BMJ Open. Pharmacology and therapeutics. Secondary Subject Heading: Epidemiology, Research methods, Cardiovascular medicine

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1 The impact of time-window bias on the assessment of the long-term effect of medication adherence: the case of secondary prevention after myocardial infarction Journal: BMJ Open Manuscript ID: bmjopen-0-00 Article Type: Research Date Submitted by the Author: -Feb-0 Complete List of Authors: Di Martino, Mirko; Department of Epidemiology, Regional Health Service, Lazio Region. Kirchmayer, Ursula; Department of Epidemiology, Regional Health Service, Lazio Region. Agabiti, Nera; Department of Epidemiology, Regional Health Service, Lazio Region. Bauleo, Lisa; Department of Epidemiology, Regional Health Service, Lazio Region. Fusco, Danilo; Department of Epidemiology, Regional Health Service, Lazio Region. Perucci, Carlo; Senior Epidemiologist Consultant., Davoli, Marina; Department of Epidemiology, Regional Health Service, Lazio Region. <b>primary Subject Heading</b>: Pharmacology and therapeutics Secondary Subject Heading: Epidemiology, Research methods, Cardiovascular medicine Keywords: THERAPEUTICS, EPIDEMIOLOGY, Myocardial infarction < CARDIOLOGY BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

2 Page of BMJ Open The impact of time-window bias on the assessment of the long-term effect of medication adherence: the case of secondary prevention after myocardial infarction Running head. The change in adherence bias. Mirko Di Martino, Ursula Kirchmayer, Nera Agabiti, Lisa Bauleo, Danilo Fusco, Carlo Alberto Perucci, Marina Davoli. () Department of Epidemiology, Regional Health Service, Lazio Region, Rome, Italy. () Senior Epidemiologist Consultant, Rome, Italy. Key Words: change in adherence bias; time-related bias; medication adherence; evidence-based drug therapy; myocardial infarction. Corresponding author: Mirko Di Martino Department of Epidemiology, Lazio Regional Health Service Via di Santa Costanza, - 00 Roma, Italy Tel.: Fax BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

3 Page of ABSTRACT Objectives. Time-window bias was described in case-control studies that analysed a dichotomous exposure and reported an overestimation of drug benefit. No studies have measured the impact of this bias on the assessment of the effect of medication adherence on health outcomes. Our goals were to estimate the association between adherence to drug therapies after myocardial infarction (MI) and the incidence of a new MI, and to quantify the error that would have been produced by a time-window bias. Design. A nested case-control study was performed. Controls were selected using both timedependent and time-independent sampling. Adherence to antiplatelets, beta-blockers, agents acting on the renin-angiotensin-system (ACEI/ARBs) and statins was calculated using the proportion of days covered (PDC). Setting. This is a population-based study. Data were obtained from the Regional Health Information Systems of the Lazio Region in Central Italy (around million inhabitants). Participants. Patients discharged after MI in were enrolled and followed through 00. Outcome measure. The study outcome was reinfarction: either mortality, or hospital admission for MI, whichever occurred first. Results. A total of,0 patients were enrolled. Using time-dependent sampling a protective effect was detected for all study drugs. Conversely, using time-independent sampling, the beneficial effect was attenuated, as in the case of antiplatelet agents and statins, or completely masked, as in the case of ACEI/ARBs and beta-blockers. For ACEI/ARBs, the time-dependent approach produced odds ratios of 0. (%CI: 0.-.) and 0. (0.-0.), respectively, for 0.<PDC 0. and PDC>0. versus 0<PDC 0.. Using the time-independent approach, the odds ratios were 0. (0.-.) and.00 (0.-.), respectively. Conclusions. A time-independent definition of a time-dependent exposure introduces a bias when the length of follow-up varies with the outcome. The persistence of time-related biases in peerreviewed papers strongly suggests the need for increased awareness of this methodological pitfall. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

