Does the association between adherence to statin medications and mortality depend on measurement approach? A retrospective cohort study
© The Author(s). 2017
Received: 5 October 2016
Accepted: 7 April 2017
Published: 20 April 2017
The aim of this study was to examine the relationship between mortality and statin adherence using two different approaches to adherence measurement (summary versus repeated-measures).
A retrospective cohort study was conducted using administrative data from Saskatchewan, Canada between 1994 and 2008. Eligible individuals received a prescription for a statin following hospitalization for acute coronary syndrome (ACS). Adherence was measured using proportion of days covered (PDC) expressed either as: 1) a fixed summary measure, or 2) as a repeatedly measured covariate. Multivariable Cox-proportional hazards models were used to estimate the association between adherence and mortality.
Among 9,051 individuals, optimal adherence (≥80%) modeled with a fixed summary measure was not associated with mortality benefits (adjusted HR 0.97, 95% CI 0.86 to 1.09, p = 0.60). In contrast, repeated-measures approach resulted in a significant 25% reduction in the risk of death (adjusted HR 0.75, 95% CI 0.67 to 0.85, p < 0.01).
Unlike the summary measure, the repeated measures approach produces a significant reduction of all-cause mortality with optimal adherence. This effect may be a result of the repeated measures approach being more sensitive, or more prone to survival bias. Our findings clearly demonstrate the need to undertake (and report) multiple approaches when assessing the benefits of medication adherence.
KeywordsCompliance/adherence Mortality Treatment Lipids and cholesterol Secondary prevention
Observational studies using health-administrative databases have reported low mortality rates among individuals exhibiting high adherence to statin medications (HMG Co-A reductase inhibitors) . However, these studies have produced highly variable estimates of benefit. Depending on the study, individuals exhibiting high adherence have been associated with 20%,  50%, [3, 4] or even 81%  lower risks of death. An important source of variability may be the approach used to measure adherence, even if the adherence data source is the same.
In studies using electronic refill databases, adherence is often measured by the ‘medication possession ratio’ (MPR) or the ‘proportion of days covered’ (PDC). This approach estimates the percentage of days during a defined observation period where medication was available for consumption based on the total quantity obtained from pharmacy refills . In descriptive studies, adherence is typically expressed as a single measure summarizing the entire observation period often lasting 1 year or more [7–9]. Although the summary measure of adherence offers a simple and straightforward approach to represent the entire period of follow-up, it does not account for the possibility that adherence may change during this period.
Medication adherence can also be measured repeatedly using defined intervals within a period of follow-up and treated as a time-dependent variable [10, 11]. This measurement method may have advantages over the summary measure because it is more sensitive to changes in adherence. For example, a summary adherence measure of 58% calculated over a 1-year period could actually reflect an individual with 16% adherence during the first 6 months and 100% adherence in the last 6 months of observation. It has been suggested that the repeated-measures approach is superior to the summary approach for identifying associations between adherence and mortality [11–13]. However, we can find no empirical data or theoretical paradigm to support this claim. No studies have investigated the impact of measurement strategy on the estimates of benefit of statin adherence. Thus, our purpose was to compare the estimated impact of statin adherence on mortality using two measurement approaches, a fixed summary measure versus repeated-measures, for a cohort of individuals with acute coronary syndrome (ACS).
Administrative data maintained by the Saskatchewan Ministry of Health were used for this study. Saskatchewan Ministry of Health databases contain comprehensive data and have been used previously to produce high quality pharmacoepidemiological studies [14–18]. Specifically, we used information from the population registry, prescription drug file (pharmacy dispensations), physician claims, and hospital services databases. The Saskatchewan Ministry of Health data covers almost 99% of the province’s residents for both physician and hospital services. The only exceptions are federal prisons inmates, members of the armed forces, and the Royal Canadian Mounted Police, who are recipients of the federal government’s health benefits. On the other hand, the prescription drug database captures medication dispensations for 90% of the provincial population; it excludes individuals who receive federal prescription coverage such as the First Nations (aboriginal) population. Information on medications available “over-the-counter” or excluded from the provincial drug formulary were not available in this study.
