statins. As an alternative, analyses restricted to all subjects without the contra-indication (i.e. those with high cholesterol) could be done. However, this may result in selection bias, as men with elevated cholesterol will have greater risk of additional co-morbidities. Nevertheless, it would allow identification of whether statins, independent of cholesterol reduction, influence prostate cancer-death. In conclusion, even advanced causal inference methods are unable to make clear inferences about the association between statins and risk of prostate cancer-death using
6
7 8
Nielsen SF, Nordestgaard BG, Bojesen SE. Statin use and reduced cancer-related mortality. N Engl J Med 2012; 367: 1792–802 Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000; 11: 550–60 Van Hemelrijck M, Wigertz A, Sandin F et al. Cohort profile: the National Prostate Cancer Register of Sweden and Prostate Cancer data Base Sweden 2.0. Int J Epidemiol 2013; 42: 956–67 Fewell ZH, Hernán MA, Wolfe F, Tilling K, Choi H, Sterne JA. Controlling for time–dependent confounding using marginal structural models. Stata J 2004; 4: 402–20. Available at: http://www.stata-journal .com/article.html?article=st0075. Accessed Novemeber 2014 Braga-Basaria M, Dobs AS, Muller DC et al. Metabolic syndrome in men with prostate cancer undergoing long-term androgen-deprivation therapy. J Clin Oncol 2006; 24: 3979–83 Suarez D, Borras R, Basagana X. Differences between marginal structural models and conventional models in their exposure effect estimates: a systematic review. Epidemiology 2011; 22: 586–8 Yu O, Eberg M, Benayoun S et al. Use of statins and the risk of death in patients with prostate cancer. J Clin Oncol 2014; 32: 5–11 Scher HI, Halabi S, Tannock I et al. Design and end points of clinical trials for patients with progressive prostate cancer and castrate levels of testosterone: recommendations of the Prostate Cancer Clinical Trials Working Group. J Clin Oncol 2008; 26: 1148–59
Correspondence: Jennifer Melvin, King’s College London, School of Medicine, Division of Cancer Studies, Cancer Epidemiology Group, Research Oncology, 3rd Floor, Bermondsey Wing, Guy’s Hospital, London SE1 9RT, UK. e-mail: [email protected] Abbreviations: ADT, androgen-deprivation therapy; IPW, inverse probability weights; MSM, marginal structural model; RCT, randomised clinical trial; TDC, time-dependent confounder.
© 2014 The Authors BJU International © 2014 BJU International
683
Introduction Recent studies suggest statins reduce risk of fatal cancer by inhibiting 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, a key enzyme for cholesterol synthesis [1]. For prostate cancer, there are inconsistent and often incomparable results due to differences in study design and timing of statin use (i.e. before/after diagnosis).
Fig. 1 All patients were selected from the National Prostate Cancer Registry (NPCR) of Sweden, and all covariate information was retrieved from PCBaSe 2.0.
Men diagnosed with prostate cancer 1/1/2006 – 31/12/2009 (n=36 965)
Existing studies are observational, with ensuing limitations of confounding and bias. Therefore, application of causal inference methods to observational data to explore potential protective effects of statin use and prostate cancer-death is of interest. These methods mimic randomised clinical trials (RCTs) and reduce limitations of traditional statistical analyses [2]. Here, we aimed to identify whether statins given after prostate cancer diagnosis have an effect on prostate cancer-death using causal inference methods for a large population-based study.
Missing risk stage, low risk, or metastatic disease ( n=15 969)
Aged ≥90 years (n=240)
Prescribed statins before diagnosis (n=5771)
Dead within a month (n=59)
Patients and Methods PCBaSe Sweden is based on the National Prostate Cancer Register (NPCR) and includes data on tumour characteristics and primary treatment [3]. We estimated the effect of statins on prostate cancer-death in the presence of time-dependent confounders (TDCs) using causal inference methods (Fig. 1 and Table 1). These methods have been described in detail elsewhere [4]. Causal Inference Methods Briefly, inverse probability weights (IPW) estimation of a marginal structural model (MSM) are used to account for TDCs. Apart from lowering cholesterol levels, statins may also reduce risk of (prostate cancer) death by approximating other lifestyle © 2014 The Authors BJU International © 2014 BJU International | doi:10.1111/bju.12935,1297771 Published by John Wiley & Sons Ltd. www.bjui.org
Men included to study (n=14 926)
characteristics (e.g. diet). Furthermore, the probability of exposure to statins is associated with the decision to measure cholesterol, which may be associated with characteristics influencing prostate cancer-death (e.g. socio-demographic factors or disease stage). For instance, men on androgen-deprivation therapy (ADT) may have a higher prescription rate of statins due to increased risk of metabolic syndrome [5].
