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Abstract

There is a need for statistical methods appropriate for the analysis of clinical trials from a personalized-medicine viewpoint as opposed to the common statistical practice that simply examines average treatment effects. This article proposes an approach to quantifying, reporting and analyzing individual benefits of medical or behavioral treatments to severely ill patients with chronic conditions, using data from clinical trials. The approach is a new development of a published framework for measuring the severity of a chronic disease and the benefits treatments provide to individuals, which utilizes regression models with random coefficients. Here, a patient is considered to be severely ill if the patient’s basal severity is close to one. This allows the derivation of a very flexible family of probability distributions of individual benefits that depend on treatment duration and the covariates included in the regression model. Our approach may enrich the statistical analysis of clinical trials of severely ill patients because it allows investigating the probability distribution of individual benefits in the patient population and the variables that influence it, and we can also measure the benefits achieved in specific patients including new patients. We illustrate our approach using data from a clinical trial of the anti-depressant imipramine.

Keywords

Random effects linear models personalized medicine illness severity imipramine distribution of individual benefits empirical Bayes predictors variance components two-dimensional personalized medicine models benefit prediction chronic diseases

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References

Schork

. Time for one-person trials. Nature 2015; 520: 609–611.

Food and Drug Administration. Paving the way for personalized medicine: FDA’s role in a new era of medical product development. U.S. Department of Health and Human Services. Silver Spring, MD: Food and Drug Administration, 2013.

Kravitz RL, Duan N (eds) and the DEcIDE Methods Center N-of-1 Guidance Panel. Design and implementation of N-of-1 trials: a user’s guide. AHRQ Publication No. 13(14)-EHC122-EF. Rockville, MD: Agency for Healthcare Research and Quality, February 2014, www.effectivehealthcare.ahrq.gov/N-1-Trials.cfm.

Scuffham

Nikles

Mitchell

et al.

Using N-of-1 trials to improve patient management and save costs. J Gen Intern Med 2010; 25: 906–913.

Diaz

. Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed-effects models. Stat Med 2016; 35: 4077–4092.

Bertolet

Brooks

Bittner

. Tree-based identification of subgroups for time-varying covariate survival data. Stat Meth Med Res 2016; 25: 488–501.

Tanniou

van der Tweel

Teerenstra

et al.

Level of evidence for promising subgroup findings in an overall non-significant trial. Stat Meth Med Res 2016; 25: 2193–2213.

Lipkovich

Dmitrienko

D’Agostino

Sr.R.B

. Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials. Stat Med 2017; 36: 136–196.

Alosh

Huque

. A flexible strategy for testing subgroups and overall population. Stat Med 2009; 28: 3–23.

10.

Jones

Ohlssen

Neuenschwander

et al.

Bayesian models for subgroup analysis in clinical trials. Clin Trials 2011; 8: 129–143.

11.

Weisberg

Pontes

. Posthoc subgroups in clinical trials: anathema or analytics? Clin Trials 2015; 12: 357–364.

12.

Yip

W-K

Bonetti

Cole

et al.

Subpopulation Treatment Effect Pattern Plot (STEPP) analysis for continuous, binary, and count outcomes. Clin Trial 2016; 13: 382–390.

13.

Zhu

. Individualizing drug dosage with longitudinal data. Stat Med 2016; 35: 4474–4488.

14.

Cho

Wang

. Personalized treatment for longitudinal data using unspecified random-effects model. Stat Sinica 2017; 27: 187–205.

15.

Diaz

Yeh

H-W

de Leon

. Role of statistical random-effects linear models in personalized medicine. Curr Pharmacognomics Person Med 2012; 10: 22–32.

16.

Diaz

de Leon

. The mathematics of drug dose individualization should be built with random effects linear models. Ther Drug Monit 2013; 35: 276–277.

17.

Diaz

Rivera

Josiassen

et al.

Individualizing drug dosage by using a random intercept linear model. Stat Med 2007; 26: 2052–2073.

18.

Diaz

Berg

Krebill

et al.

Random-effects linear modeling and sample size tables for two special cross-over designs of average bioequivalence studies: the 4-period, 2-sequence, 2-formulation and 6-period, 3-sequence, 3-formulation designs. Clin Pharmacokinet 2013; 52: 1033–1043.

19.

Diaz

Cogollo

Spina

et al.

Drug dosage individualization based on a random-effects linear model. J Biopharm Stat 2012; 22: 463–484.

20.

Botts

Diaz

Santoro

et al.

Estimating the effects of co-medications on plasma olanzapine concentrations by using a mixed model. Prog Neuro-Psychoph 2008; 32: 1453–1458.

21.

