The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement

被引:229
作者
Kent, David M. [1 ]
Paulus, Jessica K. [1 ]
van Klaveren, David [1 ,2 ,16 ]
D'Agostino, Ralph [3 ]
Goodman, Steve [4 ,17 ]
Hayward, Rodney [5 ,18 ]
Ioannidis, John P. A. [4 ,19 ]
Patrick-Lake, Bray [6 ,20 ]
Morton, Sally [7 ,21 ]
Pencina, Michael [6 ,22 ]
Raman, Gowri [1 ,8 ]
Ross, Joseph S. [9 ,10 ]
Selker, Harry P. [1 ,11 ,12 ]
Varadhan, Ravi [13 ,23 ]
Vickers, Andrew [14 ]
Wong, John B. [1 ,24 ]
Steyerberg, Ewout W. [15 ,25 ]
机构
[1] Tufts Med Ctr, Inst Clin Res & Hlth Policy Studies, Predict Analyt & Comparat Effectiveness PACE Ctr, 800 Washington St,Box 63, Boston, MA 02111 USA
[2] Erasmus MC, Rotterdam, Netherlands
[3] Boston Univ, Math & Stat Dept, 111 Cummington St, Boston, MA 02215 USA
[4] Stanford Univ, Meta Res Innovat Ctr Stanford METRICS, Stanford, CA 94305 USA
[5] Univ Michigan, Ann Arbor, MI 48109 USA
[6] Duke Univ, Duke Clin Res Inst, Durham, NC USA
[7] Virginia Polytech Inst & State Univ, Blacksburg, VA 24061 USA
[8] Tufts Med Ctr, Inst Clin Res & Hlth Policy Studies, Ctr Clin Evidence Synth, Boston, MA 02111 USA
[9] Yale Univ, Sch Med, POB 208093, New Haven, CT 06520 USA
[10] Yale Univ, Sch Publ Hlth, New Haven, CT USA
[11] Tufts Med Ctr, Inst Clin Res & Hlth Policy Studies, Ctr Cardiovasc Hlth Serv Res, Boston, MA 02111 USA
[12] Tufts Clin & Translat Sci Inst, Boston, MA USA
[13] Johns Hopkins Univ, Ctr Aging & Hlth, Baltimore, MD USA
[14] Mem Sloan Kettering Canc Ctr, 485 Lexington Ave,2nd Floor, New York, NY 10017 USA
[15] Leiden Univ, Med Ctr, Leiden, Netherlands
[16] Erasmus Univ, Med Ctr, Doctor Molewaterpl 40, NL-3015 GD Rotterdam, Netherlands
[17] Stanford Univ, Sch Med, 150 Governors Lane,Room T265, Stanford, CA 94305 USA
[18] VA Ann Arbor Hlth Serv Res & Dev, 2800 Plymouth Rd,Bldg 14,G100-36, Ann Arbor, MI 48109 USA
[19] Stanford Prevent Res Ctr, 1265 Welch Rd, Stanford, CA 94305 USA
[20] Evidat Hlth, 167 2nd Ave, San Mateo, CA 94401 USA
[21] Virginia Tech, North End Ctr, Suite 4300,300 Turner St NW, Blacksburg, VA 24061 USA
[22] Duke Clin Res Inst, 200 Trent St, Durham, NC 27710 USA
[23] Johns Hopkins Univ, Div Biostat & Bioinformat, 550 North Broadway,Suite 1103-A, Baltimore, MD 21205 USA
[24] Tufts Med Ctr, 800 Washington St 302, Boston, MA 02111 USA
[25] Erasmus Univ, Med Ctr, POB 2040, NL-3055 PC Rotterdam, Netherlands
关键词
SUBGROUP ANALYSES; CLINICAL-TRIALS; INDIVIDUAL PATIENTS; PLASMINOGEN-ACTIVATOR; RANDOMIZED-TRIALS; RISK MODELS; BENEFIT; MEDICINE; THERAPY; IDENTIFICATION;
D O I
10.7326/M18-3667
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.
引用
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页码:35 / +
页数:15
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