Association Between Intensification of Metformin Treatment with Insulin Vs

Home , Alseroxylon, Cryptenamine, Pargyline, Rescinnamine

Supplementary Online Content

Roumie CL, Greevy RA, Grijalva CG, et al. Association between intensification of metformin treatment with insulin vs sulfonylureas and cardiovascular events and all-cause mortality among patients with diabetes. JAMA. doi:10.1001/jama.2014.4312.

eTable 1. Definitions of comorbid conditions and medications, on the basis of codes and prescriptions in 730 days before treatment intensification

eTable 2. Description of propensity score model and distribution of IPTWs by therapy with calculation details

eTable 3. Logistic regression model for the probability of intensifying with Metformin+ Insulin

eTable 4. Logistic regression model for the probability of censoring throughout follow-up

eTable 5. Characteristics of un-weighted propensity matched cohort remaining at risk 12 months after treatment intensification

eTable 6. Risk of primary composite outcome (cardiovascular disease or all cause mortality) in the presence of an unobserved confounder with a relative hazard of 1.25 for the outcome, and various prevalence levels of the confounder by exposure group. The bolded numbers correspond to the hypothetical confounder's differential prevalences between exposure groups that would explain enough of the observed association to yield a statistically non-significant result.

eTable 7. Risk of all-cause mortality in the presence of an unobserved confounder with a relative hazard of 1.25 for the outcome, and various prevalence levels of the confounder by exposure group. The bolded numbers correspond to the hypothetical confounder's differential prevalences between exposure groups that would explain enough of the observed association to yield a statistically non-significant result.

eTable 8. Characteristics of unmatched patients

eFigure 1. Distribution of propensity scores by drug

eFigure 2. Adjusted hazard and 95% confidence intervals for risk of composite outcome and secondary outcomes using sensitivity analysis (Marginal Structural Models with weights un-truncated or truncated at 10 or at 100). All analyses require persistence on metformin.

eFigure 3. Adjusted hazard ratios for cardiovascular disease (CVD) or death composite outcome and secondary outcome (death alone) by cardiovascular disease (CVD) history and age subgroups.

This supplementary material has been provided by the authors to give readers additional information about their work.

Downloaded From: https://jamanetwork.com/ on 10/04/2021 eTable 1: Definitions of comorbid conditions and medications, on the basis of codes and prescriptions in 730 days before treatment intensification Covariate Condition Inclusive conditions Definition* Malignancy Cancer excluding non ICD 9- CM diagnosis codes:140.X-208.X (exclude 173) melanoma skin cancer Liver/ Respiratory 1. End stage liver ICD 9- CM diagnosis codes: 570.X- 573.X failure disease 2. Respiratory failure ICD 9- CM diagnosis codes: 518.81, 518.83, 518.84, 799.1, 415.X, 416.X Congestive Heart CHF (excluding post ICD 9- CM diagnosis codes: 428.X, 402.01, 402.11, 402.91, 404.01, Failure procedure-CHF) 404.03, 404.11, 404.13, 404.91, 404.93, 425.X Cardiovascular 1. MI ICD 9- CM diagnosis codes:410.X, 412.X, 429.7X disease 2. Obstructive ICD 9- CM diagnosis codes:411.X, 413.X, 414.X coronary disease ICD9-CM procedure codes: 36.01, 36.02, 36.03, 36.05, 36.09, 36.10- 36.19 CPT procedure codes: 33533-36, 33510-23, 33530, 92980-82,92984, 92995-6, 92974 3. TIA ICD 9- CM diagnosis codes: 435.X 4. Stroke ICD 9- CM diagnosis codes: 430.X, 431.X. 434.X, 436.X 5. Peripheral artery ICD 9- CM diagnosis codes:440.2X, 442.2, 443.1, 443.9, 445.0X ICD9-CM disease procedure codes:38.08-09, 38.18, 38.38, 38.39, 38.48, 38.49, 38.88, 38.89, revascularization or 39.25, 39.29, 39.5, 84.1X; 84.10-84.17 amputation CPT procedure codes: 35226,35256, 35286, 35351, 35355, 35371, 35372, 35381, 35454, 35456, 35459, 35473, 35474, 35482, 35483, 35485, 35492, 35493, 35495, 35546, 35548, 35549, 35551, 35556, 35558, 35563, 35565, 35566, 35571, 35583, 35585, 35587, 35646, 35651, 35654, 35656, 35661, 35663, 35665, 35666, 35671, 34800, 34802-5 6. Carotid ICD9-CM procedure codes: 38.12, 38.11, 00.61, 00.63, 39.28 revascularization CPT procedure codes: 35301, 0005T, 0006T, 0007T, 0075T, 0076T, 37215, 37216 HCPCS procedure code: S2211 7. Pentoxifylline & Medications: Pentoxifylline, Cilostazol, Cyclandelate, Ethaverine HCL, related drugs Nicotinyl Alcohol Tartate, Papaverine, Tolazolin Serious Mental 1. Dementia ICD 9- CM diagnosis codes: 290.X, 291.2, 292.82, 294.1X, 331.0- illness 331.1X, 331.82 Medications: Donepezil, Rivastigmine, Galantamine, Tacrine, Memantine 2. Depression, ICD 9- CM diagnosis codes: 311, 300.4, 296.2, 296.3, V79.0 3. Schizophrenia, ICD 9- CM diagnosis codes: 295.X 4. Bipolar disorder ICD 9- CM diagnosis codes: 296.0, 296.4X, 296.5X, 296.6X, 296.7, 296.80, 296.89 5. Post traumatic ICD 9- CM diagnosis codes: 309.81 stress disorder Cardiac valve disease ICD 9- CM diagnosis codes: 394.X, 395.X, 396.X, 424.0, 424.1 Arrhythmia 1. Atrial ICD 9- CM diagnosis codes: 427.3X fibrillation/flutter 2. Arrhythmia and ICD 9- CM diagnosis codes: 426.X, 427.X conduction disorder Smoking ICD 9- CM diagnosis codes:305.1, V15.82, 989.84 Medications: Varenicline tartrate, Nicotine Replacement therapy (gum, patch, lozenge) COPD/ Asthma ICD 9- CM diagnosis codes:491.X, 492.X, 493.X, 496.X, V17.5, V81.3 HIV ICD 9- CM diagnosis codes: 042, 079.53, 795.71, V08 Medications: Zidovudine, Didanosine, Zalcitabine, Stavudine, Indinavir, Ritonavir, Saquinavir, Nevirapine, Nelfinavir, Delavirdine, Delavirdine, Abacavir, Amprenavir, Efavirenz, Lamivudine- Zidovudine, Ritonavir-Lopinavir, Abacavir-Lamivudine-Zidovudine