4 Page of BMJ Open STRENGTHS AND LIMITATIONS OF THIS STUDY In case-control studies, it was already demonstrated the importance of ensuring an identical time to measure dichotomous drug exposure (i.e., presence or absence of the study medication) for cases and controls. If this condition is not satisfied, time-window bias may originate producing false finding of a drug benefit. No studies have measured the impact of time-window bias in evaluating the effect of medication adherence on health outcomes. We measured this impact by analysing the effect of evidence-based therapies for secondary prevention after myocardial infarction on the incidence of a new ischemic heart attack. After the first infarction, as the length of the follow-up increases, the probability of a patient being classified as adherent progressively decreases. In a case-control study, if controls are selected using a time-independent sampling, an over-representation of adherent cases will introduce a bias and an underestimation of the protective effect of medication adherence. Conversely, by using the proper time-dependent sampling, a protective effect was clearly detected for the evidence-based study drugs. The persistence of time-related biases in peerreviewed publications confirms the need for increased awareness of this problem. Population-based design, large numbers and robustness of analytical procedures are the main strengths. This study contributes to the debate on the complex methodology to estimate the effectiveness of medication in clinical practice. Our pharmaceutical database does not contain information on the prescribed daily doses and adherence to drug treatment was estimated on the basis of the DDDs. Although this is a useful instrument for comparing the results from different studies, misclassification of drug utilization may have occurred. However, both methods (time-independent and time-dependent sampling) were performed and compared in the presence of the same conditions. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

5 Page of BACKGROUND Randomised controlled trials are invaluable for assessing the efficacy of medications and obtaining regulatory approval. However, observational studies offer several advantages over clinical trials. A study based on a large and unselected population is more representative and allows for greater generalisation. Of note, in clinical practice some disturbing factors, such as the reduced adherence to chronic therapies, make therapeutic goals difficult to achieve []. The availability of computerised health databases contributes decisively to observational studies of the impact of medications. However, the use of a non-experimental setting to analyse the associations between drug exposures and health outcomes carries the risk of specific biases that may lead to erroneous results, especially when drug treatments and outcomes are both measured in the same time window. Samy Suissa described the effect of the time-window bias in case-control studies, analysed a dichotomous exposure (presence or absence of the study medication) and demonstrated a substantial overestimation of the drug benefit. The bias was caused by the time-independent method used to select controls, who were defined as all the subjects who did not experience the study outcome during the observation period. Thus, the exposure was assessed during a shorter time interval for cases than for controls; this discrepancy produced an over-representation of unexposed cases and a false finding of a drug benefit []. No studies have measured how the time-window bias affects assessments of the effect of medication adherence on health outcomes. Our goals were to properly estimate the association between the adherence to evidence-based drug therapies for secondary prevention after an acute myocardial infarction (MI) [, ] and the incidence of a new MI and to quantify both the direction and magnitude of the error that would have been produced by time-window-bias. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

6 Page of BMJ Open MATERIALS AND METHODS Data sources Our Department has access to regional health information systems that contain mortality, hospital admission, and drug claims data. The details of the individual information systems are reported in the Appendix. Setting and study cohort This observational study was based on the population living in the Lazio region of Italy, which comprises approximately million persons. Using data from the regional Hospital Information System (HIS), the study included a cohort consisting of all patients discharged from hospitals between January, 00 and November 0, 00 with a diagnosis of MI (index admission). An MI was defined as either a primary diagnosis of ICD--CM codes 0.xx or as a primary diagnosis of an MI-related condition along with a secondary diagnosis of 0.xx (Appendix). Patients aged -00 years at discharge were considered for inclusion in the study. Only incident cases of MI were included. Patients with hospital admission during the previous years for infarction, percutaneous coronary intervention (PCI), bypass, coronary disease, or surgery of the heart and great vessels were excluded. Patients who were not registered in the regional health assistance file were excluded because they could not be retrieved from the regional health information system (note that health care is offered to all resident citizens without restriction). Finally, patients who were hospitalised longer than half their individual follow-up interval or who received an outpatient regimen for less than 0 days were excluded because they were considered extremely complex or instable patients. The patient characteristics are described in detail elsewhere []. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

7 Page of Follow-up and outcome Individual follow-up was considered to start on the first day after discharge from the index admission. The end of the observation period was defined as either the end of the study period (December, 00), the time of death not related to MI or the date of an outcome, whichever occurred first. The study outcome was reinfarction (either mortality, ICD--CM 0-, or hospital admission for MI, according to the inclusion criteria, whichever occurred first). The first 0 days after discharge were considered a buffer period to ensure that all patients had the chance to achieve clinical stability and obtain drug treatment and to guarantee a minimum observation period of month. Drug exposure Exposure information was collected from the regional registry of all drugs dispensed by public and private pharmacies (Pharm); this registry is described in detail elsewhere []. All drugs in this study were included in the patients health care plans and are equally available to all residents through the universal health care coverage provided to residents of Italy. The association between evidence-based drug exposure and outcome was separately analysed in the following sub-cohorts: patients treated with platelet aggregation inhibitors (ATC: B0AC0, B0AC0, B0AC0), patients treated with beta-blockers (ATC: C0), patients treated with agents acting on the renin-angiotensin system (ACEI/ARBs, ATC: C0), and patients treated with statins (ATC: C0AA). For each of the sub-cohorts considered, cohort entry was defined as the date of discharge of the index MI. Adherence was measured for each drug as the proportion of days covered (PDC) based on the defined daily doses (DDD). This approach was based on preliminary research []. Adherence was categorised into the following three levels based on the PDC values: 0 < PDC 0.; 0. < PDC 0.; and PDC > 0.. After preliminary analysis, we decided to use BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

