A methodological survey of the analysis, reporting and interpretation of Absolute Risk ReductiOn in systematic revieWs (ARROW): a study protocol Surveying physicians to determine the minimal important difference: implications for sample-size calculation. What constitutes a clinically important pain reduction in patients after third molar surgery? Relative versus absolute change in forced vital capacity in idiopathic pulmonary fibrosis Minimal clinically important differences of 3 patient-rated outcomes instruments. Key Methodological Features of Randomized Controlled Trials of Alzheimer’s Disease Therapy Clinically useful measures of effect in binary analyses of randomized trials. Number Needed to Treat (or Harm) Using Numerical Results from Systematic Reviews in Clinical Practice Minimal clinically important difference on the Beck Depression Inventory - II according to the patient's perspective Photographic and clinical techniques for outcome assessment in sixth nerve palsy. Defining hematoma expansion in intracerebral hemorrhage Minimally important clinical difference of the Timed 25-Foot Walk Test: results from a randomized controlled trial in patients with multiple sclerosis
Background Clinicians , providers and guideline panels use absolute effects to weigh the advantages and downsides of treatment alternatives . Relative measures have the potential to mislead readers . However , little is known about the reporting of absolute measures in systematic review s. The objectives of our study are to determine the proportion of systematic review s that report absolute measures of effect for the most important outcomes , and ascertain how they are analyzed , reported and interpreted . Methods / design We will conduct a method ological survey of systematic review s published in 2010 . We will conduct a 1:1 stratified r and om sampling of Cochrane vs. non-Cochrane systematic review s. We will calculate the proportion of systematic review s reporting at least one absolute estimate of effect for the most patient-important outcome for the comparison of interest . We will conduct multivariable logistic regression analyses with the reporting of an absolute estimate of effect as the dependent variable and pre-specified study characteristics as the independent variables . For systematic review s reporting an absolute estimate of effect , we will document the methods used for the analysis , reporting and interpretation of the absolute estimate . Discussion Our method ological survey will inform current practice s regarding reporting of absolute estimates in systematic review s. Our findings may influence recommendations on reporting , conduct and interpretation of absolute estimates . Our results are likely to be of interest to systematic review authors , funding agencies , clinicians , guideline developers and journal editors The minimal important difference ( MID ) is the smallest benefit of treatment that would result in clinicians recommending it to their patients . The MID is necessary to calculate sample size for r and omized clinical trials , but its chosen value is often arbitrary . This study set out to determine the practicability of surveying physicians to elicit the MID for clinical trial sample -size calculation . Using a mail survey , we elicited the MID of different physician specialties ( family medicine , internal medicine , vascular surgery ) for using propranolol  to slow abdominal aortic aneurysm ( AAA ) growth assuming that propranolol was efficacious in this condition . We used different outcome measures ( growth rate or proportion of patients requiring surgery ) and different methods of data presentation for the proportion of patients requiring surgery ( absolute risk reduction or number needed to treat ) . The MID varied significantly by physician specialty , experience with AAA and propranolol , and the method used to elicit the MID . Consequently , sample -size calculations using these various MIDs varied from 116 to 3015 . Future attempts to elicit the MID need to consider carefully who is surveyed , how data are presented , and how opinions are elicited BACKGROUND For patients with surgical third molar removal , it is unknown what constitutes a clinical ly important change in patients ' visual analogue scale ( VAS ) reports of pain intensity . OBJECTIVES To determine what constitutes a clinical ly important change in pain intensity on a VAS following surgical removal of the third molar . METHODS The study population consisted of patients participating in three r and omized trials . Patients were asked to rate their pain three times per day over a period of seven days on a 100 mm VAS after surgical removal of the third molar . Global Perceived Effect was measured on day 1 and day 7 and was used as the