Hospital length of stay (LOS) is a common and suitable secondary, economic end point for Perioperative Surgical Home and Enhanced Recovery type interventions.1 LOS can be benchmarked using publically available national data.2 Each 1-day increase in hospital median LOS is associated with odds of readmission nationwide of 1.05 (P = .012) and odds of transfer to short-term care facility of 1.47 (P = .0008).3 LOS can be influenced by perioperative anesthesia care.4 The vast majority of anesthesiologists nationwide think that their clinical decisions influence LOS: 90% think “anesthesiologist intraoperative management should reduce overall LOS”; 81% think that “anesthesiologist coordination of all preoperative care should reduce overall LOS”; and 81% think “anesthesiologist coordination of all postoperative care should reduce overall LOS.”5
Surprisingly, there are limited data on appropriate inferential analyses for comparing brief hospital LOS between groups. Intensive care unit LOS is often short (eg, overnight for many surgical patients). From Moran and Solomon6 and Verburg et al’s7 analyses of intensive care unit LOS, treating LOS data as coming from a log-normal distribution can be appropriate. Student t test with unequal variances has performed well for distributions with similar shapes.8 On the other hand, merely analyzing the percentage of patients with LOS ≤1 day has the advantage of ensuring a negligible chance for large type I errors,9 because such a binary criterion results in binomial distributed data.10
In this article, we compare type I and II error rates for the analysis of the mean LOS versus the percentages of hospital LOS that are overnight. Both have strong economic interpretation. The mean LOS has an economic interpretation in terms of cost per day.1 However, brief reductions in LOS generally have proportionately less reduction in costs.1,11,12 The proportion of patients with LOS ≤1 day indicates the potential for surgery to be performed at for-profit ambulatory surgery centers and/or hospitals’ outpatient surgery departments. At such facilities, limited or regulated hospital beds are not constraints. Consequently, perioperative economics becomes that of operating room management (ie, surgeons and anesthesiologists are in control, not hospital executives and “nursing”).1 Although the statistical plan for clinical trials often is prespecified, rarely is the sample size chosen based on an economic end point. Thus, we consider the economic end point to be prespecified, but selected among meaningful options based on an already determined sample size.
We use pooled LOS data for thoracoscopic wedge resections and lung lobectomy, to match analgesia studies of thoracoscopic surgery.4
The 2013 Nationwide Readmissions Database includes nearly all relevant discharges from each of 21 participating states’ hospitals.13,14 The procedures studied in adults were International Classification of Diseases, Ninth Revision, Clinical Modification 32.20 “thoracoscopic excision of lesion or tissue of lung” (ie, thoracoscopic wedge resection) and 32.41 thoracoscopic lobectomy of lung (Supplemental Digital Content, Table A, http://links.lww.com/AA/B967). There were 26 hospitals with at least 100 of these cases, totaling N = 5052 discharges (Figure; Supplemental Digital Content, Table A, http://links.lww.com/AA/B967).
To simulate type I error rates for 2-group comparisons, we resampled with replacement from the LOS of the 5052 discharges (eg, to simulate 2 groups each with N = 100 we drew 200 cases with replacement from the 5052). As explained in the introduction, unequal variances t test (ie, Welch test or Welch-Satterthwaite test), and Fisher exact test, with the binary end point being LOS ≤1 day or not, were used. We used Fisher exact test rather than the more powerful9 χ2 test based on the expectation that the statistical analysis plan would be prespecified, but the expected number of observations meeting the binary end point known poorly a priori. We did not use the Poisson distribution for LOS, since the variance of LOS is severalfold greater than the mean (χ2 test P < 10−8).a
Although we used N ≥ 100 per group, previous randomized or observational studies often use smaller sample sizes,4 and thus we also included resampling with replacement using sizes of N = 75, 50, and 25 per group. Simulations of N = 1000 per group were performed to examine the asymptotic performance of the 2 tests. The standard errors of the percentage type I error rates were calculated using the Clopper-Pearson method.
To assess type II error rates, we made all (325) pairwise comparisons of the 26 hospitals using each hospital’s observed LOS data (eg, hospital A’s LOS were compared to hospital B’s). The calculated odds ratios were for Fisher exact test obtaining statistical significance (P < .05 or P < .01) while unequal variances t test did not. The 2-sided 95% confidence intervals and P values were exact (StatXact-11; Cytel, Inc, Cambridge, MA).
The Wilcoxon-Mann-Whitney test does not have a reliable interpretation as a test for differences of mean (or median) for skewed distributions such as LOS (ie, is not interpretable as an economic measure).15,16 However, readers interested in LOS as a clinical secondary end point may seek to infer whether a randomly sampled LOS from 1 group (eg, hospital) is likely to be less than a randomly selected LOS from the other group (ie, Wilcoxon-Mann-Whitney odds).16,17 For right-skewed distributions with many zeros (and 1’s for LOS), Wilcoxon rank sum test can have greater statistical power than Fisher exact test.9,18 Therefore, we also performed the analyses using the Wilcoxon rank sum test. The normal approximation for the U statistic included the correction for tied ranks.19
The log-normal distribution has been suitable for many continuous end points in anesthesia such as case duration, since these data follow log-normal distributions.20–29 We simulated the type I error rates for comparisons of mean LOS using Zhou et al’s30 maximum likelihood Z-score test. We used the same process as we simulated type I error rates for Fisher exact test and Welch method, with 1 million simulations of N = 25 per group. Previous studies found that when the data were not log-normally distributed, comparisons of log-normal means had poor type I error rates.20,31,32 We therefore assessed the normality of the log-transformed LOS data using Lilliefors tests, overall and by hospital.