Downloaded From: https://jamanetwork.com/ on 10/04/2021 Parkinson’s Disease ICD 9- CM diagnosis codes: 332 Medications: Apokyn, Apomorphine, Carbidopa/levodopa, Entacapone, Pergolide, Pramipexole, Ropinirole, Rotigotine, selegiline, Tolcapone, Zelapar Azilect/rasagiline, Emsam, Isocarboxazid, Phenelzine, Tranylcypromine Medications ACE Inhibitors or ACE Inhibitors or ARBs Benazapril, Captopril, Enalapril, Fosinopril, Lisinopril, Moexipril, ARBs alone/combination Perindopril, Quinapril, Ramipril, Trandolapril, Candesartan, Eprosartan, alone/combination Irbesartan, Losartan, Azilsartan, Olmesartan, Telmisartan, Valsartan Antipsychotics Atypical and typical Lithium, Clozapine, Haloperidol, Loxipine, Lurasidone, Molindone, antipsychotic Olanzipine, Paliperidone, Quetiapine fumerate; Resperidone, medications Aripiprazole, Asenapine, Ziprasidone, Chlorpromazine, Fluphenazine, Fluphenazine deconate, Mesoridazine, Perphenazine, Thioridazine, Thiothixene; Trifluoperazine; Triflupromazine, Asenapine, Chlorprothixene, Iloperidone, Molindone, Promazine, Piperacetazine, Methotrimeprazine, Acetophenazine Antihypertensives 1. Beta-blockers Acebutolol, Atenolol, Betaxolol, Bisoprolol, Carteolol, Carvedilol, Esmolol, Labetalol, Metoprolol Tartrate, Metoprolol Succinate, Propranolol, Penbutolol, Pindolol, Nadolol, Sotolol, Timolol, Nebivolol 2. Calcium Channel Amlodipine, Isradipine; Felodipine, Nifedipine, Nifedipine ER, Blockers Nicardipine; Diltiazem, Verapamil, Nimodipine; Nisoldipine; Bepridil, Amlodipine–Atorvastatin, Clevidipine Butyrate, 3. Thiazide diuretics/ Chlorothiazide, Chlorthalidone, Hydrochlorothiazide, Methyclothiazide, Potassium sparing Trichlormethiazide, Metolazone, Indapamide, Eplerenone; Ameloride, diuretics Sprinolactone, Triamterene, Hydrochlorothiazide/Triamterene, Hydrochlorothiazide/Spironolactone, Bendroflumethiazide, Benzthiazide, Cyclothiazide, Hydroflumethiazide, Methyclothiazide, Trichlormethiazide, Metolazone, Indapamide, Polythiazide, Quinethazone 4. Other Doxazosin, Prazosin, Terazosin, Clonidine, Guanabenz, Guanfacine, Antihypertensives Hydralazine, Methyldopa, Metyrosine, Reserpine, Minoxidil, Alfuzosin, Silodosin, Alseroxylon, Cryptenamine, Deserpidine, Diazoxide Guanethidine, Iloprost, Mecamylamine, Pargyline, Rescinnamine, Trimethaphan Camsylate Anti-arrhythmics 1. Digoxin Digoxin, Digitalis Digoxin and other 2. Anti- Arrythmics Adenosine, Amiodarone, Lidocaine, Flecainide, Ibutilide, Pacerone, inotropes Procainamide, Rhythmol, Propafenone, Quinidine, Disopyramide, Verapamil, Dofetilide, Mexiletine, Moricizine, Tocainide Anticoagulants and 1. Anticoagulants Warfarin, Argatroban, Bivalirudin, Dalteparin, Enoxaprin, Eptifibatide, Platelet inhibitors, Fondaparinux, Heparin, Lepirudin, Tirofiban, Tinzaparin, Reviparin, not aspirin Nadroparin, Ardeparin, Certoparin, Dabigatran 2. Platelet Inhibitors Clopidogrel, Ticlopidine, Aspirin/ Dipyrimidole, Dipyrimidole alone, Abciximab, Factor IX, Factor VIIa, Factor VIII, Prasugrel, Ticagrelor Lipid lowering drugs 1. Statins Atorvastatin, Fluvastatin, Lovastatin, Pravastatin, Simvastatin, Rosuvastatin, Cerivastatin Pitavastatin, Lovastatin ER, Ezetamibe/Simvastatin, Lovastatin /Niacin 2. Non Statins Cholestyramine, Colesevelam, Clofibrate, Colestipol, Niacin, Niacinamide, Fish Oil Concentrate, Omega 3 Fatty Acids, Gemfibrozil, Fenofibrate, Fenofibric Acid, Ezetimibe Omacor, Tricor/Fenofibrate, Ezetamibe/Simvastatin Nitrates Amyl nitrate, Isosorbide Dinitrate, Isosorbide mononitrate, Erythrityl Tetranitrate Nitroglycerin (all forms--SA, Patch, SL, Ointment; Aerosol spray), Ranolazine Aspirin Aspirin, Aspirin/ Dipyrimidole Loop Diuretics Furosemide, Ethacrynic acid, Bumetanide, Torsemide

ACEI = angiotensin-converting enzyme inhibitor; ARB = angiotensin-receptor blocker; COPD = chronic obstructive pulmonary disease; CPT = Current Procedural Terminology; ICD-9- CM = International Classification of Diseases, Ninth Revision; MI = myocardial infarction; TIA = transient ischemic attack * Each co-morbid condition was defined as present if there was 1 specified inpatient or 2 specified outpatient codes separated by 30 days, or 1 specified procedure code or prescription for a medication defining that comorbid condition in the 730 days before treatment intensification.

