Dissecting clinical trials with ‘number needed to treat’

Clinical trials produce a mountain of data that can be difficult to interpret and apply to clinical practice. When reading about studies such as the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) for schizophrenia, you may wonder:

• How large is the effect being measured?
  
• Is it clinically important?
  
• Are we dealing with a result that may be statistically significant but irrelevant for day-to-day patient care?
  

Number needed to treat (NNT) and number needed to harm (NNH)—two tools of evidence-based medicine (EBM, Box 1^1,2)—can help answer these questions. This article shows how to calculate NNT and NNH, then applies these tools to published results from CATIE phases 1 and 2.

Box 1

What is nnt?

NNT helps us gauge effect size—or clinical significance. It is different from knowing if a clinical trial result is statistically significant.

NNT allows us to place a number on how often we can expect to see a difference between two interventions. If we see a therapeutic difference once every 100 patients (an NNT of 100), the difference between two treatments is not of great concern under most circumstances. But if a difference in outcome is seen once in every 5 patients being treated with one intervention versus another (an NNT of5), the result will likely influence day-to-day practice. Together with calculating a confidence interval (Box 2),³ the NNT can help you judge the clinical significance of a statistically significant result.

Box 2

NNT is useful when examining differences in binary outcomes such as treatment response (yes/no), remission (yes/no), or avoidance of hospitalization (yes/no). NNT also is useful when we compare two medications’ side effects. Under these circumstances, we call NNT the “number needed to harm” (NNH).

Calculating nnt and nnh

NNT and NNH are easy to calculate:

• First determine the difference between the frequencies of the outcome of interest for two interventions.
  
• Then calculate the reciprocal of this difference.
  

For example, let’s say drugs A and B are used to treat depression, and they result in 6-week response rates of 55% and 75%, respectively. The NNT to see a difference between drug B versus drug A in terms of responders at 6 weeks can be calculated as follows:

• Difference in response rates=0.75–0.55=0.20
  
• NNT=1/0.20=5.
  

In this example, you would need to treat 5 patients with drug B instead of drug A to see 1 extra responder. If the NNT had been 5.5, you would round up to the next whole number (6) because you can’t treat a fraction of a person.

Interpreting the importance of NNT values is easy, too. The smaller the NNT, the larger the clinical difference between interventions; the larger the NNT, the smaller the difference.

 

• An NNT of 100 or more usually means little difference exists between interventions for the outcome of interest.
  
• An NNT of 2 would be hugely important and is rarely encountered.
  

Keep in mind, however, that some NNTs may be clinically important even though they are relatively large. An NNT of 500, for example, could be important if the outcome measured is death. Similarly, relatively small NNTs may be clinically irrelevant, such as an NNT of 5 when the outcome is a mild dry mouth.

Example. We can calculate the NNT (actually, NNH) for risk of new-onset diabetes mellitus attributable to second-generation antipsychotics (SGAs), using data from a study that compared diabetes rates in patients given SGAs versus conventional antipsychotics.⁴ Differences in new-onset diabetes rates across ≤25 months were 2.03%, 0.80%, 0.63%, and 0.05% for clozapine, quetiapine, olanzapine, and risperidone, respectively, versus first-generation antipsychotics (FGAs).

The NNH for clozapine compared with FGAs is 1/0.0203=49. This means you would need to treat 49 patients with clozapine instead of an FGA for up to 25 months to encounter 1 extra case of new-onset diabetes mellitus. NNH calculations for quetiapine, olanzapine, and risperidone compared with FGAs would be 125, 159, and 2,000, respectively.

Applying nnt and nnh to catie

An ongoing controversy in schizophrenia treatment is the relative merit of using the more-expensive SGAs versus FGAs. The National Institute of Mental Health-funded CATIE study addressed this issue.^5-7

In CATIE phase 1, which was double-blinded, 1,493 patients with schizophrenia were randomly assigned to 1 of 5 antipsychotics—perphenazine, olanzapine, quetiapine, risperidone, or ziprasidone—for up to 18 months. Patients who discontinued phase 1 before 18 months could participate in phase 2, where 543 patients were randomly assigned to 1 of 5 SGAs that they did not receive in phase 1. Those who prematurely discontinued phase 2 were offered open-label treatment with one or two antipsychotics. When they enrolled, patients were told these switches were possible.

