mistry et al 2022 national institutes of health stroke scale as an outcome in stroke research value of ancova over

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Stroke is available at www.ahajournals.org/journal/str

The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.

Correspondence to: Eva A. Mistry, MBBS, MSCI, Department of Neurology and Rehabilitation Medicine, 260 Stetson St, Suite 2300, Cincinnati, OH 45219. Email [email protected]

This manuscript was sent to Scott E. Kasner, Guest Editor, for review by expert referees, editorial decision, and final disposition.

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/STROKEAHA.121.034859.

For Sources of Funding and Disclosures, see page e154.

COMMENTS AND OPINIONS

National Institutes of Health Stroke Scale as an Outcome in Stroke Research: Value of ANCOVA Over Analyzing Change From Baseline

Eva A. Mistry , MBBS, MSCI; Sharon D. Yeatts , PhD; Pooja Khatri , MD, MSc; Akshitkumar M. Mistry , MD;

Michelle Detry, PhD; Kert Viele, PhD; Frank E. Harrell, Jr, PhD; Roger J. Lewis , MD, PhD

ABSTRACT: National Institutes of Health Stroke Scale (NIHSS), measured a few hours to days after stroke onset, is an attractive outcome measure for stroke research. NIHSS at the time of presentation (baseline NIHSS) strongly predicts the follow-up NIHSS. Because of the need to account for the baseline NIHSS in the analysis of follow-up NIHSS as an outcome measure, a common and intuitive approach is to define study outcome as the change in NIHSS from baseline to follow-up (∆NIHSS).

However, this approach has important limitations. Analyzing ∆NIHSS implies a very strong assumption about the relationship between baseline and follow-up NIHSS that is unlikely to be satisfied, drawing into question the validity of the resulting statistical analysis. This reduces the precision of the estimates of treatment effects and the power of clinical trials that use this approach to analysis. ANCOVA allows for the analysis of follow-up NIHSS as the dependent variable while adjusting for baseline NIHSS as a covariate in the model and addresses several challenges of using ∆NIHSS outcome using simple bivariate comparisons (eg, a t test, Wilcoxon rank-sum, linear regression without adjustment for baseline) for stroke research.

In this article, we use clinical trial simulations to illustrate that variability in NIHSS outcome is less when follow-up NIHSS is adjusted for baseline compared to ∆NIHSS and how a reduction in this variability improves the power. We outline additional, important clinical and statistical arguments to support the superiority of ANCOVA using the final measurement of the NIHSS adjusted for baseline over, and caution against using, the simple bivariate comparison of absolute NIHSS change (ie, delta).

Key Words: goal ◼ ischemic stroke ◼ National Institutes of Health (US) ◼ patients ◼ statistics

I

n patients with stroke, a key goal in treatment is mini- mization of the functional disability.¹ A common predictor of long-term disability, usually measured few months after the incident stroke, is short-term functional assessment done using the National Institutes of Health Stroke Scale (NIHSS).^2,3 Despite known limitations, such as disproportionate representation of anterior cerebral circulation and left hemispheric deficits as well as inabil- ity to account for the baseline versus new neurological deficits,^4–6 NIHSS is the most widely utilized measure of stroke severity.⁷ Baseline and follow-up NIHSS are clinically important parameters commonly obtained via clinical examination as a routine practice in patients with ischemic stroke.¹ Both baseline and follow-up NIHSS are

strong predictors of functional outcomes in patients with ischemic stroke.^3,8 Clinical trials evaluating the efficacy of treatment strategies for acute ischemic stroke often evaluate the follow-up NIHSS, measured as early as few hours after stroke onset, as the primary or a key secondary outcome.^9–13 The follow-up NIHSS is strongly influ- enced by the baseline NIHSS, for example, measured at the time of patient presentation.¹⁴ Thus, to increase the sensitivity of statistical analyses designed to detect beneficial effects of experimental treatment strategies, the statistical analysis of the follow-up NIHSS needs to account or adjust for the baseline measurement.