external criterion for assessing clinical ly important pain reduction . Global Perceived Effect scores of 6 ( ' much improved ' ) or higher were classified as clinical ly ' successful ' , and scores of 5 ( ' slightly improved ' ) or below were classified as clinical ly ' unsuccessful ' . For each trial , the mean absolute and relative changes in VAS scores were calculated for both ' successful ' and ' unsuccessful ' treatments . Sensitivity and specificity analyses were performed . RESULTS The patients who reported ' successful ' pain reduction showed a relative pain reduction of ≥69 % and an absolute pain reduction > 2.5 cm on the VAS , whereas patients who classified their pain reduction as ' unsuccessful ' had a relative pain reduction of ≤18.5 % and an absolute pain reduction < 0.5 cm on the VAS . Furthermore , sensitivity and specificity analyses showed that a cut-off point of ≥50 % relative pain reduction exhibited the best balance of sensitivity and specificity . CONCLUSION Relative pain reduction of ≥50 % and an absolute pain reduction of ≥2.5 cm on the VAS were most accurate in predicting a successful pain reduction after a given treatment Background Decline in forced vital capacity ( FVC ) over time reliably predicts mortality in patients with idiopathic pulmonary fibrosis . The use of this measure in clinical practice is recommended by current evidence -based guidelines . It is unknown if the method of calculating decline in FVC ( relative vs absolute change ) impacts its frequency or its ability to predict mortality . Methods Patients with idiopathic pulmonary fibrosis from two prospect i ve cohorts were included if they had a baseline and 12-month follow-up FVC . A ≥10 % decline in FVC from baseline was calculated in two ways : a relative decline of 10 % ( eg , from 60 % predicted to 54 % predicted ) and an absolute decline of 10 % ( eg , from 60 % predicted to 50 % predicted ) . The frequency of a ≥10 % decline in FVC and its ability to predict 2-year transplant-free survival were compared between these two methods . Declines in FVC of ≥5 % and ≥15 % were similarly compared . Analyses were performed unadjusted and adjusted for age , gender , use of oxygen , baseline FVC and baseline diffusion capacity for carbon monoxide . Results The frequency of any given FVC decline was significantly greater using the relative change in FVC method . For ≥10 % decline , both methods predicted 2-year transplant-free survival with similar accuracy , and remained significant predictors after adjusting for baseline characteristics . The absolute change method appeared more predictive for ≥5 % decline . Conclusions Using the relative change in FVC maximises the chance of identifying a ≥10 % decline in FVC without sacrificing prognostic accuracy . This may not hold true for ≥5 % decline in FVC . These findings have important implication s for clinical practice and the design of clinical trials PURPOSE Patient-rated instruments are increasingly used to measure orthopedic outcomes . However , the clinical relevance of modest score changes on such instruments is often unclear . This study was design ed to define the minimal clinical ly important differences ( MCIDs ) of the Disabilities of the Arm , Shoulder , and H and ( DASH ) , QuickDASH ( subset of DASH ) , and Patient-Rated Wrist Evaluation ( PRWE ) question naires for atraumatic conditions of the h and , wrist , and forearm . METHODS We prospect ively analyzed 102 patients undergoing nonoperative treatment for isolated tendinitis , arthritis , or nerve compression syndromes from the forearm to the h and . By phone , patients completed the DASH , QuickDASH , and PRWE at enrollment and at 2 weeks ( n = 78 used in the analysis ) and 4 weeks ( n = 24 used in the analysis ) after initiating treatment . Patients reporting clinical improvement each contributed a single data point categorized as no change ( n = 41 ) , minimal improvement ( n = 30 ) , or marked improvement ( n = 31 ) via a vali date d anchor-based approach . We calculated the MCID as the mean change score for each outcome measure in the minimal improvement group . RESULTS The MCID ( 95 % confidence interval ) for the DASH was 10 ( 5 - 15 ) . The MCID for the QuickDASH was 14 ( 9 - 20 ) . The MCID was 14 ( 8 - 20 ) for the PRWE . The MCID values were significantly different from changes in these outcome measures at times of either no change or marked improvement . The MCID values positively correlated with baseline outcome measure scores to a greater degree than final outcome measure scores . CONCLUSIONS Longitudinal changes on the DASH of 10 points , on the QuickDASH of 14 points , and on the PRWE of 14 points represent minimal clinical ly important changes . We recommend application of these MCID values for