scholarly journals Is conventionally calculated anchor-based minimum clinically important difference value catches the real clinical increment? Determining the situations that make the answer “no” by a simulation study

2019 ◽  
Vol 1 (1) ◽  
pp. 522-528
Author(s):  
Selcen Yüksel ◽  
Pervin Demir ◽  
Afra Alkan
Keyword(s):  
Sample Size ◽  
Simulation Study ◽  
Roc Analysis ◽  
Minimum Clinically Important Difference ◽  
Change Score ◽  
Post Treatment ◽  
Alternative Methods ◽  
Response Patterns ◽  
The Real ◽  
Clinically Important Difference

Abstract The aim of this study was to examine the accuracy of conventionally used method-optimal cutoff of Receiver Operating Characteristic (ROC) curve- to determine the minimum clinically important difference (MCID), which is the estimator of responsiveness for scales, by a simulation study. The baseline person parameters were firstly generated and, by using these values, two gold standard groups were constructed as “improved” and “non-improved” after the treatment. Five point-likert response patterns were obtained for 20 items in each group, representing pre- and post-treatment responses of individuals. The mean change score between post treatment and baseline scores for the improved group was considered as real MCID (MCIDR), after baseline and post-treatment total scores were calculated from response patterns. The cut-off for change score specified by ROC analysis, which best discriminates between improved group and not improved group, MCIDROC, was compared to MCIDR. The scenarios of simulation were consisted of sample size and distribution of total scores for improved group. The data were generated for each of 40 scenarios with 1000 MCMC repeats. It was observed that the MCIDR and MCIDROC were not so affected by sample size. However, MCIDROC overestimated the MCIDR values in all scenarios. Briefly, the cut-off points obtained by ROC analysis found to be greater than the real MCID values. Therefore, alternative methods are required to calculate MCID.

scholarly journals Statistical analysis of two arm randomized pre-post designs with one post-treatment measurement

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Wan
Keyword(s):  
Treatment Effect ◽  
Repeated Measures ◽  
Informed Choice ◽  
Clinical Effectiveness ◽  
Change Score ◽  
Post Treatment ◽  
Analysis Of Covariance ◽  
Alternative Methods ◽  
Main Effect ◽  
Comparable Performance

Abstract Background Randomized pre-post designs, with outcomes measured at baseline and after treatment, have been commonly used to compare the clinical effectiveness of two competing treatments. There are vast, but often conflicting, amount of information in current literature about the best analytic methods for pre-post designs. It is challenging for applied researchers to make an informed choice. Methods We discuss six methods commonly used in literature: one way analysis of variance (“ANOVA”), analysis of covariance main effect and interaction models on the post-treatment score (“ANCOVAI” and “ANCOVAII”), ANOVA on the change score between the baseline and post-treatment scores (“ANOVA-Change”), repeated measures (“RM”) and constrained repeated measures (“cRM”) models on the baseline and post-treatment scores as joint outcomes. We review a number of study endpoints in randomized pre-post designs and identify the mean difference in the post-treatment score as the common treatment effect that all six methods target. We delineate the underlying differences and connections between these competing methods in homogeneous and heterogeneous study populations. Results ANCOVA and cRM outperform other alternative methods because their treatment effect estimators have the smallest variances. cRM has comparable performance to ANCOVAI in the homogeneous scenario and to ANCOVAII in the heterogeneous scenario. In spite of that, ANCOVA has several advantages over cRM: i) the baseline score is adjusted as covariate because it is not an outcome by definition; ii) it is very convenient to incorporate other baseline variables and easy to handle complex heteroscedasticity patterns in a linear regression framework. Conclusions ANCOVA is a simple and the most efficient approach for analyzing pre-post randomized designs.

scholarly journals Precision and Sample Size Requirements for Regression-Based Norming Methods for Change Scores

Assessment ◽  
2020 ◽  
pp. 107319112091360
Author(s):  
Zhengguo Gu ◽  
Wilco H. M. Emons ◽  
Klaas Sijtsma
Keyword(s):  
Sample Size ◽  
Simulation Study ◽  
Change Score ◽  
Minimum Sample Size ◽  
Change Scores ◽  
Satisfactory Precision ◽  
Similarities And Differences ◽  
Minimum Sample

To interpret a person’s change score, one typically transforms the change score into, for example, a percentile, so that one knows a person’s location in a distribution of change scores. Transformed scores are referred to as norms and the construction of norms is referred to as norming. Two often-used norming methods for change scores are the regression-based change approach and the T Scores for Change method. In this article, we discuss the similarities and differences between these norming methods, and use a simulation study to systematically examine the precision of the two methods and to establish the minimum sample size requirements for satisfactory precision.

