Last Observation Carried Forward Imputation Research Articles

Estimation of treatment effects in short-term depression studies. An evaluation based on the ICH E9(R1) estimands framework.

Estimands aim to incorporate intercurrent events in design, data collection and estimation of treatment effects in clinical trials. Our aim was to understand what estimands may correspond to efficacy analyses commonly employed in clinical trials conducted before publication of ICH E9(R1). We re‐analysed six clinical trials evaluating a new anti‐depression treatment. We selected the following analysis methods—ANCOVA on complete cases, following last observation carried forward (LOCF) imputation and following multiple imputation; mixed‐models for repeated measurements without imputation (MMRM), MMRM following LOCF imputation and following jump‐to‐reference imputation; and pattern‐mixture mixed models. We included a principal stratum analysis based on the predicted subset of the study population who would not discontinue due to adverse events or lack of efficacy. We translated each analysis into the implicitly targeted estimand, and formulated corresponding clinical questions. We could map six estimands to analysis methods. The same analysis method could be mapped to more than one estimand. The major difference between estimands was the strategy for intercurrent events, with other attributes mostly the same across mapped estimands. The quantitative differences in MADRS10 population‐level summaries between the estimands were 4–8 points. Not all six estimands had a clinically meaningful interpretation. Only a few analyses would target the same estimand, hence only few could be used as sensitivity analyses. The fact that an analysis could estimate different estimands emphasises the importance of prospectively defining the estimands targeting the primary objective of a trial. The fact that an estimand can be targeted by different analyses emphasises the importance of prespecifying precisely the estimator for the targeted estimand.

Considerations for choosing an imputation method for addressing sparse measurement issues dictated by the study design - An illustration from per-protocol analysis in pragmatic trials

Last Observation Carried Forward (LOCF) is an ad-hoc method, with known limitations. In recent years, several methods publications have used LOCF in estimating the per-protocol effect via inverse probability of adherence weighted (IPAW) model, when a time-varying factor is partially measured by the study design. We compare the statistical performances of LOCF and multiple imputation approaches for estimating the per-protocol effects via the IPAW model in the presence of incomplete treatment adherence. We used a validated pragmatic trial data generating simulation algorithm to generate datasets under 7 different simulation scenarios, where a post-randomization prognostic factor was measured after regular intervals. Unmeasured values of a partially observed factor were imputed using LOCF and multiple imputation approaches, and IPAW model was fitted on the imputed data to obtain the estimates, and statistical performances were assessed. When confounding exists, for higher variability of the time-varying factor, multiple imputation approach shows desirable statistical properties under MCAR assumption; otherwise, LOCF approach can be adequate. Both imputation methods performed well in terms of statistical properties, when there is no confounding or when all necessary confounders are adjusted. A case study from Coronary Primary Prevention Trial data was presented, which included some participants with incomplete treatment adherence.

A Phase III Prospective Active and Placebo-Controlled Randomized Trial of Vilazodone in the Treatment of Major Depressive Disorder.

BackgroundDepression is a leading cause of psychiatric morbidity in the modern world, and the introduction of selective serotonin reuptake inhibitors (SSRIs) is a revolution in the treatment of depression. Vilazodone, a novel SSRI and 5-HT1A partial agonist, received FDA approval in 2011 to treat the major depressive disorder (MDD) in adults. This study conducted in India aimed to evaluate the efficacy and safety of vilazodone when compared to escitalopram or placebo in patients with MDD.MethodsThis was a prospective, multicentre, randomized, comparative study of 375 participants over eight weeks of treatment with either vilazodone (10-40mg/day) or escitalopram (10-40 mg/day) or placebo in adult patients with MDD. Primary efficacy was assessed using the Hamilton Rating Scale for Depression (HAM-D-17); secondary efficacy was assessed using the Montgomery-Asberg Depression Rating Scale (MADRS) score and Hamilton Anxiety Scale (HAM-A) score. Safety parameters included adverse events (AEs), clinical laboratory results, vital signs, electrocardiogram ( ECG), and Columbia-Suicide Severity Rating Scale (C-SSRS).ResultsMean change in the HAM-D-17 total score from baseline to week 8 for vilazodone, escitalopram, and placebo-treated patients in intent-to-treat (ITT) population was: -18.9 (± 7.49), -17.8 (± 6.06), and -7.4 (± 6.32); in ITT population (with Last Observation Carried Forward( LOCF) imputation) was: -17.9 (± 7.71), -17.4 (± 6.19), and -6.4 (± 6.84), and in per-protocol (PP) population was: -19.1 (± 7.20), -17.8 (± 6.08), and -7.7 (± 6.29), respectively. The upper limit of 95% CI (0.56 (ITT); 0.90 (ITT with LOCF Imputation); 0.23 (PP)) of difference in HAM-D-17 between vilazodone 40mg and escitalopram 40mg, which is lower than the defined non-inferiority margin (3.56), proving non-inferiority. The difference between vilazodone 40mg, escitalopram 40mg, and the placebo was statistically significant (p<0.0001). No deaths or serious adverse events were reported in this study.ConclusionVilazodone demonstrated comparable efficacy to escitalopram and superior efficacy over the placebo in the treatment of MDD.