Nearly one-half of all patients who enrolled finished 18 months of follow-up. What resulted, however, was a morass of percentages and p values that were misinterpreted by various parties—including The New York Times, which published an article headlined, “Little difference found in schizophrenia drugs.”⁸ We can apply NNT and NNH to the CATIE study results, however, and discover that:

• important differences do exist between the drugs tested
  
• these differences are clinically and statistically significant.³
  

Overall effectiveness in the CATIE trial was measured by determining how long patients remained on the medications to which they were randomly assigned. All-cause discontinuation—the primary outcome measure—included discontinuation because of:

• lack of efficacy
  
• poor tolerability
  
• patient decision.
  

Thus, both clinician and patient input—and both efficacy and tolerability—affected all-cause discontinuation. In CATIE phase 1, 74% of patients stopped participating (all-cause discontinuation) before 18 months. The percentage of patients who ended phase 1 early ranged from 64% for olanzapine to 82% for quetiapine. Thus, calculating NNT comparing olanzapine and quetiapine on this measure yields:

• NNT=1/(difference in discontinuation rates)=1/(0.82 - 0.64)=1/0.18=5.6. By convention, we round up to the next whole number, in this case 6. This means that for every 6 patients randomized to olanzapine treatment, 1 extra patient completed phase 1 on his or her initially initial medication, compared with patients randomized to quetiapine treatment.
  

Similarly, we can calculate the NNT for all-cause discontinuation for olanzapine compared with ziprasidone, perphenazine, and risperidone, and find NNT of 7, 9, and 11, respectively. In general, a single-digit NNT is sufficiently small for the result to be clinically relevant in day-to-day patient treatment.

In measuring the number of hospitalizations for exacerbation of schizophrenia symptoms per total person-year of exposure, NNT ranged from 3 to 7 in favor of olanzapine compared with the other antipsychotics. This means that for every 3 to 7 patients treated with olanzapine versus another antipsychotic, 1 hospitalization was avoided.

Tolerability. Calculating NNH can show how often you could expect specific tolerability outcomes when comparing medications. In CATIE, differences in tolerability emerged among the medications, and each antipsychotic had a unique profile of relative strengths and weaknesses that can be expressed in NNT and NNH. For example, in CATIE phase 1:

• For every 5 to 8 patients treated with olanzapine compared to other antipsychotics, 1 additional patient gained >7% in body weight (NNH is 5 to 8; not corrected for duration of exposure to the medication)
  
• For every 13 to 18 patients treated with olanzapine versus another antipsychotic, 1 additional patient discontinued because of weight gain or metabolic effects.
  

Data from phase 2 were largely consistent with those from phase 1, with important advantages noted for clozapine. NNT in favor of clozapine for all-cause discontinuation was 3, 4, and 7 compared with quetiapine, risperidone, and olanzapine, respectively. In phases 1 and 2, ziprasidone presented with the most favorable metabolic profile, whereas risperidone appeared to have the best overall tolerability.

 

Potential pitfalls

Different studies can provide different estimates of outcomes such as response, remission, hospitalization, or adverse events. Two studies of the risk of new-onset diabetes with antipsychotics demonstrate that these differences can be difficult to interpret, particularly when populations and study designs differ.

• A Department of Veterans Affairs study of data on 56,849 patients⁴ produced an NNH of 159 when olanzapine was compared with conventional antipsychotics, meaning 1 extra case of new-onset diabetes was encountered for every 159 patients treated with olanzapine compared to conventional antipsychotics.
  
• In the CATIE study,⁵ examining new prescriptions of antidiabetic agents yields an NNH of 61 when olanzapine is compared with perphenazine, meaning that 1 extra case of a new prescription of an antidiabetic agent was encountered for every 61 patients treated with olanzapine versus perphenazine.
  

A statistically significant NNT or NNH should carry more weight than a result that is not statistically significant. Even so, make sure the study included patients similar to individuals in your practice before applying the results.