The cohort included individuals at least 30 years of age who received at least one dispensation for a statin medication within 90 days [2, 19] of hospital discharge and for whom the responsible/primary diagnosis in the hospital record was ACS. This included myocardial infarction (MI) and unstable angina (UA). All individuals who met the cohort inclusion criteria between January 1st, 1994 and December 31st, 2008 were retained. Individuals were required to have continuous beneficiary status for 1825 days (i.e., 5 years) before the index hospitalization. Additionally, in order to offer the opportunity to exhibit adherence or non-adherence behaviour, individuals had to survive and maintain provincial beneficiary status for at least 102 days after their first statin dispensation. Individuals were excluded if they underwent a revascularization procedure without a diagnosis of MI or UA, could not be followed for at least 102 days, or received any statin medication within 365 days prior to the index hospitalization . The codes used to identify MI and UA conditions (Appendix 1) were shown to have positive predictive, sensitivity, and specificity estimates of 85 to 98% [21–26]. For individuals with several eligible hospitalizations, the earliest hospital discharge date for ACS was deemed the index date.
The assessment of adherence was applied in two ways. In method “A”, a single summary measure of adherence was calculated between the date of the first dispensation and the date of death, provincial health coverage termination, or end of the study period (December 31, 2008). In method “B”, the same period of follow-up was divided into 3-month intervals (i.e., 102 days) where adherence was measured in each. Unused supplies from a previous interval were applied to the subsequent interval to prevent underestimation. The 102 days interval was chosen because, in Saskatchewan, prescriptions are usually filled in 1 month supply quantity (34 days) .
The association between statin adherence and mortality was described using Kaplan-Meier estimator and was assessed using a time-to-event analysis with multivariable Cox proportional-hazard regression models. The outcome was time to death, starting 102 days following the first statin dispensation. The model covariates included demographic, condition-related, therapy-related, patient-related, and health-system-related variables, as categorical variables (Appendix 2, Appendix 3 and Appendix 4), in addition to dichotomous adherence variable (i.e., PDC ≥80% versus PDC < 80%). For Method A, the summary measure of adherence was entered in the model as a fixed covariate (i.e. a covariate that does not differ in value over time), whereas for Method B, adherence was included in the model as a time-dependent repeated-measure covariate (i.e. a covariate that differs in value over time) assessed every 102 days.
We estimated the adjusted hazard ratios (HRs) with 95% confidence intervals (95% CIs) for adherent individuals (i.e. PDC ≥ 80%) compared with non-adherent individuals (i.e., PDC < 80%). The proportional hazards assumption was assessed visually using the log cumulative hazard (the “log-log”) plot and Schoenfeld residuals versus observed event time’s plot . Multicollinearity amongst the non-adherence variables was examined by calculating the variance inflation factor (VIF); values greater than 10 were interpreted as representing substantial multicollinearity . Baseline variables that demonstrated evidence of multicollinearity were excluded from the model.
In a sensitivity analysis, adherence was re-classified into three categories (instead of two): PDC 20%, 21–79%, and ≥80% to assess whether estimates of benefit were substantially affected. We used SAS 9.3 software (SAS Institute Inc., Cary, NC, USA) to perform all analyses.