Double Exponential Smoothing Berimputasi LOCF Dan Linear Interpolation Dalam Akurasi Peramalan Harga Harian Emas

Gold is another kind of investment that often experiences price change, mostly every day. Because of its price fluctuation, forecasting is needed to help the investor in investment decision making. But during this Coronavirus Disease 2019 (Covid-19), the gold price is fluctuating extremely than the past 4 years so a better forecasting method approached and analysis technique is needed due to this case. Double Exponential Smoothing method is chosen to forecast this daily gold price. On the other hand, there are so many missing values spreading around the main dataset so the imputation method is needed too, Last Observation Carried Forward (LOCF) and linear interpolation are chosen for imputing the missing values. In this research, the main dataset was split into 3 (three) datasets, which are Precovid-19 (before Covid-19, used only for visualizing the actual fluctuation condition during this pandemic), Incovid-19 (during Covid-19 based on the date where the first Covid-19 case occurred in Indonesia), and Combination (a binding dataset of Pracovid-19 and Incovid-19). Although Incovid-19’s MAPE value is higher than Pracovid-19 and Combination’s MAPE values, in evaluation session showed that Incovid-19’s MAPE of forecast results has the lowest value rather than Combination’s MAPE of forecast results, so the conclusion of this research is Incovid-19 dataset with LOCF imputation is the most adaptive with the actual condition and it is used to forecast the daily gold price until the last period of the main dataset then.

MON-LB57 Impact of Imputation Method and Response Cutoffs on Results From the Phase 3 OPTIMAL Study of Oral Octreotide Capsules in Adult Patients With Acromegaly

Objective: The phase 3 CHIASMA OPTIMAL study assessed efficacy and safety of oral octreotide capsules (OOC) in patients with acromegaly controlled on injectable somatostatin receptor ligands (SRLs). Sensitivity analyses were conducted for efficacy endpoints using two methods of imputation (i.e., the process of replacing clinical data with substitution values) to address missing data points due to some subjects reverting back to their prior injectable SRL treatment. Methods: Patients were ≥18 years of age and had evidence of active acromegaly with an average IGF-I ≤ 1.0 x ULN (utilizing the IDS iSYS assay calibrated to WHO recombinant reference standard 02/254). At baseline, patients were randomized to receive OOC or placebo for 36 weeks. The primary endpoint was proportion of patients maintaining biochemical response, defined as IGF-I ≤1.0 x ULN (2-value average at weeks 34 and 36) (Samson et al. ENDO 2020). Per study protocol, patient study discontinuations were considered non-responders regardless of clinical response at the time of discontinuation (non-response imputation). Additional exploratory analyses were performed utilizing the last observation carried forward (LOCF) analysis, as well as a completers analysis of response among the subgroup that completed the entire 36 weeks on study drug. The response rates reported for the primary end point are slightly adjusted for stratification differences as prespecified in the statistical analysis plan. Results: Twenty-eight patients received OOC and 12 failed to maintain biochemical response based on the primary endpoint. Seven of these 12 patients discontinued treatment early - 5 due to treatment failure and 2 due to AEs. The remaining 5 patients completed the 36-week protocol on study drug. Of these 5 patients, 4 had IGF-I values between >1.0 and ≤1.3 x ULN and 1 completed the study with an IGF-I of 1.7 x ULN with no clinical symptoms. 58.2% of patients in the OOC group met the primary endpoint of maintenance of biochemical response at the end of study using the non-response imputation. Using LOCF imputation, 64.3% (18/28) of patients met this endpoint. Of those completing the study (N=21), 76.2% maintained response. Conclusion: CHIASMA OPTIMAL primary endpoint was assessed using the non-response imputation for patients who discontinued treatment early, with a 58.2% response rate. However, when assessing the response rate based on LOCF imputation, or in study completers, similar to other phase 3 studies for acromegaly, the rate was imputed at 64.3% and 76.2%, respectively.

