E P I DE M I O L O G Y A N D HE A L T H S E R V IC E S RE SE AR CH

BJD

British Journal of Dermatology

Statistical reporting in randomized controlled trials from the dermatology literature: a review of 44 dermatology journals M. McClean1 and J.I. Silverberg1,2 Departments of 1Dermatology and 2Preventive Medicine and Medical Social Sciences, Northwestern University Feinberg School of Medicine, Suite 1600, 676 N. St Clair St, Chicago, IL 60611, U.S.A.

Summary Correspondence Jonathan I. Silverberg. E-mail: [email protected]

Accepted for publication 3 May 2015

Funding sources No external funding.

Conflicts of interest None declared. DOI 10.1111/bjd.13907

Background The validity of randomized controlled trials (RCTs) is determined by several statistical factors. Objectives To determine the level of recent statistical reporting in RCTs from the dermatology literature. Methods We searched MEDLINE for all RCTs published between 1 May 2013 and 1 May 2014 in 44 dermatology journals. Results Two hundred and ten articles were screened, of which 181 RCTs from 27 journals were reviewed. Primary study outcomes were met in 122 (67
4%) studies. Sample size calculations and beta values were reported in 52 (28
7%) and 48 (26
5%) studies, respectively, and nonsignificant findings were supported in only 31 (17
1%). Alpha values were reported in 131 (72
4%) of studies with 45 (24
9%) having two-sided P-values, although adjustment for multiple statistical tests was performed in only 16 (9
9% of studies with ≥ two statistical tests performed). Sample size calculations were performed based on a single outcome in 44 (86
3%) and multiple outcomes in six (11
8%) studies. However, among studies that were powered for a single primary outcome, 20 (45
5%) made conclusions based on multiple primary outcomes. Twenty-one (41
2%) studies relied on secondary/unspecified outcomes. There were no differences for primary outcome being met (Chi-square, P = 0
29), sample size calculations (P ≥ 0
55), beta values (P = 0
89), alpha values (P = 0
65), correction for multiple statistical testing (P = 0
59), two-sided alpha (P = 0
64), support of nonsignificant findings (Fisher’s exact, P = 0
23) based on the journal’s impact factor. Conclusions Levels of statistical reporting are low in RCTs from the dermatology literature. Future work is needed to improve these levels of reporting.

What’s already known about this topic?

•

Previous studies have found varied levels of statistical reporting across different medical specialties.

What does this study add?

•

The present study demonstrates a low level of statistical reporting in randomized controlled trials from the dermatology literature.

Randomized controlled trials (RCTs) are the gold standard of single-study designs. Advantages include reliable data collection, randomization that ensures subjects’ equality with 172

British Journal of Dermatology (2015) 173, pp172–183

respect to disease severity, socio-demographic and other factors, and control of inclusion and exclusion criteria. The validity of a RCT is determined by several key factors, which © 2015 British Association of Dermatologists

Statistical reporting in RCTs from the dermatology literature, M. McClean and J.I. Silverberg 173

need to be sufficiently reported for the astute reader to assess the validity of the study. A key component of RCTs and other study types is statistical power and sample size calculations. If a study is insufficiently powered for a particular outcome, one can have no confidence in a nonsignificant test result. Recent studies in fields other than dermatology have demonstrated a lack of reporting of power calculations in RCTs. Systematic reviews of RCTs in plastic surgery and rehabilitation medicine found that only 19%1 and 57
3%2 of studies reported formal power and sample size calculation, respectively. Another key component of RCTs is the level of statistical significance and Type I error rate. A Type I error is the probability of finding a difference in the study sample, while there is no difference in the population, i.e. a false rejection of the null hypothesis. Most studies arbitrarily consider a P-value of 0
05 or less as statistically significant. However, as the number of statistical tests performed in a study increases, so does the probability of making at least one Type I error. To address the problem of ‘multiple testing’, there are several correction methods available. Yet, many studies do not address the issue of multiple testing. A recent systematic review of two orthopaedic journals found that only 15% and 6% of published studies performed correction for multiple testing.3 We sought to perform a systematic review of RCTs in the dermatology literature to assess reporting of power, sample size calculations, critical P-values and correction for multiple statistical testing.