scholarly journals Brief Pain Inventory–Facial minimum clinically important difference

2015 ◽  
Vol 122 (1) ◽  
pp. 180-190 ◽  
Author(s):  
Sukhmeet K. Sandhu ◽  
Casey H. Halpern ◽  
Venus Vakhshori ◽  
Keyvan Mirsaeedi-Farahani ◽  
John T. Farrar ◽  
...  
Keyword(s):  
Pain Intensity ◽  
Statistical Methods ◽  
Brief Pain Inventory ◽  
Minimum Clinically Important Difference ◽  
Change Score ◽  
Point Method ◽  
Cutoff Point ◽  
Optimal Cutoff ◽  
Clinically Important Difference ◽  
Optimal Cutoff Point

OBJECT Neurosurgeons are frequently the primary physicians measuring pain relief in patients with trigeminal neuralgia (TN). Unfortunately, the measurement of pain can be complex. The Brief Pain Inventory–Facial (BPI-Facial) is a reliable and validated multidimensional tool that consists of 18 questions. It measures 3 domains of pain: 1) pain intensity (worst and average pain intensity), 2) interference with general activities of daily living (ADL), and 3) face-specific pain interference. The objective of this paper is to determine the patient-reported minimum clinically important difference (MCID) using the BPI-Facial. METHODS The authors conducted a retrospective study of 234 patients with TN seen in a single neurosurgeon's office. Patients completed baseline and 1-month follow-up BPI-Facial questionnaires. The MCID was calculated using an anchor-based approach in which the defined anchor was the 7-point patient global impression of change (PGIC). Two statistical methods were employed: mean change score and optimal cutoff point. RESULTS Using the mean change score method, the investigators calculated the MCID for the 3 domains of the BPIFacial: 44% and 30% improvement in pain intensity at its worst and average, respectively, 54% improvement in interference with general ADL, and 63% improvement in interference with facial ADL. Using the optimal cutoff point method, they also calculated the MCID for the 3 domains of the BPI-Facial: 57% and 28% improvement in pain intensity at its worst and average, respectively, 75% improvement in interference with general ADL, and 62% improvement in interference with facial ADL. CONCLUSIONS The BPI-Facial is a multidimensional pain scale that measures 3 domains of pain. Although 2 statistical methods were used to calculate the MCID, the optimal cutoff point method was the superior one because it used data from the majority of subjects included in this study. A 57% improvement in pain intensity at its worst and a 28% improvement in pain intensity at its average were the MCIDs for patients with facial pain. A greater improvement was needed to achieve the MCID for interference with general and facial ADL. A 75% improvement in interference with general ADL and a 62% improvement in interference with facial ADL were needed to achieve an MCID. While pain intensity is easier to measure, pain's interference with ADL may be more important for patient outcomes when designing or evaluating interventions in the field of TN. The BPI-Facial is a useful instrument to measure changes in multidimensional aspects of pain in patients with TN.

Establishing the Minimum Clinically Important Difference in NDI and mJOA for Adult Cervical Deformity

2017 ◽  
Vol 17 (10) ◽  
pp. S50
Author(s):  
Alexandra Soroceanu ◽  
Jeffrey L. Gum ◽  
Michael P. Kelly ◽  
Peter G. Passias ◽  
Justin S. Smith ◽  
...  
Keyword(s):  
Minimum Clinically Important Difference ◽  
Cervical Deformity ◽  
Clinically Important Difference

scholarly journals The minimum clinically important difference: which direction to take

2019 ◽  
Vol 26 (6) ◽  
pp. 850-855 ◽  
Author(s):  
T. H. P. Draak ◽  
B. T. A. de Greef ◽  
C. G. Faber ◽  
I. S. J. Merkies ◽  
Keyword(s):  
Minimum Clinically Important Difference ◽  
Clinically Important Difference

Minimum clinically important difference in pain, disability, and quality of life after neural decompression and fusion for same-level recurrent lumbar stenosis: understanding clinical versus statistical significance

2012 ◽  
Vol 16 (5) ◽  
pp. 471-478 ◽  
Author(s):  
Scott L. Parker ◽  
Stephen K. Mendenhall ◽  
David N. Shau ◽  
Owoicho Adogwa ◽  
William N. Anderson ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Minimum Clinically Important Difference ◽  
Leg Pain ◽  
Lumbar Stenosis ◽  
Detectable Change ◽  
Calculation Methods ◽  
Recurrent Stenosis ◽  
Minimum Detectable Change ◽  
Clinically Important Difference