Non-Adherent* (n = 4,112)
Adherent (n = 4,939)
Total (n = 9,051)
Type of index diagnosis
ACS+ revascularization procedure
Duration (in days) of index hospitalization
Time (in days) from index to statin prescription
At least one prescription in post-index year
other lipid drugs
At least a statin prescription with 28 days’ supply (as an evidence of unit-of-use packaging)
High statin dose on first prescription post index€
Atorvastatin on first prescription post index
>4 distinct non-statin medications received in post-index year
Chronic disease score ≥4
Diagnosis in pre-index year
Specialty of prescribing physician of the first statin prescription
≥5 physician’s visits in the first 3 months
Any hospitalization in pre-index year
Deprivation index quintile
1 (most deprived)
5 (least deprived)
In contrast, optimal adherence measured as a time-dependent variable was clearly associated with a lower risk for death (Fig. 4) (crude HR 0.80, 95% CI 0.71 to 0.89, p < 0.01; adjusted HR 0.75, 95% CI 0.67 to 0.85, p < 0.01). Similar results were obtained when non-adherence was categorized as <20% (i.e., rather than < 80%) in a sensitivity analysis (data not shown). Additionally, almost indistinguishable results were obtained when the required time for filling statin prescription was reduced to 60 days or increased to 120,180, or 210 days. Similarly, when adherence was considered a continuous variable, it was not associated with mortality using the fixed-summary measure method (crude HR 1.05; 95% CI 0.88 to 1.25; p = 0.63 and adjusted HR 1.00; 95% CI 0.83 to 1.20, p = 0.97 for an increase of 1 unit of PDC), but it was strongly associated with mortality using the repeated-measures method (crude HR 0.65; 95% CI 0.56 to 0.75, p < 0.01 and adjusted HR 0.66; 95% CI 0.56 to 0.76, p < 0.01 for an increase of 1 unit of PDC).
We examined the impact of adherence during two distinct periods: the last follow-up interval (i.e. the last 102 days of the follow-up) and the first follow-up interval (i.e., the first 102 days of follow-up). Treating the adherence in the final interval as a fixed variable was significantly associated with mortality with a crude HR of 0.83, (95% CI 0.75 to 0.93, p < 0.01) and adjusted HR of 0.85 (95% CI 0.75 to 0.95, p < 0.01). On the other hand, considering only the first period to measure adherence did not yield a significant association with mortality with a crude HR of 0.95, 95% CI 0.84 to 1.09, p = 0.47, and adjusted HR of 0.88, 95% CI 0.77 to 1.01, p = 0.07.
In all cases, the proportionality assumption of the Cox model was met, and no collinearity was observed in included covariates.
We examined the association between statin adherence and the risk of death using two distinct adherence measures that have been used in previous studies . The association was substantially impacted by the measurement approach despite an identical adherence metric (i.e., PDC) and threshold (i.e., ≥80%) for defining optimal adherence. Optimal statin adherence defined by the fixed summary measure was not associated with a beneficial effect on mortality (adjusted HR 0.97, 95% CI 0.86 to 1.09, p = 0.60). In contrast, optimal adherence to statins defined by a time-dependent variable was associated with a significantly lower risk of death (adjusted HR 0.75, 95% CI 0.67 to 0.85). Although medication non-adherence is widely understood as a public health epidemic, it should be recognized that the estimated benefits of optimal adherence are not robust to changes in measurement approaches.
It is well known that the format of an independent variable (time-dependent or fixed at baseline) can impact its statistical association with an outcome [37, 38]. In certain situations, the use of time-dependent variables is clearly appropriate. For example, consider a study examining the occurrence of bleeding due to anticoagulant use. Exposure to anticoagulants could be modeled as a time-dependent variable because bleeding risk increases promptly after exposure (from a reversible pharmacologic process) and resolves following discontinuation. In contrast, statin medications modulate the vascular atherosclerotic disease process. Thus, they would be expected to have a much slower onset of protection and sustained benefit following drug discontinuation. In fact, it could be argued that the duration of past adherence (estimated more effectively with a summary adherence measure of statin medications) is more important than recent adherence due to the requirement of statin exposure-time for modification of vascular atherosclerosis.
The reasons for such conflicting estimates on the association between statin adherence measurements are not entirely clear. Our study was carried out on the same cohort, over the same observation period, and accounted for identical confounders with the exception of adherence measurement. The repeated-measures approach appeared to take account of changes in adherence over time, especially situations where individuals improved their adherence behavior over time. Indeed, non-concordance between adherence measures was virtually always observed as patients assessed as non-adherent by the summary measure while adherent by the repeated measure. Low adherence at any point in an observation period will always penalize the final summary adherence assessment, whereas the repeated-measures approach adjusts estimates based on independent adherence measurements at 3-month intervals .