NNT and NNH are best calculated from well-controlled clinical trials. However, the underlying study design and potential biases may affect how NNT and NNH apply to clinical practice. A more complete discussion of the CATIE NNT and NNH secondary analysis can be found elsewhere,³ but issues to consider include the impact of differential switching⁹ and the possible effects of dosages.¹⁰

Related resources

• Guyatt G, Rennie D. Users’ guides to the medical literature: a manual for evidence-based clinical practice. Chicago: AMA Press; 2001.
  
• Straus SE, Richardson WS, Glasziou P, et al. Evidence-based medicine: how to practice and teach EBM, 3rd ed. Edinburgh, UK: Elsevier/Churchill Livingstone; 2005.
  

Drug brand names

• Clozapine • Clozaril
  
• Olanzapine • Zyprexa
  
• Perphenazine • Trilafon
  
• Quetiapine • Seroquel
  
• Risperidone • Risperdal
  
• Ziprasidone • Geodon
  

Disclosure

Dr. Citrome receives research support from AstraZeneca Pharmaceuticals, Barr Pharmaceuticals, Bristol-Myers Squibb, Eli Lilly and Company, Forest Pharmaceuticals, Janssen Pharmaceutica, and Pfizer. He is a consultant to Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Jazz Pharmaceuticals, and Pfizer, and a speaker for Abbott Laboratories, AstraZeneca Pharmaceuticals, Eli Lilly and Company, and Pfizer.

Clinical trials produce a mountain of data that can be difficult to interpret and apply to clinical practice. When reading about studies such as the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) for schizophrenia, you may wonder:

• How large is the effect being measured?
  
• Is it clinically important?
  
• Are we dealing with a result that may be statistically significant but irrelevant for day-to-day patient care?
  

Number needed to treat (NNT) and number needed to harm (NNH)—two tools of evidence-based medicine (EBM, Box 1^1,2)—can help answer these questions. This article shows how to calculate NNT and NNH, then applies these tools to published results from CATIE phases 1 and 2.

Box 1

What is nnt?

NNT helps us gauge effect size—or clinical significance. It is different from knowing if a clinical trial result is statistically significant.

NNT allows us to place a number on how often we can expect to see a difference between two interventions. If we see a therapeutic difference once every 100 patients (an NNT of 100), the difference between two treatments is not of great concern under most circumstances. But if a difference in outcome is seen once in every 5 patients being treated with one intervention versus another (an NNT of5), the result will likely influence day-to-day practice. Together with calculating a confidence interval (Box 2),³ the NNT can help you judge the clinical significance of a statistically significant result.

Box 2

NNT is useful when examining differences in binary outcomes such as treatment response (yes/no), remission (yes/no), or avoidance of hospitalization (yes/no). NNT also is useful when we compare two medications’ side effects. Under these circumstances, we call NNT the “number needed to harm” (NNH).

Calculating nnt and nnh

NNT and NNH are easy to calculate:

• First determine the difference between the frequencies of the outcome of interest for two interventions.
  
• Then calculate the reciprocal of this difference.
  

For example, let’s say drugs A and B are used to treat depression, and they result in 6-week response rates of 55% and 75%, respectively. The NNT to see a difference between drug B versus drug A in terms of responders at 6 weeks can be calculated as follows:

• Difference in response rates=0.75–0.55=0.20
  
• NNT=1/0.20=5.
  

In this example, you would need to treat 5 patients with drug B instead of drug A to see 1 extra responder. If the NNT had been 5.5, you would round up to the next whole number (6) because you can’t treat a fraction of a person.

Interpreting the importance of NNT values is easy, too. The smaller the NNT, the larger the clinical difference between interventions; the larger the NNT, the smaller the difference.

 

• An NNT of 100 or more usually means little difference exists between interventions for the outcome of interest.
  
• An NNT of 2 would be hugely important and is rarely encountered.
  

Keep in mind, however, that some NNTs may be clinically important even though they are relatively large. An NNT of 500, for example, could be important if the outcome measured is death. Similarly, relatively small NNTs may be clinically irrelevant, such as an NNT of 5 when the outcome is a mild dry mouth.