of the manuscript, if there was no clear indication that they were RCTs. Institutional review board approval for this systematic review was waived. Both reviewers, M.M. and J.S., independently performed data extraction from these studies and any differences were resolved by discussion. The following data items were collected: study design; number of treatment groups; documentation of a power calculation; whether the study was powered for single or multiple outcomes; reporting of an alpha-value and whether it was one- or two-sided; adjustment for multiple comparisons; reporting of a beta value; whether negative findings were supported by power calculation; whether the primary outcome reported was significant; and whether conclusions were based on a single or multiple primary outcomes, or secondary/unspecified outcomes. Correction for multiple comparisons was assessed in any studies where multiple statistical tests were performed for efficacy-related outcomes. Statistical analysis Statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, U.S.A.). The primary outcomes were proportions of statistical reporting for manuscripts published in each journal. Chi-square and Fisher exact tests were used in secondary analyses to compare proportions of statistical reporting by the journal impact factor (tertiles).

Results Methods Literature search Literature search and data extraction We searched MEDLINE for all manuscripts published on line, in print and in press in the following 44 dermatology journals: Pigment Cell & Melanoma Research; Journal of Investigative Dermatology; British Journal of Dermatology; Journal of the American Academy of Dermatology; JAMA Dermatology; Seminars in Cutaneous Medicine and Surgery; Journal of Drugs in Dermatology; Wound Repair and Regeneration; Experimental Dermatology; Journal of the European Academy of Dermatology and Venereology; Dermatology; Journal of Dermatological Science; Acta Dermato-Venereologica; Skin Pharmacology and Physiology; Archives of Dermatological Research; Dermatologic Surgery; Melanoma Research; American Journal of Clinical Dermatology; Clinics in Dermatology; Dermatologic Therapy; Contact Dermatitis; Journal of Cutaneous Pathology; American Journal of Dermatopathology; Journal of Dermatological Treatment; Journal of Dermatology; BMC Dermatology; Dermatologic Clinics; Journal of Cosmetic and Laser Therapy; International Journal of Dermatology; Pediatric Dermatology; International Journal of Lower Extremity Wounds; European Journal of Dermatology; Clinical and Experimental Dermatology; Journal of the German Society of Dermatology; Leprosy Review; Australian Journal of Dermatology; Journal of Cutaneous Medicine and Surgery; Journal of Cosmetic Dermatology; Skin Research and Technology; Journal of Clinical and Aesthetic Dermatology; Indian Journal of Dermatology, Venereology and Leprology; Cutis; Dermatitis and Annals of Dermatology. Search results were limited to RCTs published between 1 May 2013 and 1 May 2014. Studies were then excluded based on manual review © 2015 British Association of Dermatologists

The literature search yielded 210 articles. After manuscript review, two were excluded for being duplicates and 27 were excluded for not being RCTs. This left 181 articles in 27 journals to be included in the review.4–183 Of these, 137 studies had parallel-armed control groups, 42 had placebo control groups, one was a cross-over design and one used no treatment as the control group. The PRISMA flow diagram is presented in Figure 1. Sample size calculation and beta values The primary study outcome was met in 122 (67
4%) studies, although only 45 (24
9%) reported whether P-values were one- or two-sided. Overall, only 52 (28
7%) manuscripts reported sample size calculations and beta values (Table 1). Beta values were reported in all studies that presented sample size calculations and ranged from 0
05 to 0
2. Nonsignificant findings were supported by power and sample size calculations in only 31 (17
1%) studies, not supported in 13 (7
2%) studies and not clearly addressed in 137 (75
7%) studies. Alpha values and correction for multiple statistical tests Alpha values were reported directly or indirectly in 131 (72
4%) manuscripts. However, adjustment for multiple statisBritish Journal of Dermatology (2015) 173, pp172–183