An early target for acute ischemic stroke therapies is to minimize the neurological worsening, as measured

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COMMENTS AND OPINIONS by an increase in the NIHSS from the baseline, and is

known to strongly associate with reduction in long-term disability.^15–18 Because of the need to account for the baseline NIHSS during analysis, a common and intui- tively appealing approach is to define the primary study outcome as the change in NIHSS (∆NIHSS) from baseline to follow-up.^9–13 For example, ∆NIHSS from baseline was used as a key secondary end point in the SWIFT- PRIME (Solitaire With the Intention for Thrombectomy as Primary Endovascular Treatment; 27±3 hours),¹¹ ASTER (Contact Aspiration vs Stent Retriever for Suc- cessful Revascularization; 24 hours),¹³ phase 1/2a study of the agent SB623 (24 months),¹² and Amphetamine- Enhanced Stroke Recovery (3 months) trials,⁹ and as the primary end point in the NINDS-rtPA (National Institute of Neurological Disorders and Stroke–Recombinant Plasminogen Activator; 24 hours) trial.¹⁰ It has been extensively used as primary outcome in observational studies.^18–20 Although defining the treatment effect as a

∆NIHSS from baseline to follow-up for the purposes of analysis is appealing, this approach has important limitations. Analyzing acute ischemic stroke outcomes as the

∆NIHSS implies a very strong assumption about the relationship between baseline and follow-up NIHSS that is unlikely to be satisfied, drawing into question the validity of the resulting statistical analysis. This reduces the precision of the estimates of treatment effects and the power of clinical trials that use this approach to analysis.

Our goals here are to clarify the limitations associated with the analysis of NIHSS as a change from baseline, propose a more appropriate and powerful approach, and illustrate the advantages of the proposed approach through a simulation study of trial outcomes.

CLINICAL CONCERNS REGARDING USE OF CHANGE IN NIHSS AS A CLINICAL OUTCOME

In the setting of acute ischemic stroke, a variety of patient characteristics are likely responsible for differences in baseline NIHSS and in the rate and magnitude of worsening in the progression to follow-up NIHSS, for example, cause of stroke, age, extent of collateral ves- sels, and recanalization status.^21,22 By analyzing ∆NIHSS, one makes an inherent assumption that it is independent of baseline NIHSS and that the average benefit of treatment, as measured by ∆NIHSS, is independent of the baseline NIHSS. However, ∆NIHSS depends on baseline NIHSS.²³ Furthermore, it is highly unlikely that each unit of change in NIHSS would have the same implica- tion on functional outcomes for patients. For example, a 4-unit change in NIHSS would have widely different effects on functional outcome in a patient with a baseline NIHSS of 6 compared to one with a baseline NIHSS of 22. An additional consideration, unique to stroke patients,

is the laterality of the lesion. Because left cerebral hemisphere is disproportionately represented in the NIHSS, a given baseline and ∆NIHSS may lead to differential long-term clinical outcomes in patients with left versus right hemispheric infarcts.^4,5 Finally, if patient centrality is to be considered of paramount importance when selecting outcome measures,^24,25 the follow-up NIHSS is likely to carry more clinical relevance and have higher implica- tions on functional outcomes than the ∆NIHSS. In other words, final neurological status is likely to be more rel- evant to the patient than how such status was achieved.

STATISTICAL CONCERNS REGARDING USE OF CHANGE IN NIHSS FOR

ANALYSIS

The issues associated with change from baseline measures have been described by Harrell and Slaughter,²⁶ among others. Briefly, analysis of ∆NIHSS assumes that (1) baseline NIHSS is linearly related to follow- up NIHSS and (2) that this relationship has a slope of 1 (ie, for every 1 unit increase in baseline NIHSS, the expected follow-up NIHSS also increases by 1 unit).