In a secondary analysis, optimal adherence during the final 3-month interval of follow-up was significantly associated with a lower risk of death. From a pharmacologic perspective, this association is difficult to justify due to the short period of statin exposure. However, it can be argued that this approach permitted the influence of survival bias [12, 13]. Healthy individuals with long-standing non-adherence may have had greater opportunity to exhibit optimal adherence in the latter part of the observation period using a repeated-measures approach. In this situation, high adherence in the last 3 months of follow-up may have been a marker rather than a causal determinant of good health. Although the repeated-measures approach undoubtedly captures more granular information about exposure, [37, 40] the relationship between current exposure and protection against life-threatening outcomes is not as clearly defined for cholesterol-lowering therapy. Moreover, it may be more vulnerable to bias or confounding factors that impact the outcome.
Our study identified a dramatic improvement in statin adherence over the past decade. This trend has been reported previously in other jurisdictions with statins and other medications also [41, 42]. Considering these trends, along with steady population decreases in coronary heart diseases event rates over time,  it is possible that the consequences of poor statin adherence may in fact be less dramatic in recent years. Although conflicting results from observational studies could be ideally resolved if randomized trial results were available, the nature of this phenomenon prevents rigorous examination using experimental design.
The most important strength of our study is that we performed both measurement approaches on the same cohort of individuals with the same characteristics that can affect adherence (such as socio-economic status, motivation, and attitudes). However, some limitations can be noted in this study. First, although PDC is a validated adherence measurement method, our adjustment to prevent overestimation is not validated, and may have affected our estimates. However, it is unlikely that this adjustment disadvantaged one of the methods only (i.e., the summary approach or the repeated-measures approach). Second, requiring individuals to fill a statin prescription within 90 days of their ACS hospitalization may have excluded individuals exhibiting non-adherence at the beginning of follow-up (primary non-adherence). If true, this could have weakened the association through a biased selection of individuals. Third, requiring individuals to survive for at least 102 days after their first statin prescription may have biased our inclusion to include patients with less severe heart disease. However, events occurring within the first 3 months after beginning statin therapy are likely not related to statin adherence levels. Fourth, having a record of statin dispensation does not certainly indicate that the medication was in fact consumed. Lastly, the choice of 102 days (3 months) to assess adherence in the repeated-measures method may have influenced the associations observed. However, shorter intervals would result in lower granularity of the measure and longer periods would result in lower sensitivity to periodic changes in adherence.
Statin adherence after acute coronary syndromes has improved dramatically since the 1990s and is nearing 80% in recent years. Estimates for the benefits of statin adherence on mortality are significantly influenced by measurement methods used and a gold-standard approach cannot be established using conventional techniques. Further, estimates of population benefits of statin adherence may have been exaggerated due to the lack of verification with different approaches. Although surveillance of adherence and health outcomes should continue, estimates must be scrutinized using different measures until the most valid approach can be identified.
Acute coronary syndrome
Medication possession ratio
Proportion of days covered
Variance inflation factor
This study is based in part on de-identified data provided by the Saskatchewan Ministry of Health. The interpretation and conclusions contained herein do not necessarily represent those of the Government of Saskatchewan or the Saskatchewan Ministry of Health.
Funding for this study was solely provided from the Chair in Patient Adherence to Drug Therapy within the College of Pharmacy and Nutrition, University of Saskatchewan. Dr. Blackburn’s position as the Chair in Patient Adherence was created through unrestricted financial support from AstraZeneca Canada, Merck Canada, Pfizer Canada, and the Province of Saskatchewan Ministry of Health. As a graduate student in Dr. Blackburn’s research team, Dr. Wasem Alsabbagh also received support from these funds. Dr. Eurich receives salary support though a population health investigator award from the Alberta Heritage Foundation for Medical Research and is a Canadian Institutes of Health Research New Investigator. Dr. Lix is supported by a Manitoba Research Chair. However, none of the sponsors were involved in developing this study or writing the manuscript.
Availability of data and material
The data that support the findings of this study are available from the Saskatchewan Ministry of Health but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Saskatchewan Ministry of Health.
