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 Measured enthusiasm: does the method of reporting trial results alter perceptions of therapeutic effectiveness? Setting the benchmark for research in the management of acute pain in emergency departments. Negative results of randomized clinical trials published in the surgical literature: equivalency or error? Changes in cerebral perfusion after revascularization of symptomatic carotid artery stenosis: CT measurement. Randomized discontinuation trials: utility and efficiency. Talking about Treatment: The Language of Populations and the Language of Individuals Minimum clinically important difference in lumbar spine surgery patients: a choice of methods using the Oswestry Disability Index, Medical Outcomes Study questionnaire Short Form 36, and pain scales. Outcome measures in chronic low back pain Testing minimal clinically important difference: consensus or conundrum? Reporting Height Restoration in Vertebral Compression Fractures
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 OBJECTIVE To compare clinicians ' ratings of therapeutic effectiveness when different trial end points were presented as percent reductions in relative compared with absolute risk and as numbers of patients treated to avoid one adverse outcome . DESIGN Survey , with r and om allocation of two question naires . SETTING Toronto teaching hospitals . RESPONDENTS Convenience sample of 100 faculty and housestaff in internal medicine and family medicine . INTERVENTION One question naire presented results for three end points of the Helsinki Heart Study as separate drug trials using only absolute differences in events ; the other showed the same end points as relative differences . Both question naires included a fourth " trial , " showing person-years of treatment needed to prevent one myocardial infa rct ion . MAIN OUTCOME MEASURE The " trials " were each rated on an 11-point scale , from treatment " harmful " to " very effective . " RESULTS Respondents ' ratings of effectiveness varied with the end point . Controlling for end point , ratings of effectiveness by the 50 participants receiving absolute event data were lower than those by 50 participants responding to relative risk reductions ( P < 0.001 ) ; however , no end-point difference was more than 0.6 scale points . For a " trial " reporting that 77 persons were treated for 5 years to prevent one myocardial infa rct ion , mean ratings were 2.3 or 1.8 scale points lower , respectively ( both P < 0.001 ) , than when the same data were shown as relative or absolute risk reductions . CONCLUSIONS Clinicians ' views of drug therapies are affected by the common use of relative risk reductions in both trial reports and advertisements , by end-point emphasis , and , above all , by underuse of summary measures that relate treatment burden to therapeutic yields in a clinical ly relevant manner OBJECTIVE The aim of this study was to define the degree of change on a visual analogue scale that corresponded to a clinical ly meaningful reduction in the level of pain reported by patients experiencing acute pain . METHOD Secondary analysis of data was performed from prospect i ve , descriptive studies of 152 adults and 73 children presenting to emergency departments with acute pain . At presentation and at 20 min intervals to a maximum of three measurements , patients marked the level of their pain on a 100 mm , non-hatched visual analogue scale . At each follow up , they also gave a verbal rating of their pain as ' a lot better ' , ' a little better ' , ' much the same ' , ' a little worse ' or ' much worse ' . Data from the groups reporting that pain was ' a lot better ' and ' about the same ' were analysed for mean and median absolute change in visual analogue scale and compared . RESULTS Of 559 pain comparisons , 60 were rated as ' a lot better ' and 257 as ' about the same ' . The mean absolute change in visual analogue scale for the group reporting that pain was ' a lot better ' was 24 mm ( median = 19 mm , 95 % confidence interval = 17 - 31 mm ) . For the group reporting that pain was ' about the same ' , the mean visual analogue scale change was 0 mm ( median = 0 mm , 95 % confidence interval = -1 to 1 mm ) . CONCLUSION When evaluating treatments for acute pain , a difference in visual analogue scale pain score of less than about 20 mm is unlikely to be clinical ly meaningful . Studies should be design ed and reported relative to this benchmark rather than the minimum clinical ly significant difference in visual analogue scale pain score HYPOTHESIS We hypothesized that review of r and omized controlled clinical trials ( RCTs ) with nonstatistically significant or " negative " results published in the surgical literature do not have appropriate statistical power to demonstrate equivalency between treatment arms . DATA SOURCES AND STUDY SELECTION The MEDLINE data base was search ed to obtain reports of all RCTs with negative results published in 3 surgical journals from 1988 to 1998 . Manual review of one year ( 1997 ) of publications for each journal was