174 Statistical reporting in RCTs from the dermatology literature, M. McClean and J.I. Silverberg

Fig 1. PRISMA flow diagram of the review.

tical tests was performed in only 16 (9
9%) manuscripts with ≥ two statistical tests performed. Only one study reported the actual number of statistical tests performed.115 The most common method of correction was that of Bonferroni (seven studies, 43
8%), followed by Holm–Bonferroni’s (three studies, 18
8%) and Dunn’s (two studies, 12
5%). Among the studies without correction for multiple testing, an average  standard deviation of 20
9  19
2 statistical tests were presented (maximum: 108). For reference, the family-wise error rates (FWERs) for performing 5, 15 and 108 statistical tests are 22
6%, 53
7% and 99
6%, respectively. This means that a study with 108 statistical tests has a 99
6% probability of falsely rejecting the null hypothesis, i.e. a false-positive finding. Sample size calculations Sample size calculations were performed based on a single outcome in 44 (86
3%) and multiple outcomes in six (11
8%) (Table 2). However, among studies that were powered for a single primary outcome, 20 (45
5%) made conclusions that were supported by multiple primary outcomes. In addition, 21 (41
2%) studies with any sample size calculations based their conclusions on secondary or unspecified outcomes that were not considered in the sample size calculation. Differences of reporting by impact factor To determine whether there were differences of statistical reporting based on the journal’s impact factor (IF), we compared the journal’s IF (ranked into tertiles) with the above British Journal of Dermatology (2015) 173, pp172–183

outcomes. There were no significant differences for reporting of primary outcome being met (Chi square, P = 0
29), sample size calculations (P ≥ 0
55), alpha values (P = 0
65), reporting of one- or two-sided statistical testing (P = 0
64), correction for multiple statistical testing (P = 0
59), beta values (P = 0
89) or support of nonsignificant findings (Fisher’s exact, P = 0
23) based on IF (Table 3).

Discussion The present review found low levels of reporting of power, sample size calculations, alpha values and correction for multiple testing in RCTs from the dermatology literature. There were no significant differences of reporting between journals with lower and higher IF. It is necessary to point out that the results of this review by no means undermine the results of those studies. It is probable that many if not most of the included studies did consider power and perform sample size calculation. However, if these issues are not documented, the clinician, peer-reviewer, editor and authors of subsequent systematic reviews and meta-analyses have limited ability to assess the validity and conclusions of a study. A previous review of all studies published between January and June 2003 in two top-tier dermatology journals found that only 10
3% reported power and sample size calculations. The higher proportions of reporting for sample size calculation (28
7%) likely arise from exclusion of lower quality non-RCTs and improved reporting over time. Despite such improvements, the results of our review highlight the need for improved reporting of key statistical features in RCTs in dermatology. © 2015 British Association of Dermatologists

Statistical reporting in RCTs from the dermatology literature, M. McClean and J.I. Silverberg 175 Table 1 Frequency of statistical reporting for randomized controlled trials in 27 dermatology journals [n (%)]

Journal name, n (%) Acta Dermato-Venereologica (5) American Journal of Clinical Dermatology (1) Archives of Dermatological Research (2) British Journal of Dermatology (20) Clinical and Experimental Dermatology (4) Contact Dermatitis (5) Cutis (1) Dermatologic Surgery (24) Dermatology (2) European Journal of Dermatology (1) Indian Journal of Dermatology, Venereology and Leprology (1) International Journal of Dermatology (6) International Journal of Lower Extremity Wounds (1) Journal of the American Academy of Dermatology (14) Journal of Cosmetic Dermatology (3) Journal of Cosmetic and Laser Therapy (6) Journal of Cutaneous Medicine and Surgery (1) Journal of Dermatology (5) Journal of Dermatological Treatment (28) Journal of Drugs in Dermatology (22) Journal of the European Academy of Dermatology and Venereology (9) JAMA Dermatology (11) Journal of Investigative Dermatology (1) Pediatric Dermatology (3) Skin Pharmacology and Physiology (1) Skin Research and Technology (2) Wound Repair and Regeneration (2) Total studies (181)