Example. We can calculate the NNT (actually, NNH) for risk of new-onset diabetes mellitus attributable to second-generation antipsychotics (SGAs), using data from a study that compared diabetes rates in patients given SGAs versus conventional antipsychotics.⁴ Differences in new-onset diabetes rates across ≤25 months were 2.03%, 0.80%, 0.63%, and 0.05% for clozapine, quetiapine, olanzapine, and risperidone, respectively, versus first-generation antipsychotics (FGAs).

The NNH for clozapine compared with FGAs is 1/0.0203=49. This means you would need to treat 49 patients with clozapine instead of an FGA for up to 25 months to encounter 1 extra case of new-onset diabetes mellitus. NNH calculations for quetiapine, olanzapine, and risperidone compared with FGAs would be 125, 159, and 2,000, respectively.

Applying nnt and nnh to catie

An ongoing controversy in schizophrenia treatment is the relative merit of using the more-expensive SGAs versus FGAs. The National Institute of Mental Health-funded CATIE study addressed this issue.^5-7

In CATIE phase 1, which was double-blinded, 1,493 patients with schizophrenia were randomly assigned to 1 of 5 antipsychotics—perphenazine, olanzapine, quetiapine, risperidone, or ziprasidone—for up to 18 months. Patients who discontinued phase 1 before 18 months could participate in phase 2, where 543 patients were randomly assigned to 1 of 5 SGAs that they did not receive in phase 1. Those who prematurely discontinued phase 2 were offered open-label treatment with one or two antipsychotics. When they enrolled, patients were told these switches were possible.

Nearly one-half of all patients who enrolled finished 18 months of follow-up. What resulted, however, was a morass of percentages and p values that were misinterpreted by various parties—including The New York Times, which published an article headlined, “Little difference found in schizophrenia drugs.”⁸ We can apply NNT and NNH to the CATIE study results, however, and discover that:

• important differences do exist between the drugs tested
  
• these differences are clinically and statistically significant.³
  

Overall effectiveness in the CATIE trial was measured by determining how long patients remained on the medications to which they were randomly assigned. All-cause discontinuation—the primary outcome measure—included discontinuation because of:

• lack of efficacy
  
• poor tolerability
  
• patient decision.
  

Thus, both clinician and patient input—and both efficacy and tolerability—affected all-cause discontinuation. In CATIE phase 1, 74% of patients stopped participating (all-cause discontinuation) before 18 months. The percentage of patients who ended phase 1 early ranged from 64% for olanzapine to 82% for quetiapine. Thus, calculating NNT comparing olanzapine and quetiapine on this measure yields:

• NNT=1/(difference in discontinuation rates)=1/(0.82 - 0.64)=1/0.18=5.6. By convention, we round up to the next whole number, in this case 6. This means that for every 6 patients randomized to olanzapine treatment, 1 extra patient completed phase 1 on his or her initially initial medication, compared with patients randomized to quetiapine treatment.
  

Similarly, we can calculate the NNT for all-cause discontinuation for olanzapine compared with ziprasidone, perphenazine, and risperidone, and find NNT of 7, 9, and 11, respectively. In general, a single-digit NNT is sufficiently small for the result to be clinically relevant in day-to-day patient treatment.

In measuring the number of hospitalizations for exacerbation of schizophrenia symptoms per total person-year of exposure, NNT ranged from 3 to 7 in favor of olanzapine compared with the other antipsychotics. This means that for every 3 to 7 patients treated with olanzapine versus another antipsychotic, 1 hospitalization was avoided.

Tolerability. Calculating NNH can show how often you could expect specific tolerability outcomes when comparing medications. In CATIE, differences in tolerability emerged among the medications, and each antipsychotic had a unique profile of relative strengths and weaknesses that can be expressed in NNT and NNH. For example, in CATIE phase 1:

• For every 5 to 8 patients treated with olanzapine compared to other antipsychotics, 1 additional patient gained >7% in body weight (NNH is 5 to 8; not corrected for duration of exposure to the medication)
  
• For every 13 to 18 patients treated with olanzapine versus another antipsychotic, 1 additional patient discontinued because of weight gain or metabolic effects.
  

