Clarifying data requirements

The user wants a response in Chinese. They've shared 90-day RMST results and a template for Table 2, which seems to be missing the Q4 label. It looks like they want us to fill in that row. The data includes comparisons between Q1, Q2, and Q3 for RMST, with differences between Q3 and Q1, and Q2 vs Q1.

Sorting out RMST data

The user shared their 90-day RMST results but seems to want me to focus only on the 30-day data for Table 2. The 30-day data appears to be in the last block (with estimates around 28 days), while the other rows are likely for 90-day RMST (around 80-84 days). I think they only want me to complete the 30-day section of the template.

Decoding the template request

The user provided 90-day data but the template is for 30-day and 365-day results. It seems they may want the 90-day data formatted to match the template, even though Q2 and Q3 data isn't included in their 30-day block. Their template suggests they want 90-day results in the 30-day format.

Sorting through RMST data

The user provided a mix of 90-day and 30-day RMST results. The template shows 30-day RMST for Q1, Q2, Q3, but the numbers don't match their input. The final row likely represents Q4, where they want us to fill in 30-day data (28.23, p 0.097). The user's note says "90-day," suggesting some confusion between the two datasets.

Preparing 90-day RMST table

The user wants a table following the same template but with 90-day RMST results. The format for Q1, Q2, and Q3 is clear, but we cannot include Q4 for 90-day due to missing data. I'll adjust the section heading to "90-day RMST" while keeping the overall template intact. Notably, I'll round the numbers as requested and format the p-values appropriately. I'll also note the "—" for the missing Q4 data.

Thought for 1m 11s

Formatting RMST tables

I need to reorganize the 180-day RMST data the same way the previous table was formatted for 90-day data. This involves creating columns for HGI quartiles, RMST values with 95% CI, ΔRMST with 95% CI, and P values, with a header like "180-day RMST." The user has asked to present the data similarly, except now, the 180-day data shows Q2 and Q3 pairs, plus a Q4 block with some unclear values.

Evaluating RMST data

It looks like the earlier dataset had some confusing elements. The 90-day data showed Q4 and Q1 values around 28 days, which didn't match the rest. For the 180-day data I now have, the first two blocks seem correct, but Q4 and Q1 values seem off, likely from 365-day data mislabeled as 180-day. I'll present the 180-day Q1-Q3 data but note this discrepancy for clarity.

Handling complex data

The instruction says not to ask for clarification if things are unclear, so I’ll aim to present my best effort. I’ll show the 180-day RMST data for Q1-Q3 and flag that the Q4 data seems inconsistent with 180-day values. I could also optionally present a separate table for the suspected 365-day data and include a note.

Organizing RMST data

I'll follow the previous approach: include only Q1–Q3 results, note that Q4 seems inconsistent with 180-day data, and likely reflects 365-day data. I'll present the Q4 data in a separate, concise snippet labeled "Likely 365-day RMST." I'll ensure everything is clear, rounding decimals, and formatting CIs and p-values as specified.

Thought for 45s

Adding Q4 to RMST Table

To add Q4, it seems the numbers they provided are more fitting for a 365-day horizon, given the earlier suspicion about the RMST values. I could include Q4 in the table but clearly label it as 365-day, to stay consistent with the user's request while clarifying the discrepancy in the data.

Addressing Template Inconsistency

I see a slight mismatch in the template title (30-day/365-day), but the user wants 90-day/180-day results included. Their request to "整理" likely means organizing as per the template style. I'll follow their instructions, adding a Q4 row, and briefly note the inconsistency with the 180-day data.

Calculating RMST Values

I’m going through the 180-day sections now. For Q1, the RMST is 158.22 with a confidence interval of 153.77–162.67. For Q2, RMST is 167.50, though part of the confidence interval isn’t fully visible. I'll round the numbers to two decimal places as required.

Computing RMST Values

Let’s go through the numbers for the 180-day data:

• Q1 RMST: 158.22 (153.77–162.67)
• Q2 RMST: 167.50 (164.06–170.95), Δ 9.29 (3.66–14.91), P = 0.0012
• Q3 RMST: 163.00 (159.01–166.98), Δ 4.78 (−1.19–10.75), P = 0.1169

For Q4, the value is 330.45 (322.26–338.64), but it seems this might belong to a different time period (365-day), based on the baseline differences.

Thought for 40s

Updating Q4 tables

The Δ for Q4 is 5.93 days, with a 95% CI of [0.04, 11.83] and a p-value around 0.0486, which is significant at the 0.05 level. For the 90-day data, the CI crosses zero, with a p-value near 0.055. I need to update the tables and add the Q4 row, aligning with the previous format, but adjusting the title to reflect the 90-day and 180-day results.

Updating table headings and Q4 data

The table title remains "30-day and 365-day RMST by HGI quartiles in AF," but we can add a 90-day RMST and 180-day RMST subheader for clarity, following the previous format. For the 90 and 180-day data, I’ll add the Q4 row with the new values. We’ll ensure all formatting is consistent with two decimal places and CIs portrayed correctly.

Incorporating p-values

We need to include the p-values for Q2 and Q3 exactly as given earlier: 0.0011 for 90-day Q2 and 0.1094 for 90-day Q3. For 180-day, 0.0012 for Q2 and 0.1169 for Q3. For Q4, we’ll approximate p-values: 0.055 for 90-day and 0.049 for 180-day, rounding to three decimals for consistency.

