Measuring Clinical Effectiveness by Calculating the Number Needed to Treat

Objective: Existing systematic reviews and meta-analyses indicate that acupuncture has similar clinical effectiveness in the prevention of headache disorders (HDs) as drug therapy, but with fewer side effects. As such, examining acupuncture’s use in a pragmatic, real-world setting would be valuable. The purpose of this study was to compare the effects of acupuncture and prophylactic drug treatment (PDT) on headache frequency in patients with HDs, under real-world clinical conditions. Methods: Retrospective cohort study of patients with HDs referred to a pain clinic, using electronic health record data. Patients continued with tertiary care (treatment of acute headache attacks and lifestyle, meditation, exercise and dietary instructions) with PDT, or received 12 sessions of acupuncture over 3 months, instead of PDT under conditions of tertiary care. The primary outcome data were the number of days with headache per month, and groups were compared at baseline and at the end of the third month of treatment. Results: Data were analysed for 482 patients with HDs. The number of headache days per month decreased by 3.7 (standard deviation (SD) = 2.9) days in the acupuncture group versus 2.9 (SD = 2.3) in the PDT group (p = 0.007). The proportion of responders was 39.5% versus 16.3% (p < 0.001). The number needed to treat was 4 (95% confidence interval = 3–7). Conclusion: Our study has shown that patients with HDs in tertiary care who opted for treatment with acupuncture appeared to receive similar clinical benefits to those that chose PDT, suggesting these treatments may be similarly effective of the prevention of headache in a real-world clinical setting.

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"P Value" or "Number Needed to Treat": Which One Is Better for Evaluating Clinical Treatments’ Benefits?

International journal of basic science in medicine ◽

10.34172/ijbsm.2021.13 ◽

2021 ◽

Vol 6 (3) ◽

pp. 74-75

Author(s):

Soudabeh Hamedi-Shahraki ◽

Farshad Amirkhizi

Keyword(s):

Clinical Significance ◽

Effect Size ◽

Statistical Significance ◽

Clinical Effectiveness ◽

P Value ◽

Number Needed To Treat ◽

Effect Size Measure ◽

Size Measure ◽

Therapeutic Measures

Statistical significance does not necessarily mean clinical significance. A P value less than 0.05 does not guarantee the clinical effectiveness of a treatment. To assess the clinical valuable of a treatment, the effect size must be calculated. The number needed to treat (NNT) is an example of an effect size measure that can be very helpful in determining the clinical significance of a treatment. Therefore, it is recommended for all researchers and physicians to look beyond the P value and calculate the NNT for assessing the clinical significance of therapeutic measures and agents.

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Abstract #267 Clinical Effectiveness of Glp-1 Ras in Patients with Metabolic Syndrome Diseases

Endocrine Practice ◽

10.1016/s1530-891x(20)46611-x ◽

2019 ◽

Vol 25 ◽

pp. 104-105

Author(s):

Aloysius Ikwuka

Keyword(s):

Metabolic Syndrome ◽

Clinical Effectiveness

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Abstract #364 Real-World Clinical Effectiveness and Tolerability of Hydroxychloroquine 400mg in T2dm

Endocrine Practice ◽

10.1016/s1530-891x(20)46708-4 ◽

2019 ◽

Vol 25 ◽

pp. 164

Author(s):

Amit Gupta

Keyword(s):

Real World ◽

Clinical Effectiveness

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Clinical Effectiveness in Allied Health Practices

JAMA ◽

10.1001/jama.1994.03520160092050 ◽

1994 ◽

Vol 272 (16) ◽

pp. 1300

Author(s):

Hannah L. Hedrick

Keyword(s):

Allied Health ◽

Clinical Effectiveness ◽

Health Practices

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Rice Fortified with Iron Given Weekly Increases Hemoglobin Levels and Reduces Anemia in Infants: a Community Intervention Trial

International Journal for Vitamin and Nutrition Research ◽

10.1024/0300-9831/a000145 ◽

2013 ◽

Vol 83 (1) ◽

pp. 59-66 ◽

Cited By ~ 4

Author(s):

Francisco Plácido Nogueira Arcanjo ◽

Paulo Roberto Santos ◽

Álvaro Jorge Madeiro Leite ◽

Francisco Sulivan Bastos Mota ◽

Sérgio Duarte Segall

Keyword(s):

Community Intervention ◽

Capillary Blood ◽

Number Needed To Treat ◽

Intervention Trial ◽

Day Care Centers ◽

Control Center ◽

Ferric Pyrophosphate ◽

Child Day Care ◽

Child Day Care Centers ◽

The Impact

More than two billion people suffer from anemia worldwide, and it is estimated that more than 50 % of cases are caused by iron deficiency. In this community intervention trial, we evaluated infants aged 10 to 23 months of age (n = 171) from two public child day-care centers. Intervention lasted 18 weeks. The 50-g individual portion (uncooked) of fortified rice provided 56.4 mg of elemental iron as ferric pyrophosphate. Capillary blood samples to test for anemia were taken at baseline and at endpoint. The objective of this study was to evaluate the impact of rice fortified with iron (Ultrarice®) on hemoglobin and anemia prevalence compared with standard household rice. For the fortified rice center, baseline mean hemoglobin was 113.7 ± 9.2 g/L, and at endpoint 119.5 ± 7.7 g/L, p < 0.0001; for the standard rice center, baseline mean hemoglobin value was 113.5 ± 40.7 g/L, and at endpoint 113.6 ± 21.0, p = 0.99. Anemia prevalence for the fortified rice center was 27.8 % (20/72) at baseline, and 11.1 % (8/72) at endpoint, p = 0.012; for the control center, 47.1 % (33/70) were anemic at baseline, and 37.1 % (26/70) at the end of the study, p = 0.23. The Number Needed to Treat (NNT) was 4. In this intervention, rice fortified with iron given weekly was effective in increasing hemoglobin levels and reducing anemia in infants.

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Translocation Relative to Range

Methodology ◽

10.1027/1614-2241.4.3.132 ◽

2008 ◽

Vol 4 (3) ◽

pp. 132-138 ◽

Cited By ~ 3

Author(s):

Michael Höfler

Keyword(s):

Risk Difference ◽

Binary Outcomes ◽

Number Needed To Treat ◽

Likert Scales ◽

Psychological Science ◽

The Absolute ◽

The Difference ◽

Variance Parameters ◽

Standardized Index ◽

Maximum Possible Magnitude

A standardized index for effect intensity, the translocation relative to range (TRR), is discussed. TRR is defined as the difference between the expectations of an outcome under two conditions (the absolute increment) divided by the maximum possible amount for that difference. TRR measures the shift caused by a factor relative to the maximum possible magnitude of that shift. For binary outcomes, TRR simply equals the risk difference, also known as the inverse number needed to treat. TRR ranges from –1 to 1 but is – unlike a correlation coefficient – a measure for effect intensity, because it does not rely on variance parameters in a certain population as do effect size measures (e.g., correlations, Cohens d). However, the use of TRR is restricted on outcomes with fixed and meaningful endpoints given, for instance, for meaningful psychological questionnaires or Likert scales. The use of TRR vs. Cohens d is illustrated with three examples from Psychological Science 2006 (issues 5 through 8). It is argued that, whenever TRR applies, it should complement Cohens d to avoid the problems related to the latter. In any case, the absolute increment should complement d.

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