Missing outcome data management in acute stroke trials testing iv thrombolytics. Is there risk of bias?

Missing outcome data may undermine interpretation of randomised clinical trials by weakening power and limiting apparent effect size. We assessed bias and inefficiency of two imputation methods commonly used in stroke trials evaluating the efficacy of iv thrombolysis. We searched the virtual international stroke trials archive (VISTA)-acute for ischaemic stroke patients with 90-day modified Rankin scale as an outcome, and known thrombolysis status. We excluded any with missing 30-day modified Rankin scale. We planned two analyses; first, we calculated odds ratios for outcome in thrombolysed versus not thrombolysed from imputed-only data, (a) among patients with missing modified Rankin scale 90 and (b) among matched patients with intact data (using propensity score methods and relevant covariates). Imputation approaches were last observation carried forward (LOCF) or multiple imputation. Outcome comparisons used dichotomisation and shift analysis. Thereafter, we calculated whole-population odds ratios using LOCF and multiple imputation (also through dichotomisation and shift analysis); first with the original 1.5% missing outcome data, and then artificially increasing the burden (5%; 10%; 20%; 30%). We considered 9657 patients from eight of the studies included in VISTA, 3034 (31%) thrombolysed. Missing data replacement by LOCF with analysis by dichotomisation gave the highest estimate of thrombolysis influence. Imputing while increasing the burden of missing data progressively raised the odds ratios estimates, though thresholds for overestimation were 10% for LOCF; 20% for multiple imputation.Discussion: Replacing missing outcome data tended to overestimate differences of thrombolysed versus non-thrombolysed patients, but had minimal impact below a 10% burden of missing data.Conclusion: In the specific context of acute stroke trials testing iv thrombolytics, replacing missing data by carrying forward the last observation tended to overestimate treatment odds ratios more than multiple imputation.

Methods to Handle Incomplete Data

Context: The major question for data analysis is determining the appropriate analytic approach in the presence of incomplete observations. The most common solution to handle missing data in a data set is imputation, where missing values are estimated and filled in. An important problem of imputation is to maintain the statistical significance of the data set. Aim: To compare different imputation techniques − complete case analysis, last observation carried forward (LOCF), mean imputation, hot deck Imputation, regression imputation, and multiple imputation (MI). Settings and Design: The data for the study were collected from a prospective study to find out the predictors of early response to treatment in drug naive schizophrenia patients from a tertiary care centre, India. Methods and Material: The present study tries to compare four imputation methods: complete case analysis, LOCF, mean imputation, hot deck Imputation, regression imputation and MI, in filling up the missing values of the outcome variable. Statistical analysis used: Paired t test was used to compare the imputation methods. Results: At the fourth week, the positive and negative syndrome scale scores were missing for about a minority of the subjects (41%). Mean imputation differed significantly from LOCF (P = 0.001), regression imputation (P = 0.010) and MI (P = 0.002). LOCF differed significantly from all these methods − regression imputation (P = 0.001), hot deck imputation (P = 0.011) and MI (P = 0.001). Conclusions: LOCF and mean imputation methods are different from other imputation methods, and there is no difference between hot deck imputation, MI, and regression imputation.