group-level analysis when conducting research and interpreting data examining groups of patients as opposed to assessing individual patients . These MCID values may provide a basis for sample size calculations for future investigation using these common patient-rated outcome measures . TYPE OF STUDY /LEVEL OF EVIDENCE Diagnostic III Background : The results of clinical trials are routinely presented in terms of statistical significance , which may or may not indicate clinical significance . Analysis of the minimal clinical ly important difference ( MCID ) of cognitive scales has received little attention to date . Objectives : By review ing the key method ological features ( sample size , duration , statistical and clinical significance ) of clinical trials examining the efficacy of tacrine in the treatment of Alzheimer ’s disease ( AD ) , we assessed their ability to detect clinical ly important changes in cognition . Design : The value for the MCID of the  Mini-Mental State Examination ( MMSE )  was determined by surveying specialists in neurology and geriatric medicine . This value was then used to interpret the clinical significance of the results of published r and omized controlled trials ( RCTs ) assessing the efficacy of tacrine in the treatment of AD and to retrospectively determine their optimal sample size and trial duration . Results : The mean survey MCID for the MMSE was 3.72 ( 95 % confidence interval 3.50–3.95 ) points . Only 2 of 12 tacrine RCTs using the MMSE found a statistically significant difference in MMSE scores for patients taking tacrine  compared with those taking placebo . These improvements were not clinical ly significant when compared with the survey MMSE MCID . For parallel trials of tacrine in AD , the smallest sample size and minimum trial duration required to demonstrate a clinical ly significant difference were calculated to be 53 subjects and 1 year , respectively . Five of the 7 parallel trials met the required sample size ; however , none of them met the criteria for trial duration . Conclusions : When using the MMSE as an outcome measure , no tacrine trial reported results that were clinical ly significant as perceived by clinicians working with dementia patients . Application of a range of plausible MCIDs to the parallel design RCTs also demonstrated that 2 of 7 of these trials did not have sufficient sample size , and none had sufficient duration of treatment to reliably detect clinical ly meaningful changes in cognition . Future clinical trials in this area will need to incorporate the evolving knowledge of MCIDs in order to increase their chance of detecting clinical ly relevant results The results of a r and omized clinical trial can be reported using relative and /or absolute estimators of treatment effect . These various measures convey different information , and the choice can influence the physician 's appreciation of the size of treatment effect and , subsequently , treatment decisions . We compare the estimators with respect to the clinical ly relevant information conveyed to physicians , and identify which clinical questions can and can not be answered directly by each . We also identify opportunities for misinterpretation when one estimator is substituted for another , or when an estimator is mislabeled . Clinical ly important questions are addressed most directly by reporting both relative and absolute effects using relative risk and its complement , relative risk reduction , and risk difference and its reciprocal , number needed to treat . This is true of estimates of treatment effect derived from a single trial and also from meta- analysis of a group of trials . Because the control group 's risk affects the numerical value of the odds ratio , the odds ratio can not substitute for the risk ratio in conveying clinical ly important information to physicians . This is especially important when large treatment effects are shown in trials carried out in population s at high baseline risk The effect of a treatment versus controls may be expressed in relative or absolute terms . For rational decision-making , absolute measures are more meaningful . The number needed to treat , the reciprocal of the absolute risk reduction , is a powerful estimate of the effect of a treatment . It is particularly useful because it takes into account the underlying risk ( what would happen without the intervention ? ) . The number needed to treat tells us not only whether a treatment works but how well it works . Thus , it informs health care professionals about the effort needed to achieve a particular outcome . A number needed to treat should be accompanied by information about the experimental intervention , the control intervention against which the experimental intervention has