BJU Int 2015; 116: 681–686 wileyonlinelibrary.com
Comments
Table 1 Illustration of differences between the ‘ideal’ clinical trial, and the one which we have emulated here. Trial characteristics Ideal Statin naive Intermediate prostate cancer risk
Baseline cholesterol
50/50 split in both trial arms Knowledge of compliance to treatment (i.e. statins)
Difficulties encountered Emulated
Prescribed Drug Register (PDR) to identify all statin naive men (at time of diagnosis). National Prostate Cancer Register (NPCR) of Sweden to identify men diagnosed with intermediate-risk prostate cancer. Based on assumption that statin naive men had normal cholesterol levels. Observational data does not guarantee even distribution between trial arms. Per-protocol/intention-to-treat
Cholesterol levels (ongoing)
We did not have this information.
PSA levels (ongoing) to monitor disease severity
ADT initiation was included as a proxy variable because PSA levels were not available.
PDR captures all outpatient prescriptions with details of dose and duration from July 2005. NPCR captures >96% of all newly diagnosed, biopsy confirmed prostate cancers registered in Sweden. All men diagnosed between January 2006 and December 2009 were included. Not ideal because men with higher cholesterol levels could see an increased benefit with regards to prostate cancer prognosis as a result of a greater percentage decrease in cholesterol levels. IPW was used to inflate under-represented sections of sample populations. Particularly, without cholesterol levels we have no option but to assume compliance (and essentially effectiveness) to statin prescriptions, a generally invalid method. We attempted to account for this through the use of information on anti-hypertensive, anti-diabetic, and anti-obesity drugs as well as obesity related hospital visits as a proxy for hyperlipidaemia. Prostate cancer severity was an important confounder (OR for ADT initiation and prostate cancer death: 7.93 (95% CI 5.82–10.80)), therefore a more accurate measure of severity itself would be highly desirable.
Estimates of the association between statins and prostate cancer-death may be biased due to a combination of different components associated with cholesterol levels and/or disease stage/ongoing prostate cancer management. Thus, serum cholesterol may affect future prescription of statins as well as prostate cancer-death, an example of confounding by indication. Statin prescriptions before diagnosis affect serum cholesterol levels and thus represent a TDC affected by the exposure variable itself. Standard statistical methods handle TDCs, but do not allow study of exposure effect on the outcome transmitted via this TDC. Hence, IPW and MSMs are required as they relax the assumption that TDCs cannot be affected by previous exposure [2].
Table 2 Odds ratios (ORs) and 95% CIs for all three models.
Thus, IPW is an alternative to conducting a RCT. IPW allows for creation of a pseudo-population in which neither treatment nor study continuation are influenced by measured subject-specific characteristics. More specifically, it inflates the weight of under-represented sections of the sampled population so that the data becomes representative of a RCT, with equal distribution of patient characteristics between drug and placebo groups. Restricting the analysis of observational data to individuals with complete data, without re-weighting, would introduce bias when missingness is not completely at random. As a result, the analysis of observational data mimics a pseudo-population free of confounding and dropout.
indicate sizeable confounding, and reducing variability via truncation effectively blocks the mechanism by which MSMs deal with confounding. Thus, truncation masked the failure of IPW to adequately account for confounding, but did not resolve the issue. Hence, our results highlight the need for a clinical trial.
Results A protective effect of statins on prostate cancer-death was seen initially, but the estimates were not significant when TDCs were included (Table 2). However, the IPWs were extremely variable, even after truncation [6]. Extreme weights
682
© 2014 The Authors BJU International © 2014 BJU International
Model Baseline adjusted/unweighted* MSM using IPW† MSM with truncated weights‡
IPW range
OR (95% CI)
– 0.0019–13 574.00 0.035–13.16
0.62 (0.51–0.75) 0.89 (0.69–1.14) 0.86 (0.81–0.91)
*Adjusted for baseline characteristics (region, education, civil status, initial treatment, stage of disease, mode of detection, year of diagnosis, country of birth, Charlson comorbidity index) with time-dependent confounders entered as time-updated covariates; †baseline characteristics, anti-hypertensive drugs, anti-diabetic drugs, anti-obesity drugs and related hospital visits, 5-α reductase inhibitors, and initiation of ADT; ‡as above, but with the top and bottom 1% of IPW truncated to reduce variability.