The LOS data were right-skewed with mean ± standard deviation of 3.58 ± 3.09 days (Figure); two-thirds of the 5052 discharges were 0, 1, 2, or 3 days, and 76.6% (lower confidence limit, 75.6%) were 0–4 days. The LOS equaled 0 days (ie, logarithm undefined) for 1.43% of the observations. When those zero values were treated as LOS of 1 day, and Lilliefors’ test (of normality) applied, there was P < .0001 for 2-parameter log-normal distribution. Thus, the LOS did not follow a log-normal distribution. The consequent type I error rates for differences in the log-normal means tested using the maximum likelihood Z-score test30 were larger than nominal: 11.97% ± 0.03% instead of 5.0% and 2.76% ± 0.02% instead of 1.0%, respectively. When analyses were repeated by hospital, the Lilliefors’ tests P ≤ .0007 for all 26 hospitals.
Fisher exact test was conservative (Table 1), as previously reviewed.9 Unequal variances t test reached the nominal type I error rate of 0.05 or 0.01 for N = 1000 per group, but was conservative for smaller N. Wilcoxon-Mann-Whitney type I error rates did not differ from the nominal rates, 0.05 or 0.01.
There was heterogeneity in LOS among the 26 hospitals, as indicated by the interquartile range among hospitals for the percentage of LOS ≤1 day: 9.2%–24.0% of cases. Fisher exact test obtained P < .05 for 64.9% of all 325 pairwise comparisons between the 26 hospitals, and P < .01 for 52.3% of comparisons (Table 2). Unequal variances t test obtained P < .05 for 54.8% of the 325 comparisons and P < .01 for 44.3% of comparisons. Using McNemar test for pairwise comparisons, Fisher exact test obtained P < .05 for more comparisons than did unequal variances t test (P = .0015), and for more P < .01 comparisons too (P = .0049) (Table 2). The estimated odds ratio for obtaining P < .05 with Fisher exact test versus unequal variances t test was 1.94, and for obtaining P < .01 was 1.96 (Table 2). Fisher exact test and Wilcoxon-Mann-Whitney test had comparable statistical power in terms of differentiating LOS between hospitals (Table 2).
We studied 2 approaches to compare brief LOS between groups for use when LOS is being included as a planned secondary, economic end point. Our findings show that if P < .05 for LOS after thoracoscopic lung resection surgery, then for both unequal variances t test and Fisher exact test, the finding of statistical significance is likely reliable. This was true even though the LOS were not normally distributed (Figure). Since multiple similar procedures are often combined in analgesia studies, and the studies designed with LOS as only a secondary end point, the LOS distributions commonly represent a mixture of procedures (Supplemental Digital Content, Table A, http://links.lww.com/AA/B967).4 Fisher exact test was conservative (ie, type I error rates less than nominal)9 for the analysis of brief LOS, but had greater statistical power (ie, significant for more comparisons) to detect differences among hospitals than did unequal variances t test. This was likely due to the relatively large percentage of LOS ≤1 day (Figure). Substantive statistical power while treating the data as binary was found previously for another right-skewed distribution with few different sampled values: narcotic doses in the postanesthesia care unit.18
Although we used Fisher exact test, any of the exact statistical methods to compare 2 proportions could be used; their relative advantages have been extensively studied.9,33 Our study was not intended to represent a complete survey of the many options available. Even though LOS has been a common economic end point for decades,1 we failed to identify a single previous Monte Carlo simulation study comparing its analyses for randomized trials.
Our findings are useful because thoracic surgery is an area of substantial interest in acute postoperative analgesia, including the transition from acute to chronic pain. General thoracic surgery hospital costs are principally attributable to operating room time and hospital LOS.34 Randomized trials with LOS as a primary or secondary end point are warranted and expected to continue.1 On the other hand, our results may be limited to thoracic procedures. The distributions of LOS are likely to differ little for other procedures with brief LOS, because the LOS cannot practically differ substantially being limited to units of days. Thus, when LOS is considered as a secondary end point, likely applying our results to other procedures would be reasonable. However, previous simulation findings have found heterogeneity of results for different statistical methods depending on the probability distributions.35 Consequently, we cannot recommend that our results be assumed to apply to other procedures when used as a primary end point, particularly when patients will be randomly assigned to groups. Studies of relatively discrete distributions have not found the unequal variances t test (Welch method) to be as conservative as for our LOS data.18,36,37 Our findings provide a methodology that others can repeat for different procedures. Since both of our tests that compared means between groups performed less well than treating the data as binary, we recommend paying particular attention to the probability distributions of LOS among included patients. Realistically, the distributions will often represent mixture distributions. The vast majority of surgical procedures are too uncommon to be studied individually.21,38–44 Furthermore, studies of individual surgical procedures often do not accurately reflect the heterogeneous populations to which the results are applied.
In conclusion, for studies with LOS to be used as a secondary end point of economic interest, we considered the planned statistical analysis of the percentage of patients suitable for ambulatory surgery (ie, binary outcome of hospital LOS 0 or 1 midnight). Our results show that there need not be a loss of statistical power to compare groups with the Fisher exact test versus Welch method or the Wilcoxon rank sum test.
Name: Franklin Dexter, MD, PhD.
Contribution: This author helped design the study, obtain the data, perform the analysis, and write the manuscript.
Name: Emine O. Bayman, PhD.
Contribution: This author helped design the study.
Name: Elisabeth U. Dexter, MD, FACS.
Contribution: This author helped interpret the data and write the manuscript.
This manuscript was handled by: Edward J. Mascha, PhD.
Acting EIC on final acceptance: Thomas R. Vetter, MD, MPH.
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