W.A., contributed to the design of the study, wrote the draft of the manuscript, conducted statistical analyses, reviewed and edited the manuscript. D.E. contributed to the design of the study, provided direction for the manuscript, reviewed and edited the manuscript. L.L. contributed to the design of the study, provided direction for the manuscript, reviewed and edited the manuscript. T.W. contributed to the design of the study, provided direction for the manuscript, reviewed and edited the manuscript. D.B. contributed to the design of the study, provided direction for the manuscript, reviewed and edited the manuscript. All authors approved the final version of the manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
The approval for this study was obtained from the Biomedical Research Ethics Board (Bio-REB #10-162) at the University of Saskatchewan. Consent was not needed due to the retrospective nature of this study.
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- Simpson Jr RJ, Mendys P. The effects of adherence and persistence on clinical outcomes in patients treated with statins: a systematic review. J Clin Lipidol. 2010;4(6):462–71.View ArticlePubMedGoogle Scholar
- Rasmussen JN, Chong A, Alter DA. Relationship between adherence to evidence-based pharmacotherapy and long-term mortality after acute myocardial infarction. JAMA. 2007;297(2):177–86.View ArticlePubMedGoogle Scholar
- Newby LK, LaPointe NM, Chen AY, Kramer JM, Hammill BG, DeLong ER, Muhlbaier LH, Califf RM. Long-term adherence to evidence-based secondary prevention therapies in coronary artery disease. Circulation. 2006;113(2):203–12.View ArticlePubMedGoogle Scholar
- Ho PM, Magid DJ, Shetterly SM, Olson KL, Maddox TM, Peterson PN, Masoudi FA, Rumsfeld JS. Medication nonadherence is associated with a broad range of adverse outcomes in patients with coronary artery disease. Am Heart J. 2008;155(4):772–9.View ArticlePubMedGoogle Scholar
- Wei L, Wang J, Thompson P, Wong S, et al. Adherence to strain treatment and readmission of patients after myocardial infarction: A 6 year follow up study. Heart. 2002;88(3):229–33.View ArticlePubMedPubMed CentralGoogle Scholar
- Raebel MA, Ellis JL, Carroll NM, Bayliss EA, McGinnis B, Schroeder EB, Shetterly S, Xu S, Steiner JF, Raebel MA, Ellis JL, Carroll NM, Bayliss EA, McGinnis B, Schroeder EB, Shetterly S, Xu S, Steiner JF. Characteristics of patients with primary non-adherence to medications for hypertension, diabetes, and lipid disorders. J Gen Intern Med. 2011;27(1):57–64.View ArticlePubMedPubMed CentralGoogle Scholar
- Esposti LD, Saragoni S, Benemei S, Batacchi P, Geppetti P, Di Bari M, Marchionni N, Sturani A, Buda S, Esposti ED. Adherence to antihypertensive medications and health outcomes among newly treated hypertensive patients. Clinicoecon Outcomes Res. 2011;3:47–54.View ArticlePubMedPubMed CentralGoogle Scholar
- Wei L, Fahey T, MacDonald TM. Adherence to statin or aspirin or both in patients with established cardiovascular disease: exploring healthy behaviour vs. drug effects and 10-year follow-up of outcome. Br J Clin Pharmacol. 2008;66(1):110–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Kettani F, Dragomir A, Côté R, Roy L, Bérard A, Blais L, Lalonde L, Moreau P, Perreault S. Impact of a Better Adherence to Antihypertensive Agents on Cerebrovascular Disease for Primary Prevention. Stroke. 2009;40(1):213–20.View ArticlePubMedGoogle Scholar
- Hong J, Novick D, Treuer T, Montgomery W, Haynes VS, Wu S, Haro JM. Predictors and consequences of adherence to the treatment of pediatric patients with attention-deficit/hyperactivity disorder in Central Europe and East Asia. Patient Prefer Adherence. 2013;7:987–95.View ArticlePubMedPubMed CentralGoogle Scholar