Diaz

Eap

Ansermot

et al.

Can valproic acid be an inducer of clozapine metabolism?

Pharmacopsychiatry 2014; 47: 89–96.

22.

Senn

. Mastering variation: variance components and personalised medicine. Stat Med 2016; 35: 966–977.

23.

Senn

. Individual therapy: new dawn or false dawn. Drug Inf J 2001; 35: 1479–1494.

24.

Pillai

Mentré

Steimer

J-L

. Non-linear mixed effects modeling – from methodology and software development to driving implementation in drug development science. J Pharmacokinet Phar 2005; 32: 161–183.

25.

Kalow

Endrenyi

Tang

. Repeat administration of drugs as a means to assess the genetic component in pharmacological variability. Pharmacology 1999; 58: 281–284.

26.

Gough

Hutchison

Keene

et al.

Assessment of dose proportionality: report from the statisticians in the Pharmaceutical industry/Pharmacokinetics UK Joint Working Party. Drug Inf J 1995; 29: 1039–1048.

27.

Schall

Luus

. On population and individual bioequivalence. Stat Med 1993; 12: 1109–1124.

28.

Neale

Cardon

. Methodology for genetic studies of twins and families, London: Kluwer, 1992.

29.

Bock

Measurement of human variation: a two stage model. In: Bock

(ed). Multilevel analysis of educational data, New York: Academic Press, 1989.

30.

Whiting

Kelman

Grevel

. Population pharmacokinetics. Theory and clinical application. Clin Pharmacokinet 1986; 11: 387–401.

31.

Sheiner

Rosenberg

Melmon

. Modelling of individual pharmacokinetics for computer-aided drug dosage. Comp Biomed Res 1972; 5: 411–459.

32.

Henderson

. Estimation of variance and covariance components. Biometrics 1953; 9: 226–252.

33.

Reisby

Gram

Bech

et al.

Imipramine: Clinical effects and pharmacokinetic variability. Psychopharmacology 1977; 54: 263–272.

34.

Frees

. Longitudinal and panel data, Cambridge: Cambridge University Press, 2004.

35.

Robinson

. That BLUP is a good thing: the estimation of random effects. Stat Sci 1991; 6: 15–32.

36.

Efron

Morris

. Limiting the risk of Bayes and empirical Bayes estimators – Part II: The empirical Bayes case. J Amer Statist Assoc 1972; 67: 130–139.

37.

Morris

. Parametric empirical Bayes inference: theory and applications. J Am Stat Assoc 1983; 78: 47–55.

38.

Carlin

Louis

. Bayesian methods for data analysis, 3rd ed. Boca Raton, FL: Chapman and Hall/CRC, 2008.

39.

Liu

X-Q

Rong

J-Y

Liu

X-Y

. Best linear unbiased prediction for linear combinations in general mixed linear models. J Multivar Anal 2008; 99: 1503–1517.

40.

Hedeker

Gibbons

. Longitudinal data analysis, Hoboken: John Wiley & Sons, Inc., 2006.

41.

Casella

Berger

. Statistical inference, 2nd ed. Pacific Grove: Duxbury, 2002.

42.

Brown

Prescott

. Applied mixed models in medicine, 3rd ed. Chichester: John Wiley & Sons, Ltd., 2015.

43.

Mackenzie

Donovan

McArdle

. Regression spline mixed models: a forestry example. J Agr Biol Envir St 2005; 10: 394–410.

44.

Simpkin

Metcalfe

Martin

et al.

Longitudinal prostate-specific antigen reference ranges: choosing the underlying model of age-related changes. Stat Meth Med Res 2016; 25: 1875–1891.

45.

Durbán

Harezlak

Wand

et al.

Simple fitting of subject-specific curves for longitudinal data. Stat Med 2005; 24: 1153–1167.

46.

Diaz

McDonald

Pinter

et al.

Measuring and statistically testing the size of the effect of a chemical compound on a continuous in-vitro pharmacological response through a new statistical model of response detection limit. J Biopharm Stat 2015; 25: 757–780.

47.

Kepner

. Saturation behavior: a general relationship described by a simple second-order differential equation. Theor Biol Med Model 2010; 7: 11–11.

48.

Fava

Rush

Trivedi

et al.

Background and rationale for the sequenced treatment alternatives to relieve depression (STAR*D) study. Psychiatr Clin North Am 2003; 26: 457–494.

49.

Harrell

. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis, 2nd ed. New York, NY: Springer, 2015.

50.

Steyerberg

. Clinical prediction models: a practical approach to development, validation, and updating, New York, NY: Springer, 2009.

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Estimating individual benefits of medical or behavioral treatments in severely ill patients

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