The DEcIDE Methods Center publishes a monthly literature scan of current articles of interest to the field of comparative effectiveness research.

You can find them all here.

January 2012

CER Scan [Epub ahead of print]

  1. Pharmacoepidemiol Drug Saf. 2011 Dec 8. doi: 10.1002/pds.2256. [Epub ahead of print]
  2. Applying propensity scores estimated in a full cohort to adjust for confounding in subgroup analyses. Rassen JA, Glynn RJ, Rothman KJ, Setoguchi S, Schneeweiss S. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.

    BACKGROUND: A correctly specified propensity score (PS) estimated in a cohort ("cohort PS") should, in expectation, remain valid in a subgroup population.
    OBJECTIVE: We sought to determine whether using a cohort PS can be validly applied to subgroup analyses and, thus, add efficiency to studies with many subgroups or restricted data. METHODS: In each of three cohort studies, we estimated a cohort PS, defined five subgroups, and then estimated subgroup-specific PSs. We compared difference in treatment effect estimates for subgroup analyses adjusted by cohort PSs versus subgroup-specific PSs. Then, over 10 million times, we simulated a population with known characteristics of confounding, subgroup size, treatment interactions, and treatment effect and again assessed difference in point estimates. RESULTS: We observed that point estimates in most subgroups were substantially similar with the two methods of adjustment. In simulations, the effect estimates differed by a median of 3.4% (interquartile (IQ) range 1.3-10.0%). The IQ range exceeded 10% only in cases where the subgroup had < 1000 patients or few outcome events. CONCLUSIONS: Our empirical and simulation results indicated that using a cohort PS in subgroup analyses was a feasible approach, particularly in larger subgroups. Copyright © 2011 John Wiley & Sons, Ltd.
    PMID: 22162077  [PubMed – as supplied by publisher]

  3. Stat Methods Med Res. 2011 Nov 8. [Epub ahead of print]
  4. Extension of the modified Poisson regression model to prospective studies with correlated binary data. Zou GY, Donner A. Department of Epidemiology & Biostatistics, and Robarts Clinical Trials of Robarts Research Institute, Schulich School of Medicine & Dentistry, Canada.

    The Poisson regression model using a sandwich variance estimator has become a viable alternative to the logistic regression model for the analysis of prospective studies with independent binary outcomes. The primary advantage of this approach is that it readily provides covariate-adjusted risk ratios and associated standard errors. In this article, the model is extended to studies with correlated binary outcomes as arise in longitudinal or cluster randomization studies. The key step involves a cluster-level grouping strategy for the computation of the middle term in the sandwich estimator. For a single binary exposure variable without covariate adjustment, this approach results in risk ratio estimates and standard errors that are identical to those found in the survey sampling literature. Simulation results suggest that it is reliable for studies with correlated binary data, provided the total number of clusters is at least 50. Data from observational and cluster randomized studies are used to illustrate the methods.
    PMID: 22072596  [PubMed – as supplied by publisher]

  5. J Clin Psychopharmacol. 2011 Dec 22. [Epub ahead of print]
  6. Treating Depression After Initial Treatment Failure: Directly Comparing Switch and Augmenting Strategies in STAR*D. Gaynes BN, Dusetzina SB, Ellis AR, Hansen RA, Farley JF, Miller WC, Stürmer T. Department of Psychiatry, School of Medicine, UNC at Chapel Hill, Chapel Hill, NC; Department of Health Care Policy, Harvard Medical School, Boston, MA; Harrison School of Pharmacy, Auburn University, Auburn, AL; Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, and Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.