8 Page of BMJ Open different thresholds for beta-blockers (i.e., 0. and 0.0). In fact, in clinical practice, beta-blockers are prescribed at dosages lower than the DDD for secondary prevention after an MI []. Study design and statistical analysis For each of the study drug groups, the probability of drug adherence (PDC > 0.) and its % confidence interval was estimated as a function of the follow-up time, starting from the date of discharge of the index MI, using logistic regression models. In each of the sub-cohorts described above, a nested case-control study was performed to assess the relationship between adherence to treatment and outcome. All cases of reinfarction were included in the analyses. We selected the controls using two methods, the time-dependent sampling and the biased time-independent sampling, so that the effect of these two sampling methods could be compared. Up to four controls were individually matched to each case on sex and age. Median observation time (days) for cases and controls was reported comparing the time-dependent and the time-independent sampling. Conditional logistic regression was performed to measure the adherence effect. Potential confounders were selected in two steps. First, the following potential risk factors were selected based on a priori knowledge of the disease: the duration of the index admission, revascularisation procedures during the index admission (PCI or bypass), co-morbidities retrieved from the hospital records for both the index admission and the previous years (Appendix), use of the study drugs during the months prior to the index admission (defined as at least prescriptions), and concomitant therapy with any of the other three evidence-based drug groups, classified as follows: no treatment, 0 < PDC 0. (0.0 for beta-blockers), and PDC > 0. (0.0 for beta-blockers). Second, the a priori risk factors were further selected using a bootstrap stepwise procedure to determine which factors were actually associated with the outcomes of interest []. Using this approach, 000 replicated bootstrap samples were selected from the original cohort. A bootstrap sample is a sample of the same size as the original dataset chosen with replacement. Thus, a given subject in the original cohort may be selected multiple times, only once, or not at all in a specific BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

9 Page of bootstrap sample. A stepwise procedure, using thresholds of p=0.0 for variable selection and elimination, was applied to each replicated sample, and only the risk factors selected in at least 0% of the procedures were included in the conditional logistic regression models as confounders. Odds ratios (ORs) and % confidence intervals (% CIs) were calculated with 0 < PDC 0. defined as the reference group to which all other categories were compared. Time-independent sampling: description of the bias A major challenge in this type of observational study is that both exposure and outcomes are measured in the same time window. Consequently, the time of onset of an outcome affects the classification of the drug regimen. When exposure and outcome are measured within the same time window, patients exposed to drug treatment who experience early outcomes are more likely to be classified as adherent to treatment than are patients with no or late outcomes during follow-up. In fact, the probability of complying with drug therapy after an MI decreases over time []. Consequently, using a time-independent sampling, treated patients who become "early cases" will have a greater chance of being classified as adherent. This happens because the density of prescriptions is very high immediately after the acute event and then declines towards intermittent or cyclical patterns of drug intake, or to definitive treatment discontinuation (figure ). The reduction in adherence over time can produce an over-representation of adherent cases and an underestimate of any beneficial adherence effect. This systematic error can be termed change in adherence bias and could be considered a special case of the time-window bias when the following conditions occur: the exposure is a measure of medication adherence, the observation period begins with an acute and traumatic event, the study medications are chronic drug therapies, and the patients are followed for long periods. This time-related bias can be easily avoided by using time-dependent sampling, according to the principle of incidence density sampling [0]. This method assigns the same length of observation to cases and to their matched controls to ensure equal time windows to measure exposure (figure ) []. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