Impact factor

Primary outcome met

Sample size calculation

Beta reported

Nonsignificant findings supported

Alpha reported

Two-sided test reported

Correction for multiple testinga

3
49 3
26

3 (60
0) 0 (0
0)

2 (40
0) 1 (100
0)

2 (40
0) 0 (0
0)

2 (40
0) 0 (0
0)

4 (80
0) 1 (100
0)

2 (40
0) 0 (0
0)

2 (40
0) 0 (0
0)

2
62

1 (50
0)

0 (0
0)

0 (0
0)

0 (0
0)

1 (100
0)

0 (0
0)

0 (0
0)

4
67

14 (70
0)

7 (35
0)

6 (30
0)

0 (0
0)

15 (75
0)

7 (35
0)

0 (0
0)

1
56

2 (50
0)

0 (0
0)

0 (0
0)

0 (0
0)

1 (25
0)

0 (0
0)

0 (0
0)

1 0 1 1 0

1 0 1 1 0

0 0 1 1 0

0 0 8 0 1

2 0 1 0 0

2
37 0
74 1
71 2
04 1
23

4 1 15 1 1

(80
0) (100
0) (62
5) (50
0) (100
0)

(20
0) (0
0) (4
2) (50
0) (0
0)

(20
0) (0
0) (4
2) (50
0) (0
0)

(0
0) (0
0) (4
2) (50
0) (0
0)

2 1 14 2 1

(40
0) (100
0) (58
3) (100
0) (100
0)

(0
0) (0
0) (33
3) (0
0) (100
0)

(40
0) (0
0) (4
2) (0
0) (0
0)

1
42

0 (0
0)

0 (0
0)

0 (0
0)

0 (0
0)

0 (0
0)

0 (0
0)

0 (0
0)

0
93

4 (66
7)

3 (50
0)

3 (50
0)

2 (33
3)

5 (83
3)

0 (0
0)

0 (0
0)

1
19

0 (0
0)

1 (100
0)

1 (100
0)

1 (100
0)

1 (100
0)

0 (0
0)

0 (0
0)

5
37

10 (71
4)

6 (42
9)

6 (42
9)

4 (28
6)

4 (28
6)

2 (14
3)

12 (85
7)

1
44

3 (100
0)

0 (0
0)

0 (0
0)

0 (0
0)

3 (100
0)

1 (33
3)

0 (0
0)

1
41

3 (50
0)

0 (0
0)

0 (0
0)

0 (0
0)

4 (66
7)

1 (16
7)

0 (0
0)

0
81

0 (0
0)

0 (0
0)

0 (0
0)

0 (0
0)

1 (100
0)

1 (100
0)

1 (100
0)

2
89 1
7

4 (80
0) 22 (78
6)

0 (0
0) 6 (21
4)

0 (0
0) 5 (17
9)

0 (0
0) 5 (17
9)

4 (80
0) 22 (78
6)

0 (0
0) 6 (21
4)

0 (0
0) 1 (3
6)

1
28

16 (72
7)

8 (36
4)

8 (36
4)

6 (27
3)

15 (68
2)

6 (27
3)

4 (18
2)

3
31

5 (55
6)

4 (44
4)

3 (33
3)

2 (22
2)

6 (66
7)

1 (11
1)

1 (11
1)

2
44 5
44

7 (63
6) 1 (100
0)

7 (63
6) 0 (0
0)

7 (63
6) 0 (0
0)

5 (45
5) 0 (0
0)

8 (72
7) 1 (100
0)

5 (45
5) 0 (0
0)

0 (0
0) 1 (100
0)

1
71 2
43

2 (66
7) 1 (100
0)

3 (100
0) 1 (100
0)

3 (100
0) 1 (100
0)

2 (66
7) 0 (0
0)

