From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer

BMC Medical Research Methodology

C Daniel Mullins²  Rikard Althin³  Prasun Subedi¹  Zhiyuan Zheng²  Richard J Willke¹ 

[1] Pfizer, Inc, 235 E. 42nd Street, New York, NY 10017, USA;University of Maryland School of Pharmacy, 220 Arch Street, 12th Floor, Baltimore, MD, 21201, USA;Pfizer, Inc, 500 Arcola Road, Collegeville, PA, 19426-3982, USA

关键词: Comparative effectiveness research;    Estimation techniques;    Risk adjustment;    Heterogeneity;   

Others  :  1126351
DOI  :  10.1186/1471-2288-12-185

 received in 2012-07-31, accepted in 2012-12-03,  发布年份 2012

【 摘 要 】

Implicit in the growing interest in patient-centered outcomes research is a growing need for better evidence regarding how responses to a given intervention or treatment may vary across patients, referred to as heterogeneity of treatment effect (HTE). A variety of methods are available for exploring HTE, each associated with unique strengths and limitations. This paper reviews a selected set of methodological approaches to understanding HTE, focusing largely but not exclusively on their uses with randomized trial data. It is oriented for the “intermediate” outcomes researcher, who may already be familiar with some methods, but would value a systematic overview of both more and less familiar methods with attention to when and why they may be used. Drawing from the biomedical, statistical, epidemiological and econometrics literature, we describe the steps involved in choosing an HTE approach, focusing on whether the intent of the analysis is for exploratory, initial testing, or confirmatory testing purposes. We also map HTE methodological approaches to data considerations as well as the strengths and limitations of each approach. Methods reviewed include formal subgroup analysis, meta-analysis and meta-regression, various types of predictive risk modeling including classification and regression tree analysis, series of n-of-1 trials, latent growth and growth mixture models, quantile regression, and selected non-parametric methods. In addition to an overview of each HTE method, examples and references are provided for further reading.

By guiding the selection of the methods and analysis, this review is meant to better enable outcomes researchers to understand and explore aspects of HTE in the context of patient-centered outcomes research.

