{
  "abstract": "In observational research, statistical detectability, robustness to adjustment, and predictive utility are distinct properties of a candidate predictor, yet they are often conflated. A statistically significant association may weaken after accounting for co-varying factors and may add little to out-of-sample prediction. We illustrate this distinction using atmospheric pressure and daily attendance at a Japanese psychiatric day-care as a worked example. Using four consecutive fiscal years of facility-level daily attendance, we evaluated same-day atmospheric pressure as a pre-specified candidate predictor in three stages: statistical detectability, robustness to adjustment for co-varying meteorological factors and seasonality, and incremental out-of-sample predictive utility beyond institutional scheduling variables and recent attendance history. Same-day pressure was positively associated with attendance in all four years (incidence rate ratios 1.003–1.007 per hPa). However, effect-estimate stability varied across years, with marked attenuation in FY2022, moderate attenuation after precipitation adjustment in FY2023, and specification-sensitive precision in FY2024, whereas FY2025 was comparatively stable. In contrast, the structural baseline incorporating scheduling variables and recent attendance history provided strong out-of-sample prediction, explaining 55%–69% of the variance across years. Adding atmospheric pressure produced only small and unstable changes in the primary expanding-window cross-validated performance (mean ΔR2 ≈ +0.005), with increments varying in sign across years. Thus, a candidate predictor that was repeatedly statistically detectable showed limited stability of effect magnitude or precision under reasonable adjustment and little incremental predictive utility beyond a strong structural baseline. In this highly schedule-driven, single-facility setting, the weather signal examined contributed little incremental information for out-of-sample prediction of aggregate attendance. More broadly, this worked example illustrates why candidate predictors should be evaluated separately for detectability, robustness of effect estimates, and out-of-sample predictive utility before statistically significant associations are interpreted as meaningful or actionable.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Psychiatry, Sodegaura Satsukidai Hospital, Sodegaura, Chiba, Japan"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Takahiro Okuma"
    },
    {
      "name": "Minoru Ishige"
    },
    {
      "name": "Shuichi Kikuchi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1j9qcm41qwx8p.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0352385",
  "fulltext_url": null,
  "id": "1j9qcm41qwx8p",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0352385",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2613|Statistical Methods in Epidemiology"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Psychological adjustment"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Meteorology"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Meteorology/Humidity"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Meteorology/Rain"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Meteorology/Weather"
    ],
    [
      "plos",
      "/Earth sciences/Seasons"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing/Autocorrelation"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Autocorrelation"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Autocorrelation"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Psychological adjustment"
    ]
  ],
  "openalex": "W7225397675",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/1j9qcm41qwx8p.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:20:24Z",
      "native_id": "10.1371/journal.pone.0352385",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0352385"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "plos:meteorology",
      "subfield": "atmospheric"
    },
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    }
  ],
  "title": "Evaluating a candidate predictor beyond statistical significance: Atmospheric pressure and psychiatric day-care attendance as a worked example",
  "title_text": "Evaluating a candidate predictor beyond statistical significance: Atmospheric pressure and psychiatric day-care attendance as a worked example",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:20:48Z",
  "url": "https://research.draconitic.com/paper/1j9qcm41qwx8p",
  "venue": "PLOS One",
  "versions": []
}