been tested , the length of the observation period , the underlying risk of the study population , and an exact definition of the endpoint . A 95 % confidence interval around the point estimate should be calculated . An isolated number needed to treat is rarely appropriate to summarize the usefulness of an intervention ; multiple numbers needed to treat for benefit and harm are more helpful . Absolute risk reduction and number needed to treat should become st and ard summary estimates in r and omized controlled trials As professionals , we want to use the best treatments ; as patients , we want to be given them . Knowing whether an intervention works ( or does not work ) is fundamental to clinical decision making . However , clinical decision making involves more than simply taking published results of research directly to the bedside . Physicians need to consider how similar their patients are to those in the published studies , to take the values and preferences of their patients into account , and to consider their own experience with a given test or treatment . Evidence from clinical research is becoming increasingly important in medical- practice decisions as more and better evidence is published . But when is the evidence strong enough to justify changing a practice ? Individual studies that involve only small numbers of patients may have results that are distorted by the r and om play of chance and thus lead to less than optimal decisions . As is clear from other papers in this series , systematic review s identify , critically appraise , and review all the relevant studies on a clinical question and are more likely to give a valid answer . They use explicit methods and quality st and ards to reduce bias . Their results are the closest we can come to reaching the truth given our current state of knowledge . The questions about an intervention that a systematic review should answer are the following : 1 . Does it work ? 2 . If it works , how well does it work in general and compared with placebo , no treatment , or other interventions that are currently in use ? 3 . Is it safe ? 4 . Will it be safe and effective for my patients ? Whereas the critical appraisal and qualitative synthesis provided by review articles can be interpreted directly , the numerical products of quantitative review s can be more difficult to underst and and apply in daily clinical practice . This paper provides guidance on how to interpret the numerical and statistical results of systematic review s , translate these results into more underst and able terms , and apply them directly to individual patients . Many of these principles can also be used to interpret the numerical results of individual clinical studies . They are particularly relevant to systematic review s , however , because such review s contain more information than do primary studies and often exert greater influence than do individual studies . Making Sense of the Numerical Results of Clinical Studies Although the results of clinical studies can be expressed in intuitively meaningful ways , such results do not always easily translate into clinical decision making . For example , results are frequently expressed in terms of risk , which is an expression of the frequency of a given outcome . ( Risks are probabilities , which can vary between 0.0 and 1.0 . A probability of 0.0 means that the event will never happen , and a probability of 1.0 means that it always happens . ) Consider a hypothetical study of the recurrence of migraine headaches in a control group receiving placebo and a treatment group receiving a new antimigraine preparation , drug M ( a secondary prevention trial ) . Suppose that at the end of the trial , migraines recurred in 30 % of the control group ( the risk for recurrence was 0.30 ) but in only 5 % of the drug M group ( risk of 0.05 ) ( Table 1 ) . Table 1 . Numerical Expression of Hypothetical Clinical Trial Results The outcomes of the study are clear enough for the two groups when they are examined separately . But clinicians and patients are more interested in the comparative results , that is , the outcome in one group relative to the outcome in the other group . This overall ( comparative ) result can be expressed in various ways . For example , the relative risk , which is the risk in the treatment group relative to that in the control group , is simply the ratio of the risks in the two groups . In other words , relative risk is the risk in the treatment group divided by that in the control group , 0.05 0.30 , or 0.17 . The comparison can also be expressed as the reduction in relative risk , which is the ratio between the decrease in risk ( in the treatment group ) and the risk in the control group , 0.25 0.30 , or 0.83 ( Table 1 ) . ( The relative