    OBJECTIVE: Augmenting and switching antidepressant medications are the 2 most common next-step strategies for depressed patients failing initial medication treatment. These approaches have not been directly compared; thus, our objectives are to compare outcomes for medication augmentation versus switching for patients with major depressive disorder in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) clinical trial. METHODS: We conducted a retrospective analysis of participants aged 18 to 75 years with DSM-IV nonpsychotic depression who failed to remit with initial treatment in the STAR*D clinical trial (N =1292). We compared depressive symptom remission, response, and quality of life among participants in each study arm using propensity score matching to minimize selection bias. RESULTS: The propensity-score-matched augment (N = 269) and switch (N = 269) groups were well balanced on measured characteristics. Neither the likelihood of remission (risk ratio, 1.14; 95% confidence level, 0.82-1.58) or response (risk ratio, 1.14; 95% confidence level, 0.82-1.58), nor the time to remission (log-rank test, P = 0.946) or response (log-rank test, P = 0.243) differed by treatment strategy. Similarly, quality of life did not differ. Post hoc analyses suggested that augmentation improved outcomes for patients tolerating 12 or more weeks of initial treatment and those with partial initial treatment response. CONCLUSIONS: For patients receiving and tolerating aggressive initial antidepressant trials, there is no clear preference for next-step augmentation versus switching. Findings tentatively suggest that those who complete an initial treatment of 12 weeks or more and have a partial response with residual mild depressive severity may benefit more from augmentation relative to switching.
    PMID: 22198447  [PubMed – as supplied by publisher]

  7. J Clin Psychopharmacol. 2011 Dec 22. [Epub ahead of print]
  8. Variation in Antipsychotic Treatment Choice Across US Nursing Homes. Huybrechts KF, Rothman KJ, Brookhart MA, Silliman RA, Crystal S, Gerhard T, Schneeweiss S. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School; Department of Epidemiology, Boston University School of Public Health, Boston, MA; RTI Health Solutions, Research Triangle Park; UNC, Gillings School of Global Public Health, Chapel Hill, NC; Department of Medicine, Boston University School of Medicine, Boston, MA; and Rutgers University, New Brunswick, NJ.

    OBJECTIVE: Despite serious safety concerns, antipsychotic medications continue to be used widely in US nursing homes. The objective of this study was to quantify the variation in antipsychotic treatment choice across US nursing homes, and to characterize its correlates.
    METHODS: Prescribing practices were assessed in a cohort of 65,618 patients 65 years or older in 45 states who initiated treatment with an antipsychotic medication after nursing home admission between 2001 and 2005, using merged Medicaid; Medicare; Minimum Data Set; and Online Survey, Certification, and Reporting data. We fit mixed-effects logistic regression models to examine how antipsychotic treatment choice at the patient-level depends on patient and nursing home fixed and random effects. RESULTS: Among antipsychotic medication users, 9% of patients initiated treatment with a conventional agent. After adjustment for case-mix and facility characteristics, 95% of nursing homes had a predicted conventional antipsychotic prescribing rate between 2% and 20%. Individually, patient characteristics accounted for 36% of the explained variation, facility characteristics for 23%, and nursing home prescribing tendency for 81%. Results were consistent in the subgroup of nursing home patients with a diagnosis of dementia. The prescribing physician was not considered as a determinant of treatment choice owing to data limitations.
    CONCLUSION: These findings indicate that antipsychotic treatment choice is to some extent influenced by a nursing home’s underling prescribing "culture." This culture may reveal strategies for targeting quality improvement interventions. In addition, these findings suggest that a nursing home’s tendency for specific antipsychotics merits further exploration as an instrumental variable for improved confounding adjustment in comparative effectiveness studies.
    PMID: 22198446  [PubMed – as supplied by publisher]

  9. Stat Med. 2011 Dec 4. doi: 10.1002/sim.4413. [Epub ahead of print]
  10. Diagnosing imputation models by applying target analyses to posterior replicates of completed data. He Y, Zaslavsky AM. Department of Health Care Policy, Harvard Medical School, Boston, MA, 02115, USA.

    Multiple imputation fills in missing data with posterior predictive draws from imputation models. To assess the adequacy of imputation models, we can compare completed data with their replicates simulated under the imputation model. We apply analyses of substantive interest to both datasets and use posterior predictive checks of the differences of these estimates to quantify the evidence of model inadequacy. We can further integrate out the imputed missing data and their replicates over the completed-data analyses to reduce variance in the comparison. In many cases, the checking procedure can be easily implemented using standard imputation software by treating re-imputations under the model as posterior predictive replicates. Thus, it can be applied for non-Bayesian imputation methods. We also sketch several strategies for applying the method in the context of practical imputation analyses. We illustrate the method using two real data applications and study its property using a simulation. Copyright © 2011 John Wiley & Sons, Ltd.
    PMID: 22139814  [PubMed – as supplied by publisher]

CER Scan [published within the last 30 days]

  1. Epidemiology. 2012 Jan;23(1):151-8.
  2. Is probabilistic bias analysis approximately bayesian? Maclehose RF, Gustafson P. From the Divisions of Biostatistics, and Epidemiology and Community Health, University of Minnesota, Minneapolis, MN; and Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.

