Concept Architecture
Concept
Theoretically, Disease Control Rate (DCR) is a composite clinical outcome measure representing the proportion of patients who achieve complete response, partial response or stable disease following treatment, irrespective of the duration of stable disease unless otherwise specified by the study protocol. It is widely used in oncology to quantify the overall proportion of patients whose disease has not progressed during treatment. The concept exists to provide a broader assessment of treatment activity than objective response rate by including patients with disease stabilisation.
Mathematically, Disease Control Rate is expressed as the proportion of evaluable patients who experience complete response, partial response or stable disease according to predefined response criteria, most commonly RECIST. The measure is estimated as a binomial proportion and is typically reported with confidence intervals derived using exact or asymptotic statistical methods.
In practice, Disease Control Rate is calculated using tumour response assessments obtained during clinical trials or observational studies. Each patient is classified according to their best overall response, and the number achieving disease control is divided by the total number of evaluable patients. In health economics, DCR is commonly used as an intermediate clinical endpoint to inform decision-analytic models, comparative effectiveness studies and early health technology assessments when mature survival outcomes are unavailable.
Purpose
Used to quantify the proportion of patients whose disease is controlled by treatment, supporting evaluation of oncology therapies and informing health economic analyses.
Mathematical Formulae
Primary Formula
DCR = (CR + PR + SD) � N ? 100%
Supporting Formulae
As a proportion:
DCR = (CR + PR + SD) � N
Standard error:
SE(DCR) = �(DCR ? (1 ? DCR) � N)
95% confidence interval (normal approximation):
DCR � 1.96 ? SE(DCR)
Related Mathematical Methods
- Objective Response Rate
- Clinical Benefit Rate
- Binomial Proportion Estimation
- Confidence Interval Estimation
- Exact Binomial Methods
- Survival Analysis
Example
An oncology study evaluates 150 patients.
Complete response (CR) = 12
Partial response (PR) = 48
Stable disease (SD) = 45
Disease Control Rate:
DCR = (12 + 48 + 45) � 150 ? 100%
DCR = 105 � 150 ? 100%
DCR = 70%
Therefore, 70% of patients achieved disease control during treatment.
Excel Implementation
| Function | Example Formula | Health Economics Application |
|---|---|---|
| SUM | =SUM(CR,PR,SD) | Calculates the total number of patients achieving disease control. |
| COUNT | =COUNT(PatientRange) | Determines the number of evaluable patients. |
| IF | =((CR+PR+SD)/N)*100 | Calculates the Disease Control Rate. |
| SQRT | =SQRT((DCR*(1-DCR))/N) | Estimates the standard error of the proportion. |
| CONFIDENCE.NORM | =CONFIDENCE.NORM(0.05,SE,N) | Estimates the confidence interval for the Disease Control Rate. |
VBA (Optional)
VBA can automate calculation of Disease Control Rate, confidence intervals and subgroup analyses across oncology clinical trial datasets.
Sources
- Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). European Journal of Cancer. 2009.
- FDA. Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics.
- Kaplan EL, Meier P. Nonparametric estimation from incomplete observations.
- Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the Economic Evaluation of Health Care Programmes.
- Briggs A, Claxton K, Sculpher M. Decision Modelling for Health Economic Evaluation.
- CHEERS 2022 Statement.
Related Concepts (2)
Library
Publications
1
A Systematic Review of the Effectiveness of Adalimumab, Etanercept and Infliximab for the Treatment of Rheumatoid Arthritis in Adults and an Economic Evaluation of Their Cost-Effectiveness — Chen, Jobanputra, Barton, Jowett, Bryan, Clark, Fry-Smith & Burls, Vol. 10, No. 42 ed., 2006 (Health Technology Assessment (NIHR))
A landmark NIHR HTA monograph systematically reviewing the clinical effectiveness and modelling the cost-effectiveness of anti-TNF biologics (adalimumab, etanercept, infliximab) for rheumatoid arthritis using the Birmingham Rheumatoid Arthritis Model, an exemplar of HTA-body economic evaluation in a musculoskeletal disease.
Frequently Asked Questions (6)
What is the disease control rate?
A cancer outcome measure combining complete response, partial response, and stable disease rates, characterising the proportion whose disease did not progress.
Source: Eisenhauer et al. 2009
What share of patients does the disease control rate count?
The disease control rate counts the share of patients whose cancer did not progress on treatment, bringing together those with a complete response, a partial response, and stable disease. Unlike a response rate, which counts only tumours that shrank, it credits keeping the disease in check as a form of control, so it captures the value of treatments that halt growth without reversing it. This makes it a fuller measure of a treatment's grip on the cancer. The proportion whose disease was held in check is what it captures. Eisenhauer and colleagues (2009) describe this.
Source: Eisenhauer et al. 2009
What does the disease control rate combine?
The disease control rate combines complete response, in which the cancer disappears; partial response, in which it shrinks; and stable disease, in which it neither grows nor shrinks significantly, into a measure of the proportion whose disease did not progress. So the disease control rate combines responses and stable disease, which is why it measures disease control, since it includes all patients whose disease was controlled, whether through shrinkage or stability, and combining these captures the proportion whose disease did not progress, giving a measure of disease control that is broader than counting only those whose tumours responded.
Source: Eisenhauer et al. 2009
How does the disease control rate differ from response rate?
The disease control rate differs from response rate in that it includes stable disease as well as complete and partial responses, whereas response rate counts only responses. So the disease control rate is broader than response rate, which is why it includes stable disease, since it captures all patients whose disease was controlled, including those whose disease was stable, and this broader measure reflects disease control rather than only tumour response, making the disease control rate useful for characterising the proportion of patients whose disease did not progress, beyond those whose tumours shrank or disappeared.
Source: Eisenhauer et al. 2009
Why is the disease control rate used?
The disease control rate is used to characterise the proportion of patients whose disease is controlled, including those with stable disease, giving a measure of benefit through disease control rather than only tumour shrinkage. So the disease control rate is used to measure disease control, which is why it includes stable disease, since some patients benefit from a treatment that keeps their disease stable, and counting these alongside responses characterises the proportion whose disease did not progress, making the disease control rate useful where preventing progression, not only shrinking the tumour, is a meaningful outcome of treatment.
Source: Eisenhauer et al. 2009
How does the disease control rate relate to the clinical benefit rate?
The disease control rate relates to the clinical benefit rate in that both combine complete response, partial response, and stable disease, capturing patients whose disease is controlled, and the two are closely related measures. So the disease control rate and clinical benefit rate are closely related, which is why they overlap, since both include responses and stable disease to capture benefit through disease control, and the two describe similar concepts of the proportion of patients whose disease did not progress, giving a broader picture of benefit than response rate alone by including stable disease alongside responses.
Source: Eisenhauer et al. 2009
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Verified by Dr Darrin Baines
British health economist
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Verification date: 14 May 2026
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