116. Comparison of imputation methods for evaluating long-term clinical outcomes following lumbar total disc replacement

BACKGROUND CONTEXT The problem of missing data is common in clinical trials, and it can have a significant impact on the conclusions that can be drawn from the data. Long-term results are particularly susceptible as the lost to follow-up (LTFU) rates increase over the course of a trial. Imputation methods are often used to account for missing data points, but some may either under or overestimate the true treatment effect. PURPOSE In this post-hoc analysis of a randomized clinical trial, we evaluated the clinical success of three motion preserving artificial discs up to 7 years following lumbar total disc replacement (TDR) using different imputation methods. STUDY DESIGN/SETTING Randomized, controlled, multicenter clinical IDE trial. PATIENT SAMPLE A total of 324 subjects with single level degenerative disc disease and discogenic pain. OUTCOME MEASURES Radigraphic range of motion on flexion-extension, Oswestry Disability Index (ODI), neurologic exam, adverse events, subsequent surgery. METHODS Subjects were randomized to receive either a semi-constrained mobile core disc (n=218) or a control device (n=106) which was either a constrained (n=65) or unconstrained (n=41) core disc. Several imputation methods were used to calculate clinical success, including: missing values=failure, missing values=success, last observation carried forward (LOCF), complete case, best case and worst case scenarios. Clinical success scores were compared between the treatment (semi-constrained) and control (constrained or unconstrained) groups.Clinical outcomes were collected from baseline through to 7 years following single level (L4-L5 or L5-S1) TDR. Clinical success was defined as a ≥15 point improvement in ODI from baseline, maintenance or improvement in ROM and neurological evaluation (motor and sensory), the absence of index-level subsequent surgical interventions (SSI) and serious device-related adverse events (SDAE). RESULTS By imputing missing values as failures, individual success scores decreased at 5 and 7 years compared to 2 years postop for both groups. Only ROM success at 2 years was significantly greater in the treatment compared to control group (58.7% vs 42.5%, p=0.0065). The overall success score, which is a composite of the individual success scores, also decreased over time, but it was significantly greater in the treatment group at 2 years (42.2% vs 28.3%, p=0.0020). Using complete case data, clinical success at 5 and 7 years was maintained close to 2-year values for overall and individual success scores, except for SDAE success which decreased at 7 years from 2 years for both the treatment (86.1% to 67.1%) and control groups (78.7% to 63.2%). Similarly, LOCF clinical outcomes were the same between 5 and 7 years postop for both groups. By imputing missing values as successes, the results were comparable to scores yielded by complete case and LOCF imputation methods. CONCLUSIONS The missing=failure imputation method is most susceptible to LFTU and may underestimate the treatment effect if there is a large LTFU rate, which is inherent in most long-term clinical studies. Complete case and LOCF imputation methods may be more accurate estimates of long-term clinical outcomes for clinical studies with a large patient enrollment. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

Allowing for uncertainty due to missing and LOCF imputed outcomes in meta-analysis.

The use of the last observation carried forward (LOCF) method for imputing missing outcome data in randomized clinical trials has been much criticized and its shortcomings are well understood. However, only recently have published studies widely started using more appropriate imputation methods. Consequently, meta‐analyses often include several studies reporting their results according to LOCF. The results from such meta‐analyses are potentially biased and overprecise. We develop methods for estimating summary treatment effects for continuous outcomes in the presence of both missing and LOCF‐imputed outcome data. Our target is the treatment effect if complete follow‐up was obtained even if some participants drop out from the protocol treatment. We extend a previously developed meta‐analysis model, which accounts for the uncertainty due to missing outcome data via an informative missingness parameter. The extended model includes an extra parameter that reflects the level of prior confidence in the appropriateness of the LOCF imputation scheme. Neither parameter can be informed by the data and we resort to expert opinion and sensitivity analysis. We illustrate the methodology using two meta‐analyses of pharmacological interventions for depression.

Accounting for uncertainty due to 'last observation carried forward' outcome imputation in a meta-analysis model.

Missing outcome data are a problem commonly observed in randomized control trials that occurs as a result of participants leaving the study before its end. Missing such important information can bias the study estimates of the relative treatment effect and consequently affect the meta-analytic results. Therefore, methods on manipulating data sets with missing participants, with regard to incorporating the missing information in the analysis so as to avoid the loss of power and minimize the bias, are of interest. We propose a meta-analytic model that accounts for possible error in the effect sizes estimated in studies with last observation carried forward (LOCF) imputed patients. Assuming a dichotomous outcome, we decompose the probability of a successful unobserved outcome taking into account the sensitivity and specificity of the LOCF imputation process for the missing participants. We fit the proposed model within a Bayesian framework, exploring different prior formulations for sensitivity and specificity. We illustrate our methods by performing a meta-analysis of five studies comparing the efficacy of amisulpride versus conventional drugs (flupenthixol and haloperidol) on patients diagnosed with schizophrenia. Our meta-analytic models yield estimates similar to meta-analysis with LOCF-imputed patients. Allowing for uncertainty in the imputation process, precision is decreased depending on the priors used for sensitivity and specificity. Results on the significance of amisulpride versus conventional drugs differ between the standard LOCF approach and our model depending on prior beliefs on the imputation process. Our method can be regarded as a useful sensitivity analysis that can be used in the presence of concerns about the LOCF process.