    Case-control studies are particularly susceptible to differential exposure misclassification when exposure status is determined following incident case status. Probabilistic bias analysis methods have been developed as ways to adjust standard effect estimates based on the sensitivity and specificity of exposure misclassification. The iterative sampling method advocated in probabilistic bias analysis bears a distinct resemblance to a Bayesian adjustment; however, it is not identical. Furthermore, without a formal theoretical framework (Bayesian or frequentist), the results of a probabilistic bias analysis remain somewhat difficult to interpret. We describe, both theoretically and empirically, the extent to which probabilistic bias analysis can be viewed as approximately Bayesian. Although the differences between probabilistic bias analysis and Bayesian approaches to misclassification can be substantial, these situations often involve unrealistic prior specifications and are relatively easy to detect. Outside of these special cases, probabilistic bias analysis and Bayesian approaches to exposure misclassification in case-control studies appear to perform equally well.
    PMID: 22157311  [PubMed – in process]

  3. BMC Med Inform Decis Mak. 2011 Dec 14;11(1):75. [Epub ahead of print]
  4. Evaluation of an automated safety surveillance system using risk adjusted Sequential Probability Ratio Testing. Matheny ME, Normand SL, Gross TP, Marinac-Dabic D, Loyo-Berrios N, Vidi VD, Donnelly S, Resnic FS.

    BACKGROUND: Automated adverse outcome surveillance tools and methods have potential utility in quality improvement and medical product surveillance activities. Their use for assessing hospital performance on the basis of patient outcomes has received little attention. We compared risk-adjusted sequential probability ratio testing (RA-SPRT) implemented in an automated tool to Massachusetts public reports of 30-day mortality after isolated coronary artery bypass graft surgery. METHODS: A total of 23,020 isolated adult coronary artery bypass surgery admissions performed in Massachusetts hospitals between January 1, 2002 and September 30, 2007 were retrospectively re-evaluated. The RA-SPRT method was implemented within an automated surveillance tool to identify hospital outliers in yearly increments. We used an overall type I error rate of 0.05, an overall type II error rate of 0.10, and a threshold that signaled if the odds of dying 30-days after surgery was at least twice than expected. Annual hospital outlier status, based on the state-reported classification, was considered the gold standard. An event was defined as at least one occurrence of a higher-than-expected hospital mortality rate during a given year. RESULTS: We examined a total of 83 hospital-year observations. The RA-SPRT method alerted 6 events among three hospitals for 30-day mortality compared with 5 events among two hospitals using the state public reports, yielding a sensitivity of 100% (5/5) and specificity of 98.8% (79/80). CONCLUSIONS: The automated RA-SPRT method performed well, detecting all of the true institutional outliers with a small false positive alerting rate. Such a system could provide confidential automated notification to local institutions in advance of public reporting providing opportunities for earlier quality improvement interventions.
    PMID: 22168892  [PubMed – as supplied by publisher]

    Free Full Text:

  5. Stat Med. 2011 Dec 20;30(29):3447-60. doi: 10.1002/sim.4355.
  6. Gaussian-based routines to impute categorical variables in health surveys. Yucel RM, He Y, Zaslavsky AM. Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, SUNY, One University Place, Rensselaer, NY 12144-3456, USA.

    The multivariate normal (MVN) distribution is arguably the most popular parametric model used in imputation and is available in most software packages (e.g., SAS PROC MI, R package norm). When it is applied to categorical variables as an approximation, practitioners often either apply simple rounding techniques for ordinal variables or create a distinct ‘missing’ category and/or disregard the nominal variable from the imputation phase. All of these practices can potentially lead to biased and/or uninterpretable inferences. In this work, we develop a new rounding methodology calibrated to preserve observed distributions to multiply impute missing categorical covariates. The major attractiveness of this method is its flexibility to use any ‘working’ imputation software, particularly those based on MVN, allowing practitioners to obtain usable imputations with small biases. A simulation study demonstrates the clear advantage of the proposed method in rounding ordinal variables and, in some scenarios, its plausibility in imputing nominal variables. We illustrate our methods on a widely used National Survey of Children with Special Health Care Needs where incomplete values on race posed a valid threat on inferences pertaining to disparities. Copyright © 2011 John Wiley & Sons, Ltd.
    PMID: 21976366  [PubMed – in process]