performed to vali date our search strategy . Equivalency was evaluated using the Two One-Sided Tests Procedure and post hoc power calculations . DATA SYNTHESIS Ninety reports of RCTs with negative results were identified in the surgical literature between 1988 and 1998 . The manual review of 1997 showed a 100 % retrieval rate for our search strategy . After applying the Two One-Sided Tests Procedure , 35 reports ( 39 % ) met the criteria for demonstrating equivalency . The other 55 reports ( 61 % ) contained at least a 10 % absolute difference in the 90 % confidence interval of Delta . Using the power calculation method , only 22 ( 24 % ) articles had a power greater than.80 to detect a 50 % difference in therapeutic effect . Only 29 % of the reports included a formal sample size calculation and these studies were more likely to demonstrate equivalency than those without a sample size estimate ( P<.01 ) . CONCLUSIONS Many reports from negative RCTs published in the surgical literature lack sufficient statistical power to establish that clinical ly important differences are not present . Surgeons should perform appropriate sample size calculations when design ing RCTs and recognize the utility of confidence intervals when reporting negative results PURPOSE To prospect ively evaluate changes in brain perfusion computed tomographic ( CT ) parameters after revascularization of unilateral symptomatic carotid artery stenosis and to determine whether pretreatment perfusion CT parameters can be used to predict changes in cerebral hemodynamics after treatment . MATERIAL S AND METHODS This study was medical ethics committee approved , and written informed consent was obtained from all patients . Thirty-six patients ( 23 men , 13 women ; mean age , 67 years ) with unilateral symptomatic carotid artery stenosis underwent multi-detector row perfusion CT before and after revascularization .  Mean transit time ( MTT ) , cerebral blood volume ( CBV ) , and cerebral blood flow ( CBF ) were calculated , and relative values based on the comparison between symptomatic and asymptomatic hemispheres-specifically , relative CBV , relative CBF , and difference in MTT-were derived . The absolute and relative perfusion values before treatment were assessed and compared with posttreatment values . These analyses were performed for the group as a whole by using the t test and after subdividing patients into three tertiles according to the difference in MTT by using the Wilcoxon signed rank test . RESULTS Among the absolute perfusion values , only the MTT in the symptomatic hemisphere improved significantly after treatment ( P < .01 ) . All relative values ( difference in MTT , relative CBV , and relative CBF ) changed significantly after treatment ( P < .05 ) . When the patients were subdivided into three tertiles according to difference in MTT , no significant change in any relative perfusion value could be demonstrated in the lowest tertile , only the difference in MTT improved significantly ( P = .004 ) in the middle tertile , and all relative perfusion values changed significantly ( P = .002 ) in the highest tertile . CONCLUSION Compared with relative CT perfusion values based on interhemispheric comparison , absolute perfusion CT values are less suited for demonstrating changes in cerebral perfusion after revascularization in patients with unilateral symptomatic carotid artery stenosis The r and omized discontinuation trial ( RDT ) is a two-phase trial . In phase I all patients are openly treated with the medication being evaluated . In phase II , those who have responded are r and omly assigned to continue the same treatment or switch to placebo . Usually , non-compliers and " adverse reactors " identified in phase I are excluded from phase II . To investigate the value of this design , we review ed the advantages and limitations of discontinuation studies , and compared the RDT design to the classic r and omized clinical trial design in terms of clinical utility and efficiency ( sample size ) . A computer model was used to study the efficiency of the two design s under a broad range of assumptions . The RDT design is particularly useful in study ing the effect of long-term , non-curative therapies , especially when the clinical ly important effect is relatively small , and the use of placebo should be minimized for ethical or feasibility reasons . However , its use is limited if the objective of an investigation is to estimate the magnitude of absolute treatment effects and toxic effects in the source population , or to evaluate a potentially curative treatment . Our results indicate that selecting responders prior to r and omization has a very strong effect on the relative efficiency of the trial . Further improvement may be achieved by excluding non-compliers and adverse reactors . Under the assumptions tested in our model , the sample size required in phase II of an RDT was only 20 - 50 % of that in a classic trial In recent years , clinical decisions about treatment have been increasingly guided by the findings of r and omized clinical trials . These