risk reduction can also be calculated as 1 relative risk ) . Although the clinical meaning of relative risk ( and relative risk reduction ) is reasonably clear , relative risk has the distinct disadvantage that a given value ( for example , 0.17 ) is the same whether the risk with treatment decreases from 0.80 to 0.14 , from 0.30 to 0.05 , from 0.001 to 0.00017 , and so forth . The clinical implication s of these changes clearly differ from one another enormously and depend on the specific disease and intervention . An important alternate expression of comparative results , therefore , is the absolute risk reduction . Absolute risk reduction is determined by subtracting the risk in one group from the risk in the other ( for example , the risk in the treatment group is subtracted from the risk in the placebo group ) . In the case of our migraine study , the absolute risk reduction would be 0.30 0.05 , which equals 0.25 , or 25 percentage points . In contrast , for a study in which the risk decreased from 0.001 to 0.00017 , the absolute risk reduction would be only 0.00083 , or 0.083 percentage points , which is a trivial change in comparison ( Table 1 ) . This arithmetic emphasizes the difficulty of expressing the results of clinical studies in meaningful ways . Relative risk and relative risk reduction clearly give a quantitative sense of the effects of an intervention in proportional terms but provide no clue about the size of an effect on an absolute scale . In contrast , although it tells less about proportional effects , absolute risk says a great deal about whether an effect is likely to be clinical ly meaningful . Despite this benefit , even absolute risk is problematic because it is a dimensionless , abstract number ; that is , it lacks a direct connection with the clinical situation in which the patient and physician exist . However , another way of expressing clinical research results can provide that clinical link : the number needed to treat ( NNT ) . Number Needed To Treat The NNT for a given therapy is simply the reciprocal of the absolute risk reduction for that treatment [ 1 , 2 ] . In the case of our hypothetical migraine study ( in which risk decreased from 0.30 without treatment with drug M to 0.05 with treatment with drug M , for a relative risk of 0.17 , a relative risk reduction of 0.83 , and an absolute risk reduction of 0.25 ) , the NNT would be 1 0.25 , or 4 . In concrete clinical terms , an NNT of 4 means that you would need to treat four patients with drug M to prevent migraine from recurring in one patient . To emphasize the difference between the concepts embodied in NNT and relative risk , recall the various situations mentioned above , in all of which the relative risk was 0.17 but in which the absolute risk decreased from 0.80 to 0.14 in one case and from 0.001 to 0.00017 in another . Note that the corresponding NNTs in these two other cases are 1.5 and 1204 , respectively : that is , you would need to treat 1.5 and 1204 patients to obtain a therapeutic result in these two situations compared with 4 patients with drug M ( Table 1 ) . The NNT can be calculated easily and kept as a single numerical reminder of the effectiveness ( or , as we will see , the potential for harm ) of a particular therapy . As we suggested , the NNT has the crucial advantage of direct applicability to clinical practice because it shows the effort that is required to achieve a particular therapeutic target . The NNT has the additional advantage that it can be applied to any beneficial outcome or any adverse event ( when it becomes the number needed to harm [ NNH ] ) . The concept of NNT always refers to a comparison group ( in which patients receive placebo , no treatment , or some other treatment ) , a particular treatment outcome , and a defined period of treatment . In other words , the NNT is the number of patients that you will need to treat with drug or treatment A to achieve an improvement in outcome compared with drug or treatment B for a treatment period of C weeks ( or other unit of time ) . To be fully specified , NNT and NNH must always specify the comparator , the therapeutic outcome , and the duration of treatment that is necessary to achieve the outcome . Important Qualities of the Number Needed To Treat The NNT is treatment specific . It describes the difference between treatment and control in achieving a particular clinical outcome . Table 2 shows NNTs from a selection of systematic review s and large r and omized , controlled trials . Table 2 . Numbers Needed To Treat from Systematic Review s and R and omized , Controlled Trials A very small NNT ( that is , one that approaches 1 ) means that a favorable outcome occurs in nearly every patient who receives the treatment and in few patients