JANUARY THEME: Applications of MSMs for Dealing with Time-varying Exposure

  1. Int J Biostat. 2011;7(1):Article 34. Epub 2011 Sep 8.
  2. Antihypertensive medication use and change in kidney function in elderly adults: a marginal structural model analysis. Odden MC, Tager IB, van der Laan MJ, Delaney JA, Peralta CA, Katz R, Sarnak MJ, Psaty BM, Shlipak MG. Oregon State University, USA.

    BACKGROUND: The evidence for the effectiveness of antihypertensive medication use for slowing decline in kidney function in older persons is sparse. We addressed this research question by the application of novel methods in a marginal structural model.
    METHODS: Change in kidney function was measured by two or more measures of cystatin C in 1,576 hypertensive participants in the Cardiovascular Health Study over 7 years of follow-up (1989-1997 in four U.S. communities). The exposure of interest was antihypertensive medication use. We used a novel estimator in a marginal structural model to account for bias due to confounding and informative censoring.
    RESULTS: The mean annual decline in eGFR was 2.41 ± 4.91 mL/min/1.73 m(2). In unadjusted analysis, antihypertensive medication use was not associated with annual change in kidney function. Traditional multivariable regression did not substantially change these estimates. Based on a marginal structural analysis, persons on antihypertensives had slower declines in kidney function; participants had an estimated 0.88 (0.13, 1.63) ml/min/1.73 m(2) per year slower decline in eGFR compared with persons on no treatment. In a model that also accounted for bias due to informative censoring, the estimate for the treatment effect was 2.23
    (-0.13, 4.59) ml/min/1.73 m(2) per year slower decline in eGFR.
    CONCLUSION: In summary, estimates from a marginal structural model suggested that antihypertensive therapy was associated with preserved kidney function in hypertensive elderly adults. Confirmatory studies may provide power to determine the strength and validity of the findings.
    PMCID: PMC3204667 [Available on 2012/9/8]
    PMID: 22049266  [PubMed – in process]

  3. Epidemiology. 2011 Nov;22(6):877-8.
  4. Hormonal contraception and HIV risk: evaluating marginal-structural-model assumptions. Chen PL, Cole SR, Morrison CS.

    Letter to the editor

    PMID: 21968782  [PubMed – in process]

  5. Pharmacoepidemiol Drug Saf. 2011 Jul 22. doi: 10.1002/pds.2175. [Epub ahead of print] Comparative effectiveness of individual angiotensin receptor blockers on risk of mortality in patients with chronic heart failure. Desai RJ, Ashton CM, Deswal A, Morgan RO, Mehta HB, Chen H, Aparasu RR, Johnson ML. Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.

    OBJECTIVE: There is little evidence on comparative effectiveness of individual angiotensin receptor blockers (ARBs) in patients with chronic heart failure (CHF). This study compared four ARBs in reducing risk of mortality in clinical practice. METHODS: A retrospective analysis was conducted on a national sample of patients diagnosed with CHF from 1 October 1996 to 30 September 2002 identified from Veterans Affairs electronic medical records, with supplemental clinical data obtained from chart review. After excluding patients with exposure to ARBs within the previous 6 months, four treatment groups were defined based on initial use of candesartan, valsartan, losartan, and irbesartan between the index date (1 October 2000) and the study end date (30 September 2002). Time to death was measured concurrently during that period. A marginal structural model controlled for sociodemographic factors, comorbidities, comedications, disease severity (left ventricular ejection fraction), and potential time-varying confounding affected by previous treatment (hospitalization). Propensity scores derived from a multinomial logistic regression were used as inverse probability of treatment weights in a generalized estimating equation to estimate causal effects. RESULTS: Among the 1536 patients identified on ARB therapy, irbesartan was most frequently used (55.21%), followed by losartan (21.74%), candesartan (15.23%), and valsartan (7.81%). When compared with losartan, after adjusting for time-varying hospitalization in marginal structural model, candesartan (OR = 0.79, 95%CI = 0.42-1.50), irbesartan (OR = 1.17, 95%CI = 0.72-1.90), and valsartan (OR = 0.98, 95%CI = 0.45-2.14) were found to have similar effectiveness in reducing mortality in CHF patients. CONCLUSION: Effectiveness of ARBs in reducing mortality is similar in patients with CHF in everyday clinical practice. Copyright © 2011 John Wiley & Sons, Ltd.
    PMID: 21786364  [PubMed – as supplied by publisher]

