Clinical trials rarely, if ever, occur in a vacuum. Generally, large amounts of clinical data are available prior to the start of a study, particularly on the current study’s control arm. There is obvious appeal in using (i.e., ‘borrowing’) this information. With historical data providing information on the control arm, more trial resources can be devoted to the novel treatment while retaining accurate estimates of the current control arm parameters. This can result in more accurate point estimates, increased power, and reduced type I error in clinical trials, provided the historical information is sufficiently similar to the current control data. If this assumption of similarity is not satisfied, however, one can acquire increased mean square error of point estimates due to bias and either reduced power or increased type I error depending on the direction of the bias. In this manuscript, we review several methods for historical borrowing, illustrating how key parameters in each method affect borrowing behavior, and then, we compare these methods on the basis of mean square error, power and type I error. We emphasize two main themes. First, we discuss the idea of ‘dynamic’ (versus ‘static’) borrowing. Second, we emphasize the decision process involved in determining whether or not to include historical borrowing in terms of the perceived likelihood that the current control arm is sufficiently similar to the historical data. Our goal is to provide a clear review of the key issues involved in historical borrowing and provide a comparison of several methods useful for practitioners.

On the basis of plasma exposures and safety data, enzastaurin 525 mg once daily is the recommended phase II dose. Enzastaurin is well tolerated up to 700 mg/d. Evidence of early activity was seen with significant stable disease.

Treatment with enzastaurin was well-tolerated and associated with prolonged FFP in a small subset of patients with relapsed or refractory DLBCL. Further studies of enzastaurin in DLBCL are warranted.

Minimization is a dynamic randomization technique that has been widely used in clinical trials for achieving a balance of prognostic factors across treatment groups, but most often it has been used in the setting of equal treatment allocations. Although unequal treatment allocation is frequently encountered in clinical trials, an appropriate minimization procedure for such trials has not been published. The purpose of this paper is to present novel strategies for applying minimization methodology to such clinical trials. Two minimization techniques are proposed and compared by probability calculation and simulation studies. In the first method, called naïve minimization, probability assignment is based on a simple modification of the original minimization algorithm, which does not account for unequal allocation ratios. In the second method, called biased-coin minimization (BCM), probability assignment is based on allocation ratios and optimized to achieve an 'unbiased' target allocation ratio. The performance of the two methods is investigated in various trial settings including different number of treatments, prognostic factors and sample sizes. The relative merits of the different distance metrics are also explored. On the basis of the results, we conclude that BCM is the preferable method for randomization in clinical trials involving unequal treatment allocations. The choice of different distance metrics slightly affects the performance of the minimization and may be optimized according to the specific feature of trials.

BACKGROUND An Investigational New Drug (IND) treatment program allows patients access to a drug that has shown activity against a serious or life‐threatening disease prior to full Food and Drug Administration (FDA) review and approval. This treatment IND program, in which patients with locally advanced or metastatic pancreatic carcinoma were treated with gemcitabine, began in 1995. METHODS Eligibility criteria were ≤1 prior chemotherapy regimen; a Karnofsky performance status (KPS) of ≥50; and adequate bone marrow, liver, and renal function. Gemcitabine was given at a dose of 1000 mg/m2 weekly × 7 followed by a week of rest, then weekly × 3 every 4 weeks thereafter. In this program, disease‐related symptom improvement (DRSI) was defined retrospectively as 1) improvement in pain (on a 7‐point scale) and/or analgesic class (e.g., morphine improving to codeine) and/or KPS (≥20 points), or 2) stability of these three parameters with a 7% increase in weight from baseline. RESULTS A total of 3023 patients enrolled. At baseline, 80% of them had Stage IV disease, and 84% had a baseline KPS ≥ 70. The median age was 65 years, and 56% of the patients were male. The cumulative DRSI response rate after the fourth cycle was 18.4%. Of 982 patients with tumor response data, there were 14 with complete response and 104 with partial response, for an overall response rate of 12.0% (95% confidence interval [CI], 10.0–14.0%). For 2380 patients with survival data, the median survival was 4.8 months (95% CI, 4.5–5.1 months) and the 12‐month survival was 15%. Gemcitabine was well tolerated; only 4.6% of discontinuations were due to adverse events. CONCLUSIONS Notable disease‐related symptom improvement and survival were seen with gemcitabine in this large, compassionate‐use setting, and these findings were in agreement with those of earlier registration trials. Cancer 1999;85:1261–8. © 1999 American Cancer Society.

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As predicted in preclinical testing, daily oral LY353381.HCl is safe, is well tolerated at all tested dose levels, and may be clinically beneficial in patients with extensively pretreated metastatic breast cancer. Further studies with LY353381 to evaluate the efficacy in patients with or without prior exposure to tamoxifen and fewer overall prior regimens are under way.

These results do not demonstrate any benefit to adding LY to gemcitabine in unpretreated patients with advanced pancreatic carcinoma.