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In the 1920s, Ronald Fisher developed the theory behind the p value and Jerzy Neyman and Egon Pearson developed the theory of hypothesis testing. These distinct theories have provided researchers important quantitative tools to confirm or refute their hypotheses. The p value is the probability to obtain an effect equal to or more extreme than the one observed presuming the null hypothesis of no effect is true; it gives researchers a measure of the strength of evidence against the null hypothesis. As commonly used, investigators will select a threshold p value below which they will reject the null hypothesis. The theory of hypothesis testing allows researchers to reject a null hypothesis in favor of an alternative hypothesis of some effect. As commonly used, investigators choose Type I error (rejecting the null hypothesis when it is true) and Type II error (accepting the null hypothesis when it is false) levels and determine some critical region. If the test statistic falls into that critical region, the null hypothesis is rejected in favor of the alternative hypothesis. Despite similarities between the two, the p value and the theory of hypothesis testing are different theories that often are misunderstood and confused, leading researchers to improper conclusions. Perhaps the most common misconception is to consider the p value as the probability that the null hypothesis is true rather than the probability of obtaining the difference observed, or one that is more extreme, considering the null is true. Another concern is the risk that an important proportion of statistically significant results are falsely significant. Researchers should have a minimum understanding of these two theories so that they are better able to plan, conduct, interpret, and report scientific experiments.

The increasing volume of research by the medical community often leads to increasing numbers of contradictory findings and conclusions. Although the differences observed may represent true differences, the results also may differ because of sampling variability as all studies are performed on a limited number of specimens or patients. When planning a study reporting differences among groups of patients or describing some variable in a single group, sample size should be considered because it allows the researcher to control for the risk of reporting a false-negative finding (Type II error) or to estimate the precision his or her experiment will yield. Equally important, readers of medical journals should understand sample size because such understanding is essential to interpret the relevance of a finding with regard to their own patients. At the time of planning, the investigator must establish (1) a justifiable level of statistical significance, (2) the chances of detecting a difference of given magnitude between the groups compared, ie, the power, (3) this targeted difference (ie, effect size), and (4) the variability of the data (for quantitative data). We believe correct planning of experiments is an ethical issue of concern to the entire community.

The Kaplan-Meier estimator is the current method for estimating the probability of an event to occur with time in orthopaedics. However, the Kaplan-Meier estimator was designed to estimate the probability of an event that eventually will occur for all patients, ie, death, and this does not hold for other outcomes. For example, not all patients will experience hip arthroplasty loosening because some may die first, and some may have their implant removed to treat infection or recurrent hip dislocation. Such events that preclude the observation of the event of interest are called competing events. We suggest the Kaplan-Meier estimator is inappropriate in the presence of competing events and show that it overestimates the probability of the event of interest to occur with time. The cumulative incidence estimator is an alternative approach to Kaplan-Meier in situations where competing risks are likely. Three common situations include revision for implant loosening in the long-term followup of arthroplasties or implant failure in the context of limb-salvage surgery or femoral neck fracture.

From individual randomized studies it is unclear whether the patella should be replaced during total knee replacement. We did a meta-analysis to provide quantitative data to compare patellar resurfacing with nonresurfacing during total knee arthroplasty. Only randomized, controlled trials reported between January 1966 and August 2003 comparing patellar replacement with patella retention were included for a total of 12 studies. Two reviewers assessed trial quality and extracted data from papers. The outcomes identified were reoperations for patellar problems, anterior knee pain, knee scores, stair climbing, and patient satisfaction. The resurfaced patella performed better, and we found an increased relative risk (defined by the ratio of the risk of the event in the resurfaced group on the risk of the event in the nonresurfaced group) for reoperation, for significant anterior knee pain, and for significant pain during stair climbing when the patella was left unresurfaced. No differences were observed between the two groups for International Knee Society function score, Hospital for Special Surgery score, and for patient satisfaction. Despite these general findings, forming a definitive conclusion is difficult because many confounding factors, such as component design, surgeon experience, and technical aspects of the surgery, might influence the result in a patient.

Most of the early failures of total knee replacements are related to technical flaws. Conventional ancillary devices achieve good alignment in the frontal plane in only an average of 75% of total knee replacements. Computer-assisted surgery may improve the technical quality of implantation surgery. The aim of our study was to evaluate the use of computer-assisted surgery using a quality control process. Seventy-eight total knee arthroplasties were done with a CT-based computer-assisted surgery system. The outcomes studied were alignment of the lower limb, implant positioning, and operative time. The target for alignment was 180 degrees +/- 3 degrees. Cusum analysis showed that the three outcomes were controlled during the study. The cusum test identified any existing outliers. Because few data were available at the beginning of this study regarding computer-assisted surgery for total knee replacement, a randomized study was not relevant. However, a control of the procedure was mandatory. The cusum technique allowed continuous evaluation of the performance of the new procedure, and is a useful tool in assessing new technology. The results of this study showed that it is possible to do a randomized study to determine if computer-assisted surgery can improve the technical result of total knee replacement.