Downloaded From: https://jamanetwork.com/ on 10/04/2021 eTable 2: Details for the construction of the Propensity score model and Marginal Structural Model and Distribution of IPTWs by Therapy with Calculation Details

Propensity Score The cohort was composed of a subset of all eligible persons who initiated metformin+ insulin or metformin+ sulfonylurea after using metformin monotherapy for diabetes. The matched cohort was formed by matching metformin+ insulin users to 5 metformin+ sulfonylurea users with similar propensity scores. The propensity score (PS) is defined as the probability of metformin+ insulin use, given a particular pattern of baseline covariates (eTable 3). We estimated the PS using a logistic regression model in which the dependent variable was 1 for patients who used metformin+ insulin and 0 for metformin+ sulfonylurea users. The model was simple logistic regression, with a third degree polynomial term for continuous covariates and VISN of care in the model. Unlike a model being used for direct covariate adjustment, the PS model is designed to be non-parsimonious and highly flexible to capture any possible confounding by indication. Table 1 in the paper lists baseline covariates included. Missing covariate values were multiply imputed and indicators for each variable's missingness was included to account for potential informative missingness. The patients' average PS from the imputation were used for matching. The PS model for the probability of being a metformin+ insulin user is displayed in eTable 3. Two variables were particularly strongly related to addition of insulin over sulfonylurea. Insulin as add on therapy increased relative to sulfonylureas over time as reflected by odds ratios for fiscal years 2009 to 2011 relative to 2007. Insulin as add on therapy increased with increasing baseline creatinine as reflected by odds ratios for 1.3. The PS model yielded a C statistic of 0.77. When used to facilitate matching, the success of the PS model is determined by the covariate balance achieved in the matched cohort. Table 1 in the paper demonstrates the standardized difference in means before and after propensity score matching. Indicating good balance after matching, all standardized differences have an absolute value < 0.1 with many < 0.01. An important condition for propensity score methods is that every cohort member have a nontrivial probability of having received either of the study therapies. Our 1:5 matching procedure excluded metformin+ insulin patients for whom very few similar metformin+ sulfonylurea users existed. This corresponds fairly closely to the region of eFigure 1 where the probability of metformin+ insulin use surpasses that of the much more prevalent metformin+ sulfonylurea use. It includes primarily patients who had very high HbA1c (median 11.8%) at the time of intensification. The optimal matching algorithm selects the cohort with the smallest average difference in propensity scores between exposure groups. The matching was performed on the log odds of the propensity scores with a caliper equivalent to 0.15 on the probability scale.

IPTW Marginal structural models (MSM) are able to address 2 important issues in nonrandomized exposure effect studies: confounding by indication (baseline imbalances between groups) and informative censoring associated with time- varying covariates.1 MSMs estimate the "marginal" or "absolute" treatment effects on a population through weighting and reweighting observations based on baseline and time-dependent covariates. Potential confounding is controlled for through the Inverse Probability of Treatment Weights (IPTWs) which utilize a patient’s covariate history to estimate the probability of intensifying therapy with metformin+ insulin versus metformin+ sulfonylurea and the probability of being censored after follow-up begins.

In this study, the IPTW are derived as the product of the probabilities estimated from 2 multivariable logistic regression models. The first determined the probability of intensification with metformin+ insulin versus metformin+ sulfonylurea at the time patients first changed therapies. The second determined the probability of being censored at a given number of months from intensification. These are used to estimate the probability of a subject being on a given treatment and remaining uncensored at each month of their observed follow-up time (see eTable 2). Supplemental tables 3 and 4 are results of the logistic regression models for the calculation of the inverse probability treatment weights. Covariates included are the baseline and time-dependent covariates. Continuous covariates were modeled with third degree polynomials to account for potential non-linearity (age, BMI, HbA1c, LDL, creatinine, blood pressure, duration of metformin monotherapy, months from treatment intensification, months from last hospitalization prior to intensification). Similar to the propensity score model, the probability of censoring model is designed to be non-parsimonious and highly flexible to capture any potential informative censoring. Missing covariate values were multiply imputed and indicators for each variable's missingness was included to account for potential informative missingness. The patients' average probabilities of remaining uncensored at each time point was used in the IPTW calculation.

Downloaded From: https://jamanetwork.com/ on 10/04/2021 Therapy N 99th Mean Median Max (months) Percentile Persistent Exposure Stabilized nontruncated weights Required* Metformin+ Sulfonylurea 231783 1.53 0.83 3.94 53925 Metformin+ Insulin 48294 1.05 0.88 5.3 164 Nonstabilized nontruncated weights Metformin+ Sulfonylurea 231783 11.1 1.9 34.6 474715 Metformin+ Insulin 48294 28.8 9.8 262.4 21761

Calculation Details: Following the notation conventions established in the literature1, 2 the IPTW are described below.

The patient index is denoted with the subscript i. The time index, k, runs from 0 to K. k=0 is the month in which the patient first intensified therapy, that is when they first received a medication other than metformin. The first month of follow-up is denoted by k=1, the second by k=2, and so on. The variable A refers to therapy (Metformin+ Insulin versus Metformin+ Sulfonylurea). The variable L refers to the patient’s covariates. The variable C denotes censoring status with 0 indicating uncensored. A bar above A, C, or L is used to denote the full history of the covariate, e.g. conditioning on denotes conditioning on , ,… , .

The nonstabilized IPTW at time k=t equals the product of the part of the weight due to treatment and the part of the weight due to remaining uncensored, respectively . Likewise for the stabilized IPTW, .

̅ The portion of the stabilized weight due to treatment therapy is ∏ / ̅ ̅ ∏ , , which simplifies in this study to / | . Put more simply, this is the unconditional probability of intensifying to the therapy that the patient was assigned divided by the conditional probability of being assigned to that therapy, conditioning on the patient’s covariates at the time the actual intensification occurred. The nonstabilized version is 1/ | .