- Fitzgerald AA, Powers JD, Ho PM, Maddox TM, Peterson PN, Allen LA, Masoudi FA, Magid DJ, Havranek EP. Impact of Medication Nonadherence on Hospitalizations and Mortality in Heart Failure. J Card Fail. 2011;17(8):664–9.View ArticlePubMedGoogle Scholar
- Yu AP, Nichol MB. A time-varying survival model for the association of adherence with HMG-COA inhibitors to the risk of adverse events [abstract]. Value Health. 2003;6:191–2.Google Scholar
- Shore S, Carey EP, Turakhia MP, Jackevicius CA, Cunningham F, Pilote L, Bradley SM, Maddox TM, Grunwald GK, Baron AE, Rumsfeld JS, Varosy PD, Schneider PM, Marzec LN, Ho PM. Adherence to dabigatran therapy and longitudinal patient outcomes: Insights from the Veterans Health Administration. Am Heart J. 2014;167(6):810–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Lamb D, Eurich D, McAlister F, Tsuyuki R, Semchuk W, Wilson T, Blackburn D. Changes in adherence to evidence-based medications in the first year after initial hospitalization for heart failure: observational cohort study from 1994 to 2003. Circ Cardiovasc Qual Outcomes. 2009;2(3):228.View ArticlePubMedGoogle Scholar
- Tulloch J, Evans B. Evaluation of the Accuracy of the Saskatchewan Health Pharmaceutical Information Program for Determining a Patient’s Medication Use Immediately before Admission. Can J Hosp Pharm. 2009;62(1):21–7.PubMedPubMed CentralGoogle Scholar
- Liu L, Reeder B, Shuaib A, Mazagri R. Validity of stroke diagnosis on hospital discharge records in Saskatchewan, Canada: implications for stroke surveillance. Cerebrovasc Dis. 1999;9(4):224–30.View ArticlePubMedGoogle Scholar
- Blackburn DF, Dobson RT, Blackburn JL, Wilson TW. Cardiovascular morbidity associated with nonadherence to statin therapy. Pharmacotherapy. 2005;25(8):1035–43.View ArticlePubMedGoogle Scholar
- Blackburn DF, Dobson RT, Blackburn JL, Wilson TW, Stang MR, Semchuk WM. Adherence to statins, beta-blockers and angiotensin-converting enzyme inhibitors following a first cardiovascular event: a retrospective cohort study. Can J Cardiol. 2005;21(6):485–8.PubMedGoogle Scholar
- Austin PC, Tu JV, Ko DT, Alter DA. Factors associated with the use of evidence-based therapies after discharge among elderly patients with myocardial infarction. CMAJ. 2008;179(9):901–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol. 2003;158(9):915–20.View ArticlePubMedGoogle Scholar
- Varas-Lorenzo C, Castellsague J, Stang MR, Tomas L, Aguado J, Perez-Gutthann S. Positive predictive value of ICD-9 codes 410 and 411 in the identification of cases of acute coronary syndromes in the Saskatchewan Hospital automated database. Pharmacoepidemiol Drug Saf. 2008;17(8):842–52.View ArticlePubMedGoogle Scholar
- Wahl PM, Rodgers K, Schneeweiss S, Gage BF, Butler J, Wilmer C, Nash M, Esper G, Gitlin N, Osborn N, Short LJ, Bohn RL. Validation of claims-based diagnostic and procedure codes for cardiovascular and gastrointestinal serious adverse events in a commercially-insured population. Pharmacoepidemiol Drug Saf. 2010;19(6):596–603.View ArticlePubMedGoogle Scholar
- Petersen LA, Wright S, Normand SL, Daley J. Positive predictive value of the diagnosis of acute myocardial infarction in an administrative database. J Gen Intern Med. 1999;14(9):555–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Lee DS, Stitt A, Wang X, Yu JS, Gurevich Y, Kingsbury KJ, Austin PC, Tu JV. Administrative Hospitalization Database Validation of Cardiac Procedure Codes. Med Care. 2013;51(4):e22–6.View ArticlePubMedGoogle Scholar