Although follow-up NIHSS is associated with baseline NIHSS, the slope of this relationship is far from 1.^4,23 If the baseline is not strongly associated with the follow- up (ie, if the correlation between baseline NIHSS and follow-up NIHSS is any <0.5), then ∆NIHSS will have higher variance than follow-up NIHSS. However, it can be speculated that patients with lower baseline NIHSS may have smaller ∆NIHSS compared to those who start at a higher baseline NIHSS, because of the more lim- ited room for improvement or the milder nature of initial lesion. Furthermore, because of regression to the mean, it is impossible to make the ∆NIHSS truly independent from baseline NIHSS.²⁷ Calculated ∆NIHSS is subject to measurement error from two sources, both the baseline and final measurement, whereas the follow-up NIHSS is only subject to its own measurement error. Practical measurement of NIHSS at baseline is subject to additional variability such as measurement by noncertified raters and within a pressing timeline.

This error will be further enhanced if baseline NIHSS is used as an inclusion/exclusion criterion for a study,²⁶ as is the case for most acute stroke trials. In this case, it becomes necessary to obtain a second baseline to get a meaningful ∆NIHSS.²⁶

To reduce bias in observational studies and to maxi- mize power and precision in randomized trials, it is imperative to adjust any NIHSS outcome measure for baseline NIHSS using regression modeling.²⁶ In this case, ∆NIHSS is confusing as it requires baseline NIHSS to be on both the left-hand and right-hand side of the model equa- tion. Because ∆NIHSS (computed as follow-up minus baseline NIHSS) is a function of baseline NIHSS, the

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COMMENTS AND OPINIONS

researcher analyzing the ∆NIHSS as an outcome measure indirectly considers baseline NIHSS as a response to the treatment, which is not logical.²⁸ It is also important to note that missing data on baseline NIHSS would be difficult to account for when ∆NIHSS is used as an outcome measure.

ALTERNATE APPROACH: ANCOVA

ANCOVA addresses the challenges noted above. It is equivalent to a regression model that includes the baseline value as one of the predictors. The ANCOVA model also includes a categorical variable (such as treatment A versus treatment B) as a covariate. In this case, ANCOVA allows for the analysis of follow-up

NIHSS as the dependent variable while adjusting for baseline NIHSS as a covariate in the model. This is a superior approach compared to analysis of ∆NIHSS using simple bivariate comparisons (for example, a t test, Wilcoxon rank-sum, linear regression without adjustment for baseline) for stroke trials as well as observational research.^28–30

As mentioned above, it is imperative to adjust for baseline NIHSS using regression modeling whenever NIHSS is used as an outcome measure. This approach correctly partitions variability associated with the baseline NIHSS and thus is more powerful. In fact, ANCOVA is more efficient than analysis of ∆NIHSS for all levels of correlation between baseline and follow-up NIHSS other than a correlation coefficient of 1.0.²⁶ Consequently, for

Figure 1. Variability associated with change in National Institutes of Health Stroke Scale (NIHSS), vs final NIHSS adjusted for baseline, in a single simulated randomized clinical trial for 2000 patients.

A and D illustrate the baseline and follow-up NIHSS, with measurement errors, for the control and treated groups. The mean baseline NIHSS in the control group was 10±4.9 (median with interquartile range [IQR], 10 [2–18]). Mean follow-up NIHSS in the control group was 14.7±5.04 (median with IQR, 15 [8–22]). Mean baseline NIHSS was 10.39 ±4.9 (median with IQR, 10 [2–18]) and mean follow-up NIHSS was 10.2 ±4.9 (median with IQR, 11 [4–18]) in the treated group. ∆NIHSS (follow-up minus baseline NIHSS) for the control and treated groups are shown in B and E. The mean ∆NIHSS for the was 4.4±5.2 (median with IQR 5 [−2 to 12]) for the control group and −0.14±4.9 (median with IQR 0 [−6 to 6]) for the treatment group. C and F outline the residuals (the difference between follow-up NIHSS predicted by a given baseline NIHSS and the actual follow-up NIHSS for the given baseline) of a linear model of follow-up NIHSS adjusted for the baseline adjusted NIHSS was calculated using linear regression of follow-up NIHSS over the baseline. The mean of these residuals for the control group was 0±4.5 (median with IQR −0.04 [−3.04 to 3.12]) and for the treated group was 0±4.2 (median with IQR −0.09 [−3.05 to 2.95]). The variance for the ∆NIHSS (B and E) is larger than that of the final NIHSS adjusted for the baseline in a model (C and F), as shown by the shorter length of the box plot and smaller SD for each plot (5.2 vs 4.5 and 4.9 vs 4.2) for C and F compared to B and E. Additional methodological details and R code for trial simulations is provided in the Supplemental Material.