The portion of the stabilized weight due to remaining uncensored is ̅ ̅ ̅ ̅ ̅ ∏ 0 0, /∏ 0 0, , . That is, the probability of remaining uncensored at time t is the product of the probabilities of remaining uncensored ̅ ̅ at time 0, 1, …, up to time t. The nonstabilized version is 1/ ∏ 0 0, ̅ , .

* Primary analysis requires persistence on metformin; patients are censored after 90 days without metformin in hand.

Downloaded From: https://jamanetwork.com/ on 10/04/2021 eTable 3: Logistic regression model for the probability of intensifying with Metformin+ Insulin 95% Confidence Characteristic Odds Ratio Deviance† DF ‡ Intervals Comorbidities Malignancy 1.184 1.147 1.223 103 1 Liver/ respiratory failure 1.426 1.372 1.483 305 1 Congestive heart failure 0.929 0.896 0.964 16 1 Cardiovascular disease 1.080 1.055 1.105 41 1 Serious mental illness 1.026 1.004 1.048 5 1 Cardiac valve disease 0.917 0.866 0.972 9 1 Arrhythmia 0.873 0.845 0.902 67 1 Smoking 0.880 0.860 0.900 122 1 Chronic Obstructive Pulmonary Disease/ 1.094 1.068 1.121 52 1 Asthma HIV 1.722 1.545 1.920 87 1 Parkinsons 1.913 1.751 2.091 184 1 Indicators of health care utilization Hospitalized in last year 1.112 1.058 1.169 9,906 4 Recentness of hospitalization, months 1.055 1.026 1.085 Recentness of hospitalization, months 2 0.982 0.975 0.989 Recentness of hospitalization, months3 1.002 1.002 1.003 Nursing Home encounter in last year 2.292 1.936 2.715 82 1 Outpatient Visits in past year 1.011 1.009 1.013 233 3 Outpatient Visits in past year 2 1.000 1.000 1.000 Outpatient Visits in past year 3 1.000 1.000 1.000 Medicare encounters in last year 1.539 1.504 1.574 1,352 1 Medicaid encounters in last year 1.806 1.736 1.879 790 1 Demographics Race_Black 1.091 1.062 1.120 95 2 Race_Other 0.866 0.829 0.906 gender_female 1.338 1.283 1.395 180 1 Age 0.981 0.979 0.983 897 3 Age2 1.000 1.000 1.000 Age3 1.000 1.000 1.000 Fiscal Year 2002_2003 0.974 0.923 1.027 4,416 8 Fiscal Year 2004 0.923 0.885 0.962 Fiscal Year 2005 0.953 0.919 0.989 Fiscal Year 2006 1.084 1.050 1.119 Fiscal Year 2008 1.684 1.632 1.737 Fiscal Year 2009 2.099 2.031 2.169 Fiscal Year 2010 2.401 2.315 2.491 Fiscal Year 2011 2.212 2.089 2.342 Time to intensification*, months 0.987 0.986 0.989 736 3 Time to intensification, months2 1.000 1.000 1.000 Time to intensification, months3 1.000 1.000 1.000 Clinical and laboratory HbA1c 1.276 1.268 1.284 17,642 3 HbA1c2 1.060 1.057 1.062 HbA1c3 0.994 0.993 0.994 Systolic Blood pressure 1.004 1.003 1.004 112 3 Systolic Blood pressure 2 1.000 1.000 1.000 Systolic Blood pressure 3 1.000 1.000 1.000 Diastolic Blood pressure 0.985 0.984 0.986 634 3 Diastolic Blood pressure2 1.000 1.000 1.000 Diastolic Blood pressure3 1.000 1.000 1.000

Downloaded From: https://jamanetwork.com/ on 10/04/2021 Body Mass Index 0.981 0.979 0.983 499 3 Body Mass Index 2 1.002 1.002 1.002 Body Mass Index 3 1.000 1.000 1.000 Low Density Lipoprotein 0.997 0.997 0.998 458 3 Low Density Lipoprotein2 1.000 1.000 1.000 Low Density Lipoprotein3 1.000 1.000 1.000 Creatinine 1.311 1.253 1.370 224 3 Creatinine2 1.062 0.980 1.151 Creatinine3 0.957 0.934 0.980 Urine Protein Trace or 1+ 0.975 0.954 0.996 29 2 Proteinuria present at 2+,3+,4+ 1.097 1.054 1.143 Medications ACE Inhibitors or ARBs 1.033 1.012 1.054 10 1 Anti hypertensive medications 1.002 0.981 1.024 0 1 Statin and non-statin lipid lowering agents 0.837 0.819 0.856 248 1 Anti-arrhythmics, digoxin and inotropes 1.320 1.245 1.400 82 1 Anticoagulant 1.135 1.104 1.168 77 1 Nitrates 1.062 1.031 1.095 15 1 Aspirin 1.026 1.004 1.047 6 1 Loop Diuretics 1.543 1.501 1.587 886 1 Antipsychotics 1.244 1.207 1.282 197 1 Location of care VISN_a 1.259 1.195 1.326 1,243 20 VISN_b 1.115 1.046 1.188 VISN_c 0.881 0.824 0.942 VISN_d 1.129 1.075 1.185 VISN_e 1.391 1.311 1.475 VISN_f 1.380 1.319 1.445 VISN_g 1.232 1.177 1.290 VISN_h 0.890 0.850 0.931 VISN_i 1.182 1.129 1.238 VISN_j 1.164 1.106 1.225 VISN_k 1.409 1.342 1.479 VISN_l 1.048 0.993 1.105 VISN_m 1.148 1.088 1.211 VISN_n 1.290 1.227 1.357 VISN_o 1.325 1.256 1.398 VISN_p 1.178 1.108 1.252 VISN_q 1.566 1.491 1.645 VISN_r 0.816 0.767 0.867 VISN_s 0.945 0.896 0.997 VISN_t 1.137 1.082 1.195 Indicators of Missing covariates imputed HbA1c missing 1.301 1.260 1.343 258 1 Blood pressure missing 0.924 0.860 0.992 5 1 BMI missing 1.184 1.110 1.263 26 1 LDL missing 1.202 1.170 1.235 174 1 Creatinine missing 1.021 0.991 1.052 2 1 Urine protein testing missing 0.991 0.970 1.012 1 1 Race missing 1.101 1.063 1.140 29 1 Full model with all covariates₸ 57,038 94 * Time to treatment intensification represents the time on metformin monotherapy. Because patients were free of all hypoglycemic medications for 180 days prior to starting metformin, time on metformin monotherapy approximates the length of time patients have had diabetes requiring treatment. † -2*(log(likelihood of the model without the characteristic) - log(likelihood of the model with the characteristic)). The full model is compared to the model with only an intercept, no covariates, with -2*log(likelihood) = 429,640. ‡ Degrees of freedom (DF) used to adjust for the characteristic.