3 (100
0) 1 (100
0)

2 (66
7) 0 (0
0)

0 (0
0) 1 (100
0)

1
57

1 (50
0)

0 (0
0)

0 (0
0)

0 (0
0)

1 (50
0)

0 (0
0)

0 (0
0)

3
06

1 (50
0)

0 (0
0)

0 (0
0)

0 (0
0)

1 (50
0)

1 (50
0)

0 (0
0)

52 (28
7)

48 (26
5)

31 (17
1)

131 (72
4)

45 (24
9)

16 (9
9)

122 (67
4)

a Percentages in this column are based on a denominator that reflects the number of studies where multiple statistical tests were performed, i.e. where adjustment for multiple testing was necessary (total n = 162).

Several strategies may be employed to improve the reporting of statistical findings in publications. The Consolidated Standards of Reporting Trials (CONSORT) guidelines provide © 2015 British Association of Dermatologists

a template for proper reporting of RCTs, including the objectives, design, participants, interventions, outcomes, sample size, randomization, blinding, statistics, results and British Journal of Dermatology (2015) 173, pp172–183

176 Statistical reporting in RCTs from the dermatology literature, M. McClean and J.I. Silverberg Table 2 Number of outcomes used for powering and drawing conclusions

Journal name, n (%)

Powered outcomes (n)

Acta Dermato-Venereologica (5)

Single (1) Multiple (1) Single (1) N/A Single (6) Multiple (1) N/A Single (1) N/A Single (1) Single (1) N/A N/A Single (3)

American Journal of Clinical Dermatology (1) Archives of Dermatological Research (2) British Journal of Dermatology (20) Clinical and Experimental Dermatology (4) Contact Dermatitis (5) Cutis (1) Dermatologic Surgery (24) Dermatology (2) European Journal of Dermatology (1) Indian Journal of Dermatology, Venereology and Leprology (1) International Journal of Dermatology (6) International Journal of Lower Extremity Wounds (1) Journal of the American Academy of Dermatology (14)

Journal of Drugs in Dermatology (22)

Single (1) Single (5) Multiple (1) N/A N/A N/A N/A Single (4) Multiple (1) Single (8)

Journal of the European Academy of Dermatology and Venereology (9)

Single (4)

JAMA Dermatology (11)

Single (5) Multiple (2) N/A Single (3)

Journal Journal Journal Journal Journal

of of of of of

Cosmetic Dermatology (3) Cosmetic and Laser Therapy (6) Cutaneous Medicine and Surgery (1) Dermatology (5) Dermatological Treatment (28)

Journal of Investigative Dermatology (1) Pediatric Dermatology (3) Skin Pharmacology and Physiology (1) Skin Research and Technology (2) Wound Repair and Regeneration (2) Total (181)

a

Unclear (1) N/A N/A Single (44) Multiple (6) Unclear (1)

Conclusions based on single or multiple primary outcomes in studies powered on a single outcome Multiple (1, 100%) Single (1, 100%) N/A Single (5, 83
3%) Multiple (1, 16
7%) N/A Single (1, 100%) N/A Single (1, 100%) Single (1, 100%) N/A N/A Multiple (3, 100%) Multiple (1, 100%) Single (4, 80%) Multiple (1, 20%) N/A N/A N/A N/A Single (2, 50%) Multiple (2, 50%) Single (3, 37
5%) Multiple (5, 62
5%) Single (2, 50
0%) Multiple (2, 50
0%) Single (2, 40
0%) Multiple (3, 60
0%) N/A Single (2, 66
7%) Multiple (1, 33
3%) N/A N/A N/A Single (24, 54
5%) Multiple (20, 45
5%)