trials use the strongest type of study design for treatment assessment . They offer the best approach to reducing bias and balancing the distribution of known or unknown patient attributes that could threaten the internal validity of the study ( 1 ) . The quantitative measures of risk reduction used to report r and omized clinical trials describe the collective experience of the participants in the trial . To apply the results of such trials in clinical practice , physicians must translate the concepts and measures used to describe groups of patients into a language that can inform the decisions of an individual patient . A clinical vignette illustrates the tensions inherent in this process of translation . You are caring for a 74-year-old man whose blood pressure averages approximately 175/85 mm Hg . He has type 2 diabetes mellitus that is controlled by oral medications . On the basis of the findings of a r and omized clinical trial , the Systolic Hypertension in the Elderly Program ( SHEP ) ( 2 ) , you believe that treatment with antihypertensive medication will reduce his risk for a cardiovascular event . The patient asks you , If I take this medication , how will it help me ? ( The Table provides data from the SHEP trial to inform your answer . ) Table . Measures of Treatment Efficacy from the Systolic Hypertension in the Elderly Program ( SHEP ) Trial Three problems impede an accurate response to your patient 's seemingly simple question . First , reports of r and omized clinical trials do not usually account for individual characteristics that modify the effect of treatment ( 3 ) . Second , clinicians and patients often have difficulty interpreting quantitative data about risk and risk reduction ( 4 , 5 ) . Finally , the results of treatment studies can be expressed , or framed , in different ways ; this can influence how both physicians and patients perceive the efficacy of treatment ( 6 ) . You suspect that diabetes increases your patient 's risk for cardiovascular disease . He may therefore receive more benefit from treatment of hypertension . In the SHEP trial , as in most r and omized clinical trials , some participants had risk factors that increased their likelihood of a cardiovascular event ; 13 % of enrolled patients were current smokers , 10 % had a personal history of diabetes , and 5 % had previous myocardial infa rct ion ( 2 ) . However , this particular trial provides only the average estimated benefit for all participants rather than an estimate that takes your patient 's diabetes into account . Interpreting the Data The measures of risk reduction in a r and omized clinical trial are calculated from two proportions : the proportion of patients in the control group with an adverse outcome ( P C ) and the proportion of patients in the treatment group with the same adverse outcome ( P T ) . These proportions can be used to calculate two measures of treatment effect : relative risk reduction and absolute risk reduction . Formulas for both calculations are provided in the Table . The relative risk reduction is the traditional method of reporting the findings of a r and omized clinical trial . To describe to your patient the reduction in all  cardiovascular events  observed in the SHEP trial by using the relative risk reduction , you could say , Taking  antihypertensive medications for 5 years will reduce your risk for stroke or cardiac event by 30 % . Because the event rate appears in both the numerator and the denominator of the relative risk reduction , it cancels out of their ratio ( 3 ) . As a result , the same relative risk reduction can be observed for adverse outcomes that occur at much different rates . For example , the relative risk reduction for all adverse cardiovascular events in the SHEP trial ( 30 % ) was approximately the same as the relative risk reduction for a fatal stroke ( 29 % ) , even though 12.2 % of treated patients experienced some type of cardiovascular event and only 0.4 % had a fatal stroke ( Table ) . Laupacis and colleagues ( 7 ) proposed that clinical decisions about treatment should incorporate the reciprocal of the absolute risk reduction , calculated as 1/(P C P T ) , which they termed the number needed to treat . The number needed to treat to prevent any cardiovascular event in the SHEP trial was 19 , roughly 30-fold less than the number needed to treat to prevent a fatal stroke ( Table ) . You could describe the number needed to treat to your patient by saying , About 19 persons need to be treated for hypertension for 5 years to prevent one stroke or cardiac event . The number needed to treat has become the st and ard for presenting the results of r and omized clinical trials in ACP Journal Club ( 8) . Framing the Effectiveness of Treatment The relative risk reduction and number needed to treat frame therapeutic decisions differently . The number needed to treat unambiguously assumes the population perspective because it explicitly states the number of people who must be treated and for what length of time to protect one person . Although the relative risk reduction is also a