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COMMENTS AND OPINIONS a given treatment effect and sample size, ANCOVA using

follow-up NIHSS adjusted for baseline NIHSS renders more power compared to the simple bivariate analysis, for example using the t test, of ∆NIHSS. In addition, missing value of baseline NIHSS can be easily handled in ANCOVA using multiple imputation.

We performed trial simulations to illustrate these arguments. First, we generated baseline and follow-up NIHSS data (with measurement errors) for a total of 2000 patients, randomly assigned to control and treatment groups in a 1:1 ratio (Figure 1). The distribution of the baseline and follow-up NIHSS are shown in Fig- ure 1A and 1D. In Figure 1B and 1E, we show the distribution of ∆NIHSS (follow-up minus baseline NIHSS).

To illustrate the variance of follow-up NIHSS adjusted for the baseline, we plot the residuals (the difference between follow-up NIHSS predicted by a given baseline in a linear model and the actual follow-up NIHSS for the given baseline) in Figure 1C and 1F. The variance in residuals is smaller than the variance of the

∆NIHSS. To illustrate how this reduction in variability improves power, we simulate 10 000 trials for each combination of treatment effect (difference in NIHSS between treatment and control arms) ranging from −10 to −2 (increments of 0.25), with sample size ranging from 20 to 200 (increments of 1; Figure 2). The 80%

and 95% probability of a trial deemed “positive” with a p≤0.05 is plotted for ANCOVA (solid line; difference in follow-up NIHSS between treatment and control group, adjusted for the baseline NIHSS) and a t test (dotted line; difference in the ∆NIHSS between treatment and control group). Figure 2 demonstrates that ANCOVA yields 80% and 95% power to detect a difference in the follow-up NIHSS between the treatment and control arm (treatment effect) with fewer number of patients compared with simple bivariate comparison of ∆NIHSS using t test. To illustrate further how the

correlation coefficient of baseline and follow-up NIHSS impacts this power, we perform similar trial simulations for correlation coefficients ranging from 0.25 to 0.75. It is notable that lower correlation coefficients have a drastic impact on the differences in the power of ANCOVA versus t test. Additional statistical details and the R code for trial simulations are provided in the Supplemental Material. The authors declare that all supporting data are available within the article (and its Supplemental Material).

The ANCOVA model can be formulated to allow for a nonlinear relationship between baseline NIHSS and follow-up NIHSS. Additionally, it does not assume that

∆NIHSS is independent of baseline NIHSS. Analysis of follow-up NIHSS adjusted for baseline using ANCOVA better accounts for individual patient factors responsible for this clinical progression than the analysis of ∆NIHSS.

ANCOVA allows the researcher to select patients based on baseline NIHSS and still be able to account for it in the analysis without acquiring a second baseline to can- cel some of the regression to the mean.

It is important to note that the arguments herein favoring ANCOVA over simple bivariate comparisons not accounting for the baseline measurement can be applied to other outcomes assessed at a pretreat- ment baseline and single follow-up timepoint, such as the radiographic evaluation of infarct volume, modified Rankin Scale score for functional assessment, or the Fugl-Meier Score of motor strength. Although details of measurements of each scale and their clinical interpretation varies, the principles regarding estimation of treatment effect using final value adjusted for the baseline compared with change from baseline apply generally. For categorical scales such modified Rankin Scale score analyzing change from baseline is particu- larly flawed because each unit of change from baseline implies vastly varying clinical impacts that is dependent

Figure 2. Comparison of ANCOVA using follow-up National Institutes of Health Stroke Scale (NIHSS) adjusted for baseline as the outcome measure and t test using ∆NIHSS as outcome measure for various, treatment effects, sample sizes, and correlation coefficients (of follow-up and baseline NIHSS).