in a comparison group . Although NNTs close to 1 are theoretically possible , they are almost never found in practice . However , small NNTs do occur in some therapeutic trials , such as those comparing antibiotics with placebo in the eradication of Helicobacter pylori infection or those examining the use of insecticide for head lice ( Table 2 ) . An NNT of 2 or 3 indicates that a treatment is quite effective . In contrast , such prophylactic interventions as adding aspirin to streptokinase to reduce 5-week vascular mortality rates after myocardial infa rct ion may have NNTs as high as 20 to 40 and still be considered clinical ly effective . Limitations of the Number Needed To Treat Although NNTs are powerful instruments for interpreting clinical effects , they also have important limitations . First , an NNT is generally expressed as a single number , which is known as its point estimate . As with all experimental measurements , however , the true value of the NNT can be higher or lower than the point estimate determined through clinical studies . The 95 % CIs of the NNT are useful in this regard because they provide an indication that Background The Beck Depression Inventory , 2nd edition ( BDI-II ) is widely used in research on depression . However , the minimal clinical ly important difference ( MCID ) is unknown . MCID can be estimated in several ways . Here we take a patient-centred approach , anchoring the change on the BDI-II to the patient 's global report of improvement . Method We used data collected ( n = 1039 ) from three r and omized controlled trials for the management of depression .  Improvement on a ‘ global rating of change ’ question was compared with changes in BDI-II scores using general linear modelling to explore baseline dependency , assessing whether MCID is best measured in absolute terms ( i.e. difference ) or as percent reduction in scores from baseline ( i.e. ratio ) , and receiver operator characteristics ( ROC ) to estimate MCID according to the optimal threshold above which individuals report feeling ‘ better ’ . Results Improvement in BDI-II scores associated with reporting feeling ‘ better ’ depended on initial depression severity , and statistical modelling indicated that MCID is best measured on a ratio scale as a percentage reduction of score . We estimated a MCID of a 17.5 % reduction in scores from baseline from ROC analyses . The corresponding estimate for individuals with longer duration depression who had not responded to antidepressants was higher at 32 % . Conclusions MCID on the BDI-II is dependent on baseline severity , is best measured on a ratio scale , and the MCID for treatment-resistant depression is larger than that for more typical depression . This has important implication s for clinical trials and practice PURPOSE There are few data on the reproducibility of clinical assessment of ductions and alignment . We evaluated photographic methods that may be used for masked outcome determination in a clinical trial and compared them with clinical measures . DESIGN Interexaminer reliability study . PARTICIPANTS Twenty-three patients with unilateral sixth nerve palsy and three control participants were clinical ly evaluated by two masked examiners . MAIN OUTCOME MEASURES Abduction deficit was grade d as 0 to -5 .  Simultaneous prism and cover tests ( SPCT ) and alternate cover tests ( ACT )  were performed at distance and near fixation . Photographs were taken of abduction and distance alignment by each examiner . The photographs were evaluated by a third masked reader , who assigned abduction grade , measured absolute abduction ( mm ) and relative abduction ( % ) , and calculated alignment in prism diopters ( pd ) . Agreement was evaluated by calculating intraclass correlation coefficients ( r(i ) ) , weighted kappa statistics ( kappa ) , and Spearman rank correlation coefficients ( r(S ) ) . RESULTS There was excellent agreement between the two clinicians in clinical abduction deficit ( kappa = 0.86 ) SPCT and ACT at distance and near ( r(i ) 0.94 - 0.96 ) , between the clinical grade and masked photographic grade ( kappa = 0.83 ) , and between the two sets of photographs for absolute abduction and relative abduction ( r(i ) = 0.98 and 0.97 ) . Both photographic measures of abduction correlated well with the clinical grade ( r(S ) = -0.96 for each ) . Measurements of alignment from photographs correlated with clinical SPCT measurement ( r(i ) = 0.88 ) , but had a lower level of absolute agreement ( 38 % within 5 pd ) than between two independent SPCT measurements ( 96 % within 5 pd ) . CONCLUSIONS The excellent interexaminer agreement of our new photographic abduction assessment and of masked clinical measures suggest that these methods would be useful in clinical