Mark Chang

Author Bio

Where uncertainty gets handled with tools, not slogans

Mark Chang writes for readers who don’t want a pep talk about “data-driven decisions.” They want the machinery: how a trial changes course without breaking its own logic, how an inference can look airtight and still be wrong, how software shapes what a method becomes in practice. Across his books, the through-line is a working respect for uncertainty, not as a fog to complain about, but as constraints you can model, test, and live with. These technical books are also about judgment: adaptive designs ask you to decide what can be learned early and what rules keep a study honest.

Adaptive trials and inference under pressure

Adaptive design is often summarized as “flexibility,” but the interesting part is the price. You don’t revise a study midstream for free. You pay in careful control of error rates, in pre-specified decision rules, in simulation work that makes operating characteristics visible before enrollment begins. Implementation matters as much as theory: when a method becomes code, every vague phrase resolves into a step, a stopping rule, a scheme, and that translation is where many “understood” ideas reveal their gaps. Statistics has a reputation for certainty, yet two analysts can make different choices and both sound responsible. Adaptation multiplies decision points, and decision points multiply opportunities for misinterpretation.

Popular books by Mark Chang

• Adaptive Design Methods in Clinical Trials: methods with a clinical spine, a working approach to dose-finding, sample-size re-estimation, interim monitoring, and the moments when a plan meets the evidence it generates.
• Adaptive Design Theory and Implementation Using SAS and R: you’re not just reading about adaptive designs, you’re building them, with SAS and R as the workbench.
• Adaptive Design Theory and Implementation Using SAS and R (Chapman & Hall/Crc Biostatistics): an earlier edition on the same problem, making adaptive design legible to those who must defend it.
• Paradoxes in Scientific Inference names the moments when intuition fails: when conditioning flips a conclusion or aggregation hides a pattern.
• Modern Issues and Methods in Biostatistics signals breadth: missing data that isn’t missing at random, multiplicity from subgroup analyses, high-dimensional settings where classical asymptotics feel optimistic.

What kind of reading experience these books invite

Biostatistics is never just “applied statistics.” The applications are alive: endpoints evolve, standards shift, and the ethical stakes are immediate. Chang’s work rewards a pencil-in-hand posture. These aren’t books you skim for inspiration; they’re books you return to when a protocol is being drafted, when an interim analysis looms, or when a result feels too neat to trust. Readers searching for “The Hidden Alarm by Mark Chang” should note that the books associated with Mark Chang here center on biostatistics, scientific inference, and adaptive clinical trial design.

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