Additional methodological details and R code for trial simulations are provided in the Supplemental Material.

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on the baseline score, that is, change from modified Rankin Scale score of 0 (no symptoms) to 2 (mild disability, independent in activities of daily living and ambu- lation) is vastly dissimilar to that from modified Rankin Scale 2 to 4 (Severe disability, dependent in activities of daily living and cannot ambulate independently), as is noted in the utility values of modified Rankin Scale score categories.³¹

CONCLUSIONS

NIHSS-based outcome measures have been widely used and will be increasingly considered in the design of future stroke trials and observational studies. When used, as with any repeated measure with a prerandom- ization baseline and a post-treatment follow-up measurement, we recommend using ANCOVA to analyze final measurement adjusting for the baseline value as a covariate in the regression model. We provide clinical and statistical arguments to support the superiority of ANCOVA using final measurement of the outcome variable adjusted for baseline value over the simple bivariate comparison of absolute change (ie, delta). The concerns regarding the latter approach are upheld by examples of simulated clinical trials, and thus, we caution against using absolute change in a variable as an outcome measure for stroke research.

ARTICLE INFORMATION Affiliations

Department of Neurology and Rehabilitation Medicine, University of Cincin- nati, OH (E.A.M., P.K.). Department of Neurosurgery, University of Louisville, KY (A.M.M.). Department of Public Health Sciences, Medical University of South Carolina, Charleston (S.D.Y.). Berry Consultants LLC, Austin, TX (M.D., K.V., R.J.L.).

Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN (F.E.H.). Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, CA (R.J.L.). Department of Emergency Medicine, David Geffen School of Medicine at UCLA, CA (R.J.L.).

Sources of Funding

None.

Disclosures

Dr E. Mistry’s work is supported by National Institute of Neurological Disorders and Stroke (NINDS) K23NS113858. Dr Yeatts reports grants from NINDS during the conduct of the study; grants from NINDS, grants from National Heart, Lung. and Blood Institute (NHLBI), and other from Bard Inc outside the submitted work; and Anticipated personal fees from American Heart Association (AHA) for editorial board services with Stroke. Dr Harrell’s work on this paper was supported by Clinical and Translational Science Award program No. UL1 TR002243 from the National Center for Advancing Translational Sciences, and from National Institutes of Health (NIH) NHLBI 1OT2HL156812-01, Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Integration of Host-targeting Therapies for coronavirus disease 2019 (COVID-19) Administrative Coordinat- ing Center from the National Heart, Lung. and Blood Institute (NHLBI). Dr Khatri reports funding from NIH/NINDS U24 NS 107241. Drs Detry, Viele, and Lewis report being an Employee of Berry Consultants, a company that specializes in the design and conduct of adaptive clinical trials for pharmaceutical companies, medical device companies, government entities, patient advocacy groups, and international consortia. Dr Lewis is the Senior Medical Scientist at Berry Consul- tants, LLC, a statistical consulting firm that specializes in the design of adaptive and platform clinical trials, including trials focused on the evaluation of treatments for stroke. The other author reports no conflicts.

Supplemental Material

Supplemental Methods

REFERENCES

1. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, Biller J, Brown M, Demaerschalk BM, Hoh B, et al. 2018 guide- lines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the american heart association/

american stroke association. Stroke. 2018;49:E46–e110

2. Kasner SE. Clinical interpretation and use of stroke scales. Lancet Neurol.

2006;5:603–612. doi: 10.1016/S1474-4422(06)70495-1

3. Adams HP Jr, Davis PH, Leira EC, Chang KC, Bendixen BH, Clarke WR, Woolson RF, Hansen MD. Baseline NIH stroke scale score strongly predicts outcome after stroke: a report of the trial of Org 10172 in Acute Stroke Treatment (TOAST). Neurology. 1999;53:126–131. doi: 10.1212/wnl.53.