  7. Clin Trials. 2011 Jun;8(3):277-87. doi: 10.1177/1740774511402526.
  8. How to use marginal structural models in randomized trials to estimate the natural direct and indirect effects of therapies mediated by causal intermediates. Oba K, Sato T, Ogihara T, Saruta T, Nakao K. Translational Research and Clinical Trial Center, Hokkaido University Hospital, Hokkaido University, Japan.

    Erratum in
        Clin Trials. 2011;8(5):680.

    BACKGROUND: Although intention-to-treat analysis is a standard approach, additional supplemental analyses are often required to evaluate the biological relationship among interventions, intermediates, and outcomes. Therefore, we need to evaluate whether the effect of an intervention on a particular outcome is mediated by a hypothesized intermediate variable.
    PURPOSE: To evaluate the size of the direct effect in the total effect, we applied the marginal structural model to estimate the average natural direct and indirect effects in a large-scale randomized controlled trial (RCT). Method The average natural direct effect is defined as the difference in the probability of a counterfactual outcome between the experimental and control arms, with the intermediate set to what it would have been, had the intervention been a control treatment. We considered two marginal structural models to estimate the average natural direct and indirect effects introduced by VanderWeele (Epidemiology 2009) and applied them in a large-scale RCT – the Candesartan Antihypertensive Survival
    Evaluation in Japan (CASE-J trial) – that compared angiotensin receptor blockers and calcium-channel blockers in high-risk hypertensive patients.
    RESULTS: There were no strong blood pressure-independent or dependent effects; however, a systolic blood pressure reduction of about 1.9  mmHg suppressed all events. Compared to the blood pressure-independent effects of calcium channel blockers, those of angiotensin receptor blockers contributed positively to cardiovascular and cardiac events, but negatively to cerebrovascular events.
    LIMITATIONS: There is a particular condition for estimating the average natural direct effect. It is impossible to check whether this condition is satisfied with the available data.
    CONCLUSION: We estimated the average natural direct and indirect effects through the achieved systolic blood pressure in the CASE-J trial. This first application of estimating the average natural effects in an RCT can be useful for obtaining an in-depth understanding of the results and further development of similar interventions.
    PMID: 21730076  [PubMed – indexed for MEDLINE]

  9. J Consult Clin Psychol. 2011 Apr;79(2):225-35. A marginal structural model analysis for loneliness: implications for intervention trials and clinical practice. VanderWeele TJ, Hawkley LC, Thisted RA, Cacioppo JT. Harvard University, Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.

    OBJECTIVE: Clinical scientists, policymakers, and individuals must make decisions concerning effective interventions that address health-related issues. We use longitudinal data on loneliness and depressive symptoms and a new class of causal models to illustrate how empirical evidence can be used to inform intervention trial design and clinical practice.
    METHOD: Data were obtained from a population-based study of non-Hispanic Caucasians, African Americans, and Latino Americans (N = 229) born between 1935 and 1952. Loneliness and depressive symptoms were measured with the UCLA Loneliness Scale-Revised and Center for Epidemiologic Studies Depression Scale, respectively. Marginal structural causal models were employed to evaluate the extent to which depressive symptoms depend not only on loneliness measured at a single point in time (as in prior studies of the effect of loneliness) but also on an individual’s entire loneliness history.
    RESULTS: Our results indicate that if interventions to reduce loneliness by 1 standard deviation were made 1 and 2 years prior to assessing depressive symptoms, both would have an effect; together, they would result in an average reduction in depressive symptoms of 0.33 standard deviations, 95% CI [0.21,
    0.44], p < .0001.
    CONCLUSIONS: The magnitude and persistence of these effects suggest that greater effort should be devoted to developing practical interventions on alleviating loneliness and that doing so could be useful in the treatment and prevention of depressive symptoms. In light of the persistence of the effects of loneliness, our results also suggest that, in the evaluation of interventions on loneliness, it may be important to allow for a considerable follow-up period in assessing outcomes.
    (c) 2011 APA, all rights reserved.
    PMCID: PMC3079447 [Available on 2012/4/1]
    PMID: 21443322  [PubMed – indexed for MEDLINE]