Conclusions based on primary or secondarya outcomes Primary (1, 50
0%) Secondary (1, 50
0%) Primary (1, 100
0%) N/A Primary (5, 71
4%) Secondary (2, 28
6%) N/A Secondary (1, 100
0%) N/A Primary (1, 100
0%) Secondary (1, 100
0%) N/A N/A Primary (1, 33
3%) Secondary (2, 66
7%) Primary (1, 100
0%) Primary (4, 66
7%) Secondary (2, 33
3%) N/A N/A N/A N/A Primary (4, 80
0%) Secondary (1, 20
0%) Primary (4, 50
0%) Secondary (4, 50
0%) Primary (2, 50
0%) Secondary (2, 50
0%) Primary (4, 57
1%) Secondary (3, 42
9%) N/A Primary (2, 66
7%) Secondary (1, 33
3%) Secondary (1, 100
0%) N/A N/A Primary (30, 58
8%) Secondary (21, 41
2%)

Secondary includes unspecified outcomes.

discussion.184 This template approach has improved awareness of and compliance with proper technique and allows readers to appropriately interpret results. Nevertheless, there are still areas of statistical reporting that can be improved, such as power and sample size calculations. The Statistical Analyses and Methods in the Published Literature (SAMPL) were recently developed to provide guiding principles for reporting preliminary, primary and supplementary analyses and how to present numeric results, risk, rates and ratios, hypothesis testing and statistical tests such as analysis of variance and regression. We recommend that all dermatology journals employ checklists for CONSORT guidelines in RCT submissions and SAMPL guidelines in all submissions. We recognize British Journal of Dermatology (2015) 173, pp172–183

the growing constraints that journals face with respect to page limits and publication costs, which may preclude inclusion of this vital information in all circumstances. However, such checklists could be published as online supplementary material, which is increasingly being utilized by journals. Sample size calculations can be quite complex and depend on myriad factors (reviewed in Silverberg185). However, it is imperative that sample size calculations be performed for all RCTs. More importantly, these must be documented to allow for critical review and proper interpretation of nonsignificant findings. If a manuscript presents nonsignificant test results without any power calculation, it may be impossible to have confidence in those findings and negative conclusions. For © 2015 British Association of Dermatologists

Statistical reporting in RCTs from the dermatology literature, M. McClean and J.I. Silverberg 177 Table 3 Lack of association between impact factor and statistical reporting Impact factor Variable

Tertile 1 (0
74–1
57)

Tertile 2 (1
70–2
44)

Primary outcome met No 20 (41
7) 25 (33
8) Yes 28 (58
3) 49 (66
2) Sample size calculation No 36 (75
0) 54 (73
0) Yes 12 (25
0) 20 (27
0) Reporting if alpha was one- or two-sidedb No 14 (29
2) 21 (28
8) Yes 34 (70
8) 52 (71
2) Two-sided alpha reported No 37 (77
1) 52 (70
3) Yes 11 (22
9) 22 (29
7) Correction for multiple testingc No 35 (87
5) 65 (92
9) Yes 5 (12
5) 5 (7
1) Beta reported No 36 (75
0) 55 (74
3) Yes 12 (25
0) 19 (25
7) Nonsignificant findings supported No 1 (2
1) 4 (5
4) Unclear 38 (79
2) 56 (75
7) Yes 9 (18
8) 14 (18
9)

Tertile 3 (2
62–5
44)

P-value

16 (27
1) 43 (72
9)

0
29a

39 (66
1) 20 (33
9)

0
55a

13 (22
4) 45 (77
6)

0
65a

41 (69
5) 18 (30
5)

0
64a

46 (88
5) 6 (11
5)

0
59a

42 (71
2) 17 (28
8)

0
89a

8 (13
6) 43 (72
9) 8 (13
6)

0
23d

Chi square test. bMissing values = 2. cMissing values = 19. Fisher’s exact test.