Current Practice in Japan for the Prevention and Treatment of Missing Data in Confirmatory Clinical Trials: A Survey of Japanese and Foreign Pharmaceutical Manufacturers.

This study aims to survey the current practice in Japan for the prevention and treatment of missing data in clinical trials since the publication of regulatory guidelines on missing data issues. A web-based questionnaire was conducted among 65 member companies of the Japan Pharmaceutical Manufacturers Association in 2013. Responses were obtained on 187 clinical trials from 55 companies, including 42 based in Japan and 13 based in other countries. Missing data were most frequent in trials involving the central nervous system (65.2% had ≥10% missing data). Overall, last observation carried forward (LOCF) was the most popular method for handling missing data (45.0%), followed by mixed-effect models for repeated measures (15.5%), although this was used as frequently as LOCF imputation in central nervous system trials. Even after the publication of regulatory guidelines discouraging use of LOCF, LOCF imputation remains the most popular method for treating missing data among pharmaceutical manufacturers in Japan.

THU0162 Multiple Approaches for Implementation of Long-Term Efficacy Interpretation of Certolizumab Pegol Data: RAPID1 and RAPID2 CASE Study

BackgroundUse of imputed data vs observed data in long-term studies, as well as patient (pt) population evaluated (those completing randomized controlled trials [RCTs] vs all pts initiating treatment), can have...

General Linear Models in a Missing Outcome Environment of Clinical Trials Incorporating with Splines for Time-Invariant Continuous Adjustment

Missing data is a common occurrence in longitudinal studies of health care research. Although many studies have shown the potential usefulness of current missing analyses, e.g., (1) Complete Case (CC) analysis; (2) imputation methods such as Last Observation Carried Forward (LOCF), multiple imputations, Expectation-Maximization algorithm approach; and (3) methods using all available data such as linear mixed model and generalized estimation equations approach, the CC analysis or LOCF imputation method have been popular due to their simplicity of execution regardless of some critical drawbacks. The proposed approach employs the generalized least squares method using all available data without deletion or imputations for missing outcomes, producing the best linear unbiased estimate. A simulation study was conducted to compare the proposed approach to commonly used missing analyses under each missing data mechanism and showed the validity of the proposed approach, especially with the first order autoregressive correlation structure. B-spline is applied to the proposed model to manage non-linear relationships between outcome and continuous covariate. Application to a cell therapy clinical trial is presented.

Topiramate for neuropathic pain and fibromyalgia in adults.

Topiramate is an antiepileptic drug with multiple possible mechanisms of action. Antiepileptic drugs are widely used to treat chronic neuropathic pain (pain due to nerve damage) and fibromyalgia, and many guidelines recommend them. To assess the analgesic efficacy and associated adverse events of topiramate for chronic neuropathic pain and fibromyalgia in adults (aged 18 years and above). On 8 May 2013, we searched the Cochrane Neuromuscular Disease Group Specialized Register, CENTRAL, MEDLINE, and EMBASE. We reviewed the bibliographies of all randomised trials identified and review articles, and also searched two clinical trial databases, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform, to identify additional published or unpublished data. We included randomised controlled trials (RCTs) with double-blind assessment of participant outcomes following two weeks of treatment or longer (though the emphasis of the review was on studies of eight weeks or longer) that used a placebo or active comparator. We extracted efficacy and adverse event data, and two study authors examined issues of study quality independently. We performed analysis using two tiers of evidence. The first tier used data where studies reported the outcome of at least 50% pain reduction from baseline, lasted at least eight weeks, had a parallel group design, included 200 or more participants in the comparison, and reported an intention-to-treat analysis. First tier studies did not use last-observation-carried-forward (LOCF) or other imputation methods for dropouts. The second tier used data that failed to meet this standard; second tier results were therefore subject to potential bias. We included four studies with 1684 participants. Three parallel-group placebo comparisons were in painful diabetic neuropathy (1643 participants), and one cross-over study with diphenhydramine as an active placebo (41 participants) was in lumbar radiculopathy. Doses of topiramate were titrated up to 200 mg/day or 400 mg/day. All studies had one or more sources of potential major bias, as they either used LOCF imputation or were of small size.No study provided first tier evidence for an efficacy outcome. There was no convincing evidence for efficacy of topiramate at 200 to 400 mg/day over placebo.Eighty-two per cent of participants taking topiramate 200 to 400 mg/day experienced at least one adverse event, as did 71% with placebo, and the number needed to treat for an additional harmful effect (NNTH) was 8.6 (95% confidence interval (CI) 4.9 to 35). There was no difference in serious adverse events recorded (6.6% versus 7.5%). Adverse event withdrawals with 400 mg daily were much more common with topiramate (27%) than with placebo (8%), with an NNTH of 5.4 (95% CI 4.3 to 7.1). Lack of efficacy withdrawal was less frequent with topiramate (12%) than placebo (18%). Weight loss was a common event in most studies. No deaths attributable to treatment were reported. Topiramate is without evidence of efficacy in diabetic neuropathic pain, the only neuropathic condition in which it has been adequately tested. The data we have includes the likelihood of major bias due to LOCF imputation, where adverse event withdrawals are much higher with active treatment than placebo control. Despite the strong potential for bias, no difference in efficacy between topiramate and placebo was apparent.