Data from phase 2 were largely consistent with those from phase 1, with important advantages noted for clozapine. NNT in favor of clozapine for all-cause discontinuation was 3, 4, and 7 compared with quetiapine, risperidone, and olanzapine, respectively. In phases 1 and 2, ziprasidone presented with the most favorable metabolic profile, whereas risperidone appeared to have the best overall tolerability.

 

Potential pitfalls

Different studies can provide different estimates of outcomes such as response, remission, hospitalization, or adverse events. Two studies of the risk of new-onset diabetes with antipsychotics demonstrate that these differences can be difficult to interpret, particularly when populations and study designs differ.

• A Department of Veterans Affairs study of data on 56,849 patients⁴ produced an NNH of 159 when olanzapine was compared with conventional antipsychotics, meaning 1 extra case of new-onset diabetes was encountered for every 159 patients treated with olanzapine compared to conventional antipsychotics.
  
• In the CATIE study,⁵ examining new prescriptions of antidiabetic agents yields an NNH of 61 when olanzapine is compared with perphenazine, meaning that 1 extra case of a new prescription of an antidiabetic agent was encountered for every 61 patients treated with olanzapine versus perphenazine.
  

A statistically significant NNT or NNH should carry more weight than a result that is not statistically significant. Even so, make sure the study included patients similar to individuals in your practice before applying the results.

NNT and NNH are best calculated from well-controlled clinical trials. However, the underlying study design and potential biases may affect how NNT and NNH apply to clinical practice. A more complete discussion of the CATIE NNT and NNH secondary analysis can be found elsewhere,³ but issues to consider include the impact of differential switching⁹ and the possible effects of dosages.¹⁰

Related resources

• Guyatt G, Rennie D. Users’ guides to the medical literature: a manual for evidence-based clinical practice. Chicago: AMA Press; 2001.
  
• Straus SE, Richardson WS, Glasziou P, et al. Evidence-based medicine: how to practice and teach EBM, 3rd ed. Edinburgh, UK: Elsevier/Churchill Livingstone; 2005.
  

Drug brand names

• Clozapine • Clozaril
  
• Olanzapine • Zyprexa
  
• Perphenazine • Trilafon
  
• Quetiapine • Seroquel
  
• Risperidone • Risperdal
  
• Ziprasidone • Geodon
  

Disclosure

Dr. Citrome receives research support from AstraZeneca Pharmaceuticals, Barr Pharmaceuticals, Bristol-Myers Squibb, Eli Lilly and Company, Forest Pharmaceuticals, Janssen Pharmaceutica, and Pfizer. He is a consultant to Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Jazz Pharmaceuticals, and Pfizer, and a speaker for Abbott Laboratories, AstraZeneca Pharmaceuticals, Eli Lilly and Company, and Pfizer.

Clinical trials produce a mountain of data that can be difficult to interpret and apply to clinical practice. When reading about studies such as the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) for schizophrenia, you may wonder:

• How large is the effect being measured?
  
• Is it clinically important?
  
• Are we dealing with a result that may be statistically significant but irrelevant for day-to-day patient care?
  

Number needed to treat (NNT) and number needed to harm (NNH)—two tools of evidence-based medicine (EBM, Box 1^1,2)—can help answer these questions. This article shows how to calculate NNT and NNH, then applies these tools to published results from CATIE phases 1 and 2.

Box 1

What is nnt?

NNT helps us gauge effect size—or clinical significance. It is different from knowing if a clinical trial result is statistically significant.

NNT allows us to place a number on how often we can expect to see a difference between two interventions. If we see a therapeutic difference once every 100 patients (an NNT of 100), the difference between two treatments is not of great concern under most circumstances. But if a difference in outcome is seen once in every 5 patients being treated with one intervention versus another (an NNT of5), the result will likely influence day-to-day practice. Together with calculating a confidence interval (Box 2),³ the NNT can help you judge the clinical significance of a statistically significant result.

Box 2

NNT is useful when examining differences in binary outcomes such as treatment response (yes/no), remission (yes/no), or avoidance of hospitalization (yes/no). NNT also is useful when we compare two medications’ side effects. Under these circumstances, we call NNT the “number needed to harm” (NNH).