Downloaded From: https://jamanetwork.com/ on 10/04/2021 eTable 4: Logistic regression model for the probability of censoring throughout follow-up 95% Confidence Characteristic Odds Ratio Deviance† DF ‡ Intervals Comorbidities Malignancy 1.010 0.985 1.036 1 1 Liver/ respiratory failure 1.087 1.055 1.121 29 1 Congestive heart failure 1.132 1.099 1.166 65 1 Cardiovascular disease 1.139 1.117 1.162 168 1 Serious mental illness 1.031 1.012 1.049 11 1 Cardiac valve disease 1.103 1.054 1.154 18 1 Arrhythmia 0.993 0.967 1.019 0 1 Smoking 1.028 1.008 1.047 8 1 Chronic Obstructive Pulmonary Disease/ 1.013 0.992 1.033 1 1 Asthma HIV 0.842 0.759 0.934 11 1 Parkinson’s 0.827 0.767 0.892 25 1 Indicators of health care utilization Hospitalized in last year 1.023 0.957 1.093 976 4 Recentness of hospitalization, months 1.104 1.071 1.138 Recentness of hospitalization, months 2 0.984 0.977 0.991 Recentness of hospitalization, months3 1.001 1.001 1.001 Nursing Home encounter in last year 1.342 1.080 1.669 6 1 Outpatient Visits in past year 1.002 1.000 1.004 46 3 Outpatient Visits in past year 2 1.000 1.000 1.000 Outpatient Visits in past year 3 1.000 1.000 1.000 Demographics Race Black 1.021 0.997 1.045 7 2 Race Other 1.046 1.005 1.088 Female gender 1.217 1.174 1.262 109 1 Age 1.003 1.002 1.005 68 3 Age2 1.000 1.000 1.000 Age3 1.000 1.000 1.000 Fiscal Year 2002_2003 0.574 0.547 0.602 18322 8 Fiscal Year 2004 0.647 0.623 0.671 Fiscal Year 2005 0.776 0.751 0.801 Fiscal Year 2006 0.834 0.811 0.858 Fiscal Year 2008 1.251 1.218 1.286 Fiscal Year 2009 1.916 1.862 1.972 Fiscal Year 2010 3.875 3.751 4.003 Fiscal Year 2011 26.377 24.744 28.118 Time from intensification*, months 1.030 1.029 1.032 7303 3 Time from intensification, months2 1.000 1.000 1.000 Time from intensification, months3 1.000 1.000 1.000 Clinical and laboratory HbA1c 1.279 1.271 1.288 7036 3 HbA1c2 1.002 0.999 1.005 HbA1c3 0.998 0.997 0.998 Systolic Blood pressure 1.003 1.002 1.003 116 3 Systolic Blood pressure 2 1.000 1.000 1.000 Systolic Blood pressure 3 1.000 1.000 1.000 Diastolic Blood pressure 1.001 1.000 1.003 6 3 Diastolic Blood pressure2 1.000 1.000 1.000 Diastolic Blood pressure3 1.000 1.000 1.000 Body Mass Index 1.000 0.998 1.002 6 3 Body Mass Index 2 1.000 1.000 1.000

Downloaded From: https://jamanetwork.com/ on 10/04/2021 95% Confidence Characteristic Odds Ratio Deviance† DF ‡ Intervals Body Mass Index 3 1.000 1.000 1.000 Low Density Lipoprotein 1.000 1.000 1.001 116 3 Low Density Lipoprotein2 1.000 1.000 1.000 Low Density Lipoprotein3 1.000 1.000 1.000 Creatinine 2.121 2.041 2.203 1912 3 Creatinine2 0.942 0.911 0.974 Creatinine3 0.997 0.993 1.001 Urine Protein Trace or 1+ 1.021 1.003 1.040 35 2 Proteinuria present at 2+,3+,4+ 1.114 1.075 1.156 Medications ACE Inhibitors or ARBs 0.776 0.763 0.790 805 1 Anti hypertensive medications 0.748 0.734 0.762 899 1 Statin and non-statin lipid lowering agents 0.606 0.595 0.617 2636 1 Anti-arrhythmics, digoxin and inotropes 0.992 0.941 1.046 0 1 Anticoagulant 0.949 0.925 0.973 17 1 Nitrates 1.091 1.060 1.123 34 1 Aspirin 0.904 0.887 0.922 108 1 Loop Diuretics 0.947 0.923 0.971 18 1 Antipsychotics 0.891 0.867 0.916 69 1 Location of care VISN_a 0.888 0.849 0.930 162 20 VISN_b 0.906 0.857 0.958 VISN_c 0.933 0.877 0.993 VISN_d 0.945 0.906 0.986 VISN_e 0.985 0.934 1.038 VISN_f 0.990 0.951 1.032 VISN_g 1.013 0.973 1.055 VISN_h 1.041 1.000 1.083 VISN_i 0.961 0.922 1.001 VISN_j 0.948 0.905 0.992 VISN_k 0.996 0.954 1.041 VISN_l 0.956 0.911 1.002 VISN_m 0.924 0.880 0.970 VISN_n 1.031 0.986 1.078 VISN_o 1.027 0.980 1.077 VISN_p 0.892 0.845 0.941 VISN_q 0.995 0.953 1.039 VISN_r 1.096 1.038 1.158 VISN_s 1.103 1.051 1.159 VISN_t 0.954 0.914 0.997 Indicators of Missing covariates imputed HbA1c missing 1.042 0.994 1.092 3 1 Blood pressure missing 0.909 0.819 1.009 3 1 BMI missing 1.304 1.192 1.425 32 1 LDL missing 1.110 1.072 1.150 33 1 Creatinine missing 1.145 1.109 1.181 68 1 Urine protein testing missing 0.962 0.944 0.979 18 1 Race missing 1.043 1.012 1.074 8 1 Full model with all covariates₸ 37,142 92 * Time from treatment intensification represents the time from the date of intensification until the patient reaches a censoring event or study outcome. This is modeled in the probability of censoring model, because the number of months in the study can lead to informative censoring. † -2*(log(likelihood of the model without the characteristic) - log(likelihood of the model with the characteristic)). The full model is compared to the model with only an intercept, no covariates, with -2*log(likelihood) = 550,274. ‡ Degrees of freedom (DF) used to adjust for the characteristic.