【 授权许可】

   
2012 Willke et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】

• [1]Kravitz RL, Duan N, Breslow J: Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Q 2004, 82:661-687.
• [2]Greenfield S, Kravitz R, Duan N, Kaplan S: Heterogeneity of treatment effects: implications for guidelines, payment, and quality assessment. Am J Med 2007, 120(4A):S3-S9.
• [3]Gabler NB, Duan N, Liao D, et al.: Dealing with heterogeneity of treatment effects: is the literature up to the challenge? Trials 2009, 10:43. BioMed Central Full Text
• [4]Assmann SF, Pocock SJ, Enos LE, Kasten LE: Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet 2000, 355:1064-1069.
• [5]Pocock SJ, Assmann SE, Enos LE, Kasten LE: Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat Med 2002, 21:2917-2930.
• [6]Cook DI, Gebski VJ, Keech AC: Subgroup analysis in clinical trials. Med J Aust 2004, 180:289-91.
• [7]Rothwell PM: Treating individuals 2. Subgroup analysis in randomised controlled trials: importance, indications, and interpretation. Lancet 2005, 365:176-186.
• [8]Cui L, Hung HM, Wang SJ, Tsong Y: Issues related to subgroup analysis in clinical trials. J Biopharm Stat 2002, 12:347-358.
• [9]Kraemer HC, Frank E, Kupfer DJ: Moderators of treatment outcomes: clinical, research, and policy importance. JAMA 2006, 296(10):1286-1289.
• [10]Brookes ST, Whitely E, Egger M, et al.: Subgroup analyses in randomized trials: risks of subgroup-specific analyses; power and sample size for the interaction test. J Clin Epidemiol 2004, 57(3):229-236.
• [11]Kent DM, Rothwell PM, Ioannidis JP, et al.: Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal. Trials 2010, 11:85. BioMed Central Full Text
• [12]Debate SP: Subgroup analyses in clinical trials: fun to look at - but don't believe them! Curr Control Trials Cardiovasc Med 2000, 1:25-27. BioMed Central Full Text
• [13]Wang R, Lagakos SW, Ware JH, et al.: Statistics in medicine–reporting of subgroup analyses in clinical trials. N Engl J Med 2007, 357(21):2189-2194.
• [14]Cochran WG: The combination of estimates from different experiments. Biometrics 1954, 10:101-129.
• [15]Higgins JP, Thompson SG: Quantifying heterogeneity in a meta-analysis. Stat Med 2002, 21:1539-1558.
• [16]Sterne JA, Becker C, B J, Egger M: The funnel plot. In Publication bias in meta-analysis: prevention, assessment and adjustment. Edited by Rothstein HR, Sutton AJ, Borenstein M. Chichester: Wiley; 2005:75-98.
• [17]Duval S, Tweedie R: Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 2000, 56:455-463.
• [18]Duval S, Tweedie RL: A non-parametric "Trim and Fill" method of accounting for publication bias in meta-analysis. J Am Statist Ass 2000, 95:89-98.
• [19]Egger M, Smith GD, Schneider M, Minder C: Bias in meta-analysis detected by a simple, graphical test. Br Med J 1997, 315:629-634.
• [20]Begg CB, Mazumdar M: Operating characteristics of a rank correlation test for publication bias. Biometrics 1994, 50:1088-1101.
• [21]Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L, Moreno SG: Assessing publication bias in meta-analyses in the presence of between-study heterogeneity. J R Statist Soc A 2010, 173:575-591. Part 3
• [22]Schmid CH, Lau J, McIntosh MW, Cappelleri JC: An empirical study of the effect of the control rate as a predictor of treatment efficacy in meta-analysis of clinical trials. Stat Med 1998, 17:1923-1942.
• [23]DerSimonian R, Laird N: Meta-analysis in clinical trials. Control Clin Trials 1986, 7:177-188.
• [24]Baker WL, White CM, Cappelleri JC, et al.: Understanding heterogeneity in meta-analysis: the role of meta-regression. Int J Clin Pract 2009, 63:1426-1434.
• [25]Alatorre CI, Carter GC, Chen C, et al.: A comprehensive review of predictive and prognostic composite factors implicated in the heterogeneity of treatment response and outcome across disease areas. Int J Clinn Pract 2011, 65:831-847.
• [26]D'Agostino RB, Russell MW, Huse DM, et al.: Primary and subsequent coronary risk appraisal: new results from the Framingham study. Am Heart J 2000, 139(2 Pt 1):272-281.
• [27]Mega JL, Close SL, Wiviott SD: Cytochrome p-450 polymorphisms and response to clopidogrel. N Engl J Med 2009, 360:354-362.
• [28]Stone M, Brooks JR: Continuum regression: cross-validated sequentially constructed prediction, embracing ordinary least squares, partial least squares and principal components regression (with discussion). J R Statist Soc B 1990, 52:237-269.
• [29]Breiman L: Random forests. University of California-Berkeley: Working paper; 1999.
• [30]Vapnik V: The nature of statistical learning theory. Berlin: Springer-Verlag; 1995.
• [31]Fradkin D, Muchnik I: Support vector machines for classification. In Discrete methods in epidemiology", DIMACS series in discrete mathematics and theoretical computer science, volume 70. Edited by Abello J, Carmode G. New Brunswick: Department of Computer Science, Rutgers, The State University of New Jersey; 2006:13-20.