1.126

4. Fink JN, Selim MH, Kumar S, Silver B, Linfante I, Caplan LR, Schlaug G. Is the association of National Institutes of Health Stroke Scale scores and acute magnetic resonance imaging stroke volume equal for patients with right- and left-hemisphere ischemic stroke? Stroke. 2002;33:954–958.

doi: 10.1161/01.str.0000013069.24300.1d

5. Woo D, Broderick JP, Kothari RU, Lu M, Brott T, Lyden PD, Marler JR, Grotta JC. Does the national institutes of health stroke scale favor left hemisphere strokes? NINDS t-PA Stroke Study Group. Stroke. 1999;30:2355–2359.

doi: 10.1161/01.str.30.11.2355

6. Sato S, Toyoda K, Uehara T, Toratani N, Yokota C, Moriwaki H, Naritomi H, Minematsu K. Baseline NIH Stroke Scale Score predicting outcome in anterior and posterior circulation strokes. Neurology. 2008;70(24 Pt 2):2371–

2377. doi: 10.1212/01.wnl.0000304346.14354.0b

7. Lyden PD, Lu M, Levine SR, Brott TG, Broderick J, Group NrSS. A modified national institutes of health stroke scale for use in stroke clinical trials: preliminary reliability and validity. Stroke. 2001;32:1310–1317. doi:

10.1161/01.str.32.6.1310

8. Rost NS, Bottle A, Lee JM, Randall M, Middleton S, Shaw L, Thijs V, Rinkel GJ, Hemmen TM, Global Comparators Stroke Gc. Stroke severity is a crucial predictor of outcome: An international prospective validation study. J Am Heart Assoc. 2016;5:e002433. doi: 10.1161/JAHA.115.002433 9. Goldstein LB, Lennihan L, Rabadi MJ, Good DC, Reding MJ, Dromerick

AW, Samsa GP, Pura J. Effect of dextroamphetamine on poststroke motor recovery: a randomized clinical trial. JAMA Neurol. 2018;75:1494–1501.

doi: 10.1001/jamaneurol.2018.2338

10. National Institute of Neurological D, Stroke rt PASSG. Tissue plasminogen activator for acute ischemic stroke. N Engl J Med. 1995;333:1581–1587.

doi: 10.1056/NEJM199512143332401

11. Saver JL, Goyal M, Bonafe A, Diener HC, Levy EI, Pereira VM, Albers GW, Cognard C, Cohen DJ, Hacke W, et al. Stent-retriever thrombectomy after intravenous t-pa vs. T-pa alone in stroke. N Engl J Med. 2015;372:2285–

2295. doi: 10.1056/NEJMoa1415061

12. Steinberg GK, Kondziolka D, Wechsler LR, Lunsford LD, Kim AS, Johnson JN, Bates D, Poggio G, Case C, McGrogan M, et al. Two-year safety and clinical outcomes in chronic ischemic stroke patients after implantation of modified bone marrow-derived mesenchymal stem cells (sb623): a phase 1/2a study.

J Neurosurg. 2018;131:1462–1472. doi: 10.3171/2018.5.JNS173147 13. Lapergue B, Blanc R, Gory B, Labreuche J, Duhamel A, Marnat G, Saleme S,

Costalat V, Bracard S, Desal H, et al. Effect of endovascular contact aspiration vs stent retriever on revascularization in patients with acute ischemic stroke and large vessel occlusion: The ASTER randomized clinical trial.

JAMA. 2017;318:443–452. doi: 10.1001/jama.2017.9644

14. Appelros P, Terént A. Characteristics of the National Institute of Health Stroke Scale: results from a population-based stroke cohort at baseline and after one year. Cerebrovasc Dis. 2004;17:21–27. doi: 10.1159/000073894 15. Heit JJ, Mlynash M, Kemp SM, Lansberg MG, Christensen S, Marks MP,

Ortega-Gutierrez S, Albers GW. Rapid neurologic improvement predicts favorable outcome 90 days after thrombectomy in the DEFUSE 3 study.