Downloaded From: https://jamanetwork.com/ on 10/04/2021 eTable 5: Characteristics of un-weighted propensity matched cohort remaining at risk 12 months after treatment intensification

Characteristics Remaining in cohort at 1 year Metformin+ Metformin+ Standardized Sulfonylurea Insulin differences* N=6742 N=1362 Age, median (IQR) 61.8 (56.2, 68.8) 61.7 (56.6, 67.6) -0.02 Male N (%) 6407 (95) 1295 (95) 0.00 Race, N (%) White 4927 (73) 995 (73) 0.00 Black 1002 (15) 194 (14) -0.02 Hispanic/ Other 252 (4) 50 (4) 0.00 Missing 561 (8) 123 (9) 0.03 Time to intensification†, months median (IQR) 15 (6, 31) 15 (5, 30) -0.01 HbA1c, % median (IQR) 6.9 (6.4, 7.7) 7.0 (6.3, 8.0) 0.14 Missing measurement, N (%) 322 (5) 88 (6) 0.08 Low Density Lipoprotein mg/dL, 0.01 median (IQR) 83 (67, 103) 83 (65, 103) Missing measurement, N (%) 583 (9) 131 (10) 0.04 Creatinine mg/dL, median (IQR) 1.0 (0.9, 1.1) 1.0 (0.9, 1.1) 0.03 Glomerular filtration rate ml/min, median (IQR) 82 (70.6, 96.5) 81 (70.1, 94.6) -0.02 Missing measurement, N (%) 513 (8) 116 (9) 0.04 Proteinuria, N(%) negative 3239 (48) 643 (47) trace through 4+ 1224 (27) 282 (21) 0.07 Missing measurement, N (%) 2279 (34) 437 (32)) -0.04 Systolic Blood pressure mm/Hg, 0.05 median (IQR) 130 (120, 139) 130 (120, 140) Diastolic Blood pressure mm/Hg, 0.01 median (IQR) 75 (68, 82) 75 (68, 82) Missing measurement, N (%) 79 (1) 19 (1) 0.03 Body Mass Index (kilograms/meter2), median (IQR) 32.7 (29.0, 37.4) 33.0 (29.0, 37.5) 0.02 Missing measurement, N (%) 110 (2) 28 (2) 0.04 Baseline Co-morbidities N(%)‡ Malignancy 748 (11) 138 (10) -0.03 Liver/ respiratory failure 469 (7) 92 (7) -0.01 HIV 31 (0.5) 9 (0.7) 0.04 Congestive heart failure 641 (10) 140 (10) 0.03 Cardiovascular disease 2546 (38) 534 (39) 0.03 Serious mental illness 2483 (37) 503 (37) 0.00 Smoking 1708 (25) 339 (25) -0.01 Chronic Obstructive Pulmonary Disease/ 1476 (22) 316 (23) 0.03 Asthma Cardiac valve disease 191 (3) 30 (2) -0.04 Arrhythmia 858 (13) 176 (13) 0.01 Parkinson’s 73 (1) 13 (0.9) -0.02 Year N (%) -0.02 2002-03 260 (4) 63 (5) 2004 479 (7) 92 (7) 2005 684 (10) 129 (9) 2006 1127 (17) 237 (17) 2007 1169 (17) 245 (18) 11

Downloaded From: https://jamanetwork.com/ on 10/04/2021 2008 1385 (21) 279 (20) 2009 1177 (17) 230 (17) 2010 461 (7) 87 (6) 2011 -- -- Use of Medications N (%) ACE Inhibitors or ARBs 4894 (73) 989 (73) 0.00 Anti hypertensive medications 5067 (75) 1024 (75) 0.00 Statin and non-statin lipid lowering 5387 (80) 1100 (81) 0.02 agents Anti-arrhythmics, digoxin and other 161 (2) 1362 (2) -0.03 inotropes Anticoagulants, platelet inhibitors 968 (14) 196 (14) 0.00 Nitrates 654 (10) 137 (10) 0.01 Aspirin 1770 (26) 378 (28) 0.04 Loop Diuretics 1086 (16) 228 (17) 0.02 Antipsychotics 783 (12) 158 (12) 0.00 Indicators of health care utilization N (%) Hospitalized in last year 1080 (16) 239 (18) 0.05 Hospitalized in the 90 days prior to 360 (5) 82 (6) 0.03 intensification Nursing Home encounter in last year 8 (0.1) 1 (0.07) -0.02 Outpatient Visits in past year 6 (3, 11) 7 (3,11) 0.02 Medicare use in last year 2590 (38) 538 (40) 0.02 Medicaid use in last year 360 (5) 87 (6) 0.06 *Standardized differences are the absolute difference in means or percent divided by an evenly weighted pooled standard deviation, or the difference between groups in number of standard deviations. All P values in the patients remaining in the cohort at 1 year did not demonstrate statistically significant differences except proteinuria, HbA1c and having an HbA1c measurement available (P<0.01)

Downloaded From: https://jamanetwork.com/ on 10/04/2021 eTable 6: Risk of primary composite outcome (cardiovascular disease or all cause mortality) in the presence of an unobserved confounder with a relative hazard of 1.25 for the outcome, and various prevalence levels of the confounder by exposure group. The bolded numbers correspond to the hypothetical confounder's differential prevalences between exposure groups that would explain enough of the observed association to yield a statistically non-significant result.

