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1.
Figure 8.

Figure 8. From: Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.

Model-based estimates of the inverse growth rate distribution (left) and screening sensitivity (right), based on simulated data (dotted). Solid lines represent the same distributions based on the true parameter values.

Gabriel Isheden, et al. Stat Methods Med Res. 2019 Dec;28(12):3822-3842.

CitationFull text

2.
Figure 4.

Figure 4. From: Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.

Observed and predicted numbers of affected lymph nodes (CAHRES). The bars represent the observed numbers of affected lymph nodes, within tumour size interval 10–15 mm (left) and 35–45 mm (right), in the CAHRES dataset. Circles represent predicted probabilities from the Poisson model with k = 5, estimated on the CAHRES data set, and dots represent predicted probabilities from the random effects Poisson model with k = 4, also estimated on the CAHRES data set.

Gabriel Isheden, et al. Stat Methods Med Res. 2019 Dec;28(12):3822-3842.

CitationFull text

3.
Figure 3.

Figure 3. From: Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.

Model-based estimates of expected lymph node spread as a function of tumour size (CAHRES). Circles and bars represent averages and 95% confidence intervals of numbers of lymph nodes affected within each tumour size interval. The spread component of Model A (dotted) is plotted alongside the random effects spread model with k = 4 (solid).

Gabriel Isheden, et al. Stat Methods Med Res. 2019 Dec;28(12):3822-3842.

CitationFull text

4.
Figure 1.

Figure 1. From: Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.

Model-based estimates of expected lymph node spread as a function of tumour size, based on CAHRES. Circles and bars represent averages and 95% confidence intervals of numbers of lymph nodes affected within each tumour size interval. Model A (dotted) produces excessive spread at large tumour sizes, while model B (solid) underestimates spread at large tumour sizes.

Gabriel Isheden, et al. Stat Methods Med Res. 2019 Dec;28(12):3822-3842.

CitationFull text

5.
Figure 7.

Figure 7. From: Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.

Observed and predicted numbers of affected lymph nodes (Libro-1). The bars represent the observed numbers of affected lymph nodes, within tumour size interval 10–15 mm (left) and 35–45 mm (right), in the Libro-1 dataset. Circles represent predicted probabilities from the Poisson model with k = 5, estimated on the CAHRES data set, dots represent predicted probabilities from the random effects Poisson model with k = 4, also estimated on the CAHRES data set, and crosses represent estimated probabilities from the random effects Poisson model with k = 4, estimated on the Libro-1 data set.

Gabriel Isheden, et al. Stat Methods Med Res. 2019 Dec;28(12):3822-3842.

CitationFull text

6.
Figure 6.

Figure 6. From: Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.

Model-based estimates of expected lymph node spread as a function of tumour size based on the random effects Poisson model (k = 4), estimated on CAHRES (dotted line) and Libro-1 (solid line), along with 95% confidence intervals of average lymph node spread obtained from Libro-1.

Gabriel Isheden, et al. Stat Methods Med Res. 2019 Dec;28(12):3822-3842.

CitationFull text

7.
Figure 2.

Figure 2. From: Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.

Model-based estimates of expected lymph node spread as a function of tumour size (CAHRES). Circles and bars represent averages and 95% confidence intervals of numbers of lymph nodes affected within each tumour size interval. The spread component of Model A (dotted) produces excessive spread in large tumours, whereas in terms of expected numbers of affected lymph nodes the spread model with k = 5 (solid) fits at all tumour sizes.

Gabriel Isheden, et al. Stat Methods Med Res. 2019 Dec;28(12):3822-3842.

CitationFull text

8.
Figure 5.

Figure 5. From: Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.

Model-based estimates of expected lymph node spread as a function of tumour size (CAHRES). To the left, circles and bars represent averages and 95% confidence intervals of numbers of lymph nodes affected within each tumour size interval for screen detected cancers, and to the right the corresponding quantities for symptomatically detected cancers. On both figures, the spread component of Model A (dotted) is plotted alongside the random effects spread model with k = 4 (solid).

Gabriel Isheden, et al. Stat Methods Med Res. 2019 Dec;28(12):3822-3842.

CitationFull text

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