10 Page of BMJ Open RESULTS The study cohort included,0 patients who were incident cases of MI. Of these,.% were men. The mean age was. years for women and. years for men. The median follow-up was. days. A total of patients were treated with antiplatelet agents (.%), patients with beta-blockers (0.%), patients with ACEI/ARBs (.%), and patients with statins (.%). For any further details, the flow diagram, the demographic and clinical characteristics of the selected cohort were previously published []. As shown in figure, for each of the study medications, the probability of being classified as adherent to treatment decreased as the length of the observation period increased. This probability varied from % to slightly more than 0% for patients treated with antiplatelet agents, from % to 0% for users of beta-blockers, from 0% to 0% for patients treated with ACEI/ARBs, and from % to % for statins users. The nested case-control study was based on sub-cohorts that included cases of reinfarction among the patients treated with antiplatelet agents, cases among the patients treated with betablockers, 0 cases among the patients treated with ACEI/ARBs, and cases among the patients treated with statins. Table displays the median observation period for cases and controls using the two sampling strategies. The time-independent cumulative incidence sampling produced a marked difference in the median observation times between cases and controls. The largest difference was observed in the sub-cohort of patients treated with beta-blockers; cases were observed for a median of 0 days and controls for a median of days. In table the strength of the association between the adherence to treatment and the incidence of a new MI is reported for each of the study drugs. Using the proper, unbiased, time-dependent sampling, a protective effect was detected for all study drugs. The risk of reinfarction decreased with increasing treatment coverage. The greatest benefits were observed with antiplatelet agents (relative risk reduction for PDC > 0. versus 0 < PDC 0. : %) and beta-blockers (relative BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

11 Page 0 of risk reduction for PDC > 0.0 versus 0 < PDC 0. : %). Based on the odds ratios, a benefit was also evident for statins, but did not reach statistical significance. The use of time-independent sampling produced very different results. The protective effect of the study drugs was largely attenuated (for antiplatelet agents and statins) or completely disappeared (for beta-blockers and ACEI/ARBs). None of the associations was statistically significant. Furthermore, for all the study drugs, the risk of reinfarction increased with increasing adherence, from intermediate to the higher adherence category; this trend was the reverse of that observed using time-dependent sampling. DISCUSSION In cohort studies where exposure and outcomes are measured in the same time-window, the time of the onset of an outcome affects drug regimen classification. Similarly, in case-control studies nested within a cohort, the choice of the time window in which exposure is measured is fundamental to the production of unbiased estimates. Suissa has already demonstrated the importance of ensuring an identical time to measure dichotomous drug exposure (i.e., presence or absence of the study medication) for cases and controls. If this condition is not met, the study drug appears spuriously protective because of a time-window bias, an error that is still frequent in observational research []. In clinical practice, medication non-adherence is one of the main factors that reduce the effectiveness of drug therapies []; therefore, the aim of this study was to evaluate the impact of the time-window bias when the exposure is a measure of medication adherence. In this setting, we found that this bias leads to an opposite effect; it produces an underestimate of the beneficial effect of good medication adherence that conforms to clinical guidelines. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

12 Page of BMJ Open In the cohort of patients with a first acute myocardial infarction, as the length of the follow-up increased, the probability of a patient being classified as adherent to evidence-based treatments progressively decreased. This trend usually occurs when the observation period begins with an acute and traumatic event and the study medications are chronic, secondary prevention therapies []. Because exposure time overlaps follow-up time, patients who have already begun therapy and experience early outcomes during the exposure measurement period are more likely to be classified as adherent to treatment. In a nested study, if controls are selected using time-independent sampling, this differential misclassification produces an over-representation of adherent cases, which introduces a bias and an underestimation of the protective effect of medication adherence. We measured the impact of this change in adherence bias by analysing the effect of evidencebased therapies on the incidence of a new MI. By using the proper time-dependent sampling a protective effect was clearly detected for all study drugs, confirming the results of clinical trials and strengthening their validity in clinical practice [,, ]. Conversely, the use of a time-independent sampling spuriously attenuated the beneficial effect of the study drugs, antiplatelet agents and statins and completely masked the beneficial effect of ACEI/ARBs and beta-blockers. For this latter class of drug, we used lower PDC thresholds (0. and 0. versus 0. and 0.) than used for the other drug groups. In fact, calculating drug coverage using the DDD carries the risk of not accounting for real-life dosing when a drug is used for indications different from its principal indication. This discrepancy is particularly relevant for betablockers, which are usually prescribed at dosages lower than the DDD for secondary prevention after an MI. Unfortunately, information on daily doses prescribed to individual patients was not available. As a last remark, we used the incidence density sampling for nested case-control analyses specifically to measure the impact of time-window bias, which is specific to case-control studies. When the effect of medication adherence on health outcomes is estimated from a cohort study, models with time dependent covariates [] or marginal structural models [] are recommended. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

13 Page of CONCLUSION When the exposure of interest is adherence to chronic medication, measured during a lengthy observation period that begins with an acute and traumatic event, the selection of controls using time-independent sampling may underestimate any beneficial adherence effect. The bias that we have described is not restricted to case-control studies. In both the cohort and case-control study designs, the probability of a time-dependent exposure changes with the length of follow-up. Therefore, time-independent exposure definitions will introduce a bias when the duration of followup varies with the outcome. The persistence of this type of time-related bias in peer-reviewed publications [,, ] confirms the need for increased awareness of this methodological pitfall []. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