Stroke. 2019;50:1172–1177. doi: 10.1161/STROKEAHA.119.024928 16. Irvine HJ, Battey TW, Ostwaldt AC, Campbell BC, Davis SM, Donnan GA,

Sheth KN, Kimberly WT. Early neurological stability predicts adverse outcome after acute ischemic stroke. Int J Stroke. 2016;11:882–889. doi:

10.1177/1747493016654484

17. Ozdemir O, Giray S, Arlier Z, Baş DF, Inanc Y, Colak E. Predictors of a good outcome after endovascular stroke treatment with stent retrievers. Scienti- ficWorldJournal. 2015;2015:403726. doi: 10.1155/2015/403726

(6)

18. Rangaraju S, Frankel M, Jovin TG. Prognostic value of the 24-hour Neurolog- ical Examination in anterior circulation ischemic stroke: a post hoc analysis of two randomized controlled stroke trials. Interv Neurol. 2016;4:120–129.

doi: 10.1159/000443801

19. Heitsch L, Ibanez L, Carrera C, Binkley MM, Strbian D, Tatlisumak T, Bustamante A, Ribo M, Molina C, Davalos A, et al. Early neurological change after ischemic stroke is associated with 90-day outcome. Stroke.

2021;52:132–141. doi: 10.1161/STROKEAHA.119.028687

20. Saver JL, Altman H. Relationship between neurologic deficit severity and final functional outcome shifts and strengthens during first hours after onset.

Stroke. 2012;43:1537–1541. doi: 10.1161/STROKEAHA.111.636928 21. Ali LK, Saver JL. The ischemic stroke patient who worsens: new assess-

ment and management approaches. Rev Neurol Dis. 2007;4:85–91.

22. Yamamoto H, Bogousslavsky J, van Melle G. Different predictors of neurological worsening in different causes of stroke. Arch Neurol.

1998;55:481–486. doi: 10.1001/archneur.55.4.481

23. Weimar C, Mieck T, Buchthal J, Ehrenfeld CE, Schmid E, Diener HC, German Stroke Study C. Neurologic worsening during the acute phase of ischemic stroke. Arch Neurol. 2005;62:393–397. doi: 10.1001/archneur.62.3.393 24. Coster WJ. Making the best match: selecting outcome measures for clini-

cal trials and outcome studies. Am J Occup Ther. 2013;67:162–170. doi:

10.5014/ajot.2013.006015

25. Selby JV, Beal AC, Frank L. The Patient-Centered Outcomes Research Institute (PCORI) national priorities for research and initial research agenda. JAMA. 2012;307:1583–1584. doi: 10.1001/jama.

2012.500

26. Harrell FE, Slaughter JC. Biostatistics for biomedical research, section 14.4, 2019. Accessed October 7, 2021. https://hbiostat.org/doc/bbr.pdf.

27. Barnett AG, Dobson AJ, van der Pols JC. Regression to the mean: what it is and how to deal with it. Int J Epidemiol. 2004;34:215–220. doi:

10.1093/ije/dyh299

28. Senn S. Change from baseline and analysis of covariance revisited. Stat Med. 2006;25:4334–4344. doi: 10.1002/sim.2682

29. Bland JM, Altman DG. Comparisons against baseline within randomised groups are often used and can be highly misleading. Trials. 2011;12:264.

doi: 10.1186/1745-6215-12-264

30. Knoke JD. Nonparametric analysis of covariance for comparing change in randomized studies with baseline values subject to error. Biometrics.

1991;47:523–533.

31. Chaisinanunkul N, Adeoye O, Lewis RJ, Grotta JC, Broderick J, Jovin TG, Nogueira RG, Elm JJ, Graves T, Berry S, et al. Adopting a patient-centered approach to primary outcome analysis of acute stroke trials using a utility-weighted modified rankin scale. Stroke. 2015;46:2238–2243. doi:

10.1161/STROKEAHA.114.008547