FRI0219 Disease severity, no steroid use and type ii diabetes predict response to anakinra (kineret®) in patients with rheumatoid arthritis

Background458 patients with rheumatoid arthritis (RA) and unsatisfactory response to DMARDs alone and/or TNFα blocking agents were treated with the IL-1 receptor antagonist anakinra. The initial analysis showed no difference...

Problems in Statistical Analysis of Attrition in Randomized Controlled Clinical Trials of Antidepressant for Geriatric Depression

Attrition from clinical trials is unavoidable in geriatric psychiatry and beyond. It results in incomplete data and consequently imposes three fundamental challenges: greater bias, reduced power, and less generalizability. In an effort to assess the extent of attrition and the relevance of statistical methods applied to analyze incomplete data in geriatric psychiatry, we reviewed 69 published antidepressant randomized clinical trials conducted since 1975. The median attrition rate estimated from these trials was 26.6%; nevertheless, we found that many trials lack data analytic strategies to address the problem of attrition. Most of the applied statistical analyses involved chi-square tests, t-tests, and analysis of variance (ANOVA), each of which assume that data are missing completely at random. Even when imputation for missing data due to attrition was attempted, only the last observation carried forward (LOCF) method was implemented. The LOCF imputation can actually increase bias of the results in the analysis of repeatedly measured outcomes. In addition, despite the longitudinal nature of repeatedly measured outcomes, the statistical methods used are for analysis of cross-sectional data. Thus, the data analytic strategies did not adequately meet the challenges arising from attrition. We encourage the use of mixed-effects models to reduce the impact of attrition on bias, power and generalizability in antidepressant RCTs for geriatric depression. For imputation, we recommend use of multiple imputation methods instead of LOCF.

Cannabinoid-1 Receptor Antagonist, Rimonabant, for Management of Obesity and Related Risks

The prevalence of obesity has risen substantially during the past 25 years in the United States and most developed countries, with a related increase in type 2 diabetes mellitus.1,2 Almost one third of the adult US population is considered to be obese (body mass index [BMI] ≥30), and 1 in 20 is extremely obese (BMI ≥40).3 Nearly 17% of children and adolescents are overweight in the United States.3 Obesity is associated with increased risk for type 2 diabetes mellitus, coronary heart disease (CHD), hypertension, obstructive sleep apnea, and cancer, higher overall mortality rate,4–6 and decreased longevity.7,8 Extreme obesity can truncate life expectancy in young adults by 5 to 20 years.8 Accordingly, the expected benefits of weight reduction for obese individuals are profound. Weight loss of 5% to 10% generally lessens many health risks, including cardiovascular risks, although such improvements are most notably demonstrable in studies specifically conducted in high-risk populations, and the benefits are presumed to be greater when healthier weight is maintained for long periods.9,10 In overweight and obese individuals, weight loss achieved with most interventions over 1 to 2 years generally leads to improvements in blood pressure (BP), glycemic measures, and triglycerides (TGs). Improvements in total cholesterol, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) have been reported in studies using dietary interventions combined with exercise. When weight loss is achieved primarily via pharmacological interventions, these benefits have not occurred quite so consistently.11 Reduced caloric intake and increased physical activity are generally accepted as the foundations of any approach directed at weight reduction, but these lifestyle interventions do not appear to provide long-lasting success for obese individuals wishing to lose weight. About half of the weight lost with the help of lifestyle interventions is regained at 1 year; after …