population statistic , it seems to promise a benefit that is individual , large , and immediate . In studies of hypothetical treatment decisions , physicians ( 9 - 12 ) and patients ( 13 , 14 ) viewed treatments more favorably when benefits of these treatments were framed in terms of relative risk reduction . In a question naire study of 235 physicians ( 9 ) , 49 % of respondents reported that they would be more likely to prescribe a lipid-lowering drug with a relative risk reduction of 24 % than a drug with an absolute risk reduction of 0.4 % , even though both statistics were calculated from the Lipid Research Clinics Coronary Primary Prevention Trial ( 15 ) . In another question naire study that used data from the Helsinki Heart Study ( 16 ) , 88 % of patients indicated that they would be willing to take a medication that caused a 34 % relative risk reduction in heart attacks . However , only 31 % of patients said that they would take this medication when the same information was expressed as a number needed to treat ( 71 persons needed to be treated for 5 years to prevent one heart attack ) ( 14 ) . Thus , in hypothetical cases , physicians and patients seem more likely to promote or accept treatment when it is framed in individual terms rather than in population terms . Little research has assessed the content of actual clinical conversations about risk . In one study , discussion s about risk occurred in 26 % of audiotaped primary care office visits ( 17 ) . Only 3.4 % of risk discussion s were quantitative ; relative risk reduction was used in those conversations . The physician usually told the patient that an adverse outcome was certain unless the patient adopted the recommended treatment ; in other words , that P C = 1 ( 17 ) . These limited clinical data support studies of hypothetical treatment decisions ( 9 - 14 ) by suggesting that physicians may deliberately or unconsciously choose to discuss the benefits of a treatment by using a framing strategy that does not provide neutral information but is intended to guide the treatment choices of their patients . The Language of Population s : From Groups to Subgroups You could estimate the benefits of hypertension treatment for your patient more precisely if information were available about the subgroup of diabetic patients enrolled in the SHEP trial . A valid subgroup analysis of a r and omized clinical trial identifies characteristics of prognostic importance before data analysis , then compares outcomes between treated patients and controls within the subgroup of patients who have these characteristics ( 18 ) . Subgroup analyses can provide useful information to guide decision making when the subgroups are large , the subgroup analyses are specified in advance , and the statistical analysis is appropriately conservative ( 19 , 20 ) . Although the initial SHEP study ( 2 ) did not evaluate the effect of treatment of hypertension in persons with diabetes , a subsequent SHEP report found that the cumulative incidence of adverse cardiovascular events at 5 years among the subgroup of diabetic persons who received antihypertensive drugs  was 21.4 % compared with 31.5 % among diabetic persons receiving placebo ( 21 ) . When the formulas in the Table are used , these findings represent a relative risk reduction of 32 % and a number needed to treat of 10 . Thus , as we would expect to find in clinical practice , diabetic patients in the SHEP trial were at greater risk for adverse events and received more benefit from treatment of hypertension . With this additional information , you could say to your patient , On average , 19 patients need to be treated for hypertension for 5 years to prevent a stroke or cardiac event . For persons with diabetes , only 9 to 10 patients need to be treated for the same period to achieve the same benefit . In a similar way , you could use other single risk factorsor your overall assessment of your patient 's risk for a cardiovascular event if he does not receive antihypertensive medicationto modify the average estimate of treatment benefit from the SHEP trial . A full discussion of this process , which is called Bayesian reasoning , is beyond the scope of this paper , but the literature contains excellent introduction s ( 22 , 23 ) . In a busy clinical practice , neither the findings of the SHEP trial itself nor the additional data necessary to further particularize the benefits of treatment are readily available . Computerized decision support systems to provide such information in real time are being developed ( 24 ) . Problems of statistical power limit the applicability of subgroup analyses in r and omized clinical trials to individual decisions . The sample size for a r and omized clinical trial is calculated to provide a precise estimate of the average treatment effect . Nevertheless , estimates of the relative risk reduction or the number needed to treat from any study retain statistical uncertainty , which can BACKGROUND CONTEXT The impact of  lumbar spinal surgery  is commonly evaluated with three patient-reported outcome measures : Oswestry