  11. J Clin Psychopharmacol. 2011 Apr;31(2):226-30.
  12. Differential 3-year effects of first- versus second-generation antipsychotics on subjective well-being in schizophrenia using marginal structural models. Lambert M, Schimmelmann BG, Schacht A, Suarez D, Haro JM, Novick D, Wagner T, Wehmeier PM, Huber CG, Hundemer HP, Dittmann RW, Naber D. Psychosis Centre, Department of Psychiatry and Psychotherapy, Centre for Psychosocial Medicine, University Medical Centre Hamburg-Eppendorf, Germany.

    OBJECTIVE: This study examined the differential effects of first- (FGAs) versus second-generation antipsychotics (SGAs) on subjective well-being in patients with schizophrenia.
    METHOD: Data were collected in an observational 3-year follow-up study of 2224 patients with schizophrenia. Subjective well-being was assessed with the Subjective Well-being under Neuroleptic Treatment Scale (SWN-K). Differential effects of FGAs versus SGAs were analyzed using marginal structural models in those patients taking antipsychotic monotherapy.
    RESULTS: The marginal structural model, which analyzed the differential effect on the SWN-K total score, revealed that patients on SGAs had significantly higher SWN-K total scores, starting at 6 months (3.02 points; P = 0.0061, d = 0.20) and remaining significant thereafter (end point: 5.35 points; P = 0.0074, d = 0.36).
    CONCLUSIONS: Results of this large observational study are consistent with a small but clinically relevant superiority of SGAs over FGAs in subjective well-being extending previous positive findings of differential effects on quality of life.
    PMID: 21346606  [PubMed – indexed for MEDLINE]

  13. Arch Intern Med. 2011 Jan 24;171(2):110-8. Epub 2010 Sep 27.
  14. Similar outcomes with hemodialysis and peritoneal dialysis in patients with end-stage renal disease. Mehrotra R, Chiu YW, Kalantar-Zadeh K, Bargman J, Vonesh E. Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA 90502, USA.

    Comment in
        Arch Intern Med. 2011 Jan 24;171(2):107-9.

    BACKGROUND: The annual payer costs for patients treated with peritoneal dialysis (PD) are lower than with hemodialysis (HD), but in 2007, only 7% of dialysis patients in the United States were treated with PD. Since 1996, there has been no change in the first-year mortality of HD patients, but both short- and long-term outcomes of PD patients have improved.
    METHODS: Data from the US Renal Data System were examined for secular trends in survival among patients treated with HD and PD on day 90 of end-stage renal disease (HD, 620 020 patients; PD, 64 406 patients) in three 3-year cohorts (1996-1998, 1999-2001, and 2002-2004) for up to 5 years of follow-up using a nonproportional hazards marginal structural model with inverse probability of treatment and censoring weighting.
    RESULTS: There was a progressive attenuation in the higher risk for death seen in patients treated with PD in earlier cohorts; for the 2002-2004 cohort, there was no significant difference in the risk of death for HD and PD patients through 5 years of follow-up. The median life expectancy of HD and PD patients was 38.4 and 36.6 months, respectively. Analyses in 8 subgroups based on age (<65 and ≥65 years), diabetic status, and baseline comorbidity (none and ≥1) showed greater improvement in survival among patients treated with PD relative to HD at all follow-up periods.
    CONCLUSION: In the most recent cohorts, patients who began treatment with HD or PD have similar outcomes.
    PMID: 20876398  [PubMed – indexed for MEDLINE]

  15. Epidemiology. 2010 Jul;21(4):528-39.
  16. Estimating absolute risks in the presence of nonadherence: an application to a follow-up study with baseline randomization. Toh S, Hernández-Díaz S, Logan R, Robins JM, Hernán MA. Department of Epidemiology, Harvard School of Public Health, Boston, MA 02215

