INTRODUCTION Many of the latter Res...

INTRODUCTION

Many of the latter Research Corners in this journal have focused upon a variety of topics related to evidence-based practice. Following an evidence-based approach, therapists integrate the best evidence, the individual characteristics of the patient, and individual clinical expertise into a decision making proces that leads to optimal therapy.1 This intricate web process requires a detailed understanding of the evidence, including to what extent the evidence was derived and an appreciation of the magnitude of the benefits and/or risks. Since researchers oftentimes use a variety of statistics when reporting their issues (eg, F values, t values, chi-square values, and associated P values), making brains of data from clinical trials can be a surpassingly challenging task. Several methods have been hinted to help clinicians examine the magnitude of differences between a treatment cluster and control group. When the issue variable of interest is measured in succession a continuous scale (eg, heart rate, lung function, and oxygen uptake), the purport size or standardized difference between the means of the intervention form into groups and control group conveys the magnitude of the intervention effect2

While many trials involving patients with cardiovascular and pulmonary disease report variables that are continuous, one researchers also publish important issues measured on a dichotomous (binary) scale. Dichotomous variables are discrete consequences They may be used to measure the increase of complications or a disease proces patient satisfaction, or terminations such as death or injury. For example, Flenady and Gray3 studied the effectiveness of chest physiotherapy for preventing morbidity in babies extubated from mechanical ventilation. In another research Oldridge and colleagues4 investigated the efficacy of cardiac rehabilitation in reducing cardiovascular death and all-cause mortality in patients who have experienced a myocardial infarction.

Clinical trials with dichotomous consequences often focus on whether treatment remodels the risk of one or more facts of interest. The magnitude of the risk reduction can be intimateed in a number of different ways. The intent of this article is to describe statistics that can be used to demise information about the magnitude of the power size when some of the issue variables are dichotomous. Three usual measures, the absolute risk reduction, the relative risk reduction, and the number urgencyed to treat, are described and discussed below.

ABSOLUTE RISK REDUCTION

The absolute risk reduction (ARR) is the absolute, arithmetic difference in risk or facts between treatment and control assemblages These rates also are referr to as the experimental adventure rate (EER) and the controll incident rate (CER). In 1997, Fagevik-Olsen and colleagues"' reported the drifts of prophylactic chest physiotherapy forward respiratory complications in the first 6 days following major abdominal surgery The conclusion of interest in this clinical trial was the progress to maturity of a respiratory complication. simply 6% of the subjects in the treatment assign places to (10/172) developed respiratory complications compared to 27% (52/192) of the enthralls in the control group. In this example, the ARR is equal to the absolute difference between CER and EER = | 006 - 027 | = 021 or 21% Treated exposes were at 21%, lower risk than have the direction of subjects of experiencing a respiratory complication during the defined time frame. In general, when the adventure is considered a negative issue (eg, development of a respiratory complication), the larger the ARR, the more beneficial the treatment. In trials where the treatment increases the probability of a beneficial event, calculation of the same ratio yields the absolute benefit increase.6

RELATIVE RISK REDUCTION

The relative risk reduction (RRR) leaves to the percentage decrease in risk achieved by dint of the group receiving the intervention compared to the superintend group. This ratio is comput by the agency of dividing the ARR by the risk in the regulate group. Using the above example, the RRR produc by means of prophylactic chest physiotherapy was 021/027 = 078 or 78% Therefore, prophylactic chest physiotherapy reduc the risk of developing a respiratory complication at 78% of the risk of untreated patients. Since RRR is obtained on dividing the ARR by a number which is always les than 1 RRR is always a great deal of larger than ARR. When the experimental treatment increases the risk of a well adapted event, the same equation bring outs the "relative benefit increase."6 Disadvantages of the RRR will be discussed below.

NUMBER privationed TO TREAT

Definition

The number distressed to treat (NNT) is the number of patients who ne to be treated in order to obviate one additional bad outcome. First introduced in 1988 from Laupacis et al,7 a growing number of citations related to physical therapy issues are reporting this statistic. The NNT can be used to compare the relative effectiveness of different treatments studied by the and of randomized controlled clinical trials. If the treatment has a potentially harmful issue a similar calculation can be used to indicate the number of patients who ne to be treated to harm or lead to the death of undivided person. This statistic is referr to as the number wanted to harm (NNH).