14 Page of BMJ Open Figure. Time-independent sampling: the change in adherence bias Controls Cases Evaluation of treatment adherence Evaluation of treatment adherence Beginning of the observation period (the acute event) Vertical bars denote prescriptions filled. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

15 Page of Figure. Time-dependent sampling Evaluation of treatment adherence Case Controls Evaluation of treatment adherence Beginning of the observation period (the acute event) Vertical bars denote prescriptions filled. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

16 Page of BMJ Open Figure. Probability of being classified as adherent to treatment by follow-up time, starting from the date of discharge of the index MI Analyses were performed separately for patients treated with each specific study drug group. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

17 Page of Table. Median observation time (days) for cases and controls: a comparison between timedependent and time-independent sampling PATIENTS TREATED WITH ANTIPLATELET AGENTS Time-dependent sampling Time-independent sampling Cases Controls Cases Controls Median observation time (days) PATIENTS TREATED WITH BETA-BLOCKERS Median observation time (days) PATIENTS TREATED WITH ACEI/ARBs Median observation time (days) 0 PATIENTS TREATED WITH STATINS Median observation time (days) BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

18 Page of BMJ Open Table. Association between the levels of adherence to evidence-based drug therapies for secondary prevention after an acute myocardial infarction (MI) and the incidence of a new MI: a comparison between time-dependent and time-independent sampling methods PATIENTS TREATED WITH ANTIPLATELET AGENTS Time-dependent sampling Time-independent sampling Odds Ratio CI % P-value Odds Ratio CI % P-value 0 < PDC < PDC PDC > PATIENTS TREATED WITH BETA-BLOCKERS 0 < PDC < PDC PDC > PATIENTS TREATED WITH ACEI/ARBs 0 < PDC < PDC PDC > PATIENTS TREATED WITH STATINS 0 < PDC < PDC PDC > PDC: Proportion of Days Covered. Potential confounders included in the analysis: percutaneous coronary intervention and bypass at the index admission, heart failure, diabetes, chronic nephropathies, diseases of arteries, arterioles and capillaries, ACEI/ARBs before admission, duration of the index admission and concomitant evidence-based therapy. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

19 Page of CONTRIBUTORSHIP STATEMENT Mirko Di Martino contributed to the concept and design of the study, the analysis of data, the interpretation of results, and the writing of the article. Ursula Kirchmayer, Nera Agabiti and Danilo Fusco contributed to the design of the study, the interpretation of results, and the writing of the article. Lisa Bauleo contributed to the design of the study, the acquisition of data from the health information systems, the statistical analysis, the interpretation of results, and the writing of the article. Marina Davoli and Carlo Alberto Perucci contributed to the concept of the study, the interpretation of results, the writing of the article, and the critical revision of the paper for important intellectual content, and they have given their final approval of the version submitted for publication. All authors agree to be accountable for all aspects of the work and ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. COMPETING INTERESTS The authors declare that they have no conflict of interest. FUNDING This research received no grant from any funding agency in the public, commercial, or not-forprofit sectors. DATA SHARING STATEMENT No additional data are available. PRIVACY LAWS This study was carried out in full compliance with the current privacy laws. The Department of Epidemiology is legitimized by the Lazio Region Committee in managing and analyzing data from the regional health information systems for epidemiological purposes. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

20 Page of BMJ Open REFERENCES. Marcum ZA, Sevick MA, Handler SM. Medication nonadherence: a diagnosable and treatable medical condition. JAMA 0; 0(0): 0-.. Suissa S, Dell Aniello S, Vahey S, et al. Time-window bias in case-control studies. Statins and lung cancer. Epidemiology 0; :.. Antman EM, Hand M, Armstrong PW, et al. 00 focused update of the ACC/AHA 00 Guidelines for the management of patients with ST-elevation myocardial infarction: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Group to Review New Evidence and Update the ACC/AHA 00 Guidelines for the Management of Patients With ST-Elevation Myocardial Infarction). Circulation 00; :.. Cooper A, Skinner J, Nherera L, et al. Clinical guidelines and evidence review for post myocardial infarction: Secondary prevention in primary and secondary care for patients following a myocardial infarction. NICE Clinical Guidelines, No. ; 00.. Kirchmayer U, Agabiti N, Belleudi V, et al. Socio-demographic differences in adherence to evidence-based drug therapy after hospital discharge from acute myocardial infarction: a population-based cohort study in Rome, Italy. J Clin Pharm Ther 0; (): -.. Belleudi V, Fusco D, Kirchmayer U, et al. Definition of patients treated with evidence based drugs in absence of prescribed daily doses: the example of acute myocardial infarction. Pharmacoepidemiol Drug Saf 0; 0: -.. Kirchmayer U, Di Martino M, Agabiti N, et al. Effect of evidence-based drug therapy on longterm outcomes in patients discharged after myocardial infarction: a nested case control study in Italy. Pharmacoepidemiol Drug Saf 0; (): -.. Austin PC, Tu JV. Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. J Clin Epidemiol 00; (): -.. Filippi A, D'Ambrosio G, Giustini SE, et al. Pharmacological treatment after acute myocardial infarction from 00 to 00: a survey in Italian primary care. J Cardiovasc Med 00; 0: Rothman KJ, Greenland S. Case-control studies. In: Rothman KJ, Greenland S, eds. Modern Epidemiology. nd edition. Philadelphia: Lippincott Williams & Wilkins; :. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