- Gurevich Y, McFarlane A, Morris K, Jokovic A, Peterson GM, Webster GK. Estimating the number of coronary artery bypass graft and percutaneous coronary intervention procedures in Canada: a comparison of cardiac registry and Canadian Institute for Health Information data sources. Can J Cardiol. 2010;26(7):e249–53.View ArticlePubMedPubMed CentralGoogle Scholar
- Rawson NS, Malcolm E. Validity of the recording of ischaemic heart disease and chronic obstructive pulmonary disease in the Saskatchewan health care datafiles. Stat Med. 1995;14(24):2627–43.View ArticlePubMedGoogle Scholar
- Martin BC, Wiley-Exley EK, Richards S, Domino ME, Carey TS, Sleath BL. Contrasting Measures of Adherence with Simple Drug Use, Medication Switching, and Therapeutic Duplication. Ann Pharmacother. 2009;43(1):36–44.View ArticlePubMedGoogle Scholar
- Hess LM, Raebel MA, Conner DA, Malone DC. Measurement of adherence in pharmacy administrative databases: a proposal for standard definitions and preferred measures. Ann Pharmacother. 2006;40(7–8):1280–8.View ArticlePubMedGoogle Scholar
- Peterson AM, Nau DP, Cramer JA, Benner J, Gwadry-Sridhar F, Nichol M. A checklist for medication compliance and persistence studies using retrospective databases. Value Health. 2007;10(1):3–12.View ArticlePubMedGoogle Scholar
- Leslie SR, Gwadry-Sridhar F, Thiebaud P, Patel BV. Calculating medication compliance, adherence and persistence in administrative pharmacy claims databases. Pharmaceutical Programming. 2008;1(1):13–9.View ArticleGoogle Scholar
- Insull W. The problem of compliance to cholesterol altering therapy. J Intern Med. 1997;241(4):317–25.View ArticlePubMedGoogle Scholar
- Avorn J, Monette J, Lacour A, Bohn RL, Monane M, Mogun H, LeLorier J. Persistence of use of lipid-lowering medications: a cross-national study. JAMA. 1998;279(18):1458–62.View ArticlePubMedGoogle Scholar
- Strom BL. Pharmacoepidemiology. 4th ed. Chichester: J. Wiley; 2005.Google Scholar
- Government of Saskatchewan. Drug Plan and Extended Benefits Branch Annual Report 2010-2011. 2011.Google Scholar
- Collett 1952- D, Collett D, 1952. Modelling survival data in medical research. Boca Raton: Chapman & Hall/CRC; 2003.Google Scholar
- Belsley DA, Belsley DA. Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley; 1980.View ArticleGoogle Scholar
- Sylvestre MP, Abrahamowicz M. Flexible modeling of the cumulative effects of time-dependent exposures on the hazard. Stat Med. 2009;28(27):3437–53.View ArticlePubMedGoogle Scholar
- Fisher LD, Lin DY. Time-dependent covariates in the Cox proportional-hazards regression model. Annu Rev Public Health. 1999;20:145–57.View ArticlePubMedGoogle Scholar
- Levesque LE, Hanley JA, Kezouh A, Suissa S. Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes. BMJ. 2010;340:b5087.View ArticlePubMedGoogle Scholar
- Andersen PK, Liestol K. Attenuation caused by infrequently updated covariates in survival analysis. Biostatistics. 2003;4(4):633–49.View ArticlePubMedGoogle Scholar
- Setoguchi S, Choudhry NK, Levin R, Shrank WH, Winkelmayer WC. Temporal trends in adherence to cardiovascular medications in elderly patients after hospitalization for heart failure. Clin Pharmacol Ther. 2010;88(4):548–54.View ArticlePubMedGoogle Scholar
- Choudhry NK, Setoguchi S, Levin R, Winkelmayer WC, Shrank WH. Trends in adherence to secondary prevention medications in elderly post-myocardial infarction patients. Pharmacoepidemiol Drug Saf. 2008;17(12):1189–96.View ArticlePubMedPubMed CentralGoogle Scholar
- Tu JV, Nardi L, Fang J, Liu J, Khalid L, Johansen H, Canadian Cardiovascular Outcomes Research Team. National trends in rates of death and hospital admissions related to acute myocardial infarction, heart failure and stroke, 1994-2004. CMAJ. 2009;180(13):E118–25.View ArticlePubMedPubMed CentralGoogle Scholar