Calculating nnt and nnh

NNT and NNH are easy to calculate:

• First determine the difference between the frequencies of the outcome of interest for two interventions.
  
• Then calculate the reciprocal of this difference.
  

For example, let’s say drugs A and B are used to treat depression, and they result in 6-week response rates of 55% and 75%, respectively. The NNT to see a difference between drug B versus drug A in terms of responders at 6 weeks can be calculated as follows:

• Difference in response rates=0.75–0.55=0.20
  
• NNT=1/0.20=5.
  

In this example, you would need to treat 5 patients with drug B instead of drug A to see 1 extra responder. If the NNT had been 5.5, you would round up to the next whole number (6) because you can’t treat a fraction of a person.

Interpreting the importance of NNT values is easy, too. The smaller the NNT, the larger the clinical difference between interventions; the larger the NNT, the smaller the difference.

 

• An NNT of 100 or more usually means little difference exists between interventions for the outcome of interest.
  
• An NNT of 2 would be hugely important and is rarely encountered.
  

Keep in mind, however, that some NNTs may be clinically important even though they are relatively large. An NNT of 500, for example, could be important if the outcome measured is death. Similarly, relatively small NNTs may be clinically irrelevant, such as an NNT of 5 when the outcome is a mild dry mouth.

Example. We can calculate the NNT (actually, NNH) for risk of new-onset diabetes mellitus attributable to second-generation antipsychotics (SGAs), using data from a study that compared diabetes rates in patients given SGAs versus conventional antipsychotics.⁴ Differences in new-onset diabetes rates across ≤25 months were 2.03%, 0.80%, 0.63%, and 0.05% for clozapine, quetiapine, olanzapine, and risperidone, respectively, versus first-generation antipsychotics (FGAs).

The NNH for clozapine compared with FGAs is 1/0.0203=49. This means you would need to treat 49 patients with clozapine instead of an FGA for up to 25 months to encounter 1 extra case of new-onset diabetes mellitus. NNH calculations for quetiapine, olanzapine, and risperidone compared with FGAs would be 125, 159, and 2,000, respectively.

Applying nnt and nnh to catie

An ongoing controversy in schizophrenia treatment is the relative merit of using the more-expensive SGAs versus FGAs. The National Institute of Mental Health-funded CATIE study addressed this issue.^5-7

In CATIE phase 1, which was double-blinded, 1,493 patients with schizophrenia were randomly assigned to 1 of 5 antipsychotics—perphenazine, olanzapine, quetiapine, risperidone, or ziprasidone—for up to 18 months. Patients who discontinued phase 1 before 18 months could participate in phase 2, where 543 patients were randomly assigned to 1 of 5 SGAs that they did not receive in phase 1. Those who prematurely discontinued phase 2 were offered open-label treatment with one or two antipsychotics. When they enrolled, patients were told these switches were possible.

Nearly one-half of all patients who enrolled finished 18 months of follow-up. What resulted, however, was a morass of percentages and p values that were misinterpreted by various parties—including The New York Times, which published an article headlined, “Little difference found in schizophrenia drugs.”⁸ We can apply NNT and NNH to the CATIE study results, however, and discover that:

• important differences do exist between the drugs tested
  
• these differences are clinically and statistically significant.³
  

Overall effectiveness in the CATIE trial was measured by determining how long patients remained on the medications to which they were randomly assigned. All-cause discontinuation—the primary outcome measure—included discontinuation because of:

• lack of efficacy
  
• poor tolerability
  
• patient decision.
  

Thus, both clinician and patient input—and both efficacy and tolerability—affected all-cause discontinuation. In CATIE phase 1, 74% of patients stopped participating (all-cause discontinuation) before 18 months. The percentage of patients who ended phase 1 early ranged from 64% for olanzapine to 82% for quetiapine. Thus, calculating NNT comparing olanzapine and quetiapine on this measure yields:

• NNT=1/(difference in discontinuation rates)=1/(0.82 - 0.64)=1/0.18=5.6. By convention, we round up to the next whole number, in this case 6. This means that for every 6 patients randomized to olanzapine treatment, 1 extra patient completed phase 1 on his or her initially initial medication, compared with patients randomized to quetiapine treatment.
  