21 Page 0 of Vanuzzo L, Pilotto L, Pilotto L, et al for the WHO MONICA Project. Pharmacological treatment during MI and in secondary prevention: the scientific evidence. Published by World Health Organization (WHO) and the WHO MONICA Project investigators, Stricker BH, Stijnen T. Analysis of individual drug use as a time-varying determinant of exposure in prospective population-based cohort studies. Eur J Epidemiol 00; (): -.. Hernán MA, McAdams M, McGrath N, Lanoy E, Costagliola D. Observation plans in longitudinal studies with time-varying treatments. Stat Methods Med Res 00; (): -.. Khurana V, Bejjanki HR, Caldito G, Owens MW. Statins reduce the risk of lung cancer in humans: a large case-control study of US veterans. Chest 00; (): -.. Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol 00; (): -.. Suissa S. Randomized Trials Built on Sand: Examples from COPD, Hormone Therapy, and Cancer. Rambam Maimonides Med J 0; (): e00.. Hernández-Díaz S. Name of the bias and sex of the angels. Epidemiology 0; (): -. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

22 Page of BMJ Open APPENDIX Data sources Hospital Information System (HIS) The HIS includes the patients characteristics (the patients identifier, gender, date and place of birth, and place of residence); admission and discharge dates; discharge diagnoses (up to ); procedure codes (up to ) according to the International Classification of Disease, Ninth Revision, Clinical Modification (ICD--CM); hospital ward(s); date(s) of in-hospital transfer; and a regional code that corresponds to the admitting facility. Mortality Information System (MIS) The MIS includes the patients demographic characteristics (the patient s identifier, age, gender, place and date of birth, residence, marital status, and occupation), as well as the date, place, and cause of death (codified by ICD- codes). Drug claims registry (Pharm) Pharm registry comprises individual records for each medical prescription dispensed in public and private pharmacies within the territory of the local health authorities for the resident population. The registry is limited to those drugs prescribed for outpatient use that are reimbursed by the health care system. All the drugs in the study are included in the Pharm registry. In this registry, the drugs are identified by the national drug registry code, which refers to the international ATC classification and allows for the exact quantification of the dispensed drug. Individual patient data (the patient s identifier) and the date the drug is dispensed are reported for every prescription. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

23 Page of Algorithm for selection of the cohort ICD--CM codes for the identification of MI cases. An MI was defined either as a primary diagnosis of an acute myocardial infarction (ICD--CM 0.xx) or as a secondary diagnosis of an MI associated with one of the following conditions as the primary diagnosis: ICD--CM code Condition Other acute and subacute forms of ischemic heart disease Angina pectoris Other forms of chronic ischemic heart disease.0 Haemopericardium Conduction disorders Cardiac dysrhythmias, excluding. Cardiac arrest Heart failure. Rupture of chordae tendineae. Rupture of papillary muscle. Acquired cardiac septal defect. Certain sequelae of myocardial infarction not elsewhere classified. Other disorders of papillary muscle. Acute edema of lung, unspecified. Acute respiratory failure 0.0 Coma 0. Syncope and collapse. Cardiogenic shock. Respiratory arrest.0 Iatrogenic cerebrovascular infarction or haemorrhage. Accidental puncture or laceration during a procedure BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

