PD Research Non Monotone Branch Tag Tool

Description

Label supplied response segments under an explicit flatness tolerance.

A pharmacodynamic model relates an exposure or occupancy measure to a response under explicitly supplied parameters. Baseline, maximum increment, half-maximal concentration, effect direction, and observation windows have distinct meanings. Evaluating one model term does not fit the curve or establish its clinical validity.

Label supplied response segments under an explicit flatness tolerance.

When to use this tool

Use PD Research Non Monotone Branch Tag when you need this particular response-model term, scale conversion, panel duration, or support calculation.

How to use the tool and how it works

Enter levels, responses, tolerance. Keep parallel arrays aligned. Use the units and conventions below.
Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
Read risingSegments, fallingSegments, flatSegments, nonMonotone. Inspect nonMonotone before interpreting the associated values.

Require aligned levels and responses with at least two strictly increasing levels and nonnegative tolerance. Classify each adjacent response change as rising above tolerance, falling below negative tolerance, or flat otherwise; return zero-based segment indices and nonMonotone when rising and falling segments both occur.^[1]

Input field	Meaning and units	Accepted input
levels	Levels	Required. Array of finite number; maximum items 256.
responses	Responses	Required. Array of finite number; maximum items 256.
tolerance	Absolute response-change allowance, not a slope-per-concentration tolerance.	Required. Finite number.

Output field	Interpretation	Returned shape
risingSegments	Rising Segments	Required. Array of safe integer; maximum items 256.
fallingSegments	Falling Segments	Required. Array of safe integer; maximum items 256.
flatSegments	Flat Segments	Required. Array of safe integer; maximum items 256.
nonMonotone	Non Monotone	Required. Boolean.

json

{
  "input": {
    "levels": [
      1,
      2,
      3,
      4
    ],
    "responses": [
      1,
      3,
      2,
      2
    ],
    "tolerance": 0
  },
  "output": {
    "risingSegments": [
      0
    ],
    "fallingSegments": [
      1
    ],
    "flatSegments": [
      2
    ],
    "nonMonotone": true
  }
}

Limitations and assumptions

Tolerance is in response units and classifies changes, not slopes per level unit. The labels do not fit a smooth curve, detect a mechanism, or establish statistically significant non-monotonicity.
Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Signed zero is exported as zero.

Alternative or Complementary methods

Use a fitted and validated response model when a supplied local term is insufficient. Check exposure units, response direction, baseline, model assumptions, and observation support before comparing effects.

References

Calculation and input contract: pharmacodynamics/pd-research-non-monotone-branch-tag — tool.io

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