    The intention-to-treat (ITT) analysis provides a valid test of the null hypothesis and naturally results in both absolute and relative measures of risk. However, this analytic approach may miss the occurrence of serious adverse effects that would have been detected under full adherence to the assigned treatment. Inverse probability weighting of marginal structural models has been used to adjust for nonadherence, but most studies have provided only relative measures of risk. In this study, we used inverse probability weighting to estimate both absolute and relative measures of risk of invasive breast cancer under full adherence to the assigned treatment in the Women’s Health Initiative estrogen-plus-progestin trial. In contrast to an ITT hazard ratio (HR) of 1.25 (95% confidence interval [CI] = 1.01 to 1.54), the HR for 8-year continuous estrogen-plus-progestin use versus no use was 1.68 (1.24 to 2.28). The estimated risk difference (cases/100 women) at year 8 was 0.83 (-0.03 to 1.69) in the ITT analysis, compared with 1.44 (0.52 to 2.37) in the adherence-adjusted analysis. Results were robust across various dose-response models. We also compared the dynamic treatment regimen "take hormone therapy until certain adverse events become apparent, then stop taking hormone therapy" with no use (HR = 1.64; 95% CI
    = 1.24 to 2.18). The methods described here are also applicable to observational studies with time-varying treatments.
    PMID: 20526200  [PubMed – indexed for MEDLINE]

  17. Lifetime Data Anal. 2010 Jan;16(1):71-84. Epub 2009 Nov 6.
  18. Relation between three classes of structural models for the effect of a time-varying exposure on survival. Young JG, Hernán MA, Picciotto S, Robins JM. Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Kresge Bldg Suite 820, Boston, MA 02115, USA.

    Standard methods for estimating the effect of a time-varying exposure on survival may be biased in the presence of time-dependent confounders themselves affected by prior exposure. This problem can be overcome by inverse probability weighted estimation of Marginal Structural Cox Models (Cox MSM), g-estimation of Structural Nested Accelerated Failure Time Models (SNAFTM) and g-estimation of
    Structural Nested Cumulative Failure Time Models (SNCFTM). In this paper, we describe a data generation mechanism that approximately satisfies a Cox MSM, an SNAFTM and an SNCFTM. Besides providing a procedure for data simulation, our formal description of a data generation mechanism that satisfies all three models allows one to assess the relative advantages and disadvantages of each modeling approach. A simulation study is also presented to compare effect estimates across the three models.
    PMID: 19894116  [PubMed – indexed for MEDLINE]

  19. J Rheumatol. 2009 Mar;36(3):560-4. Epub 2009 Feb 4.
  20. Prednisone, lupus activity, and permanent organ damage. Thamer M, Hernán MA, Zhang Y, Cotter D, Petri M. Medical Technology and Practice Patterns Institute, Bethesda, MD 20814

    OBJECTIVE: To estimate the effect of corticosteroids (prednisone dose) on permanent organ damage among persons with systemic lupus erythematosus (SLE). METHODS: We identified 525 patients with incident SLE in the Hopkins Lupus Cohort. At each visit, clinical activity indices, laboratory data, and treatment were recorded. The study population was followed from the month after the first visit until June 29, 2006, or attainment of irreversible organ damage, death, loss to follow-up, or receipt of pulse methylprednisolone therapy. We estimated the effect of cumulative average dose of prednisone on organ damage using a marginal structural model to adjust for time-dependent confounding by indication due to SLE disease activity.
    RESULTS: Compared with non-prednisone use, the hazard ratio of organ damage for prednisone was 1.16 (95% CI 0.54, 2.50) for cumulative average doses > 0-180 mg/month, 1.50 (95% CI 0.58, 3.88) for > 180-360 mg/month, 1.64 (95% CI 0.58, 4.69) for > 360-540 mg/month, and 2.51 (95% CI 0.87, 7.27) for > 540 mg/month. In contrast, standard Cox regression models estimated higher hazard ratios at all dose levels.
    CONCLUSION: Our results suggest that low doses of prednisone do not result in a substantially increased risk of irreversible organ damage.
    PMID: 19208608  [PubMed – indexed for MEDLINE]

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