24 Page of BMJ Open Selection of co-morbidities from hospital discharge records Condition Index admission ICD--CM codes Malignant neoplasms Diabetes Disorders of lipoid metabolism/obesity,.0 Admissions during the years prior to index admission Hematologic diseases 0-,, 0-,, Hypertension 0-0 Heart failure Other cardiac diseases -, (excl..0),,,,.-., V., V., V., V., V.0 Conduction disorders Cardiac dysrhythmias Cerebrovascular disease Hemorrhagic stroke Ischemic stroke/ Transient cerebral ischemia Diseases of arteries, arterioles, and capillaries Chronic obstructive pulmonary disease (COPD) Chronic nephropathies Chronic liver, pancreas, and digestive diseases Gastro-oesophageal haemorrhage,.0,.0,.0, V.0, V. 0-0,,.x,,.x, 0- (excl.,.,.,., ) -, -, V.0, V., V procedures.,.,. -,.-.,,, V. 0. (00: 0.), 0.,.0,.,.,.,.0,.,.,.,.0,.,.,.,.0,.,.,.,.,.,. 0.,, -, 0,,,,,, (excl..),,.-., V., V., V., V., V.0,.0,.0,.0, V.0, V. 0-0,,.x,,.x, 0-, -,, -, V.0, V., V procedures.,.,.,. -,.-.,,, 0.0,.0-., 0,, V. 0. (00:0.), 0.,.0,.,.,.,.0,.,.,.,.0,.,.,.,.0,.,.,.,.,.,. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

25 Page of STROBE 00 (v) checklist of items to be included in reports of observational studies in epidemiology* Checklist for cohort, case-control, and cross-sectional studies (combined) Section/Topic Item # Recommendation Reported on page # Title and abstract (a) Indicate the study s design with a commonly used term in the title or the abstract (b) Provide in the abstract an informative and balanced summary of what was done and what was found Introduction Background/rationale Explain the scientific background and rationale for the investigation being reported Objectives State specific objectives, including any pre-specified hypotheses Methods Study design Present key elements of study design early in the paper Setting Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection Participants (a) Cohort study Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up, Case-control study Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls Cross-sectional study Give the eligibility criteria, and the sources and methods of selection of participants (b) Cohort study For matched studies, give matching criteria and number of exposed and unexposed Case-control study For matched studies, give matching criteria and the number of controls per case Variables Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable Data sources/ measurement * For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe, comparability of assessment methods if there is more than one group Bias Describe any efforts to address potential sources of bias, Study size 0 Explain how the study size was arrived at Not Applicable Quantitative variables Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why Statistical methods (a) Describe all statistical methods, including those used to control for confounding, on 0 January 0 by guest. Protected by copyright. (b) Describe any methods used to examine subgroups and interactions (c) Explain how missing data were addressed (d) Cohort study If applicable, explain how loss to follow-up was addressed Case-control study If applicable, explain how matching of cases and controls was addressed, Not Applicable BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from

26 Page of BMJ Open Results Participants Descriptive data Cross-sectional study If applicable, describe analytical methods taking account of sampling strategy (e) Describe any sensitivity analyses * (a) Report numbers of individuals at each stage of study eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed (b) Give reasons for non-participation at each stage (c) Consider use of a flow diagram * (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders (b) Indicate number of participants with missing data for each variable of interest (c) Cohort study Summarise follow-up time (eg, average and total amount),. This paper compares two methods for sampling controls within a nested case-control design: a timeindependent sampling versus a time-dependent sampling. Not Applicable Any further details, including the flow diagram, the demographics and clinical characteristics of the cohort, were previously published and cited along with the paper (reference number ). We have no missing Outcome data * Cohort study Report numbers of outcome events or summary measures over time Case-control study Report numbers in each exposure category, or summary measures of exposure This is a nested case- on 0 January 0 by guest. Protected by copyright. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from data.

27 Page of control study, which compare two different sampling strategies. We have three level of exposure ) by each study drug and ) by each sampling strategy. Thus, reporting all the data suggested in the checklist might overload the presentation of the results and probably it is not strictly adherent to the aim of the study. Cross-sectional study Report numbers of outcome events or summary measures Not Applicable Main results (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, % confidence interval). Make clear which confounders were adjusted for and why they were included, 0, and table (b) Report category boundaries when continuous variables were categorized (c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period This is an Etiological Other analyses Report other analyses done eg analyses of subgroups and interactions, and sensitivity analyses 0 on 0 January 0 by guest. Protected by copyright. study. Therefore the reporting method suggested by the checklist (i.e. absolute risk measures) might probably be misleading within this framework. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from

28 Page of BMJ Open Discussion Key results Summarise key results with reference to study objectives Limitations Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias Interpretation 0 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence, Generalisability Discuss the generalisability (external validity) of the study results Other information Funding Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based *Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies. Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at Annals of Internal Medicine at and Epidemiology at Information on the STROBE Initiative is available at on 0 January 0 by guest. Protected by copyright. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from