Disability Index ( ODI ) , the physical component summary ( PCS ) of the Short Form of the Medical Outcomes Study ( SF-36 ) , and pain scales . A minimum clinical ly important difference ( MCID ) is a threshold used to measure the effect of clinical treatments . Variable threshold values have been proposed as MCID for those instruments despite a lack of agreement on the optimal MCID calculation method . PURPOSE This study has three purpose s. First , to illustrate the range of values obtained by common anchor-based and distribution-based methods to calculate MCID . Second , to determine a statistically sound and clinical ly meaningful MCID for ODI , PCS , back pain scale , and leg pain scale in lumbar spine surgery patients . Third , to compare the discriminative ability of two anchors : a global health assessment and a rating of satisfaction with the results of the surgery . STUDY DESIGN This study is a review of prospect ively collected patient-reported outcomes data . PATIENT SAMPLE A total of 454 patients from a large data base of surgeries performed by the Lumbar Spine Study Group with a 1-year follow-up on either ODI or PCS were included in the study . OUTCOME MEASURES Preoperative and 1-year postoperative scores for ODI , PCS , back pain scale , leg pain scale , health transition item ( HTI ) of the SF-36 , and Satisfaction with Results scales . METHODS ODI , SF-36 , and pain scales were administered before and 1 year after spinal surgery . Several c and i date MCID calculation methods were applied to the data and the result ing values were compared . The HTI of the SF-36 was used as the anchor and compared with a second anchor ( Satisfaction with Results scale ) . RESULTS Potential MCID calculations yielded a range of values : fivefold for ODI , PCS , and leg pain , 10-fold for back pain . Threshold values obtained with the two anchors were very similar . CONCLUSIONS The minimum detectable change ( MDC ) appears as a statistically and clinical ly appropriate MCID value . MCID values in this sample were 12.8 points for ODI , 4.9 points for PCS , 1.2 points for back pain , and 1.6 points for leg pain The purpose of this prospect i ve , single site cohort quasi-experimental study was to determine the responsiveness of the numerical rating scale ( NRS ) , Rol and –Morris disability question naire ( RMDQ ) , Oswestry disability index ( ODI ) , pain self-efficacy question naire ( PSEQ ) and the patient-specific functional scale ( PSFS ) in order to determine which would best measure clinical ly meaningful change in a chronic low back pain ( LBP ) population . Several patient-based outcome instruments are currently used to measure treatment effect in the chronic LBP population . However , there is a lack of consensus on what constitutes a “ successful ” outcome , how an important improvement/deterioration has been defined and which outcome measure(s ) best captures the effectiveness of therapeutic interventions for the chronic LBP population . Sixty-three consecutive patients with chronic LBP referred to a back exercise and education class participated in this study ; 48 of the 63 patients had complete data . Five question naires were administered initially and after the 5-week back class intervention . Also at 5 weeks , patients completed a global impression of change as a reflection of meaningful change in patient status . Score changes in the five different question naires were subjected to both distribution- and anchor-based methods : st and ard error of measurement ( SEM ) and receiver operating characteristic ( ROC ) curves to define clinical improvement . From these methods , the minimal clinical ly important difference ( MCID ) defined as the smallest difference that patients and clinicians perceive to be worthwhile is presented for each instrument . Based on the SEM , a point score change of 2.4 in the NRS , 5 in the RMDQ , 17 in the ODI , 11 on the PSEQ , and 1.4 on the PSFS corresponded to the MCID . Based on ROC curve analysis , a point score change of 4 points for both the NRS and RMDQ , 8 points for the ODI , 9 points for the PSEQ and 2 points for the PSFS corresponded to the MCID . The ROC analysis demonstrated that both the PSEQ and PSFS are responsive to clinical ly important change over time . The NRS was found to be least responsive . The exact value of the MCID is not a fixed value and is dependent on the assessment method used to calculate the score change . Based on ROC curve analysis the PSFS and PSEQ were more responsive than the other scales in measuring change in patients with chronic LBP following participation in a back class programme . However , due to the small sample size , the lack of observed worsening of symptoms over time , the single centre and intervention studied these results which need to be interpreted with caution BACKGROUND CONTEXT Various method ologies have been used in attempting to eluci date a st and ard method for calculating minimal clinical ly important difference ( MCID ) . A consensus-based decision ( Initiative on Methods , Measurement , and Pain Assessment in Clinical Trials [ IMMPACT ] group ) suggested a 30 % reduction from baseline as a means to define the MCID of self-report back pain measures . Additionally , important psychometric issues need to be addressed regarding use of an independent measure of the same construct as an external criterion , instead of simply using another self-report measure , when using an anchor-based approach to MCID . PURPOSE The purpose was to test the validity of recently published guidelines regarding MCID using self-report back pain measures and objective socioeconomic outcomes . STUDY DESIGN / SETTING This is a prospect i ve study assessing change scores on commonly used spinal pain assessment measures in patients with chronic disabling occupational spinal disorders ( CDOSDs ) treated in a regional referral rehabilitation center performing interdisciplinary functional restoration . PATIENT SAMPLE The study consisted of consecutive cohort of patients ( N=1,180 ) with CDOSDs completing a functional restoration program . OUTCOMES MEASURES Self-report measures including the Oswestry Disability Index ( ODI ) and the physical component summary ( PCS ) and mental component summary ( MCS ) of the Short Form-36 ( SF-36 ) obtained before and after treatment , were compared with objective socioeconomically relevant outcomes obtained 1 year after treatment ( ie , work status and additional health-care utilization ) , that were the external criteria for evaluating MCID . METHODS Pre- to posttreatment improvement was calculated separately for each measure , and subjects were divided into two groups based on the change in scores relative to baseline : 30 % or greater versus less than 30 % improvement . One-year posttreatment objective socioeconomic outcomes were used as independent external criteria relevant to the CDOSD population . This population is often studied as the most costly and problematic cohort in spine care . RESULTS The ODI and SF-36 MCS were not associated with any of the objective 1-year outcomes used as external criteria . Reduced post-rehabilitation health-care utilization ( based on the percentage of patients pursuing health care from a new provider ) was weakly associated with 30 % or greater improvement on the SF-36 PCS , relative to patients whose scores changed by less than 30 % relative to baseline ( 17.0 % vs. 21.1 % ) . The same was true for the ODI and return-to-work . CONCLUSIONS When objective and independent criteria are used ( socioeconomic outcomes ) in a CDOSD cohort , the 30 % improvement in the ODI and SF-36 may not be a valid MCID index . This replicates similar conclusions made by an independent research group using a distribution-based approach to MCID . The validity of the MCID concept rests on future research using objective external criteria . Moreover , there remains a question whether the term " important " in MCID can be unequivocally and operationally defined as a reliable construct Study Design . Prospect i ve radiographic analysis of vertebral compression fractures ( VCFs ) that underwent vertebroplasty .  Objective . Illustrate the variability in apparent magnitude of vertebral height restoration when this outcome is reported by four different methods commonly used in the vertebroplasty literature . Propose a consensus method for reporting vertebral height restoration . Summary of Background Data . Measuring and reporting height restoration of fractured vertebrae presupposes a consensus of method that does not exist . Lack of consensus makes the interpretation of reports and comparison of outcomes of interventions that cl aim vertebral height restoration difficult . Material s and Methods . Preoperative and postoperative st and ing lateral radiographs of 65 VCFs in 41 patients were compared to assess operative vertebral height restoration . Restorations of vertebral height occurred in 23 instances and were reported by each of the following commonly used methods : ( 1 ) absolute restoration in millimeters ; ( 2 ) percent restoration relative to initial fracture height ; ( 3 ) percent restoration relative to lost vertebral height ; and ( 4 ) percent restoration relative to referent vertebral height . Results . Apparent magnitude of height restoration varied nearly four-fold depending on initial fracture severity and reporting method . Conclusions . Substantial apparent variability in the reported magnitude of identical height restorations demonstrates the need for a consensus method for measuring , reporting , and interpreting this outcome . Rationale is presented to support the recommendation that reports of vertebral height restoration should : include all index vertebral height dimensions ( posterior ( Hp ) , middle ( Hm ) and anterior ( Ha ) vertebral height ) ; include absolute measurements of all referent vertebral heights ; be reported relative to a referent normative height ; include a correction for inter-radiographic measurement error ; take into consideration the dynamic mobility of some osteoporotic VCFs ; and include the calculated precision error for all measurements
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 .