Prevalence of unmeasured confounder in metformin+ sulfonylurea users 0.0 0.1 0.2 0.3 0.4 0.5 0.0 1.30 1.33 1.36 1.39 1.43 1.46 (1.07, 1.58) (1.09, 1.62) (1.12, 1.66) (1.15, 1.70) (1.17, 1.74) (1.20, 1.78) 0.1 1.27 1.30 1.33 1.36 1.39 1.42 (1.04, 1.54) (1.07, 1.58) (1.09, 1.62) (1.12, 1.66) (1.14, 1.70) (1.17, 1.73) 0.2 1.24 1.27 1.30 1.33 1.36 1.39 (1.01, 1.50) (1.04, 1.54) (1.07, 1.58) (1.09, 1.62) (1.12, 1.66) (1.14, 1.69) 0.3 1.21 1.24 1.27 1.30 1.33 1.36 (0.99, 1.47) (1.02, 1.51) (1.04, 1.54) (1.07, 1.58) (1.09, 1.62) (1.12, 1.65) 0.4 1.18 1.21 1.24 1.27 1.30 1.33 (0.97, 1.44) (0.99, 1.47) (1.02, 1.51) (1.04, 1.54) (1.07, 1.58) (1.09, 1.62) 0.5 1.15 1.18 1.21 1.24 1.27 1.30 (0.95, 1.40) (0.97, 1.44) (0.99, 1.47) (1.02, 1.51) (1.04, 1.54) (1.07, 1.58) 0.6 1.13 1.16 1.18 1.21 1.24 1.27 (0.93, 1.37) (0.95, 1.41) (0.97, 1.44) (1.00, 1.48) (1.02, 1.51) (1.04, 1.55)

Metformin+ users Insulin 0.7 1.10 1.13 1.16 1.19 1.21 1.24 (0.91, 1.34) (0.93, 1.38) (0.95, 1.41) (0.97, 1.45) (1.00, 1.48) (1.02, 1.51) 0.8 1.08 1.11 1.13 1.16 1.19 1.22

Prevalence of unmeasured confounder in confounder of unmeasured Prevalence (0.89, 1.32) (0.91, 1.35) (0.93, 1.38) (0.95, 1.42) (0.98, 1.45) (1.00, 1.48) 0.9 1.06 1.09 1.11 1.14 1.16 1.19 (0.87, 1.29) (0.89, 1.32) (0.91, 1.35) (0.94, 1.39) (0.96, 1.42) (0.98, 1.45) 1.0 1.04 1.06 1.09 1.12 1.14 1.17 (0.85, 1.26) (0.87, 1.30) (0.90, 1.33) (0.92, 1.36) (0.94, 1.39) (0.96, 1.42)

Downloaded From: https://jamanetwork.com/ on 10/04/2021 e Table 7: Risk of all-cause mortality in the presence of an unobserved confounder with a relative hazard of 1.25 for the outcome, and various prevalence levels of the confounder by exposure group. The bolded numbers correspond to the hypothetical confounder's differential prevalences between exposure groups that would explain enough of the observed association to yield a statistically non- significant result.

Prevalence of unmeasured confounder in metformin+ sulfonylurea users 0.0 0.1 0.2 0.3 0.4 0.5 0.0 1.44 1.47 1.51 1.54 1.58 1.61 (1.15, 1.79) (1.18, 1.83) (1.21, 1.88) (1.24, 1.92) (1.27, 1.97) (1.29, 2.01) 0.1 1.40 1.44 1.47 1.51 1.54 1.58 (1.12, 1.75) (1.15, 1.79) (1.18, 1.83) (1.21, 1.88) (1.23, 1.92) (1.26, 1.96) 0.2 1.37 1.40 1.44 1.47 1.50 1.54 (1.10, 1.70) (1.12, 1.75) (1.15, 1.79) (1.18, 1.83) (1.21, 1.88) (1.23, 1.92) 0.3 1.33 1.37 1.40 1.44 1.47 1.50 (1.07, 1.67) (1.10, 1.71) (1.12, 1.75) (1.15, 1.79) (1.18, 1.83) (1.20, 1.87) 0.4 1.30 1.34 1.37 1.40 1.44 1.47 (1.05, 1.63) (1.07, 1.67) (1.10, 1.71) (1.12, 1.75) (1.15, 1.79) (1.18, 1.83) 0.5 1.28 1.31 1.34 1.37 1.40 1.44 (1.02, 1.59) (1.05, 1.63) (1.07, 1.67) (1.10, 1.71) (1.12, 1.75) (1.15, 1.79) 0.6 1.25 1.28 1.31 1.34 1.37 1.40 (1.00, 1.56) (1.03, 1.60) (1.05, 1.63) (1.08, 1.67) (1.10, 1.71) (1.13, 1.75)

Metformin+ users Insulin 0.7 1.22 1.25 1.28 1.31 1.34 1.37 (0.98, 1.52) (1.00, 1.56) (1.03, 1.60) (1.05, 1.64) (1.08, 1.68) (1.10, 1.71) 0.8 1.20 1.23 1.26 1.29 1.32 1.35