Discussion A protective effect of statins was seen in the first model, which disappeared when a weighted model accounting for TDC was performed. However, this may reflect that statins are less likely to be prescribed to men with severe prostate cancer than those with less aggressive disease, as hyperlipidaemia may not be a major concern when prostate cancer is aggressive. The protective effect of statins disappeared in the weighted model where characteristics of prostate cancer severity were handled more sophisticatedly. After IPW truncation, the association reverted to resemble that observed originally. However,
Comments
extreme variability of the weights, even after truncation, questions whether these findings can be meaningfully interpreted. Most studies do not distinguish between effects of statins administered before or after cancer diagnosis, resulting in bias by indication [6]. Prostate cancer diagnosed after statins may be different, as disease severity could be affected, so it becomes difficult to elucidate their effect on prostate cancer-death if this is not clearly distinguished. However, a more recent observational study found statins used after diagnosis reduced the risk of prostate cancer-death, with the effect more pronounced in those exposed before diagnosis [7]. Disease severity may have an effect on prescription of statins. For example, hyperlipidaemia may not be the primary cause for concern when in the presence of advanced prostate cancer. Time-dependent assessment of severity, as recommended by the Prostate Cancer Clinical Trials Working Group (i.e. repeated measures of PSA) [8], is required to handle this bias. Lacking this information, time spent on ADT was used as a proxy for severity instead. The resulting weighted model did not show an association between statins and prostate cancer-death (Table 1). Apart from disease severity, data on other potential important confounders are needed. Evidence suggests cholesterol is associated with prostate cancer-death and may be underlying the link between statins and cancer [1]. Even if the effect of cholesterol on cancer-specific death is small, serum lipid levels must be accounted for. Otherwise, we assume serum lipids do not have an effect on cancer-specific death, which contradicts pre-clinical studies.
population-based data. As yet no published studies present information on serum cholesterol levels and disease severity in a single setting. These findings show that well-defined clinical trials are needed before an effect of a (e.g. lipid-lowering) drug on cancer-specific mortality can be claimed, and research into drugs in relation to diseases other than their intended purpose in observational settings should be interpreted cautiously.
Acknowledgments The authors would like to thank Andrea Rotnitzky for her expert guidance and participation in the discussions.
Conflict of Interest None declared.
References 1 2 3
4
5
While traditional epidemiological approaches allow for time-dependent exposures and TDCs, they fail to give causally interpretable estimates when confounders themselves are influenced by previous measures of the exposure, as is the case here. An underlying assumption of IPW estimation is the positivity assumption: all treatment arms of the clinical trial are possible in the observational setting, meaning no subject has zero probability of entering either arm based on the covariates to be adjusted for. This is violated as patients with normal cholesterol levels would rarely be prescribed statins. As an alternative, analyses restricted to all subjects without the contra-indication (i.e. those with high cholesterol) could be done. However, this may result in selection bias, as men with elevated cholesterol will have greater risk of additional co-morbidities. Nevertheless, it would allow identification of whether statins, independent of cholesterol reduction, influence prostate cancer-death. In conclusion, even advanced causal inference methods are unable to make clear inferences about the association between statins and risk of prostate cancer-death using
6
7 8
Nielsen SF, Nordestgaard BG, Bojesen SE. Statin use and reduced cancer-related mortality. N Engl J Med 2012; 367: 1792–802 Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000; 11: 550–60 Van Hemelrijck M, Wigertz A, Sandin F et al. Cohort profile: the National Prostate Cancer Register of Sweden and Prostate Cancer data Base Sweden 2.0. Int J Epidemiol 2013; 42: 956–67 Fewell ZH, Hernán MA, Wolfe F, Tilling K, Choi H, Sterne JA. Controlling for time–dependent confounding using marginal structural models. Stata J 2004; 4: 402–20. Available at: http://www.stata-journal .com/article.html?article=st0075. Accessed Novemeber 2014 Braga-Basaria M, Dobs AS, Muller DC et al. Metabolic syndrome in men with prostate cancer undergoing long-term androgen-deprivation therapy. J Clin Oncol 2006; 24: 3979–83 Suarez D, Borras R, Basagana X. Differences between marginal structural models and conventional models in their exposure effect estimates: a systematic review. Epidemiology 2011; 22: 586–8 Yu O, Eberg M, Benayoun S et al. Use of statins and the risk of death in patients with prostate cancer. J Clin Oncol 2014; 32: 5–11 Scher HI, Halabi S, Tannock I et al. Design and end points of clinical trials for patients with progressive prostate cancer and castrate levels of testosterone: recommendations of the Prostate Cancer Clinical Trials Working Group. J Clin Oncol 2008; 26: 1148–59
Correspondence: Jennifer Melvin, King’s College London, School of Medicine, Division of Cancer Studies, Cancer Epidemiology Group, Research Oncology, 3rd Floor, Bermondsey Wing, Guy’s Hospital, London SE1 9RT, UK. e-mail: [email protected] Abbreviations: ADT, androgen-deprivation therapy; IPW, inverse probability weights; MSM, marginal structural model; RCT, randomised clinical trial; TDC, time-dependent confounder.
© 2014 The Authors BJU International © 2014 BJU International
683