Similarly, we can calculate the NNT for all-cause discontinuation for olanzapine compared with ziprasidone, perphenazine, and risperidone, and find NNT of 7, 9, and 11, respectively. In general, a single-digit NNT is sufficiently small for the result to be clinically relevant in day-to-day patient treatment.

In measuring the number of hospitalizations for exacerbation of schizophrenia symptoms per total person-year of exposure, NNT ranged from 3 to 7 in favor of olanzapine compared with the other antipsychotics. This means that for every 3 to 7 patients treated with olanzapine versus another antipsychotic, 1 hospitalization was avoided.

Tolerability. Calculating NNH can show how often you could expect specific tolerability outcomes when comparing medications. In CATIE, differences in tolerability emerged among the medications, and each antipsychotic had a unique profile of relative strengths and weaknesses that can be expressed in NNT and NNH. For example, in CATIE phase 1:

• For every 5 to 8 patients treated with olanzapine compared to other antipsychotics, 1 additional patient gained >7% in body weight (NNH is 5 to 8; not corrected for duration of exposure to the medication)
  
• For every 13 to 18 patients treated with olanzapine versus another antipsychotic, 1 additional patient discontinued because of weight gain or metabolic effects.
  

Data from phase 2 were largely consistent with those from phase 1, with important advantages noted for clozapine. NNT in favor of clozapine for all-cause discontinuation was 3, 4, and 7 compared with quetiapine, risperidone, and olanzapine, respectively. In phases 1 and 2, ziprasidone presented with the most favorable metabolic profile, whereas risperidone appeared to have the best overall tolerability.

 

Potential pitfalls

Different studies can provide different estimates of outcomes such as response, remission, hospitalization, or adverse events. Two studies of the risk of new-onset diabetes with antipsychotics demonstrate that these differences can be difficult to interpret, particularly when populations and study designs differ.

• A Department of Veterans Affairs study of data on 56,849 patients⁴ produced an NNH of 159 when olanzapine was compared with conventional antipsychotics, meaning 1 extra case of new-onset diabetes was encountered for every 159 patients treated with olanzapine compared to conventional antipsychotics.
  
• In the CATIE study,⁵ examining new prescriptions of antidiabetic agents yields an NNH of 61 when olanzapine is compared with perphenazine, meaning that 1 extra case of a new prescription of an antidiabetic agent was encountered for every 61 patients treated with olanzapine versus perphenazine.
  

A statistically significant NNT or NNH should carry more weight than a result that is not statistically significant. Even so, make sure the study included patients similar to individuals in your practice before applying the results.

NNT and NNH are best calculated from well-controlled clinical trials. However, the underlying study design and potential biases may affect how NNT and NNH apply to clinical practice. A more complete discussion of the CATIE NNT and NNH secondary analysis can be found elsewhere,³ but issues to consider include the impact of differential switching⁹ and the possible effects of dosages.¹⁰

Related resources

• Guyatt G, Rennie D. Users’ guides to the medical literature: a manual for evidence-based clinical practice. Chicago: AMA Press; 2001.
  
• Straus SE, Richardson WS, Glasziou P, et al. Evidence-based medicine: how to practice and teach EBM, 3rd ed. Edinburgh, UK: Elsevier/Churchill Livingstone; 2005.
  

Drug brand names

• Clozapine • Clozaril
  
• Olanzapine • Zyprexa
  
• Perphenazine • Trilafon
  
• Quetiapine • Seroquel
  
• Risperidone • Risperdal
  
• Ziprasidone • Geodon
  

Disclosure

Dr. Citrome receives research support from AstraZeneca Pharmaceuticals, Barr Pharmaceuticals, Bristol-Myers Squibb, Eli Lilly and Company, Forest Pharmaceuticals, Janssen Pharmaceutica, and Pfizer. He is a consultant to Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Jazz Pharmaceuticals, and Pfizer, and a speaker for Abbott Laboratories, AstraZeneca Pharmaceuticals, Eli Lilly and Company, and Pfizer.