29 The impact of time-window bias on the assessment of the long-term effect of medication adherence: the case of secondary prevention after myocardial infarction Journal: BMJ Open Manuscript ID: bmjopen-0-00.r Article Type: Research Date Submitted by the Author: -Apr-0 Complete List of Authors: Di Martino, Mirko; Department of Epidemiology, Regional Health Service, Lazio Region. Kirchmayer, Ursula; Department of Epidemiology, Regional Health Service, Lazio Region. Agabiti, Nera; Department of Epidemiology, Regional Health Service, Lazio Region. Bauleo, Lisa; Department of Epidemiology, Regional Health Service, Lazio Region. Fusco, Danilo; Department of Epidemiology, Regional Health Service, Lazio Region. Perucci, Carlo; Senior Epidemiologist Consultant., Davoli, Marina; Department of Epidemiology, Regional Health Service, Lazio Region. <b>primary Subject Heading</b>: Pharmacology and therapeutics Secondary Subject Heading: Epidemiology, Research methods, Cardiovascular medicine Keywords: THERAPEUTICS, EPIDEMIOLOGY, Myocardial infarction < CARDIOLOGY BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

30 Page of BMJ Open The impact of time-window bias on the assessment of the long-term effect of medication adherence: the case of secondary prevention after myocardial infarction Running head. The change in adherence bias. Mirko Di Martino, Ursula Kirchmayer, Nera Agabiti, Lisa Bauleo, Danilo Fusco, Carlo Alberto Perucci, Marina Davoli. () Department of Epidemiology, Regional Health Service, Lazio Region, Rome, Italy. () Senior Epidemiologist Consultant, Rome, Italy. Key Words: change in adherence bias; time-related bias; medication adherence; evidence-based drug therapy; myocardial infarction. Corresponding author: Mirko Di Martino Department of Epidemiology, Lazio Regional Health Service Via di Santa Costanza, - 00 Roma, Italy Tel.: Fax BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

31 Page of ABSTRACT Objectives. Time-window bias was described in case-control studies and led to a biased estimate of drug effect. No studies have measured the impact of this bias on the assessment of the effect of medication adherence on health outcomes. Our goals were to estimate the association between adherence to drug therapies after myocardial infarction (MI) and the incidence of a new MI, and to quantify the error that would have been produced by a time-window bias. Setting. This is a population-based study. Data were obtained from the Regional Health Information Systems of the Lazio Region in Central Italy (around million inhabitants). Participants. Patients discharged after MI in were enrolled in the cohort and followed through 00. Outcome measure. The study outcome was reinfarction: either mortality, or hospital admission for MI, whichever occurred first. Design. A nested case-control study was performed. Controls were selected using both time-dependent and time-independent sampling. Adherence to antiplatelets, beta-blockers, agents acting on the renin-angiotensinsystem (ACEI/ARBs) and statins was calculated using the proportion of days covered (PDC). Results. A total of,0 patients were enrolled in the cohort. Using time-dependent sampling a protective effect was detected for all study drugs. Conversely, using time-independent sampling, the beneficial effect was attenuated, as in the case of antiplatelet agents and statins, or completely masked, as in the case of ACEI/ARBs and beta-blockers. For ACEI/ARBs, the time-dependent approach produced odds ratios of 0. (%CI: 0.-.) and 0. (0.-0.), respectively, for 0.<PDC 0. and PDC>0. versus 0 PDC 0.. Using the time-independent approach, the odds ratios were 0. (0.-.) and.00 (0.-.), respectively. Conclusions. A time-independent definition of a time-dependent exposure introduces a bias when the length of follow-up varies with the outcome. The persistence of time-related biases in peer-reviewed papers strongly suggests the need for increased awareness of this methodological pitfall. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.

32 Page of BMJ Open STRENGTHS AND LIMITATIONS OF THIS STUDY Population-based design, large numbers and robustness of analytical procedures are the main strengths. This study contributes to the debate on the complex methodology to estimate the effectiveness of medication in clinical practice. Our pharmaceutical database does not contain information on the prescribed daily doses and adherence to drug treatment was estimated on the basis of the DDDs. Although this is a useful instrument for comparing the results from different studies, misclassification of drug utilization may have occurred. However, both methods (time-independent and time-dependent sampling) were performed and compared in the presence of the same conditions. BMJ Open: first published as 0./bmjopen-0-00 on 0 June 0. Downloaded from on 0 January 0 by guest. Protected by copyright.