Object Spine surgery outcome studies rely on patient-reported outcome (PRO) measurements to assess treatment effect, but the extent of improvement in the numerical scores of these questionnaires lacks a direct clinical meaning. Because of this, the concept of a minimum clinically important difference (MCID) has been used to measure the critical threshold needed to achieve clinically relevant treatment effectiveness. As utilization of spinal fusion has increased over the past decade, so has the incidence of same-level recurrent stenosis following index lumbar fusion, which commonly requires revision decompression and fusion. The MCID remains uninvestigated for any PROs in the setting of revision lumbar surgery for this pathology. Methods In 53 consecutive patients undergoing revision surgery for same-level recurrent lumbar stenosis–associated back and leg pain, PRO measures of back and leg pain were assessed preoperatively and 2 years postoperatively, using the visual analog scale for back pain (VAS-BP) and leg pain (VAS-LP), Oswestry Disability Index (ODI), Physical and Mental Component Summary categories of the 12-Item Short Form Health Survey (SF-12 PCS and MCS) for quality of life, Zung Depression Scale (ZDS), and EuroQol-5D health survey (EQ-5D). Four established anchor-based MCID calculation methods were used to calculate MCID (average change; minimum detectable change; change difference; and receiver operating characteristic curve analysis) for 2 separate anchors (health transition index of the SF-36 and the satisfaction index). Results All patients were available for 2-year PRO assessment. Two years after surgery, a significant improvement was observed for all PROs assessed. The 4 MCID calculation methods generated a range of MCID values for each of the PROs (VAS-BP 2.2–6.0, VAS-LP 3.9–7.5, ODI 8.2–19.9, SF-12 PCS 2.5–12.1, SF-12 MCS 7.0–15.9, ZDS 3.0–18.6, and EQ-5D 0.29–0.52). Each patient answered synchronously for the 2 anchors, suggesting both of these anchors are equally appropriate and valid for this patient population. Conclusions The same-level recurrent stenosis surgery-specific MCID is highly variable based on calculation technique. The “minimum detectable change” approach is the most appropriate method for calculation of MCIDs in this population because it was the only method to reliably provide a threshold above the 95% confidence interval of the unimproved cohort (greater than the measurement error). Based on this method, the MCID thresholds following neural decompression and fusion for symptomatic same-level recurrent stenosis are 2.2 points for VAS-BP, 5.0 points for VAS-LP, 8.2 points for ODI, 2.5 points for SF-12 PCS, 10.1 points for SF-12 MCS, 4.9 points for ZDS, and 0.39 QALYs for EQ-5D.

Detecting change in community participation with the Enfranchisement scale of the community participation indicators

2021 ◽  
pp. 026921552110521
Author(s):  
Jessica Kersey ◽  
Lauren Terhorst ◽  
Joy Hammel ◽  
Carolyn Baum ◽  
Joan Toglia ◽  
...  
Keyword(s):  
Community Participation ◽  
Functional Independence Measure ◽  
Minimum Clinically Important Difference ◽  
Functional Independence ◽  
Baseline Score ◽  
Sensitivity To Change ◽  
Detectable Change ◽  
Change Scores ◽  
Minimum Detectable Change ◽  
Clinically Important Difference

Objective This study determined the sensitivity to change of the Enfranchisement scale of the Community Participation Indicators in people with stroke. Data sources We analyzed data from two studies of participants with stroke: an intervention study and an observational study. Main measures The Enfranchisement Scale contains two subscales: the Importance subscale (feeling valued by and contributing to the community; range: 14–70) and the Control subscale (choice and control: range: 13–64). Data analysis Assessments were administered 6 months apart. We calculated minimum detectable change and minimal clinically important difference. Results The Control subscale analysis included 121 participants with a mean age of 61.2 and mild-moderate disability (Functional Independence Measure, mean = 97.9, SD = 24.7). On the Control subscale, participants had a mean baseline score of 51.4 (SD = 10.4), and little mean change (1.3) but with large variation in change scores (SD = 11.5). We found a minimum detectable change of 9 and a minimum clinically important difference of 6. The Importance subscale analysis included 116 participants with a mean age of 60.7 and mild-moderate disability (Functional Independence Measure, mean = 98.9, SD = 24.5). On the Importance subscale, participants had a mean baseline score of 44.1 (SD = 12.7), and again demonstrated little mean change (1.08) but with large variation in change scores (SD = 12.6). We found a minimum detectable change of 11 and a minimum clinically important difference 7. Conclusions The Control subscale required 9 points of change, and the Importance subscale required 11 points of change, to achieve statistically and clinically meaningful changes, suggesting adequate sensitivity to change.

Sign in / Sign up

Export Citation Format

Share Document