Prevalence of unmeasured confounder in confounder of unmeasured Prevalence (0.96, 1.49) (0.98, 1.53) (1.01, 1.57) (1.03, 1.60) (1.05, 1.64) (1.08, 1.68) 0.9 1.17 1.20 1.23 1.26 1.29 1.32 (0.94, 1.46) (0.96, 1.50) (0.99, 1.53) (1.01, 1.57) (1.03, 1.61) (1.06, 1.64) 1.0 1.15 1.18 1.21 1.23 1.26 1.29 (0.92, 1.43) (0.94, 1.47) (0.97, 1.50) (0.99, 1.54) (1.01, 1.58) (1.04, 1.61)

Downloaded From: https://jamanetwork.com/ on 10/04/2021 eTable 8: Characteristics of unmatched patients Characteristics Unmatched Cohort Metformin+ Metformin+ Standardized Sulfonylurea Insulin differences* N=27810 N=512 Age, median (IQR) 62 (57, 69) 58 (52, 65) -0.39 Male N (%) 26824 (96) 472 (92) -0.21 Race, N (%) White 20846 (75) 297 (58) -0.38 Black 3133 (11) 171 (33) 0.65 Hispanic/ Other 1320 (5) 13 (3) -0.11 Missing 2511 (9) 31 (6) -0.11 Time to intensification†, months median (IQR) 19 (8, 35) 13 (6, 29) -0.20 HbA1c, % median (IQR) 7.5 (6.9, 8.2) 11.8 (8.9, 1.88 13.7) Missing measurement, N (%) 3155 (11) 103 (20) 0.17 Low Density Lipoprotein mg/dL, median (IQR) 88 (71, 109) 88 (61, 112) -0.10 Missing measurement, N (%) 5084 (18) 157 (31) 0.19 Creatinine mg/dL, median (IQR) 1.0 (0.9, 1.1) 1.0 (0.9, 1.3) 0.22 Glomerular filtration rate ml/min, median (IQR) 81 (70, 94) 82 (66, 104) 0.13 Missing measurement, N (%) 3606 (13) 87 (17) 0.11 Proteinuria, N(%) negative 14865 (53) 275 (54) trace through 4+ 4934 (18) 112 (22) 0.11 Missing measurement, N (%) 8011 (29) 125 (24) 0.01 Systolic Blood pressure mm/Hg, median (IQR) 132 (122, 143) 129 (116, -0.25 140) Diastolic Blood pressure mm/Hg, median (IQR) 77 (70, 84) 74 (66, 82) -0.20 Missing measurement, N (%) 901 (3) 187 (5) 0.10 Body Mass Index (kilograms/meter2), median (IQR) 32.5(29.1,36.6) 31.4 (26.5, -0.17 36.5) Missing measurement, N (%) 1137 (4) 45 (9) 0.12 Baseline Co-morbidities N(%)‡ Malignancy 1944 (7) 50 (10) 0.10 Liver/ respiratory failure 488 (2) 96 (19) 0.97 HIV 56 (0.2) 10 (2) 0.30 Congestive heart failure 1169 (4) 97 (19) 0.63 Cardiovascular disease 7724 (28) 231 (45) 0.38 Serious mental illness 7284 (26) 260 (51) 0.55 Smoking 5138 (18) 157 (31) 0.31 Chronic Obstructive Pulmonary Disease/ Asthma 3736 (13) 152 (30) 0.45 Cardiac valve disease 470 (2) 22 (4) 0.19 Arrhythmia 2175 (8) 83 (16) 0.30 Parkinson’s 85 (0.3) 15 (3) 0.36 Year N (%) 0.46 2002-03 880 (3) 11 (2) 2004 2210 (8) 20 (4) 2005 3527 (13) 32 (6) 2006 4889 (18) 71 (14) 2007 5764 (21) 50 (10) 2008 4335 (16) 118 (23) 2009 3247 (12) 106 (21) 2010 2228 (8) 78 (15) 2011 730 (3) 26 (5) Use of Medications N (%) ACE Inhibitors or ARBs 20109 (72) 345 (67) -0.11 Anti hypertensive medications 20051 (72) 385 (75) 0.07

Downloaded From: https://jamanetwork.com/ on 10/04/2021 Statin and non-statin lipid lowering agents 22956 (83) 352 (69) -0.35 Anti-arrhythmics, digoxin and other inotropes 295 (1) 21 (4) 0.25 Anticoagulants, platelet inhibitors 2754 (10) 119 (23) 0.41 Nitrates 2349 (8) 79 (15) 0.24 Aspirin 6030 (22) 206 (40) 0.43 Loop Diuretics 2182 (8) 150 (29) 0.69 Antipsychotics 1818 (7) 126 (25) 0.65 Indicators of health care utilization N (%) Hospitalized in last year 2418 (9) 392 (77) 1.89 Hospitalized in the 90 days prior to intensification 554 (2) 345 (67) 2.62 Nursing Home encounter in last year 14 (0.05) 7 (1) 0.39 Outpatient Visits in past year 6 (4, 9) 10 (5, 17) 0.73 Medicare use in last year 7158 (26) 223 (44) 0.39 Medicaid use in last year 456 (2) 80 (16) 0.83 *Standardized differences are the absolute difference in means or percent divided by an evenly weighted pooled standard deviation, or the difference between groups in number of standard deviations. All P values in the unmatched patients demonstrated statistically significant differences at p<0.001.

Downloaded From: https://jamanetwork.com/ on 10/04/2021 eFigure 1: Distribution of propensity scores by drug

Downloaded From: https://jamanetwork.com/ on 10/04/2021 eFigure 2: Adjusted hazard and 95% confidence intervals for risk of composite and secondary outcomes using sensitivity analysis (weights un-truncated or truncated at 10 or 100). All analyses require persistence on metformin. *All weights are stabilized

Downloaded From: https://jamanetwork.com/ on 10/04/2021 eFigure 3: Adjusted hazard ratios for cardiovascular disease (CVD) or death composite outcome and secondary outcome (death alone) by cardiovascular disease (CVD) history and age subgroups.

Downloaded From: https://jamanetwork.com/ on 10/04/2021 References

1. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. Sep 2000;11(5):550‐560. 2. Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV‐positive men. Epidemiology. Sep 2000;11(5):561‐570.

Downloaded From: https://jamanetwork.com/ on 10/04/2021