Preparing Δ RMST CI

We need to compute the Δ RMST confidence intervals: For 90-day Q4, it's 2.70 (from −0.05 to 5.45), and for 180-day Q4, it's 5.93 (from 0.04 to 11.83). We should note that Q4 values are from the newly provided data with the independence assumption. Let's ensure we list Q4 correctly without repeating previous numbers. I'll format the final response crisply and concisely.

Thought for 59s

def maybe_bold(p): if p=="<0.001": return "<0.001" try: val=float(p) return f"{p}" if val<0.05 else p except: return p rows=[] for t in ["30","90","180","365"]: d={k:format_p(v) for k,v in pvals[t].items()} row=[t+"‑day", maybe_bold(d["overall"]), maybe_bold(d["2v1"]), maybe_bold(d["3v1"]), maybe_bold(d["4v1"]), maybe_bold(d["3v2"]), maybe_bold(d["4v2"]), maybe_bold(d["4v3"])] rows.append(row) rows

[['30‑day', '0.011', '<0.001', '0.092', '0.086', '0.097', '0.103', '0.977'], ['90‑day', '0.015', '0.002', '0.128', '0.045', '0.101', '0.248', '0.627'], ['180‑day', '0.010', '<0.001', '0.111', '0.048', '0.084', '0.181', '0.695'], ['365‑day', '0.015', '0.001', '0.110', '0.084', '0.096', '0.123', '0.895']]

Thought for 1m 15s

def round3(x): return round(x+0.00000000001,3) for horizon in pvals: holm=holm_bonferroni(pvals[horizon]) row={k:round3(v) for k,v in holm.items()} print(horizon,row)

Thought for 1m 54s

Proposing a better layout

The user feels the current layout isn't comfortable. Earlier, they were shown an SCI-style table, but now they want improvement. I’ll propose more compact layout options such as a pairwise P-value matrix for each horizon or a clearer table with improved headers and grouping. I’ll stick to English as they requested.

Proposing cleaner layout options

To improve the layout, I’m thinking about two panels: (A) Overall log-rank P-values for each horizon, and (B) A pairwise comparisons matrix (upper triangle, with diagonals as “—”). I can include optional columns for both unadjusted and Holm adjusted P-values as a second option. This would be more journal-friendly and easier to read.

Ensuring consistent formatting

I need to make sure the rounding is consistent. I'll round to three decimal places, representing values under 0.001 as "<0.001". For example, 30-day Q2 vs Q1 will be "<0.001", 90-day 0.0016 will be 0.002, and 365-day 0.0011 will be 0.001. I’ll be careful not to contradict earlier suggestions and maintain uniformity throughout.

Creating a readable layout

Since the user hasn't provided sample sizes, I’m proposing a cleaner two-panel layout: one for overall log-rank P-values, the other for a compact pairwise matrix per horizon. I’ll bold significant values and present unadjusted P-values. Each horizon will have a table with respective P-values and a pairwise matrix in a stacked subtable format. Markdown works well for this!

Thought for 35s

Adding footnotes to KM table

For the user's request about adding footnotes to the KM table, I can offer standard language for typical footnotes, such as definitions of quartiles, endpoints, log-rank test, and P-value formatting. If they share their original text, I can incorporate their custom language. I'll provide a version with both overall and pairwise tables, plus detailed but general footnotes.

Adding footnotes to table

I can add footnotes like "KM, HGI abbreviations" and include specific details: log-rank test results, pairwise tests, P-value formatting (bold <0.05, <0.001 shown), and note no adjustment for multiple comparisons (or optional correction). I'll maintain the minimal style used earlier and add superscript footnotes (a, b, c) referencing table details.

Creating footnotes for table

The user requested footnotes in English, which corresponds with the previous request. I'll use standard footnotes such as "Abbreviations:", "Pairwise P-values from log-rank tests are unadjusted," and definitions for quartiles, matrix, and log-rank test. For missing information, I'll use "—" and avoid speculative statements.

Footnotes and adjustments

The user asked for footnotes in English, so I’ll provide standard footnotes and a brief Chinese sentence noting that we’ve drafted standard footnotes. I’ll skip including sample sizes and corrections, as they didn’t ask for those. I’ll add a note about multiple-testing adjustment and leave the rest minimal.

Thought for 29s

Providing table footnote

The user has asked to unify the footnotes for the KM curve values to match the style used for RMST. I need to adapt the earlier KM table footnotes to reflect key terms like KM, HGI, AF, CI, and log-rank. I'll format it succinctly while providing the necessary definitions for clarity.

Creating footnotes for KM table

The user wants me to create footnotes for the KM P-values table in a consistent style. I'll write a short paragraph for the footnote: “Pairwise and overall log-rank P values for Kaplan–Meier survival at 30-, 90-, 180-, and 365-days are reported. Pairwise tests compare each quartile with Q1 (reference).” I'll also include a brief list of abbreviations at the end.

Finalizing footnote style

To unify the style with the RMST footnote, I should include the definitions for AF, HGI, and KM in the KM table footnote. I need to avoid using "CI" in this case since it's not relevant to KM data. The final footnote should read: "AF, atrial fibrillation; HGI, hemoglobin glycation index; KM, Kaplan-Meier."

Final footnote for KM table

To align with the requested style, I’ll write the footnote for the KM table as follows: “Kaplan-Meier overall and pairwise log-rank P values at 30-, 90-, 180-, and 365-day horizons are reported. Pairwise P values denote comparisons with Q1 (reference). Two-sided tests; P values are unadjusted for multiplicity. AF, atrial fibrillation; HGI, hemoglobin glycation index; KM, Kaplan-Meier. Bold indicates P<0.05; values <0.001 are shown as <0.001.”

Thought for 27s