trials . In contrast , our simple method of photographic assessment of alignment lacks excellent agreement with the clinical assessment . These data are important in planning clinical trials in strabismus Background : Hematoma expansion ( HE ) is a surrogate marker in intracerebral hemorrhage ( ICH ) trials . However , the amount of HE necessary to produce poor outcomes in an individual is unclear ; there is no agreement on a clinical ly meaningful definition of HE . We compared commonly used definitions of HE in their ability to predict poor outcome as defined by various cutpoints on the modified Rankin Scale ( mRS ) . Methods : In this cohort study , we analyzed 531 patients with ICH from the Virtual International Stroke Trials Archive . Primary outcome was mRS at 90 days , dichotomized into 0–3 vs 4–6 . Secondary outcomes included other mRS cutpoints and mRS “ shift analysis . ” Sensitivity , specificity , and predictive values for commonly used HE definitions were calculated . Results : Between 13 % and 32 % of patients met the commonly used HE definitions . All definitions independently predicted poor outcome ; positive predictive values increased with higher growth cutoffs but at the expense of lower sensitivities . All HE definitions showed higher specificity than sensitivity . Absolute growth cutoffs were more predictive than relative cutoffs when mRS 5–6 or 6 was defined as “ poor outcome . ” Conclusion : HE robustly predicts poor outcome regardless of the growth definition or the outcome definition . The highest positive predictive values are obtained when using an absolute growth definition to predict more severe outcomes . Given that only a minority of patients may have clinical ly relevant HE , hemostatic ICH trials may need to enroll a large number of patients , or select for a population that is more likely to have HE Abstract Background : Limited data define what constitutes a clinical ly significant change on the Timed 25-Foot Walk ( T25FW ) in multiple sclerosis ( MS ) ; however , most studies suggest a value of ≥20 % . Analyses were undertaken to estimate the minimally important clinical difference ( MICD ) in walking speed as measured by the T25FW in patients with MS . Methods : Data from MS-F203 , a r and omized trial of dalfampridine extended release tablets , 10 mg twice daily ( prolonged-release/sustained-release fampridine outside the US ) in patients with MS , were used to calculate the MICD , as an absolute and percentage value , for the T25FW test . Both anchor- ( using the Clinician Global Impression [ CGI ] ) and distribution-based ( 2.77 × st and ard error of measurement or 0.50 st and ard deviation units ) approaches were used . Using the anchor-based estimations , the proportion of patients in the dalfampridine and placebo groups achieving at least a MICD in MS-F203 was determined . Results : A correlation between change in T25FW speed during and CGI at the end of double-blind period was found ( Spearman r = −0.39 , p < 0.0001 ) . Irrespective of treatment group , participants categorized ‘ minimally improved ’ by the CGI had a mean improvement in T25FW speed of 0.36 feet/second or a 17.2 % relative change from an average baseline walking speed of 2.1 feet/second ( effect size = 0.49 ) ; values representing MICDs . MICD estimates of 0.35 and 0.37 feet/second were generated using distribution-based approaches . In MS-F203 , a greater proportion of patients receiving dalfampridine achieved ≥0.36 feet/second ( 12/72 vs. 78/224 , p = 0.007 ) and a 17.2 % ( 11/72 vs. 87/224 , p = 0.0005 ) improvement in T25FW speed compared to placebo . Limitations : MICD estimates from this analysis may not apply to patients with different disease characteristics from MS-F203 . A different anchor may result in a different MICD estimation . Conclusion : Our MICD estimate for an improvement in T25FW is close to previous estimates of 20 % change . Dalfampridine may improve walking speed in a considerable proportion of patients by a clinical ly relevant amount
Thus the distribution method can only be used to calculate an absolute MCID as there is no st and ard deviation of baseline score for a relative measure .
The anchor-based method relates the change in
To the Editor : The same patient outcome data from clinical trial results , when presented as absolute or relative changes , may appear different in magnitude .
Recommendations are to report both absolute and relative , or at least baseline , data from which to calculate absolute values [ 1 , 2 ] .
To inform clinical practice , outcome improvements , whether relative or absolute , must be statistically significant and clinical ly meaningful .
A minimal clinical ly important difference ( MCID ) should inform sample size calculations for clinical trials .