• [32]Bishop CM: Neural networks for pattern recognition. Oxford: Oxford University Press; 1995.
• [33]Ioannidis JP, Lau J: Heterogeneity of the baseline risk within patient populations of clinical trials: a proposed evaluation algorithm. Am J Epidemiol 1998, 148:1117-1126.
• [34]Lazar AA, Cole BF, Bonetti M, Gelber RD: Evaluation of treatment-effect heterogeneity using biomarkers measured on a continuous scale: subpopulation treatment effect pattern plot. J Clin Oncol 2010, 28:4539-44.
• [35]Crown WH: There’s a reason they call them dummy variables: a note on the use of structural equation techniques in comparative effectiveness research. Pharmaco Economics 2010, 28:947-955.
• [36]Province MA, Shannon WD, Rao DC: Classification methods for confronting heterogeneity. Adv Genet 2001, 4(2):273-286.
• [37]Green , Donald P, Kern HL: Generalizing experimental results. The annual meeting of the American political science association. DC: Washington; 2010.
• [38]Green , Donald P, Holder LK: Detecting heterogenous treatment effects in large-scale experiments using Bayesian additive regression trees. The annual summer meeting of the society of political methodology. University of Iowa; 2010.
• [39]Duncan TE, Duncan SC: An introduction to latent growth curve modeling. Behav Ther 2004, 35:333-363.
• [40]Stull DE: Analyzing growth and change: latent variable growth curve modeling with an application to clinical trials. Qual Life Res 2008, 17:47-59.
• [41]Chan D: The conceptualization and analysis of change over time: an integrative approach incorporating longitudinal mean and covariance structures analysis (LMACS) and multiple indicator latent growth modeling (MLGM). Organ Res Methods 1998, 1:421-483.
• [42]Curran P: Have multilevel models been structural equation models all along? Multivar Behav Res 2003, 38:529-569.
• [43]Wang M, Bodner TE: Growth mixture modeling: identifying and predicting unobserved subpopulations with longitudinal data. Organ Res Methods 2007, 10:635-656.
• [44]Stull DE, Wiklund I, Gale R, et al.: Application of latent growth and growth mixture modeling to identify and characterize differential responders to treatment for COPD. Contemp Clin Trials 2011, 32(6):818-828.
• [45]Lubke GH, Muthén B: Investigating population heterogeneity with factor mixture models. Psychol Methods 2005, 10(1):21-39.
• [46]Jung T, Wickrama KA: An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass 2008, 2:302-317.
• [47]Guyatt G, Sackett D, Adachi J, et al.: A clinician's guide for conducting randomized trials in individual patients. CMAJ 1988, 139:497-503.
• [48]Guyatt GH, Keller JL, Jaeschke R, et al.: The n-of-1 randomized controlled trial: clinical usefulness. Our three-year experience. Ann Intern Med 1990, 112:293-299.
• [49]Lewis JA: Controlled trials in single subjects. 2. Limitations of use. BMJ 1991, 303:175-176.
• [50]Zucker DR, Schmid CH, McIntosh MW, et al.: Combining single patient (N-of-1) trials to estimate population treatment effects and to evaluate individual patient responses to treatment. J Clin Epidemiol 1997, 50:401-410.
• [51]Karnon J, Qizlibash N: Economic evaluation alongside N-of-1 trials: getting closer to the margin. Health Econ 2001, 10:79-82.
• [52]Scuffham PA, Dip BA, Yelland MB, et al.: Are N-of-1 trials an economically viable option to improve access to selected high cost medications? The Australian experience. Value Health 2008, 11:97-109.
• [53]Djebbari H, Smith JA: Heterogeneous impacts in PROGRESA. J Econ 2008, 145:64-80.
• [54]Finkelstein A, McKnight R: What did medicare do? the initial impact of medicare on mortality and out of pocket medical spending. J Public Econ 2008, 92:1644-1668.
• [55]Shimshack JP, Ward MB, Beatty TKM: Mercury advisories: information, education, and fish consumption. J Environ Econ Manag 2007, 53:158-179.
• [56]Bitler MP, Gelbach JB, Hoynes HW: What mean impacts miss: distributional effects of welfare reform experiments. Am Econ Rev 2006, 96:988-1012.
• [57]Swindell WR: Accelerated failure time models provide a useful statistical framework for aging research. Exp Gerontol 2009, 44(3):190-200.
• [58]Müller H-G, Abramson I, Azari R: Nonparametric regression to the mean. Proc Natl Acad Sci USA 2003, 100(17):9715-9720.
• [59]Crump RK, Hotz VJ, Imbens GW, Mitnik OA: Nonparametric tests for treatment effect heterogeneity. National bureau of economic research technical working paper 324. Available from: http://www.nber.org/papers/t0324 webcite [Accessed February 12, 2012]
• [60]Lee M: Non-parametric tests for distributional treatment effect for randomly censored responses. J R Statist Soc B 2009, 71:243-264.
• [61]Frölich M: Non-parametric regression for binary dependent variables. Econ J 2006, 9:511-540.