a

d

example, the present review a priori sought to examine RCTs in the dermatology literature in the previous year to identify the most recent reporting practices. We also performed secondary analyses and found no significant differences of reporting based on journal IF. However, post-hoc power calculations revealed actual powers of 8–10%. To be sufficiently powered, > 5000 RCTs in the dermatology literature would be needed; however, there simply weren’t that many RCTs recently published in the dermatology literature. Thus, we can’t have total confidence in these nonsignificant findings. The problem of multiple statistical testing stems from the FWER, which is the probability of making at least one Type I error when multiple tests are performed. The FWER for n tests with a critical P-value of 0
05 can be calculated by the following equation: 1 – (1 – 0
5)n. Studies that perform many statistical tests may have a FWER greater than 90%, i.e. there is a 90% probability that at least one of the findings is a false positive. There are a number of different techniques available to correct for multiple statistical testing, including techniques based on FWERs and false discovery rates (FDR). The simplest and most conservative approach to FWER correction is the Bonferroni method, where for each test the alpha = 0
05 is divided by n where n = number of tests. However, this approach assumes that the multiple tests are performed independently of one another, which is to say that the result of one test cannot be predicted from the result of another. In © 2015 British Association of Dermatologists

practice, correlated tests often arise; for instance, if groups A and B differ and A and C differ, then it is likely that B and C will also differ. In this situation the Bonferroni approach is too conservative. The Holm–Bonferroni method is a stepwise approach that first sorts P-values from n different tests in order of smallest to largest and serially compares whether each P-value is less than 0
05 divided by the rank of that P-value. It is less conservative than the Bonferroni method but, in general, methods that control for FWER are quite conservative. Adjustment for multiple comparisons should be considered in studies with numerous outcomes, especially retrospective or exploratory studies and non-RCTs. In the case of RCTs, there are conflicting views about whether correction for multiple comparisons is needed in multi-arm RCTs (reviewed in Wason et al.186). Regardless, the primary and secondary/ unspecified outcomes must be clearly defined and the number of primary outcomes should be incorporated in the sample size calculations. In addition, the problem of multiple comparisons needs to be addressed when planning interim analyses (IAs) in RCTs. IAs are commonly employed to decide whether to stop a trial early. Reasons for stopping a trial include convincing treatment differences that do not warrant further enrolment, unacceptable adverse events or other ethical concerns. One challenge of IA is that the Type I error rate increases with each analysis performed. To address this issue, different stopping boundaries have been proposed that distribute the Type I error rate differently throughout each IA. One approach is the Fixed-Nominal, which uses alpha/(number of IAs) as the stopping boundary. This is conceptually similar to the abovementioned Bonferroni correction method for multiple comparisons, Alternatively, the O’Brien–Fleming approach uses relatively small alpha levels in early IAs and increases the alpha with each IA performed. This is conservative early on, i.e. is less likely to stop a trial early, and yields alphas closest to 0
05 at the final analysis. These approaches require careful planning during the planning phase of an RCT. Of note, none of the reviewed studies commented on any IAs or stopping rules. In many large-scale genetic or exploratory studies, the multiple tests may have significant commonality and not be independent, such as common sources of noise and effects of expression of a gene on many other genes (reviewed in Farcomeni187). In these scenarios, a commonly used technique is to control the FDR, which is the expected proportion of falsely rejected hypotheses. This approach allows for a higher number of Type I errors when more rejections are made.187 FDR offers more statistical power than FWER, but is less conservative and has a considerably higher risk of Type I error than FWER. There is no gold-standard approach to correction for multiple testing and selection depends on the data being analysed. It is important to recognize that borderline significant P-values approaching 0
05 in studies with many statistical tests should be interpreted with caution. Caution is advised for both studies that use more conservative approaches, e.g. FWER, for a high number of tests potentially resulting in false retention of the null hypothesis, as well as less conservative approaches, British Journal of Dermatology (2015) 173, pp172–183

178 Statistical reporting in RCTs from the dermatology literature, M. McClean and J.I. Silverberg

e.g. FDR, for a small number of tests potentially resulting in false rejection of the null hypothesis. In conclusion, reporting of statistical variables in RCTs from the dermatology literature is low, including reporting of power, sample size calculations, alpha values and correction for multiple testing. Future work is needed to increase the level of statistical reporting in studies from the dermatology literature.

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