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Estimating the Time to Benefit for Preventive Drugs with the Statistical Process Control Method: An Example with Alendronate.

  • Author(s): van de Glind, Esther MM;
  • Willems, Hanna C;
  • Eslami, Saeid;
  • Abu-Hanna, Ameen;
  • Lems, Willem F;
  • Hooft, Lotty;
  • de Rooij, Sophia E;
  • Black, Dennis M;
  • van Munster, Barbara C
  • et al.


For physicians dealing with patients with a limited life expectancy, knowing the time to benefit (TTB) of preventive medication is essential to support treatment decisions.


The aim of this study was to investigate the usefulness of statistical process control (SPC) for determining the TTB in relation to fracture risk with alendronate versus placebo in postmenopausal women.


We performed a post hoc analysis of the Fracture Intervention Trial (FIT), a randomized, controlled trial that investigated the effect of alendronate versus placebo on fracture risk in postmenopausal women. We used SPC, a statistical method used for monitoring processes for quality control, to determine if and when the intervention group benefited significantly more than the control group. SPC discriminated between the normal variations over time in the numbers of fractures in both groups and the variations that were attributable to alendronate. The TTB was defined as the time point from which the cumulative difference in the number of clinical fractures remained greater than the upper control limit on the SPC chart.


For the total group, the TTB was defined as 11 months. For patients aged ≥70 years, the TTB was 8 months [absolute risk reduction (ARR) = 1.4%]; for patients aged <70 years, it was 19 months (ARR = 0.7%).


SPC is a clear and understandable graphical method to determine the TTB. Its main advantage is that there is no need to define a prespecified time point, as is the case in traditional survival analyses. Prescribing alendronate to patients who are aged ≥70 years is useful because the TTB shows that they will benefit after 8 months. Investigators should report the TTB to simplify clinical decision making.

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