Factors predict low accrual in cancer clinical trials

Preparing treatment

for a clinical trial

Photo by Esther Dyson

Twelve factors may predict low patient accrual in cancer clinical trials, according to research published in JNCI.

Many studies have been conducted to investigate the perceived barriers to clinical trial accrual from the patient or provider perspective.

However, researchers have rarely taken a trial-level view and investigated why certain trials are able to accrue patients faster than expected while others fail to attract even a fraction of the intended number of participants.

Caroline S. Bennette, PhD, of the University of Washington in Seattle, and her colleagues conducted their study to do just that.

They analyzed information on 787 phase 2/3 clinical trials sponsored by the National Clinical Trials Network (NCTN; formerly the Cooperative Group Program) launched between 2000 and 2011.

After excluding trials that closed because of toxicity or interim results, the researchers found that 145 (18%) NCTN trials closed with low accrual or were accruing at less than 50% of target accrual 3 years or more after opening.

The team identified potential risk factors from the literature and interviews with clinical trial experts and found multiple trial-level factors that were associated with poor accrual to NCTN trials, such as increased competition for patients from currently ongoing trials, planning to enroll a higher proportion of the available patient population, and not evaluating a new investigational agent or targeted therapy.

The researchers then developed a multivariable prediction model of low accrual using 12 trial-level risk factors. The team said these factors had good agreement between predicted and observed risks of low accrual in a preliminary validation using 46 trials opened between 2012 and 2013.

Those 12 risk factors are:

1. The number of competing trials per 10,000 eligible patients per year (odds ratio [OR]=1.88)
2. Phase 3 vs phase 2 trial (OR=1.86)
3. Enrollment as percentage of eligible population for targeted therapy (OR=0.57)
4. Enrollment as percentage of eligible population for radiation therapy (OR=1.81)
5. Annual incidence of clinical condition(s) per 10,000 (OR=0.99)
6. Tissue sample required to assess eligibility (OR=1.26)
7. Investigational new drug (OR=0.34)
8. Metastatic setting (OR=1.46)
9. Sample size per 100 (OR=0.95)
10. More than one condition evaluated (OR=1.98)
11. Common solid cancer (prostate, breast, lung, or colon) vs liquid or rare solid cancers (OR=2.32)
12. Interaction term (phase 3 x investigational new drug, OR=2.47).

The researchers concluded that systematically considering the overall influence of these risk factors could aid in the design and prioritization of future clinical trials.

Preparing treatment

for a clinical trial

Photo by Esther Dyson

Twelve factors may predict low patient accrual in cancer clinical trials, according to research published in JNCI.

Many studies have been conducted to investigate the perceived barriers to clinical trial accrual from the patient or provider perspective.

However, researchers have rarely taken a trial-level view and investigated why certain trials are able to accrue patients faster than expected while others fail to attract even a fraction of the intended number of participants.

Caroline S. Bennette, PhD, of the University of Washington in Seattle, and her colleagues conducted their study to do just that.

They analyzed information on 787 phase 2/3 clinical trials sponsored by the National Clinical Trials Network (NCTN; formerly the Cooperative Group Program) launched between 2000 and 2011.

After excluding trials that closed because of toxicity or interim results, the researchers found that 145 (18%) NCTN trials closed with low accrual or were accruing at less than 50% of target accrual 3 years or more after opening.

The team identified potential risk factors from the literature and interviews with clinical trial experts and found multiple trial-level factors that were associated with poor accrual to NCTN trials, such as increased competition for patients from currently ongoing trials, planning to enroll a higher proportion of the available patient population, and not evaluating a new investigational agent or targeted therapy.

The researchers then developed a multivariable prediction model of low accrual using 12 trial-level risk factors. The team said these factors had good agreement between predicted and observed risks of low accrual in a preliminary validation using 46 trials opened between 2012 and 2013.

Those 12 risk factors are:

1. The number of competing trials per 10,000 eligible patients per year (odds ratio [OR]=1.88)
2. Phase 3 vs phase 2 trial (OR=1.86)
3. Enrollment as percentage of eligible population for targeted therapy (OR=0.57)
4. Enrollment as percentage of eligible population for radiation therapy (OR=1.81)
5. Annual incidence of clinical condition(s) per 10,000 (OR=0.99)
6. Tissue sample required to assess eligibility (OR=1.26)
7. Investigational new drug (OR=0.34)
8. Metastatic setting (OR=1.46)
9. Sample size per 100 (OR=0.95)
10. More than one condition evaluated (OR=1.98)
11. Common solid cancer (prostate, breast, lung, or colon) vs liquid or rare solid cancers (OR=2.32)
12. Interaction term (phase 3 x investigational new drug, OR=2.47).

The researchers concluded that systematically considering the overall influence of these risk factors could aid in the design and prioritization of future clinical trials.

Preparing treatment

for a clinical trial

Photo by Esther Dyson

Twelve factors may predict low patient accrual in cancer clinical trials, according to research published in JNCI.

Many studies have been conducted to investigate the perceived barriers to clinical trial accrual from the patient or provider perspective.

However, researchers have rarely taken a trial-level view and investigated why certain trials are able to accrue patients faster than expected while others fail to attract even a fraction of the intended number of participants.

Caroline S. Bennette, PhD, of the University of Washington in Seattle, and her colleagues conducted their study to do just that.

They analyzed information on 787 phase 2/3 clinical trials sponsored by the National Clinical Trials Network (NCTN; formerly the Cooperative Group Program) launched between 2000 and 2011.

After excluding trials that closed because of toxicity or interim results, the researchers found that 145 (18%) NCTN trials closed with low accrual or were accruing at less than 50% of target accrual 3 years or more after opening.

The team identified potential risk factors from the literature and interviews with clinical trial experts and found multiple trial-level factors that were associated with poor accrual to NCTN trials, such as increased competition for patients from currently ongoing trials, planning to enroll a higher proportion of the available patient population, and not evaluating a new investigational agent or targeted therapy.

The researchers then developed a multivariable prediction model of low accrual using 12 trial-level risk factors. The team said these factors had good agreement between predicted and observed risks of low accrual in a preliminary validation using 46 trials opened between 2012 and 2013.

Those 12 risk factors are:

1. The number of competing trials per 10,000 eligible patients per year (odds ratio [OR]=1.88)
2. Phase 3 vs phase 2 trial (OR=1.86)
3. Enrollment as percentage of eligible population for targeted therapy (OR=0.57)
4. Enrollment as percentage of eligible population for radiation therapy (OR=1.81)
5. Annual incidence of clinical condition(s) per 10,000 (OR=0.99)
6. Tissue sample required to assess eligibility (OR=1.26)
7. Investigational new drug (OR=0.34)
8. Metastatic setting (OR=1.46)
9. Sample size per 100 (OR=0.95)
10. More than one condition evaluated (OR=1.98)
11. Common solid cancer (prostate, breast, lung, or colon) vs liquid or rare solid cancers (OR=2.32)
12. Interaction term (phase 3 x investigational new drug, OR=2.47).

The researchers concluded that systematically considering the overall influence of these risk factors could aid in the design and prioritization of future clinical trials.